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Pelczar Microbiology Ebook PDF Free 330: A Comprehensive Guide
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If you are a student, teacher, researcher, or practitioner of microbiology, you might have heard of Pelczar Microbiology. It is one of the most popular and widely used textbooks in the field of microbiology. But what is Pelczar Microbiology exactly? How can you download it for free as a PDF file? And how can you use it effectively for your studies or work? In this article, we will answer all these questions and more. We will provide you with a comprehensive guide on Pelczar Microbiology Ebook PDF Free 330, covering its history, features, benefits, contents, structure, download process, usage methods, examples, and applications. By the end of this article, you will have a clear understanding of what Pelczar Microbiology is and how you can get the most out of it.
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What is Pelczar Microbiology?
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Pelczar Microbiology is a textbook of microbiology written by Michael J. Pelczar Jr., E.C.S. Chan, and Noel R. Krieg. It was first published in 1958 and has since been revised and updated several times. The latest edition is the seventh edition, which was published in 2010. It is also known as Pelczar Microbiology Ebook PDF Free 330 because it is available online as a free PDF file with 330 pages.
Pelczar Microbiology was originally written by Michael J. Pelczar Jr., who was a professor of microbiology at the University of Maryland. He was also the director of the Institute for Molecular and Cell Biology at the University of Maryland Biotechnology Institute. He had a distinguished career as a microbiologist, author, editor, and administrator. He wrote several books and articles on microbiology, biotechnology, biochemistry, genetics, immunology, and molecular biology. He also served as the president of the American Society for Microbiology and the International Union of Microbiological Societies.
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Pelczar Jr. collaborated with E.C.S. Chan and Noel R. Krieg to write the first edition of Pelczar Microbiology in 1958. E.C.S. Chan was a professor of microbiology at the National University of Singapore. He was also the dean of the Faculty of Science and the vice-chancellor of the university. He had a prominent role in developing microbiology education and research in Singapore and Southeast Asia. He wrote several books and articles on microbiology, biotechnology, environmental science, food science, and public health. He also served as the president of the Singapore National Academy of Science and the Federation of Asian Scientific Academies and Societies.
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Noel R. Krieg was a professor emeritus of microbiology at Virginia Tech. He was also the director emeritus of the Virginia Agricultural Experiment Station. He had an illustrious career as a microbiologist, author, editor, and consultant. He wrote several books and articles on microbiology, soil science, ecology, taxonomy, phylogeny, evolution, and biotechnology. He also served as the president of the Society for Industrial Microbiology and Biotechnology and the Bergey's Manual Trust.
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The three authors aimed to provide a comprehensive and up-to-date introduction to microbiology for undergraduate students in biology, agriculture, medicine, dentistry, pharmacy, veterinary science, engineering, and other related fields. They also intended to make the book useful for graduate students, teachers, researchers, and practitioners who need a reference or a review of microbiology. They covered various aspects of microbiology, such as its history, scope, methods, principles, diversity, structure, function, metabolism, genetics, evolution, ecology, interactions, applications, and challenges. They also included numerous examples, illustrations, tables, diagrams, exercises, questions, and references to enhance the learning and understanding of microbiology.
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The main features and benefits of Pelczar Microbiology
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Pelczar Microbiology has many features and benefits that make it one of the best textbooks in microbiology. Some of these features and benefits are:
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It is comprehensive and up-to-date. It covers all the major topics and subtopics in microbiology, from its history and scope to its applications and challenges. It also incorporates the latest developments and discoveries in microbiology, such as molecular techniques, genomics, proteomics, metabolomics, bioinformatics, nanotechnology, synthetic biology, bioremediation, biofuels, bioweapons, antibiotics resistance, emerging diseases, and climate change.
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It is clear and concise. It explains the concepts and facts of microbiology in a simple and straightforward way. It uses an informal tone, personal pronouns, active voice, brief sentences, rhetorical questions, analogies, and metaphors to engage the reader. It avoids jargon, technical terms, and unnecessary details that might confuse or bore the reader.
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It is informative and interesting. It provides relevant information about microbiology that can help the reader to learn more about the subject. It also provides interesting information about microbiology that can spark the reader's curiosity and enthusiasm for the subject. It uses examples from real life situations such as health care environmental issues food production biotechnology etc to illustrate how microbiology affects our lives.
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It is visual and logical. It uses illustrations, tables, diagrams, and graphs to present information in a clear and attractive way. It also uses headings, subheadings, bullets, and numbers to organize information in a logical and coherent way. It helps the reader to follow the flow of the article and to find the information they need easily.
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It is interactive and actionable. It includes exercises, questions, and references at the end of each chapter to test the reader's knowledge and understanding of microbiology. It also includes tips, suggestions, and warnings throughout the article to guide the reader on how to use microbiology effectively. It encourages the reader to apply microbiology to their studies or work.
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These are some of the main features and benefits of Pelczar Microbiology that make it a valuable and enjoyable resource for anyone who wants to learn more about microbiology.
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The contents and structure of Pelczar Microbiology
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Pelczar Microbiology consists of 24 chapters that are divided into six parts. Each part covers a major theme or area of microbiology. The parts and chapters are as follows:
- | Part | Title | Chapters | | --- | --- | --- | | I | Introduction to Microbiology | 1. The Scope and History of Microbiology 2. The Methods of Microbiology 3. The Principles of Microbiology | | II | The Diversity of Microorganisms | 4. The Prokaryotic Cell: Bacteria 5. The Eukaryotic Cell: Fungi, Algae, Protozoa, and Helminths 6. The Viruses | | III | The Structure and Function of Microorganisms | 7. Microbial Nutrition 8. Microbial Growth 9. Microbial Metabolism 10. Microbial Genetics | | IV | The Evolution and Ecology of Microorganisms | 11. Microbial Taxonomy and Phylogeny 12. Microbial Evolution 13. Microbial Ecology | | V | The Interactions of Microorganisms | 14. Microbial Symbiosis 15. Microbial Pathogenesis 16. Host Defense and Immunity 17. Epidemiology and Public Health | | VI | The Applications of Microorganisms | 18. Industrial Microbiology 19. Environmental Microbiology 20. Food Microbiology 21. Agricultural Microbiology 22. Medical Microbiology 23. Biotechnology and Genetic Engineering 24. Emerging Issues in Microbiology |
The structure of each chapter is similar. It starts with an introduction that gives an overview of the topic and its importance. It then presents the main points and subpoints of the topic in a clear and concise way. It ends with a summary that highlights the key takeaways and a list of exercises, questions, and references that help the reader to review and reinforce their learning.
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The advantages and disadvantages of downloading Pelczar Microbiology Ebook PDF Free 330
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Download Pelczar Microbiology Ebook PDF Free 330 from the source or website. You can click on the download button, link, or icon that is provided by the source or website. You might need to register, sign up, or log in to the source or website first. You might also need to complete a survey, captcha, or verification process first. You should follow the instructions carefully. You should also choose a suitable format, size, and location for your ebook file. You should avoid clicking on any pop-ups, ads, or redirects that might appear during the download process.
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A basic knowledge and interest in microbiology. You should have a general understanding of what microbiology is and why it is important. You should also have a curiosity and enthusiasm for learning more about microbiology.
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The methods and strategies for using Pelczar Microbiology Ebook PDF Free 330
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When using Pelczar Microbiology Ebook PDF Free 330, you need to apply some methods and strategies that will help you to use it effectively. These are:
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Set a clear goal and purpose for using Pelczar Microbiology Ebook PDF Free 330. You should know what you want to achieve and learn from using Pelczar Microbiology Ebook PDF Free 330. You should also know how you will use it for your studies or work. For example, you might want to use it as a textbook for your course, as a reference for your research, as a review for your exam, or as a source of inspiration for your project.
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Select the relevant parts and chapters of Pelczar Microbiology Ebook PDF Free 330. You should choose the parts and chapters of Pelczar Microbiology Ebook PDF Free 330 that are relevant to your goal and purpose. You should also prioritize the parts and chapters that are more important or difficult than others. For example, you might want to focus on the parts and chapters that cover the topics that are included in your syllabus, research question, exam question, or project proposal.
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Skimming and scanning. You can skim and scan the selected parts and chapters of Pelczar Microbiology Ebook PDF Free 330 to get a general idea of what they are about and to find the specific information that you need.
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Highlighting and annotating. You can highlight and annotate the important points and facts of the selected parts and chapters of Pelczar Microbiology Ebook PDF Free 330 to make them stand out and to add your own notes and comments.
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Summarizing and paraphrasing. You can summarize and paraphrase the main ideas and arguments of the selected parts and chapters of Pelczar Microbiology Ebook PDF Free 330 to condense them into your own words and to check your understanding.
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To illustrate how you can use Pelczar Microbiology Ebook PDF Free 330 effectively, here are some examples and applications of using it for different purposes:
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If you are a student of microbiology, you can use Pelczar Microbiology Ebook PDF Free 330 as a textbook for your course. You can read and study the parts and chapters that cover the topics that are included in your syllabus. You can also do the exercises, questions, and references at the end of each chapter to review and reinforce your learning. You can also use Pelczar Microbiology Ebook PDF Free 330 as a reference for your assignments, projects, or exams. You can find relevant information, examples, illustrations, tables, diagrams, and graphs that can help you to complete your tasks, present your work, or answer your questions.
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If you are a researcher of microbiology, you can use Pelczar Microbiology Ebook PDF Free 330 as a source for your research. You can use the parts and chapters as a background for defining your research problem, question, or hypothesis. You can also use the examples, illustrations, tables, diagrams, graphs, exercises, questions, references as a basis for conducting your literature review, data collection, data analysis, or data interpretation. You can also use the methods, principles, applications, challenges as a framework for developing your research design, methodology, results, discussion, or conclusion.
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If you are a practitioner of microbiology, you can use Pelczar Microbiology Ebook PDF Free 330 as a guide for your practice. You can use the parts and chapters as a reference for updating your knowledge, skills, or competencies in microbiology. You can also use the examples, illustrations, tables, diagrams, graphs, exercises, questions, references as a support for solving problems, making decisions, or creating products related to microbiology. You can also use the methods, principles, applications, challenges as a inspiration for improving your practice, innovation, or impact in microbiology.
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These are some of the examples and applications of using Pelczar Microbiology Ebook PDF Free 330 effectively for different purposes.
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Conclusion
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Pelczar Microbiology Ebook PDF Free 330 is a comprehensive and up-to-date textbook of microbiology that covers various aspects of microbiology such as its history scope methods principles diversity structure function metabolism genetics evolution ecology interactions applications challenges It is written by Michael J Pelczar Jr ECS Chan Noel R Krieg who are renowned experts in microbiology It is available online as a free PDF file with 330 pages It has many features benefits such as being clear concise informative interesting visual logical interactive actionable It is useful for anyone who wants to learn more about microbiology such as students teachers researchers practitioners To download it you need to find a reliable source website follow some steps tips consider some advantages disadvantages To use it effectively you need to have some prerequisites requirements apply some methods strategies use it for different purposes such as textbook reference review source resource guide By using it effectively you will be able to gain more knowledge understanding appreciation application of microbiology
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Here are some frequently asked questions about Pelczar Microbiology Ebook PDF Free 330:
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What is the difference between Pelczar Microbiology Ebook PDF Free 330 and other textbooks of microbiology?
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Pelczar Microbiology Ebook PDF Free 330 is different from other textbooks of microbiology in several ways such as being more comprehensive up-to-date clear concise informative interesting visual logical interactive actionable free online
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The authors of Pelczar Microbiology Ebook PDF Free 330 are Michael J Pelczar Jr ECS Chan Noel R Krieg who are renowned experts in microbiology They have written several books articles on microbiology biotechnology biochemistry genetics immunology molecular biology They have also served as presidents directors deans professors editors consultants in various institutions organizations societies related to microbiology
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How many pages does Pelczar Microbiology Ebook PDF Free 330 have?
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Pelczar Microbiology Ebook PDF Free 330 has 330 pages It consists of 24 chapters that are divided into six parts Each part covers a major theme or area of microbiology Each chapter starts with an introduction ends with a summary exercises questions references
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You can download Pelczar Microbiology Ebook PDF Free 330 by finding a reliable source website that offers it clicking on the download button link icon following the instructions choosing a suitable format size location for your ebook file avoiding any pop-ups ads redirects that might appear during the download process
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You can use Pelczar Microbiology Ebook PDF Free 330 effectively by setting a clear goal purpose for using it planning a schedule budget for using it selecting the relevant parts chapters of it reading studying them in a systematic active way using various techniques tools such as skimming scanning highlighting annotating summarizing paraphrasing outlining mapping questioning answering comparing contrasting applying analyzing evaluating synthesizing using it for different purposes such as textbook reference review source resource guide
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-
-
In the free energy formula of Eq (7.36), nonideality is expressed by the general form gex , the excess free energy. The simplifications used in the prior analyses of ideal melting and phase separation, namely neglecting sex and confining hex to the regular-solution model, are not valid for most binary systems. In order to construct phase diagrams by the common-tangent technique, more elaborate solution models are needed to relate free energy to composition for all likely phases. Figure 8.9 shows plots of g Vs xB for the three phases at six temperatures, with T6 the highest and T1 the lowest. In the six graphs, the curves for each phase keep approximately the same shape but shift relative Fig. 8.9 Free energy composition curves for an A-B binary system with two solid phases (a and P) and a liquid phase (From Ref. 1) Fig. 8.9 Free energy composition curves for an A-B binary system with two solid phases (a and P) and a liquid phase (From Ref.
-
As in Eq (7.21) for the enthalpy, the molar Gibbs free energy of a solution (g) can be written in terms of pure-component contributions (gA and gB ) and an excess value (gex ). However, an important contribution needs to be added. For a binary solution, the terms contributing to g are
Even though the thermochemical database need contain only AGo (or, equivalently, AHo and ASo ), the number of reactions that would have to be included in such a compilation is intractably large. The key to reducing data requirements to manageable size is to provide the standard free energy changes of forming the individual molecular species from their constituent elements. Particular reactions are constructed from these so-called formation reactions. For molecular compounds containing two or more elements, the basic information is the Free energy change for reactions by which the compound is created from its constituent elements, the latter in their normal state at the particular temperature. These reaction free energy changes are called standard free energies of formation of the compound. For example, the methane combustion reaction of Eq (9.1) involves one elemental compound (O2 ) and three molecular compounds (CH4 , CO2 , and H2 O).
-
Another notable solid-solid equilibrium is the graphite-to-diamond transition in the element carbon. Graphite is fairly common in the earth's crust but the rarity of diamond is the origin of its value. Under normal terrestrial conditions (300 K, 1 atm) the two forms of carbon are not in equilibrium and so, thermodynamically speaking, only one form should exist. The stable form is the one with the lowest Gibbs free energy. At 300 K, the enthalpy difference between diamond and graphite is Ahd-g 1900 J mole, with diamond less stable than graphite in this regard. Being a highly ordered structure, diamond has a molar entropy lower than that of graphite, and Asd-g -3.3 J mole-K (see Fig. 3.6). This difference also favors the stability of graphite. The combination of the enthalpy and entropy effects produces a free-energy difference of Since the phase with the lowest free energy (graphite) is stable, diamond is a metastable phase.
-
The definition has been chosen so that the activity tends to unity for pure i that is, gi , the molar free energy of pure i. Activity varies monotonically with concentration. Therefore, when component i approaches infinite dilution ai 0 and - ot. This inconvenient behavior of the chemical potential at zero concentration is avoided by using the activity in practical thermodynamic calculations involving species in solution. Another reason for the choice of the mathematical form of the relation between and ai embodied in Eq (7.29) is that the activity is directly measurable as the ratio of the equilibrium pressure exerted by a component in solution to the vapor pressure of the pure substance. This important connection is discussed in Chap. 8. Problem 7.7 shows how this equation can be used to assess the validity of formulas for hex . In an equally important application, the above equation can be integrated to give the Gibbs free energy analog of Eq (7.19) for the enthalpy
-
The equilibrium criterion of minimum Gibbs free energy (Sect. 1.11) can be applied to any of the phase transitions described in the previous section. At fixed pressure and temperature, let the system contain nI moles of phase I and nII moles of phase II, with molar Gibbs free energies of gI and gII, respectively. The total Gibbs free energy of the two-phase mixture is This is an expression of chemical equilibrium. It complements the conditions of thermal equilibrium (TI TII) and mechanical equilibrium (pI pII). Since the Gibbs free energy is defined by g h - Ts, another form of Eq (5.2) is
-
To Matthews that Tesla had entrusted two of his greatest inventions prior to his death - the Tesla interplanetary communications set and the Tesla anti-war device. Tesla also left special instructions to Otis T. Carr of Baltimore, who used this information to develop free-energy devices capable of 'powering anything from a hearing aid to a spaceship.' (73) Tesla's technology, through Carr's free-energy device, will revolutionize the world. Instead of purchasing power from the large corporations, which is then delivered to our homes via wires and cables, the new technology consists of nothing more than a small antenna that will be attached to the roof of every building
-
As in any system constrained to constant temperature and pressure, the equilibrium of a chemical reaction is attained when the free energy is a minimum. Specifically, this means that dG 0, where the differential of G is with respect to the composition of the mixture. In order to convert this criterion to an equation relating the equilibrium concentrations of the reactants and products, the chemical potentials are the essential intermediaries. At equilibrium, Eq (7.27) provides the equation
-
Irrespective of the complexity of the nonideal behavior of the phases involved, the phase diagram can always be constructed if the free energy Vs composition curves for each phase can be drawn. The link between the two graphical representations is the common-tangent rule. Because of the wide variations in the shapes of free-energy curves, the types of phase diagrams deduced from them reaches zoological proportions. In this section, a common variety called the eutectic phase diagram5 is developed by the graphical method.
-
The structure of a phase diagram is determined by the condition of chemical equilibrium. As shown in Sect. 8.2, this condition can be expressed in one of two ways either the total free energy of the system (Eq (8.1)) is minimized or the chemical potentials of the each component (Eq (8.2)) in coexisting phases are equated. The choice of the manner of expressing equilibrium is a matter of convenience and varies with the particular application. Free-energy minimization is usually used with the graphical method and chemical-potential equality is the method of choice for the analytic approach.
-
-
The chemical potential is directly related to the Gibbs free energy of a system. For a one-component system, the chemical potential is identical to the molar Gibbs free energy of the pure substance. In solutions or mixtures, the chemical potential is simply another name for the partial molar Gibbs free energy. The discussion in Sect. 7.3, in which enthalpy was used to illustrate partial molar and excess properties, applies to the Gibbs free energy one need only replace h everywhere by g. The reason that the partial molar Gibbs free energy (g) is accorded the special name chemical potential is not only to shorten a cumbersome five-word designation. More important is the role of the chemical potential in phase equilibria and chemical equilibria when the restraints are constant temperature and pressure. Instead of the symbol g, the chemical potential is designated by The connection between the Gibbs free energy of a system at fixed T and p and the equilibrium state is shown in Fig. 1.18.
-
By the time I arrived at Princeton in the fall of 1962, I was thoroughly pumped up to join the quest for controlled fusion at PPL. My under-grad senior thesis on an obscure plasma instability led to working in Jim Drummond's Plasma Physics Group at the Boeing Scientific Research Laboratories in my hometown, Seattle, during the year following graduation. In fact, this millennial dream of realizing a virtually limitless source of pollution-free energy was largely my motivation for applying to grad school, and only to Princeton.
-
That an under-ice ocean exists on Europa is remarkable. It is especially remarkable when it is realized that Jupiter sits well outside of the habitable zone (defined in Chapter 5, and see Figure 5.9) and given that the surface temperature of the moon is not much greater than 100 K. How, indeed, can this ocean exist There is not enough solar energy to warm Europa above the freezing point of water, and the moon is so small that it should have cooled off relatively rapidly after formation.19 The possibility of terraforming Europa and, indeed, the other Galilean moons has been discussed by numerous researchers, but in all cases, bar the stellifying of Jupiter option, the biggest hurdle to overcome is that of supplying enough surface heat.
-
The term on the left is A , the chemical-potential difference of overall reaction (10.21). It is the aqueous equivalent of the free-energy difference AG used in describing nonaqueous cells. The electric potential difference on the right is the cell EMF, s, so the equation is
-
Propellant to generate electricity is not an efficient way to power a satellite that could use free solar energy instead (by means of solar cells). Nevertheless, electrodynamic tether power generation could be useful for generating short bursts of electrical energy, for instance when needed for high-energy but short duration experiments involving powerful lidars (instruments similar to radar but using laser light instead of short wavelength radio waves).
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/1line/AutoGPT/autogpt/prompt.py b/spaces/1line/AutoGPT/autogpt/prompt.py
deleted file mode 100644
index 03c132acdf26d08deeee119e41a561f430957806..0000000000000000000000000000000000000000
--- a/spaces/1line/AutoGPT/autogpt/prompt.py
+++ /dev/null
@@ -1,204 +0,0 @@
-from colorama import Fore
-
-from autogpt.config import Config
-from autogpt.config.ai_config import AIConfig
-from autogpt.config.config import Config
-from autogpt.logs import logger
-from autogpt.promptgenerator import PromptGenerator
-from autogpt.setup import prompt_user
-from autogpt.utils import clean_input
-
-CFG = Config()
-
-
-def get_prompt() -> str:
- """
- This function generates a prompt string that includes various constraints,
- commands, resources, and performance evaluations.
-
- Returns:
- str: The generated prompt string.
- """
-
- # Initialize the Config object
- cfg = Config()
-
- # Initialize the PromptGenerator object
- prompt_generator = PromptGenerator()
-
- # Add constraints to the PromptGenerator object
- prompt_generator.add_constraint(
- "~4000 word limit for short term memory. Your short term memory is short, so"
- " immediately save important information to files."
- )
- prompt_generator.add_constraint(
- "If you are unsure how you previously did something or want to recall past"
- " events, thinking about similar events will help you remember."
- )
- prompt_generator.add_constraint("No user assistance")
- prompt_generator.add_constraint(
- 'Exclusively use the commands listed in double quotes e.g. "command name"'
- )
- prompt_generator.add_constraint(
- "Use subprocesses for commands that will not terminate within a few minutes"
- )
-
- # Define the command list
- commands = [
- ("Google Search", "google", {"input": ""}),
- (
- "Browse Website",
- "browse_website",
- {"url": "", "question": ""},
- ),
- (
- "Start GPT Agent",
- "start_agent",
- {"name": "", "task": "", "prompt": ""},
- ),
- (
- "Message GPT Agent",
- "message_agent",
- {"key": "", "message": ""},
- ),
- ("List GPT Agents", "list_agents", {}),
- ("Delete GPT Agent", "delete_agent", {"key": ""}),
- (
- "Clone Repository",
- "clone_repository",
- {"repository_url": "", "clone_path": ""},
- ),
- ("Write to file", "write_to_file", {"file": "", "text": ""}),
- ("Read file", "read_file", {"file": ""}),
- ("Append to file", "append_to_file", {"file": "", "text": ""}),
- ("Delete file", "delete_file", {"file": ""}),
- ("Search Files", "search_files", {"directory": ""}),
- ("Analyze Code", "analyze_code", {"code": ""}),
- (
- "Get Improved Code",
- "improve_code",
- {"suggestions": "", "code": ""},
- ),
- (
- "Write Tests",
- "write_tests",
- {"code": "", "focus": ""},
- ),
- ("Execute Python File", "execute_python_file", {"file": ""}),
- ("Task Complete (Shutdown)", "task_complete", {"reason": ""}),
- ("Generate Image", "generate_image", {"prompt": ""}),
- ("Send Tweet", "send_tweet", {"text": ""}),
- ]
-
- # Only add the audio to text command if the model is specified
- if cfg.huggingface_audio_to_text_model:
- commands.append(
- ("Convert Audio to text", "read_audio_from_file", {"file": ""}),
- )
-
- # Only add shell command to the prompt if the AI is allowed to execute it
- if cfg.execute_local_commands:
- commands.append(
- (
- "Execute Shell Command, non-interactive commands only",
- "execute_shell",
- {"command_line": ""},
- ),
- )
- commands.append(
- (
- "Execute Shell Command Popen, non-interactive commands only",
- "execute_shell_popen",
- {"command_line": ""},
- ),
- )
-
- # Only add the download file command if the AI is allowed to execute it
- if cfg.allow_downloads:
- commands.append(
- (
- "Downloads a file from the internet, and stores it locally",
- "download_file",
- {"url": "", "file": ""},
- ),
- )
-
- # Add these command last.
- commands.append(
- ("Do Nothing", "do_nothing", {}),
- )
- commands.append(
- ("Task Complete (Shutdown)", "task_complete", {"reason": ""}),
- )
-
- # Add commands to the PromptGenerator object
- for command_label, command_name, args in commands:
- prompt_generator.add_command(command_label, command_name, args)
-
- # Add resources to the PromptGenerator object
- prompt_generator.add_resource(
- "Internet access for searches and information gathering."
- )
- prompt_generator.add_resource("Long Term memory management.")
- prompt_generator.add_resource(
- "GPT-3.5 powered Agents for delegation of simple tasks."
- )
- prompt_generator.add_resource("File output.")
-
- # Add performance evaluations to the PromptGenerator object
- prompt_generator.add_performance_evaluation(
- "Continuously review and analyze your actions to ensure you are performing to"
- " the best of your abilities."
- )
- prompt_generator.add_performance_evaluation(
- "Constructively self-criticize your big-picture behavior constantly."
- )
- prompt_generator.add_performance_evaluation(
- "Reflect on past decisions and strategies to refine your approach."
- )
- prompt_generator.add_performance_evaluation(
- "Every command has a cost, so be smart and efficient. Aim to complete tasks in"
- " the least number of steps."
- )
-
- # Generate the prompt string
- return prompt_generator.generate_prompt_string()
-
-
-def construct_prompt() -> str:
- """Construct the prompt for the AI to respond to
-
- Returns:
- str: The prompt string
- """
- config = AIConfig.load(CFG.ai_settings_file)
- if CFG.skip_reprompt and config.ai_name:
- logger.typewriter_log("Name :", Fore.GREEN, config.ai_name)
- logger.typewriter_log("Role :", Fore.GREEN, config.ai_role)
- logger.typewriter_log("Goals:", Fore.GREEN, f"{config.ai_goals}")
- elif config.ai_name:
- logger.typewriter_log(
- "Welcome back! ",
- Fore.GREEN,
- f"Would you like me to return to being {config.ai_name}?",
- speak_text=True,
- )
- should_continue = clean_input(
- f"""Continue with the last settings?
-Name: {config.ai_name}
-Role: {config.ai_role}
-Goals: {config.ai_goals}
-Continue (y/n): """
- )
- if should_continue.lower() == "n":
- config = AIConfig()
-
- if not config.ai_name:
- config = prompt_user()
- config.save(CFG.ai_settings_file)
-
- # Get rid of this global:
- global ai_name
- ai_name = config.ai_name
-
- return config.construct_full_prompt()
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ .md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ .md
deleted file mode 100644
index 07e04913a6af81f924a9d045bb9723d08c5d5bc6..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ .md
+++ /dev/null
@@ -1,103 +0,0 @@
-
-
Скачать книги английский для начинающих бесплатно: как и где найти лучшие ресурсы
-
Вы хотите изучать английский язык, но не знаете, с чего начать? Вы ищете эффективный и интересный способ улучшить свои знания и навыки? Тогда вам стоит попробовать читать книги на английском языке. Это не только увлекательное, но и полезное занятие, которое поможет вам развиться в разных аспектах языка. В этой статье мы расскажем вам, почему читать книги на английском языке полезно для начинающих, как выбрать подходящие книги для чтения на английском языке, и где скачать книги английский для начинающих бесплатно.
Почему читать книги на английском языке полезно для начинающих
-
Чтение книг на английском языке имеет много преимуществ для тех, кто только начинает изучать язык. Вот некоторые из них:
-
Улучшение словарного запаса и грамматики
-
Когда вы читаете книгу на английском языке, вы неизбежно сталкиваетесь с новыми словами и выражениями, которые вы можете запомнить и использовать в своей речи. Вы также видите, как эти слова и выражения употребляются в контексте, что помогает вам понять их значение и правильное употребление. Чтение книг на английском языке также помогает вам освоить грамматические правила и структуры, которые вы можете применять в своем письме и говорении. Вы учитесь строить предложения, согласовывать времена, использовать модальные глаголы, артикли, предлоги и другие элементы языка.
-
Развитие навыков чтения и понимания
-
Чтение книг на английском языке также тренирует ваши навыки чтения и понимания. Вы учитесь сканировать текст, выделять главную идею, находить ключевые слова, делать выводы и предположения, анализировать и критиковать информацию. Вы также развиваете свою способность угадывать значение неизвестных слов по контексту, использовать синонимы и антонимы, распознавать фразовые глаголы и идиомы. Все эти навыки пригодятся вам не только при чтении книг, но и при просмотре фильмов, прослушивании подкастов, общении с носителями языка и сдаче международных экзаменов.
-
Погружение в культуру и менталитет
-
Наконец, чтение книг на английском языке дает вам возможность погрузиться в культуру и менталитет стран, где говорят на этом языке. Вы можете узнать много интересных фактов об истории, географии, политике, экономике, образовании, науке, искусстве, спорте, религии, традициях и обычаях этих стран. Вы также можете почувствовать атмосферу, юмор, эмоции и характер людей, которые живут там. Это поможет вам лучше понять и уважать разные культуры и народы, а также расширить свой кругозор и обогатить свой опыт.
-
Как выбрать подходящие книги для чтения на английском языке
-
Чтобы читать книги на английском языке с удовольствием и пользой, важно выбрать те книги, которые соответствуют вашему уровню владения языком, интересам и целям. Вот несколько советов, как это сделать:
-
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-
Определение своего уровня владения языком
-
Перед тем, как скачать книги английский для начинающих бесплатно, вы должны определить свой уровень владения языком. Это поможет вам подобрать книги, которые не будут слишком легкими или слишком сложными для вас. Существ уют разные системы определения уровня владения языком, такие как CEFR (Common European Framework of Reference for Languages), IELTS (International English Language Testing System), TOEFL (Test of English as a Foreign Language) и другие. Вы можете пройти онлайн-тест или самостоятельно оценить свои знания по таким критериям, как словарный запас, грамматика, чтение, письмо, говорение и слушание. В зависимости от вашего уровня, вы можете выбирать книги с разной степенью сложности, объема и жанра.
-
Учет своих интересов и целей
-
Чтобы читать книги на английском языке с удовольствием и мотивацией, важно выбирать те книги, которые вам интересны и соответствуют вашим целям. Например, если вы хотите изучать английский язык для работы или учебы, вы можете выбирать книги по своей специальности или профессии. Если вы хотите изучать английский язык для путешествий или общения, вы можете выбирать книги по странам, культурам, людям или ситуациям, которые вас привлекают. Если вы хотите изучать английский язык для развлечения или саморазвития, вы можете выбирать книги по своим хобби, увлечениям, вкусам или ценностям. Главное, чтобы книги были интересными и полезными для вас.
-
Поиск книг с аудио, переводом и упражнениями
-
Чтобы читать книги на английском языке эффективно и комфортно, полезно искать те книги, которые имеют дополнительные ресурсы, такие как аудио, перевод и упражнения. Аудио поможет вам слушать произношение слов и интонацию предложений, а также тренировать свое слуховое восприятие. Перевод поможет вам понять смысл слов и выражений, которые вы не знаете или не уверены. Упражнения помогут вам проверить свое понимание текста и закрепить новые знания и навыки. Вы можете искать книги с аудио, переводом и упражнениями на специализированных сайтах или приложениях для изучения английского языка.
-
Где скачать книги английский для начинающих бесплатно
-
Если вы решили читать книги на английском языке, то вам нужно знать, где вы можете скачать книги английский для начинающих бесплатно. Существует много сайтов и ресурсов, которые предлагают бесплатные книги на английском языке для разных уровней и целей. Вот некоторые из них:
-
English 4U: две книги по грамматике с упражнениями
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English 4U - это сайт для изучения английского языка онлай н. На этом сайте вы можете скачать две книги по грамматике английского языка с упражнениями: English Grammar in Use и Essential Grammar in Use. Эти книги подходят для начинающих и среднего уровня, и содержат много примеров, объяснений и тестов по разным темам грамматики. Вы можете скачать книги в формате PDF или DOC, а также аудиофайлы в формате MP3. Вы можете скачать книги по этой ссылке: .
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Grammar Teacher: учебник English Grammar Secrets с тестами
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Grammar Teacher - это сайт для изучения грамматики английского языка онлайн. На этом сайте вы можете скачать учебник English Grammar Secrets, который состоит из 16 глав, посвященных разным аспектам грамматики. Каждая глава содержит теоретическую часть, примеры, упражнения и тесты. Вы можете скачать учебник в формате PDF или DOC, а также аудиофайлы в формате MP3. Вы можете скачать учебник по этой ссылке: .
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Oxford Guide to English Grammar: полный справочник по грамматике
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Oxford Guide to English Grammar - это полный справочник по грамматике английского языка, который подходит для всех уровней. Эта книга охватывает все основные и дополнительные темы грамматики, такие как части речи, предложения, времена, модальность, пассивный залог, условные предложения и другие. Книга содержит много примеров, таблиц, схем и правил. Вы можете скачать книгу в формате PDF по этой ссылке: .
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Englishpage Online English Grammar Book: интерактивная книга по грамматике с заданиями
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Englishpage Online English Grammar Book - это интерактивная книга по грамматике английского языка, которая доступна онлайн. Эта книга состоит из 10 разделов, которые посвящены разным темам грамматики, таким как артикли, наречия, прилагательные, местоимения, фразовые глаголы и другие. Каждый раздел содержит теоретическую часть, примеры и задания. Вы можете читать книгу онлайн или скачать ее в формате PDF по этой ссылке: .
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Альдебаран: электронная библиотека с книгами по разным темам
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Альдебаран - это электронная библиотека, которая содержит более 100 тысяч книг на русском и английском языках по разным темам и жанрам. На этом сайте вы можете найти книги по литературе, истории, философии, психологии, экономике, праву, медицине и другим областям знаний. Вы также можете найти художественные произведения разных авторов и направлений. Вы можете читать книги онлайн или скачать их в форматах FB 2, RTF, TXT, DOC и другие. Вы можете посетить сайт по этой ссылке: .
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English Online Club: сайт с книгами для чтения по уровням
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English Online Club - это сайт для изучения английского языка онлайн. На этом сайте вы можете найти книги для чтения по уровням, от начального до продвинутого. Вы можете выбрать книги по разным жанрам, таким как детективы, фантастика, приключения, романы и другие. Вы также можете найти книги по классике английской и американской литературы, такие как Шекспир, Диккенс, Твен, Хемингуэй и другие. Вы можете читать книги онлайн или скачать их в форматах PDF или DOC. Вы можете посетить сайт по этой ссылке: .
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Заключение
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Чтение книг на английском языке - это один из лучших способов изучать английский язык для начинающих. Это помогает вам улучшить свой словарный запас и грамматику, развить навыки чтения и понимания, погрузиться в культуру и менталитет англоязычных стран. Чтобы читать книги на английском языке с удовольствием и пользой, важно выбирать те книги, которые соответствуют вашему уровню владения языком, интересам и целям. Вы также можете искать те книги, которые имеют дополнительные ресурсы, такие как аудио, перевод и упражнения. В интернете вы можете найти много сайтов и ресурсов, которые предлагают скачать книги английский для начинающих бесплатно. Мы надеемся, что наша статья помогла вам в этом вопросе и вы сможете насладиться чтением книг на английском языке.
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FAQ
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Вот некоторые часто задаваемые вопросы о теме скачать книги английский для начинающих бесплатно:
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Какая книга лучше всего подходит для начинающих?
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Нет однозначного ответа на этот вопрос, так как выбор книги зависит от вашего уровня владения языком, интересов и целей. Однако в целом рекомендуется выбирать те книги, которые не слишком сложные или слишком легкие для вас, которые имеют интересный сюжет или полезную информацию, которые имеют дополнительные ресурсы, такие как аудио, перевод и упражнения. Вы можете начать с книг по грамматике или лексике, а затем перейти к художественным или научно-популярным произведениям.
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Сколько времени нужно уделять чтению книг на английском языке?
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Это зависит от вашего свободного времени, мотивации и целей. Од нако, в целом, рекомендуется читать книги на английском языке регулярно и систематически, по возможности каждый день или несколько раз в неделю. Вы можете уделять чтению книг на английском языке столько времени, сколько вам комфортно, но не меньше 15-20 минут за сеанс. Вы можете читать книги на английском языке в любое удобное для вас время и место, например, дома, в общественном транспорте, в парке или в кафе.
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Как проверить свое понимание книг на английском языке?
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Чтобы проверить свое понимание книг на английском языке, вы можете использовать разные способы, такие как:
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Отвечать на вопросы по тексту, которые могут быть в конце главы или книги, или которые вы можете составить сами.
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Писать краткое содержание или рецензию на прочитанную книгу, выражая свое мнение и впечатления.
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Обсуждать прочитанную книгу с другими людьми, которые тоже читали ее или хотят прочитать.
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Искать дополнительную информацию по теме книги, сравнивая и анализируя разные источники.
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Применять новые слова и выражения из книги в своей речи и письме, используя их в разных контекстах.
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Как повысить свой уровень чтения книг на английском языке?
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Чтобы повысить свой уровень чтения книг на английском языке, вы можете следовать таким советам:
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Читать книги на английском языке постепенно усложняя их сложность, объем и жанр.
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Читать книги на английском языке разных авторов и направлений, чтобы расширить свой кругозор и стиль.
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Читать книги на английском языке с разными целями, например, для обучения, для развлечения, для саморазвития и т.д.
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Читать книги на английском языке с разными стратегиями, например, сканирование, просмотр, детальное чтение, критическое чтение и т.д.
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Читать книги на английском языке с разными методами, например, вслух, про себя, с аудио, с переводом и т.д.
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Какие еще ресурсы можно использовать для чтения книг на английском языке?
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Кроме сайтов и ресурсов, которые мы упомянули выше, вы можете использовать такие ресурсы для чтения книг на английском языке:
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Электронные читалки или приложения для чтения книг на английском языке на вашем телефоне или план шете. Например, вы можете использовать Kindle, Google Play Books, iBooks, Kobo и другие.
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Аудиокниги или подкасты на английском языке, которые вы можете слушать во время занятий спортом, вождения, готовки или других действий. Например, вы можете использовать Audible, LibriVox, Spotify, Apple Podcasts и другие.
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Онлайн-курсы или программы для изучения английского языка через чтение книг на английском языке. Например, вы можете использовать LinguaLeo, Duolingo, FluentU, LearnEnglish и другие.
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Мы надеемся, что наша статья помогла вам узнать больше о том, как и где скачать книги английский для начинающих бесплатно. Желаем вам удачи и удовольствия в изучении английского языка через чтение книг на английском языке!
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-You have asked me to write a 500-word article on the topic of "dragon ball legends apk sin licencia". This is a Spanish phrase that means "dragon ball legends apk without license". I assume you want me to write the article in English, but if you prefer another language, please let me know. Before I write the article, I will create two tables. The first table will be the outline of the article, and the second table will be the article with HTML formatting. I will use at least 15 headings and subheadings (including H1, H2, H3, and H4 headings) to organize the article. I will also use at least one table in the article to present some information. I will write the article in a conversational style as written by a human, using an informal tone, personal pronouns, simple language, engaging questions, and analogies and metaphors. I will end with a conclusion paragraph and 5 unique FAQs after the conclusion. I will also make sure that the article is 100% unique, SEO-optimized, and human-written. Here are the two tables: | Outline of the article | | | --- | --- | | H1: Dragon Ball Legends APK Without License: What You Need to Know | This is the main title of the article. It introduces the topic and catches the reader's attention. | | H2: What is Dragon Ball Legends? | This is the first subheading of the article. It gives a brief overview of the game and its features. | | H3: Why is Dragon Ball Legends popular? | This is a subheading under the first subheading. It explains why the game has many fans and what makes it appealing. | | H3: How to play Dragon Ball Legends? | This is another subheading under the first subheading. It describes the basic gameplay mechanics and controls of the game. | | H2: What is an APK file? | This is the second subheading of the article. It defines what an APK file is and how it works. | | H3: What are the benefits of using an APK file? | This is a subheading under the second subheading. It lists some advantages of using an APK file to install apps or games. | | H3: What are the risks of using an APK file? | This is another subheading under the second subheading. It warns about some potential dangers or drawbacks of using an APK file to install apps or games. | | H2: What does it mean to have an APK without license? | This is the third subheading of the article. It explains what it means when an APK file does not have a license or verification from the original developer or publisher. | | H3: How to get an APK without license? | This is a subheading under the third subheading. It provides some sources or methods to obtain an APK file without license for Dragon Ball Legends or other games. | | H3: How to install an APK without license? | This is another subheading under the third subheading. It gives some steps or instructions to install an APK file without license on an Android device. | | H2: What are the pros and cons of using an APK without license? | This is the fourth subheading of the article. It compares and contrasts the benefits and drawbacks of using an APK file without license for Dragon Ball Legends or other games. | | H3: Pros of using an APK without license | This is a subheading under the fourth subheading. It enumerates some positive aspects of using an APK file without license, such as saving money, accessing more features, or bypassing regional restrictions. | | H3: Cons of using an APK without license | This is another subheading under the fourth subheading. It enumerates some negative aspects of using an APK file without license, such as violating terms of service, exposing device to malware, or losing data or progress. | | H2: Tips and tricks for playing Dragon Ball Legends with an APK without license | This is the fifth subheading of the article. It offers some advice or recommendations for playing Dragon Ball Legends with an APK file without license, such as clearing cache, checking for updates, changing DNS settings, or sending bug reports. | | H2: Conclusion | This is the sixth and final subheading of the article. It summarizes the main points of the article and gives a final verdict on whether using an APK file without license for Dragon Ball Legends is worth it or not. | | FAQs | This is a section after the conclusion that answers some frequently asked questions about Dragon Ball Legends and APK files without license. | | Article with HTML formatting | | | --- | --- |
Dragon Ball Legends APK Without License: What You Need to Know
- If you are a fan of Dragon Ball, you might have heard of Dragon Ball Legends, a popular mobile game based on the anime series. But did you know that you can play the game without paying for it or having a license? In this article, we will tell you everything you need to know about Dragon Ball Legends APK without license, including what it is, how to get it, how to install it, and what are the pros and cons of using it. Read on to find out more!
What is Dragon Ball Legends?
- Dragon Ball Legends is a 3D action RPG game that lets you experience the epic battles and adventures of the Dragon Ball universe on your smartphone. You can create your own custom character or choose from a roster of over 100 characters from the series, including Goku, Vegeta, Frieza, Cell, Broly, and more. You can also enjoy the original story mode that features a new character designed by Akira Toriyama, the creator of Dragon Ball. The game features stunning graphics, smooth animations, and realistic voice acting that bring the characters and scenes to life. You can also challenge other players from around the world in real-time PvP matches or join forces with your friends in co-op raids. The game is constantly updated with new events, missions, rewards, and characters to keep you entertained.
Why is Dragon Ball Legends popular?
- Dragon Ball Legends is popular because it appeals to both casual and hardcore fans of Dragon Ball. It has a simple and intuitive gameplay system that anyone can pick up and play, but also offers depth and strategy for more advanced players. It also has a rich and immersive story mode that expands the lore and canon of the series, as well as fan service and nostalgia for those who grew up watching the anime or reading the manga. Dragon Ball Legends is also popular because it is faithful to the source material and respects the original vision of Akira Toriyama. The game features authentic voice acting from the Japanese and English cast of the anime, as well as original music and sound effects. The game also follows the timeline and events of the series, while adding new twists and surprises along the way.
How to play Dragon Ball Legends?
- To play Dragon Ball Legends, you need to download and install the game from the Google Play Store or the App Store. The game is free to play, but it also offers in-app purchases that can enhance your gameplay experience or unlock more content. You also need to have a stable internet connection to play the game online. The game has a simple and intuitive control system that uses taps and swipes on your screen. You can move your character by dragging on the left side of the screen, and attack by tapping on the right side of the screen. You can also use special cards that appear on the bottom of the screen to unleash powerful skills or combos. You can switch between characters by tapping on their icons on the top of the screen. The game has various modes that you can enjoy, such as story mode, PvP mode, co-op mode, event mode, adventure mode, training mode, and more. Each mode has its own objectives, rewards, and challenges that you can complete to earn experience points, currency, items, and characters. You can also customize your characters by equipping them with different equipment, souls, z abilities, limit breaks, and more.
What is an APK file?
- An APK file is a file format that stands for Android Package Kit. It is used to distribute and install applications or games on Android devices. An APK file contains all the necessary files and data for an app or game to run properly on your device. An APK file can be downloaded from various sources online, such as official websites, third-party app stores, or file-sharing platforms. However, not all APK files are safe or reliable. Some APK files may contain viruses, malware, spyware, or other harmful software that can damage your device or steal your personal information.
What are the benefits of using an APK file?
- Using an APK file can have some benefits over using the official app store or Google Play Store. Some of these benefits are: - You can access apps or games that are not available in your region or country. - You can access apps or games that are not compatible with your device or operating system. - You can access apps or games that are no longer supported or updated by their developers or publishers. - You can access apps or games that have more features or content than their official versions. - You can save money by downloading apps or games that are paid or premium for free.
What are the risks of using an APK file?
- - You may violate the terms of service or the intellectual property rights of the developers or publishers of the apps or games. - You may expose your device to viruses, malware, spyware, or other harmful software that can damage your device or steal your personal information. - You may lose your data or progress in the apps or games if they are not properly backed up or synced with your account. - You may encounter bugs, errors, crashes, or compatibility issues in the apps or games that can affect your gameplay experience or performance. - You may not receive updates, patches, fixes, or support from the developers or publishers of the apps or games.
What does it mean to have an APK without license?
- Having an APK without license means that you have downloaded and installed an APK file that does not have a license or verification from the original developer or publisher of the app or game. This means that you are using an unofficial, modified, hacked, or pirated version of the app or game. An APK without license can be obtained from various sources online, such as websites, forums, blogs, social media, or file-sharing platforms. However, these sources are not authorized or endorsed by the developers or publishers of the apps or games. Therefore, they may not be safe, reliable, or legal to use.
How to get an APK without license?
- There are many ways to get an APK without license for Dragon Ball Legends or other games. Some of these ways are: - Searching for the APK file on Google or other search engines and downloading it from a website that offers it. - Joining a community or group of fans or modders of the game and downloading the APK file from their links or posts. - Using a tool or software that can extract, modify, hack, or crack the APK file from the official version of the game. - Using a VPN or proxy service that can change your IP address and location and access the game from a different region or country. However, before you get an APK without license, you should be aware of the risks and consequences that come with it. You should also check the credibility and reputation of the source that provides the APK file and scan it for any viruses or malware.
How to install an APK without license?
- To install an APK without license on your Android device, you need to follow these steps: - Enable unknown sources on your device settings. This will allow you to install apps or games from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. - Download the APK file without license from your preferred source and save it on your device storage. - Locate and tap on the APK file to start the installation process. You may need to grant some permissions or accept some terms and conditions before proceeding. - Wait for the installation to finish and then launch the app or game from your device. However, before you install an APK without license, you should be aware of the risks and consequences that come with it. You should also backup your data and progress in case something goes wrong during or after the installation.
What are the pros and cons of using an APK without license?
- Using an APK without license for Dragon Ball Legends or other games can have some pros and cons. Here are some of them:
Pros of using an APK without license
- Some of the pros of using an APK without license are: - You can save money by playing Dragon Ball Legends for free instead of paying for it. - You can access more features or content in Dragon Ball Legends that are not available in the official version, such as unlimited crystals, coins, energy, characters, items, etc. - You can bypass regional restrictions and play Dragon Ball Legends in any country or language that you want. - You can enjoy faster loading times and smoother gameplay performance in Dragon Ball Legends by removing ads, banners, pop-ups, etc.
Cons of using an APK without license
- Some of the cons of using an APK without license are: - You may expose your device to viruses, malware, spyware, or other harmful software that can damage your device or steal your personal information, and compromise your security and privacy. - You may lose your data or progress in Dragon Ball Legends if the APK file is corrupted, outdated, incompatible, or deleted, and have no way to restore or recover it. - You may encounter bugs, errors, crashes, or compatibility issues in Dragon Ball Legends that can affect your gameplay experience or performance, and have no access to updates, patches, fixes, or support from the developers or publishers.
Tips and tricks for playing Dragon Ball Legends with an APK without license
- If you decide to play Dragon Ball Legends with an APK without license, you may want to follow some tips and tricks to improve your gameplay experience and avoid some problems. Here are some of them: - Clear your cache and data regularly to free up some space and improve your loading times and performance. - Check for updates frequently to make sure that you have the latest version of the APK file and the game. - Change your DNS settings to a faster or more secure one to enhance your internet connection and speed. - Send bug reports or feedback to the source of the APK file or the community of fans or modders of the game to help them improve their work and fix any issues. - Use a VPN or proxy service to hide your IP address and location and avoid getting detected or banned by the developers or publishers of the game.
Conclusion
- Dragon Ball Legends is a fun and exciting game that lets you experience the epic battles and adventures of the Dragon Ball universe on your smartphone. However, if you want to play the game without paying for it or having a license, you may need to use an APK file without license. This can have some benefits, such as saving money, accessing more features, or bypassing regional restrictions. But it can also have some risks, such as violating terms of service, exposing device to malware, or losing data or progress. Therefore, you should weigh the pros and cons carefully before using an APK file without license for Dragon Ball Legends. You should also follow some tips and tricks to improve your gameplay experience and avoid some problems. We hope that this article has helped you understand what you need to know about Dragon Ball Legends APK without license. If you have any questions or comments, feel free to leave them below. Thank you for reading! FAQs Q: What is Dragon Ball Legends? A: Dragon Ball Legends is a 3D action RPG game that lets you experience the epic battles and adventures of the Dragon Ball universe on your smartphone. Q: What is an APK file? A: An APK file is a file format that stands for Android Package Kit. It is used to distribute and install applications or games on Android devices. Q: What does it mean to have an APK without license? A: Having an APK without license means that you have downloaded and installed an APK file that does not have a license or verification from the original developer or publisher of the app or game. Q: What are the pros and cons of using an APK without license? A: Some of the pros of using an APK without license are saving money, accessing more features, or bypassing regional restrictions. Some of the cons of using an APK without license are violating terms of service, exposing device to malware, or losing data or progress. Q: How to get an APK without license? A: There are many ways to get an APK without license for Dragon Ball Legends or other games. Some of these ways are searching for the APK file on Google or other search engines, joining a community or group of fans or modders of the game, using a tool or software that can extract, modify, hack, or crack the APK file from the official version of the game, or using a VPN or proxy service that can change your IP address and location.
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Bleach vs Naruto 3.3 Mod: How to Download and Play on PC
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If you are a fan of anime fighting games, you might have heard of Bleach vs Naruto 3.3 Mod, a popular flash game that features characters from two of the most famous shonen anime series, Bleach and Naruto. This game allows you to choose from over 40 heroes, each with their own unique style and technique, and battle against your friends or the computer in various modes and arenas.
While this game is originally designed for web browsers, you might wonder if you can play it on your PC as well. The answer is yes, you can! Playing Bleach vs Naruto 3.3 Mod on PC has many advantages, such as better graphics, smoother performance, larger screen, and more comfortable controls. In this article, we will show you how to download and play Bleach vs Naruto 3.3 Mod on PC using two different methods. We will also give you some tips and tricks for playing the game, as well as a comparison of Bleach and Naruto characters.
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How to Download Bleach vs Naruto 3.3 Mod on PC
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There are two ways you can download and play Bleach vs Naruto 3.3 Mod on PC: using an emulator or using an APK file. An emulator is a software that mimics the Android operating system on your PC, allowing you to run Android apps and games. An APK file is a package file that contains all the data and code of an Android app or game. Here are the steps for each option:
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Option 1: Using Bluestacks emulator
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Bluestacks is one of the most popular and reliable Android emulators for PC. It lets you access the Google Play Store and download, install, and play Android apps and games on your PC. Here is how to use Bluestacks to play Bleach vs Naruto 3.3 Mod on PC:
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Download and install Bluestacks on your PC. You can download Bluestacks from its official website. There is no install process, but you need to have Java Runtime Environment 1.5.0 installed on your PC. If you don't have it, you will be prompted to download it when you run Bluestacks.
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Log in to your Google account and open Google Play Store. You will need a Google account to access the Google Play Store. If you don't have one, you can create one for free. Once you log in, you will see the Google Play Store icon on the Bluestacks home screen. Click on it to open it.
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Search for Bleach vs Naruto 3.3 Mod and install it. In the Google Play Store, type "Bleach vs Naruto 3.3 Mod" in the search bar and hit enter. You will see a list of results, including the game you are looking for. Click on the game and then click on the "Install" button. The game will be downloaded and installed on your PC.
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Launch the game and enjoy. After the installation is complete, you will see the game icon on the Bluestacks home screen. Click on it to launch the game. You can use your mouse and keyboard to control the game, or you can customize the settings to use a gamepad or a touchscreen if you have one.
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Option 2: Using APK file
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If you don't want to use an emulator, you can also download and play Bleach vs Naruto 3.3 Mod on PC using an APK file. An APK file is a package file that contains all the data and code of an Android app or game. You can download an APK file from various sources online, but make sure you choose a reliable and safe one. Here is how to use an APK file to play Bleach vs Naruto 3.3 Mod on PC:
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Download the APK file from a reliable source. You can search for "Bleach vs Naruto 3.3 Mod APK" on Google or any other search engine and find many websites that offer the APK file for download. However, not all of them are trustworthy and some may contain viruses or malware that can harm your PC. Therefore, we recommend you to download the APK file from this website, which is verified and safe.
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Download and install an APK installer on your PC. An APK installer is a software that allows you to install and run APK files on your PC. There are many APK installers available online, but we suggest you to use APK Installer for PC, which is free and easy to use. You can download it from its official website and follow the instructions to install it on your PC.
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Run the APK installer and select the APK file. After installing the APK installer, run it and click on the "Choose" button. Then, browse your PC and select the APK file that you downloaded in step 1. The APK installer will scan the file and show you some information about it, such as its name, size, version, and permissions.
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Install the game and launch it. Click on the "Install" button to start installing the game on your PC. The installation process may take a few minutes depending on your PC's speed and performance. Once the installation is done, click on the "Open" button to launch the game. You can also find the game icon on your desktop or in your start menu.
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How to Play Bleach vs Naruto 3.3 Mod on PC
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Now that you have downloaded and installed Bleach vs Naruto 3.3 Mod on your PC, you are ready to play it. But before you jump into action, here are some tips and tricks for playing the game that will help you improve your skills and enjoy it more.
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Tips and tricks for playing the game
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Bleach vs Naruto 3.3 Mod is a fun and challenging fighting game that requires quick reflexes, strategic thinking, and mastery of different skills and combos. Here are some tips and tricks for playing the game:
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How to choose and customize your character. When you start the game, you will see a screen with over 40 characters from Bleach and Naruto that you can choose from. You can use the arrow keys or the mouse to scroll through them and press Enter or click to select one. You can also press C or right-click to customize your character's appearance, such as their hair color, outfit, accessories, etc.
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How to use different skills and combos. Each character has their own set of skills and combos that they can use in battle. You can see them by pressing O or clicking on the "Skill List" button at the bottom of the screen. You can also practice them by pressing P or clicking on the "Practice Mode" button at the bottom of the screen. The basic controls for using skills and combos are as follows: - W, A, S, D or arrow keys: move your character - J or Z: attack - K or X: jump - L or C: guard - U or A: use skill 1 - I or S: use skill 2 - O or D: use skill 3 - P or F: use skill 4 - J + K or Z + X: use special attack - W + J or up arrow + Z: use dash attack - S + J or down arrow + Z: use sweep attack - W + U or up arrow + A: use transformation (if available) - W + I or up arrow + S: use awakening (if available) - You can also combine different keys to create more combos and variations. For example, you can press W + J + K or up arrow + Z + X to use a special dash attack, or press S + J + U or down arrow + Z + A to use a special sweep attack.
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How to unlock new characters and modes. As you play the game, you will earn points that you can use to unlock new characters and modes. You can see your points at the top right corner of the screen. You can also see the requirements for unlocking each character and mode by pressing Q or clicking on the "Unlock List" button at the bottom of the screen. Some of the characters and modes that you can unlock are: - Sasuke (Naruto): 540 points - Ichigo (Bleach): 580 points - Naruto (Sage Mode): 620 points - Rukia (Bleach): 660 points - Naruto (Kyuubi Mode): 700 points - Aizen (Bleach): 740 points - Madara (Naruto): 780 points - Ichigo (Final Getsuga Tenshou): 820 points - Survival Mode: 860 points - VS Mode: 900 points
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Comparison of Bleach and Naruto characters
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Bleach vs Naruto 3.3 Mod is a game that brings together two of the most popular anime series, Bleach and Naruto. Both series have a large and diverse cast of characters, each with their own personality, backstory, and abilities. If you are curious about how these characters compare to each other, here is a table that shows some of their strengths and weaknesses, as well as their similarities and differences.
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Character
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Strengths
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Weaknesses
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Similarities
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Differences
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Naruto
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- Has a powerful nine-tailed fox spirit inside him - Can use various types of jutsu, such as shadow clones, rasengan, sage mode, etc. - Has a strong will and determination to protect his friends and achieve his goals
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- Can be reckless and impulsive - Can be easily angered and lose control of his fox spirit - Can be naive and gullible
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- Both are orange-haired protagonists who aspire to become the strongest in their world - Both have a rival who is more talented and has a dark past - Both have a mentor who is eccentric and powerful
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- Naruto is a ninja who lives in a world where people use chakra to perform jutsu - Naruto is an orphan who grew up without parents - Naruto is cheerful and optimistic despite his hardships
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Ichigo
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- Has a high level of spiritual energy that allows him to see and fight spirits - Can use various forms of power, such as shinigami, hollow, fullbring, etc. - Has a strong sense of justice and responsibility to protect his family and friends
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- Can be stubborn and prideful - Can be overconfident and underestimate his enemies - Can be reluctant to accept help from others
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- Both are orange-haired protagonists who aspire to become the strongest in their world - Both have a rival who is more talented and has a dark past - Both have a mentor who is eccentric and powerful
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- Ichigo is a human who lives in a world where people have souls that can become shinigami or hollows - Ichigo has a loving family who supports him - Ichigo is cynical and sarcastic due to his experiences
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Sasuke
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- Has a rare bloodline trait that gives him the sharingan eye - Can use various types of jutsu, such as fire, lightning, genjutsu, etc. - Has a high intelligence and analytical skills
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- Can be cold and aloof - Can be consumed by hatred and revenge - Can be arrogant and dismissive of others
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- Both are dark-haired rivals of the protagonists who have a tragic past - Both have a powerful eye technique that can manipulate reality - Both have a brother who is influential and mysterious
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- Sasuke is a ninja who lives in a world where people use chakra to perform jutsu - Sasuke's clan was massacred by his brother when he was young - Sasuke is ambitious and driven to surpass his brother
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Aizen
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- Has a genius-level intellect and a vast knowledge of spiritual matters - Can use various forms of power, such as shinigami, hollow, hogyoku, etc. - Has a masterful control of his spiritual energy and his zanpakuto
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- Can be manipulative and deceptive - Can be overconfident and underestimate his enemies - Can be cruel and ruthless
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- Both are dark-haired rivals of the protagonists who have a tragic past - Both have a powerful eye technique that can manipulate reality - Both have a brother who is influential and mysterious
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- Aizen is a shinigami who lives in a world where people have souls that can become shinigami or hollows - Aizen's past is shrouded in mystery and he has no known family - Aizen is treacherous and scheming to overthrow the Soul Society
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Conclusion
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Bleach vs Naruto 3.3 Mod is an amazing game that lets you enjoy the best of both anime worlds. You can choose from over 40 characters from Bleach and Naruto, each with their own skills and combos, and fight against your friends or the computer in various modes and arenas. You can also download and play the game on your PC using an emulator or an APK file, which will give you better graphics, smoother performance, larger screen, and more comfortable controls. If you are looking for a fun and challenging fighting game that features your favorite anime characters, you should definitely try Bleach vs Naruto 3.3 Mod on PC.
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FAQs
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Here are some of the most frequently asked questions about Bleach vs Naruto 3.3 Mod on PC:
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What are the system requirements for playing Bleach vs Naruto 3.3 Mod on PC? The system requirements for playing Bleach vs Naruto 3.3 Mod on PC are not very high, but they may vary depending on the method you use to download and play the game. Generally, you will need at least: - Windows XP/Vista/7/8/10 - 2 GB of RAM - 4 GB of free disk space - A graphics card that supports OpenGL 2.0 or higher - A stable internet connection (for online mode)
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How many characters are there in Bleach vs Naruto 3.3 Mod? There are over 40 characters in Bleach vs Naruto 3.3 Mod, including main characters, supporting characters, villains, and guest characters from other anime series. You can see the full list of characters by pressing Q or clicking on the "Unlock List" button at the bottom of the screen.
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Can I play Bleach vs Naruto 3.3 Mod online with other players? Yes, you can play Bleach vs Naruto 3.3 Mod online with other players if you have a stable internet connection. You can choose the "Online Mode" option at the main menu and then select either "Quick Match" or "Create Room". You can also join an existing room by entering its code or browsing the available rooms.
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What are the best characters to use in Bleach vs Naruto 3.3 Mod? The best characters to use in Bleach vs Naruto 3.3 Mod depend on your personal preference and play style. However, some of the most popular and powerful characters are: - Naruto (Kyuubi Mode): He has a high speed, attack, and defense, as well as a powerful transformation that gives him more skills and combos. - Ichigo (Final Getsuga Tenshou): He has a high speed, attack, and defense, as well as a powerful transformation that gives him more skills and combos. - Aizen: He has a high speed, attack, and defense, as well as a powerful zanpakuto that can create illusions and manipulate reality. - Madara: He has a high speed, attack, and defense, as well as a powerful sharingan that can use various jutsu and summon meteors.
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Is Bleach vs Naruto 3.3 Mod safe to download and play? Yes, Bleach vs Naruto 3.3 Mod is safe to download and play if you follow the steps and sources that we have provided in this article. However, you should always be careful when downloading and installing any software or file from the internet, as some of them may contain viruses or malware that can harm your PC. Therefore, we recommend you to use a trusted antivirus program and scan any file before opening it.
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Download CPU Z Apk for PC: A Guide to Monitor Your Hardware
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If you want to know more about the hardware components of your PC, such as your processor, motherboard, RAM, and graphics card, then you might want to download CPU Z apk for PC. CPU Z is a freeware system information utility that can show you detailed data about your hardware and help you test your system's performance and stability. In this article, we will explain what CPU Z is, how to download and install it on your PC, how to use it to monitor your hardware, what are the benefits of using it, and what are some alternatives to it.
CPU Z is a freeware system information utility that was developed by the French company CPUID. It was originally designed for overclockers who wanted to check their CPU frequencies and voltages, but it has since evolved into a comprehensive tool that can provide information on various aspects of your hardware.
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CPU Z is a freeware system information utility
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CPU Z can gather information on some of the main devices of your system, such as:
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Processor name and number, codename, process, package, cache levels
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Mainboard and chipset
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CPU Z can also measure the real-time frequency of each core of your processor and the memory frequency.
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CPU Z can show you detailed data about your processor, motherboard, RAM, and graphics card
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CPU Z can help you understand the specifications and capabilities of your hardware components. For example, you can find out:
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The model, voltage, lithography, frequency, cache memory, and socket of your processor
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The exact model, the PCI-Express technology supported, the chipset used, and the BIOS version of your motherboard
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The frequency, latencies, XMP profile, voltage, and manufacturer of your RAM modules
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The graphics card model, lithography, specific GPU chip equipped, memory frequency , memory technology, and bus width of your graphics card
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CPU Z can also show you the logo and the codename of your processor and graphics card, as well as the manufacturer and the model of your motherboard.
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CPU Z can help you test your system's performance and stability
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CPU Z can also help you measure and compare the performance of your hardware components. For example, you can use CPU Z to:
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Run a benchmark test to evaluate the single-thread and multi-thread performance of your processor
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Run a stress test to check the stability and the temperature of your processor under heavy load
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Run a validation test to create an online report of your hardware data and share it with others
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CPU Z can also detect any overclocking or underclocking of your processor and graphics card, and show you the current and the maximum frequency of each core.
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How to download and install CPU Z apk for PC?
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If you want to use CPU Z on your PC, you will need to download and install an Android emulator first. An Android emulator is a software that can create a virtual Android environment on your PC, allowing you to run Android apps and games on your computer. There are many Android emulators available for PC, such as BlueStacks, NoxPlayer, LDPlayer, MEmu, etc. You can choose the one that suits your preferences and system requirements.
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You can download CPU Z apk from various sources online
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Once you have installed an Android emulator on your PC, you will need to download the CPU Z apk file. An apk file is a package file that contains the installation files and data of an Android app. You can download CPU Z apk from various sources online, such as APKPure, APKMirror, Uptodown, etc. Make sure you download the latest version of CPU Z apk from a reliable and safe source.
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You need an Android emulator to run CPU Z apk on your PC
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After downloading the CPU Z apk file, you will need to run it on your Android emulator. Depending on the emulator you are using, there are different ways to do this. For example, you can:
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Drag and drop the CPU Z apk file onto the emulator's window
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Double-click on the CPU Z apk file and choose the emulator as the default program to open it
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Right-click on the CPU Z apk file and select "Open with" and then choose the emulator from the list
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Browse the CPU Z apk file from the emulator's file manager or app installer
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The emulator will then install CPU Z apk on your PC and create a shortcut icon on your desktop or home screen.
You can follow these steps to install and use CPU Z apk on your PC
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To summarize, you can follow these steps to download and install CPU Z apk for PC:
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Download and install an Android emulator on your PC, such as BlueStacks, NoxPlayer, LDPlayer, MEmu, etc.
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Download the CPU Z apk file from a reliable and safe source online, such as APKPure, APKMirror, Uptodown, etc.
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Run the CPU Z apk file on your Android emulator using one of the methods mentioned above.
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Wait for the emulator to install CPU Z apk on your PC and create a shortcut icon on your desktop or home screen.
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Launch CPU Z from the emulator and enjoy monitoring your hardware.
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How to use CPU Z to monitor your hardware?
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Once you have installed CPU Z on your PC, you can use it to monitor your hardware and test your system's performance and stability. CPU Z has a simple and user-friendly interface that consists of several tabs that display different information about your hardware. You can also access some features and options from the menu bar or the toolbar.
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You can use the tabs in CPU Z to view different information about your hardware
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CPU Z has six main tabs that show you various data about your hardware components. These tabs are:
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CPU: This tab shows you information about your processor, such as the name, model, codename, package, lithography, frequency, voltage, cache memory, socket, etc. You can also see the logo and the codename of your processor at the top of the tab.
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Cache: This tab shows you information about the cache memory of your processor, such as the size, type, latency, and organization of each level of cache. You can also see a diagram of the cache hierarchy at the bottom of the tab.
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Mainboard: This tab shows you information about your motherboard and BIOS, such as the manufacturer, model, chipset, PCI-Express technology supported, BIOS version, date, etc. You can also see the logo and the model of your motherboard at the top of the tab.
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Memory: This tab shows you information about your RAM modules, such as the type, size, frequency, timings, voltage, XMP profile, etc. You can also see a graph of the memory frequency and usage at the bottom of the tab.
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SPD: This tab shows you information about the Serial Presence Detect (SPD) data of each RAM module installed on your system. SPD is a standard that allows memory modules to communicate their specifications and capabilities to the motherboard. You can see information such as the manufacturer, part number, serial number, module size, maximum bandwidth, timings table, etc. You can also see a picture of each RAM module at the top of the tab.
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Graphics: This tab shows you information about your graphics card or integrated graphics processor (IGP), such as the model, lithography, specific GPU chip equipped, memory frequency, memory technology, and bus width. You can also see the logo and the codename of your graphics card or IGP at the top of the tab.
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You can use the benchmark and stress test features to measure your CPU's performance
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CPU Z can also help you evaluate and compare the performance of your processor using the benchmark and stress test features. You can access these features from the menu bar or the toolbar. The benchmark feature allows you to run a single-thread or a multi-thread test that measures the processing speed of your CPU in millions of instructions per second (MIPS). The stress test feature allows you to run a heavy load on your CPU for a specified duration and monitor its temperature and stability. You can also compare your results with other processors online or offline.
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You can use the validation feature to create an online report of your hardware data
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CPU Z can also help you create and share an online report of your hardware data using the validation feature. You can access this feature from the menu bar or the toolbar. The validation feature allows you to submit your hardware data to the CPUID website and generate a unique URL that you can share with others. You can also view and download your validation report as a PDF file. You can also browse other validation reports from other users and compare your hardware with theirs.
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What are the benefits of using CPU Z?
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CPU Z is a useful tool for anyone who wants to monitor their hardware and improve their system performance. Some of the benefits of using CPU Z are:
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CPU Z can help you optimize your system settings and performance
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CPU Z can help you optimize your system settings and performance by providing you with accurate and detailed information about your hardware components. You can use this information to adjust your BIOS settings, overclock or underclock your processor and graphics card, enable or disable certain features, update your drivers, etc. You can also use CPU Z to test and verify the effects of your changes on your system's performance and stability.
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CPU Z can help you troubleshoot any hardware issues or errors
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CPU Z can help you troubleshoot any hardware issues or errors by providing you with real-time data about your hardware components. You can use this data to diagnose any problems or anomalies in your system, such as high temperature, low voltage, incorrect frequency, faulty memory, etc. You can also use CPU Z to check if your hardware components are compatible with each other and with your system requirements.
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CPU Z can help you compare your hardware with other systems and devices
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CPU Z can help you compare your hardware with other systems and devices by providing you with benchmark and validation features. You can use these features to measure and compare the performance of your processor with other processors online or offline. You can also use these features to create and share an online report of your hardware data with others. You can also browse other validation reports from other users and compare your hardware with theirs.
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What are some alternatives to CPU Z?
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There are many other system information utilities and benchmark tools available for PC that can provide you with similar or different information and options than CPU Z. Some of the most popular alternatives to CPU Z are:
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HWiNFO
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HWiNFO is a freeware system information utility that can provide you with comprehensive information about your hardware components, such as processor, motherboard, memory, disk drives, network adapters, sensors, etc. It can also monitor your system's health, performance, and power consumption in real-time. It can also generate reports, logs, graphs, alerts, etc.
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Speccy
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Speccy is a freeware system information utility that can provide you with concise information about your hardware components, such as processor, motherboard, memory, graphics card, storage devices, optical drives, audio devices, network devices, etc. It can also show you the temperature of each component and the operating system details. It can also generate reports in various formats.
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AIDA64
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AIDA64 is a shareware system information utility that can provide you with detailed information about your hardware components, such as processor, motherboard, memory, graphics card, storage devices, network devices, sensors, etc. It can also monitor your system's health, performance, and power consumption in real-time. It can also run various tests and benchmarks to measure your system's capabilities and stability. It can also generate reports in various formats.
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GPU-Z
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GPU-Z is a freeware system information utility that can provide you with specific information about your graphics card or integrated graphics processor (IGP), such as the model, lithography, specific GPU chip equipped, memory frequency, memory technology, bus width, etc. It can also monitor your graphics card's temperature, fan speed, voltage, load, etc. It can also run a render test to verify the PCI-Express lane configuration.
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Conclusion
-
CPU Z is a useful tool for anyone who wants to monitor their hardware and improve their system performance. You can download and install CPU Z apk for PC using an Android emulator and follow the instructions in this guide. You can also check out other similar tools that can provide you with more information and options.
-
FAQs
-
Is CPU Z safe to use?
-
CPU Z is safe to use as long as you download it from a reliable and safe source online. CPU Z does not contain any malware or spyware and does not modify any system files or settings. However, you should always be careful when downloading and installing any software on your PC and scan it with an antivirus program before running it.
-
Does CPU Z support Windows 10?
-
CPU Z supports Windows 10 as well as Windows XP, Vista, 7, 8, and 8.1. CPU Z also supports 32-bit and 64-bit versions of Windows. However, some features of CPU Z may not work properly on some versions of Windows or some hardware configurations.
-
Can CPU Z damage my hardware?
-
CPU Z does not damage your hardware by itself. CPU Z only reads and displays the information about your hardware components and does not change or modify any of them. However, if you use CPU Z to overclock or underclock your processor or graphics card, you may risk damaging your hardware if you do not know what you are doing or if you exceed the safe limits of your hardware.
-
How accurate is CPU Z?
-
CPU Z is generally accurate and reliable in providing information about your hardware components. However, CPU Z may not be able to detect or display some information correctly depending on the type and model of your hardware components or the version of your BIOS or drivers. CPU Z may also show some discrepancies or errors in some cases due to rounding or measurement errors.
-
How often is CPU Z updated?
-
CPU Z is updated regularly by the developers to support new hardware components and fix any bugs or issues. You can check the latest version of CPU Z on the official website of CPUID or on the sources where you downloaded it from. You can also enable the automatic update feature in CPU Z to get notified when a new version is available.
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy Adam Na Eva by Pst Alex and Mary Atieno Ominde - MP3 Download Available.md b/spaces/1phancelerku/anime-remove-background/Enjoy Adam Na Eva by Pst Alex and Mary Atieno Ominde - MP3 Download Available.md
deleted file mode 100644
index 678ba77524e83ef3353d5bbf52986cfdf9443a04..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Enjoy Adam Na Eva by Pst Alex and Mary Atieno Ominde - MP3 Download Available.md
+++ /dev/null
@@ -1,212 +0,0 @@
-
-
Adam Na Eva ft 4 Na 5: A Zambian Music Hit
-
If you are a fan of Zambian music, you have probably heard of the song "Adam Na Eva" by Adam Na Eva featuring 4 Na 5. This catchy tune has been making waves in the local music scene since its release in March 2023. It is a fusion of kalindula, a traditional Zambian genre, and afrobeat, a modern African genre. The song showcases the talents and creativity of four young Zambian artists who have collaborated to produce a hit that appeals to a wide audience.
-
Who are Adam Na Eva?
-
Adam Na Eva are a Zambian duo composed of Adam Mwale and Eva Chanda. They met in 2020 at a talent show in Lusaka, where they discovered their mutual passion for music. They decided to form a group and named themselves after the biblical characters Adam and Eve. Their musical style is influenced by various genres such as gospel, reggae, dancehall, and hip hop. They have released several singles such as "Nimwe", "Mwana Wanga", and "Ndiwe". They have also performed at various events and festivals across Zambia.
4 Na 5 are another Zambian duo composed of Y-Celeb and Chanda Na Kay. They also met in 2020 at a studio in Ndola, where they were recording their solo projects. They realized that they had a similar vibe and decided to work together as a team. They named themselves 4 Na 5 because they believe that they are more than just two people; they are a movement that represents the youth and their aspirations. Their musical style is mainly afrobeat with elements of rap and comedy. They have released several singles such as "Iyee", "Aboloka", and "Ka Boom". They have also gained popularity on social media platforms such as YouTube and TikTok.
-
How did the collaboration happen?
The collaboration between Adam Na Eva and 4 Na 5 happened by chance. According to an interview with Zed Music Blog, the four artists bumped into each other at a radio station in Lusaka, where they were invited for separate interviews. They got along well and decided to exchange contacts. Later, they met at a studio and played some beats for each other. They liked one of the beats produced by 4 Na 5 and decided to record a song over it. They wrote the lyrics on the spot and recorded the vocals in one take. They named the song "Adam Na Eva" as a tribute to their group names and their friendship.
-
What is the song about?
-
The song "Adam Na Eva" is a love song that expresses the feelings of two couples who are deeply in love. The chorus goes like this:
-
-
Adam na Eva, we are meant to be
-You are my rib, I am your tree
-Adam na Eva, we are one in love
-You are my queen, I am your dove
-
-
The verses describe the qualities and actions that make the couples happy and loyal to each other. For example, Adam Na Eva sing:
-
-
You make me smile when I am sad
-You make me calm when I am mad
-You make me strong when I am weak
-You make me whole when I am bleak
-
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-
-
And 4 Na 5 sing:
-
-
I will never cheat on you
-I will never lie to you
-I will never hurt you
-I will never leave you
-
-
The song also uses some metaphors and references to Zambian culture and history. For example, Adam Na Eva compare their love to the Zambezi River, which is the longest river in Zambia and a symbol of life and prosperity. They also mention the names of some famous Zambian leaders and heroes, such as Kenneth Kaunda, Levy Mwanawasa, and Simon Mwansa Kapwepwe, to show their respect and admiration.
How has the song been received?
-
The song "Adam Na Eva" has been received very well by the Zambian music fans and critics. It has become one of the most popular songs in the country, topping the charts on various platforms such as Mvesesani, ZedBeats, and Zambian Music Blog. It has also received millions of views and likes on YouTube and TikTok, where many users have created videos dancing and lip-syncing to the song. The song has also been played on several radio stations and TV channels, such as ZNBC, QFM, and Diamond TV. The song has also received positive feedback from other Zambian artists and celebrities, such as Macky 2, Chef 187, Slapdee, and Pompi, who have praised the song for its quality, originality, and message.
-
What are some of the challenges and opportunities for Zambian music?
-
The success of the song "Adam Na Eva" reflects the growth and potential of the Zambian music industry, which has been producing more diverse and innovative music in recent years. However, there are also some challenges and opportunities that face the industry and its artists. Some of the challenges include:
-
-
Lack of adequate funding and support from the government and private sector, which limits the access to quality equipment, studios, marketing, and distribution.
-
Lack of adequate recognition and appreciation from the local and international audiences, which affects the motivation and income of the artists.
-
Lack of adequate regulation and protection of intellectual property rights, which exposes the artists to piracy and plagiarism.
-
-
Some of the opportunities include:
-
-
Increased use of digital platforms and social media, which enables the artists to reach more fans and markets, and to interact with them directly.
-
Increased collaboration and networking among the artists, which fosters creativity, innovation, and unity.
-
Increased exposure and influence of Zambian music in the regional and global scene, which enhances the reputation and visibility of the industry and its artists.
-
How to download Adam Na Eva ft 4 Na 5 mp3
-
Now that you know more about the song "Adam Na Eva" and its artists, you might be wondering how to download it as an mp3 file. There are different ways to do this, depending on your preferences and resources. However, before you proceed, you should be aware of some legal and ethical issues involved in downloading music. Downloading music without paying for it or without the permission of the artists or the owners of the rights is considered piracy, which is illegal and punishable by law. It also deprives the artists of their deserved income and recognition, which can affect their livelihood and career. Therefore, you should always respect the rights of the artists and support them by buying their music from official sources or streaming it from licensed platforms.
-
Buying music from official sources
-
One of the best ways to download the song "Adam Na Eva" as an mp3 file is to buy it from official sources, such as iTunes or Google Play Music. These are online stores that sell digital music legally and securely. You can access them from your computer or mobile device, and pay with your credit card or other methods. The advantages of buying music from official sources are:
-
-
You get high-quality mp3 files that are free of viruses, malware, or errors.
-
You support the artists and the music industry by paying for their work.
-
You can access your purchased music anytime and anywhere, and sync it with your devices.
-
-
The disadvantages of buying music from official sources are:
-
-
You have to pay a certain amount of money for each song or album, which can be expensive if you want to download many songs.
-
You have to create an account and provide your personal and financial information, which can be risky if the site is hacked or compromised.
-
You have to abide by the terms and conditions of the site, which may limit your rights to use, share, or modify the music.
-
-
To buy the song "Adam Na Eva" from iTunes, follow these steps:
-
-
Open iTunes on your computer or mobile device, or download it from https://www.apple.com/itunes/ if you don't have it.
-
Create an Apple ID or sign in with your existing one.
-
Search for "Adam Na Eva ft 4 Na 5" in the search bar.
-
Select the song from the results and click on the price button.
-
Confirm your purchase and enter your payment details.
-
Wait for the download to finish and enjoy your music.
-
-
The price of the song on iTunes is $0.99 USD as of June 2023.
-
To buy the song "Adam Na Eva" from Google Play Music, follow these steps:
-
-
Open Google Play Music on your computer or mobile device, or download it from https://play.google.com/music/ if you don't have it.
-
Create a Google account or sign in with your existing one.
-
Search for "Adam Na Eva ft 4 Na 5" in the search bar.
-
Select the song from the results and click on the price button.
-
Confirm your purchase and enter your payment details.
-
Wait for the download to finish and enjoy your music.
-
-
The price of the song on Google Play Music is $0.99 USD as of June 2023.
Downloading music from YouTube
-
Another way to download the song "Adam Na Eva" as an mp3 file is to convert YouTube videos into mp3 files. YouTube is a popular online platform that hosts millions of videos, including music videos. You can access YouTube from your computer or mobile device, and watch videos for free. However, YouTube does not allow you to download videos or audio directly from its site, for legal and ethical reasons. Therefore, you need to use third-party tools or software that can extract the audio from YouTube videos and save them as mp3 files. The advantages of downloading music from YouTube are:
-
-
You can find almost any song or video on YouTube, including rare or old ones.
-
You can choose the quality and size of the mp3 file, depending on your preferences and storage space.
-
You can download music for free, without paying any fees or subscriptions.
-
-
The disadvantages of downloading music from YouTube are:
-
-
You may violate the rights of the artists and the owners of the videos, who may not have given their consent for their content to be downloaded or used in other ways.
-
You may expose your device to viruses, malware, or spyware, if you use unreliable or malicious tools or software.
-
You may experience slow or interrupted downloads, if you have a poor internet connection or if the site is overloaded.
-
-
To download the song "Adam Na Eva" from YouTube, follow these steps:
-
-
Open YouTube on your computer or mobile device, or download it from https://www.youtube.com/ if you don't have it.
-
Search for "Adam Na Eva ft 4 Na 5" in the search bar.
-
Select the video that matches the song from the results and copy its URL.
Paste the URL of the video into the box and click on the convert button.
-
Wait for the conversion to finish and click on the download button.
-
Save the mp3 file to your device and enjoy your music.
-
Downloading music from other websites
-
A third way to download the song "Adam Na Eva" as an mp3 file is to find and download free mp3 files from websites that offer royalty-free or Creative Commons music. Royalty-free music is music that you can use for personal or commercial purposes without paying any royalties or fees to the artists or the owners of the rights. Creative Commons music is music that the artists have licensed under certain conditions, such as attribution, non-commercial use, or share-alike. You can access these websites from your computer or mobile device, and download music for free or for a small donation. The advantages of downloading music from other websites are:
-
-
You can discover new and diverse music from different genres, cultures, and countries.
-
You can support independent and emerging artists who share their music for free or for a low cost.
-
You can use the music for your own projects, such as videos, podcasts, or presentations, without worrying about legal issues.
-
-
The disadvantages of downloading music from other websites are:
-
-
You may not find the exact song or artist that you are looking for, as these websites may have limited or niche collections.
-
You may encounter low-quality or corrupted mp3 files that are not suitable for listening or editing.
-
You may have to comply with certain terms and conditions of the websites, such as giving credit to the artists, linking back to the source, or not modifying the music.
-
-
To download the song "Adam Na Eva" from other websites, follow these steps:
Search for "Adam Na Eva ft 4 Na 5" or similar keywords in the search bar.
-
Select the mp3 file that matches the song from the results and click on the download button.
-
Save the mp3 file to your device and enjoy your music.
-
-
Comparing the pros and cons of each method
-
To help you decide which method to use for downloading the song "Adam Na Eva" as an mp3 file, here is a table that compares the pros and cons of each method, based on criteria such as quality, cost, convenience, and legality.
-
-
Method
Quality
Cost
Convenience
Legality
-
Buying music from official sources
High
Medium to high
Easy to moderate
Legal and ethical
-
Downloading music from YouTube
Low to medium
Free
Moderate to hard
Illegal and unethical
-
Downloading music from other websites
Low to high
Free to low
Moderate to hard
Legal and ethical (with conditions)
-
-
Conclusion
-
In conclusion, the song "Adam Na Eva" by Adam Na Eva featuring 4 Na 5 is a Zambian music hit that has been making waves in the local and regional scene. It is a fusion of kalindula and afrobeat genres that showcases the talents and creativity of four young Zambian artists. The song is a love song that expresses the feelings of two couples who are deeply in love. The song has been received very well by the Zambian music fans and critics, who have praised it for its quality, originality, and message. The song also reflects the growth and potential of the Zambian music industry, which faces some challenges and opportunities in terms of funding, recognition, regulation, digitalization, collaboration, and exposure.
-
If you want to download the song "Adam Na Eva" as an mp3 file, you have three options: buying it from official sources such as iTunes or Google Play Music; converting it from YouTube videos using online tools or software; or finding it on other websites that offer royalty-free or Creative Commons music. Each option has its pros and cons in terms of quality, cost, convenience, and legality. You should weigh these factors carefully before choosing your preferred option. However, we recommend that you buy the song from official sources if you can afford it, as this is the best way to support the artists and respect their rights. However, if you cannot or do not want to buy the song, you can use the other options with caution and discretion, and always give credit to the original sources.
-
FAQs
-
Here are some frequently asked questions and answers related to the topic of the article:
-
-
What is the meaning of the name Adam Na Eva?
-The name Adam Na Eva is a combination of the names of the two members of the duo, Adam Mwale and Eva Chanda. It is also a reference to the biblical characters Adam and Eve, who were the first human couple created by God.
-
What is the meaning of the name 4 Na 5?
-The name 4 Na 5 is a slang term that means "more than enough" or "exceeding expectations". It is also a way of expressing the duo's confidence and ambition in their music career.
-
Where can I watch the official video of the song Adam Na Eva?
-You can watch the official video of the song Adam Na Eva on YouTube, by following this link: https://www.youtube.com/watch?v=xxxxxxxx. The video was directed by XYZ and features the four artists performing the song in various locations.
-
Are there any remixes or covers of the song Adam Na Eva?
-Yes, there are several remixes and covers of the song Adam Na Eva by other Zambian artists and fans. Some of them are:
", unsafe_allow_html=True)
-
- sentence1 = st.selectbox("Selectionnez la 1ere des 5 phrases à traduire avec le dictionnaire sélectionné", df_data_src.iloc[:-4],index=int(n1) )
- n1 = df_data_src[df_data_src[0]==sentence1].index.values[0]
- if (chosen_id == "tab1"):
- display_translation(n1, Lang,1)
- else:
- display_translation(n1, Lang,2)
-
- elif chosen_id == "tab3":
-
- custom_sentence = st.text_area(label="Saisir le texte à traduire")
- l_tgt = st.selectbox("Choisir la langue cible pour Google Translate (uniquement):",lang_tgt, format_func = find_lang_label )
- st.button(label="Valider", type="primary")
- if custom_sentence!="":
- Lang_detected = lang_classifier (custom_sentence)[0]['label']
- st.write('Langue détectée : **'+lang_src.get(Lang_detected)+'**')
- audio_stream_bytesio_src = io.BytesIO()
- tts = gTTS(custom_sentence,lang=Lang_detected)
- tts.write_to_fp(audio_stream_bytesio_src)
- st.audio(audio_stream_bytesio_src)
- st.write("")
- else: Lang_detected=""
- col1, col2 = st.columns(2, gap="small")
- with col1:
- st.write(":red[**Trad. t5-base & Helsinki**] *(Anglais/Français)*")
- audio_stream_bytesio_tgt = io.BytesIO()
- if (Lang_detected=='en'):
- translation = translation_en_fr(custom_sentence, max_length=400)[0]['translation_text']
- st.write("**fr :** "+translation)
- st.write("")
- tts = gTTS(translation,lang='fr')
- tts.write_to_fp(audio_stream_bytesio_tgt)
- st.audio(audio_stream_bytesio_tgt)
- elif (Lang_detected=='fr'):
- translation = translation_fr_en(custom_sentence, max_length=400)[0]['translation_text']
- st.write("**en :** "+translation)
- st.write("")
- tts = gTTS(translation,lang='en')
- tts.write_to_fp(audio_stream_bytesio_tgt)
- st.audio(audio_stream_bytesio_tgt)
- with col2:
- st.write(":red[**Trad. Google Translate**]")
- try:
- translator = Translator(to_lang=l_tgt, from_lang=Lang_detected)
- if custom_sentence!="":
- translation = translator.translate(custom_sentence)
- st.write("**"+l_tgt+" :** "+translation)
- st.write("")
- audio_stream_bytesio_tgt = io.BytesIO()
- tts = gTTS(translation,lang=l_tgt)
- tts.write_to_fp(audio_stream_bytesio_tgt)
- st.audio(audio_stream_bytesio_tgt)
- except:
- st.write("Problème, essayer de nouveau..")
-
- elif chosen_id == "tab4":
- detection = st.toggle("Détection de langue ?")
- if not detection:
- l_src = st.selectbox("Choisissez la langue parlée :",lang_tgt, format_func = find_lang_label, index=1 )
- l_tgt = st.selectbox("Choisissez la langue cible :",lang_tgt, format_func = find_lang_label )
- audio_bytes = audio_recorder (pause_threshold=1.0, sample_rate=16000, text="Cliquez pour parler, puis attendre 2s..", \
- recording_color="#e8b62c", neutral_color="#1ec3bc", icon_size="6x",)
-
- if audio_bytes:
- st.audio(audio_bytes, format="audio/wav")
- try:
- if detection:
- # Create a BytesIO object from the audio stream
- audio_stream_bytesio = io.BytesIO(audio_bytes)
-
- # Read the WAV stream using wavio
- wav = wavio.read(audio_stream_bytesio)
-
- # Extract the audio data from the wavio.Wav object
- audio_data = wav.data
-
- # Convert the audio data to a NumPy array
- audio_input = np.array(audio_data, dtype=np.float32)
- audio_input = np.mean(audio_input, axis=1)/32768
-
- result = model_speech.transcribe(audio_input)
- st.write("Langue détectée : "+result["language"])
- Lang_detected = result["language"]
- # Transcription Whisper (si result a été préalablement calculé)
- custom_sentence = result["text"]
- else:
- Lang_detected = l_src
- # Transcription google
- audio_stream = sr.AudioData(audio_bytes, 32000, 2)
- r = sr.Recognizer()
- custom_sentence = r.recognize_google(audio_stream, language = Lang_detected)
-
- if custom_sentence!="":
- # Lang_detected = lang_classifier (custom_sentence)[0]['label']
- #st.write('Langue détectée : **'+Lang_detected+'**')
- st.write("")
- st.write("**"+Lang_detected+" :** :blue["+custom_sentence+"]")
- st.write("")
- translator = Translator(to_lang=l_tgt, from_lang=Lang_detected)
- translation = translator.translate(custom_sentence)
- st.write("**"+l_tgt+" :** "+translation)
- st.write("")
- audio_stream_bytesio_tgt = io.BytesIO()
- tts = gTTS(translation,lang=l_tgt)
- tts.write_to_fp(audio_stream_bytesio_tgt)
- st.audio(audio_stream_bytesio_tgt)
- st.write("Prêt pour la phase suivante..")
- audio_bytes = False
- except KeyboardInterrupt:
- st.write("Arrêt de la reconnaissance vocale.")
- except:
- st.write("Problème, essayer de nouveau..")
-
-
-
diff --git a/spaces/Detomo/ai-comic-generation/src/components/ui/avatar.tsx b/spaces/Detomo/ai-comic-generation/src/components/ui/avatar.tsx
deleted file mode 100644
index 88aeea9d9368f2bd7385f0a0885829bf6d789492..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/src/components/ui/avatar.tsx
+++ /dev/null
@@ -1,50 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as AvatarPrimitive from "@radix-ui/react-avatar"
-
-import { cn } from "@/lib/utils"
-
-const Avatar = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-))
-Avatar.displayName = AvatarPrimitive.Root.displayName
-
-const AvatarImage = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-))
-AvatarImage.displayName = AvatarPrimitive.Image.displayName
-
-const AvatarFallback = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-))
-AvatarFallback.displayName = AvatarPrimitive.Fallback.displayName
-
-export { Avatar, AvatarImage, AvatarFallback }
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/mapper/training/coach.py b/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/mapper/training/coach.py
deleted file mode 100644
index fd38eb226106a21e19beb306cd9b0de6a1e7db04..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/mapper/training/coach.py
+++ /dev/null
@@ -1,242 +0,0 @@
-import os
-
-import clip
-import torch
-import torchvision
-from torch import nn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-import criteria.clip_loss as clip_loss
-from criteria import id_loss
-from mapper.datasets.latents_dataset import LatentsDataset
-from mapper.styleclip_mapper import StyleCLIPMapper
-from mapper.training.ranger import Ranger
-from mapper.training import train_utils
-
-
-class Coach:
- def __init__(self, opts):
- self.opts = opts
-
- self.global_step = 0
-
- self.device = 'cuda:0'
- self.opts.device = self.device
-
- # Initialize network
- self.net = StyleCLIPMapper(self.opts).to(self.device)
-
- # Initialize loss
- if self.opts.id_lambda > 0:
- self.id_loss = id_loss.IDLoss(self.opts).to(self.device).eval()
- if self.opts.clip_lambda > 0:
- self.clip_loss = clip_loss.CLIPLoss(opts)
- if self.opts.latent_l2_lambda > 0:
- self.latent_l2_loss = nn.MSELoss().to(self.device).eval()
-
- # Initialize optimizer
- self.optimizer = self.configure_optimizers()
-
- # Initialize dataset
- self.train_dataset, self.test_dataset = self.configure_datasets()
- self.train_dataloader = DataLoader(self.train_dataset,
- batch_size=self.opts.batch_size,
- shuffle=True,
- num_workers=int(self.opts.workers),
- drop_last=True)
- self.test_dataloader = DataLoader(self.test_dataset,
- batch_size=self.opts.test_batch_size,
- shuffle=False,
- num_workers=int(self.opts.test_workers),
- drop_last=True)
-
- self.text_inputs = torch.cat([clip.tokenize(self.opts.description)]).cuda()
-
- # Initialize logger
- log_dir = os.path.join(opts.exp_dir, 'logs')
- os.makedirs(log_dir, exist_ok=True)
- self.log_dir = log_dir
- self.logger = SummaryWriter(log_dir=log_dir)
-
- # Initialize checkpoint dir
- self.checkpoint_dir = os.path.join(opts.exp_dir, 'checkpoints')
- os.makedirs(self.checkpoint_dir, exist_ok=True)
- self.best_val_loss = None
- if self.opts.save_interval is None:
- self.opts.save_interval = self.opts.max_steps
-
- def train(self):
- self.net.train()
- while self.global_step < self.opts.max_steps:
- for batch_idx, batch in enumerate(self.train_dataloader):
- self.optimizer.zero_grad()
- w = batch
- w = w.to(self.device)
- with torch.no_grad():
- x, _ = self.net.decoder([w], input_is_latent=True, randomize_noise=False, truncation=1)
- w_hat = w + 0.1 * self.net.mapper(w)
- x_hat, w_hat = self.net.decoder([w_hat], input_is_latent=True, return_latents=True, randomize_noise=False, truncation=1)
- loss, loss_dict = self.calc_loss(w, x, w_hat, x_hat)
- loss.backward()
- self.optimizer.step()
-
- # Logging related
- if self.global_step % self.opts.image_interval == 0 or (
- self.global_step < 1000 and self.global_step % 1000 == 0):
- self.parse_and_log_images(x, x_hat, title='images_train')
- if self.global_step % self.opts.board_interval == 0:
- self.print_metrics(loss_dict, prefix='train')
- self.log_metrics(loss_dict, prefix='train')
-
- # Validation related
- val_loss_dict = None
- if self.global_step % self.opts.val_interval == 0 or self.global_step == self.opts.max_steps:
- val_loss_dict = self.validate()
- if val_loss_dict and (self.best_val_loss is None or val_loss_dict['loss'] < self.best_val_loss):
- self.best_val_loss = val_loss_dict['loss']
- self.checkpoint_me(val_loss_dict, is_best=True)
-
- if self.global_step % self.opts.save_interval == 0 or self.global_step == self.opts.max_steps:
- if val_loss_dict is not None:
- self.checkpoint_me(val_loss_dict, is_best=False)
- else:
- self.checkpoint_me(loss_dict, is_best=False)
-
- if self.global_step == self.opts.max_steps:
- print('OMG, finished training!')
- break
-
- self.global_step += 1
-
- def validate(self):
- self.net.eval()
- agg_loss_dict = []
- for batch_idx, batch in enumerate(self.test_dataloader):
- if batch_idx > 200:
- break
-
- w = batch
-
- with torch.no_grad():
- w = w.to(self.device).float()
- x, _ = self.net.decoder([w], input_is_latent=True, randomize_noise=True, truncation=1)
- w_hat = w + 0.1 * self.net.mapper(w)
- x_hat, _ = self.net.decoder([w_hat], input_is_latent=True, randomize_noise=True, truncation=1)
- loss, cur_loss_dict = self.calc_loss(w, x, w_hat, x_hat)
- agg_loss_dict.append(cur_loss_dict)
-
- # Logging related
- self.parse_and_log_images(x, x_hat, title='images_val', index=batch_idx)
-
- # For first step just do sanity test on small amount of data
- if self.global_step == 0 and batch_idx >= 4:
- self.net.train()
- return None # Do not log, inaccurate in first batch
-
- loss_dict = train_utils.aggregate_loss_dict(agg_loss_dict)
- self.log_metrics(loss_dict, prefix='test')
- self.print_metrics(loss_dict, prefix='test')
-
- self.net.train()
- return loss_dict
-
- def checkpoint_me(self, loss_dict, is_best):
- save_name = 'best_model.pt' if is_best else 'iteration_{}.pt'.format(self.global_step)
- save_dict = self.__get_save_dict()
- checkpoint_path = os.path.join(self.checkpoint_dir, save_name)
- torch.save(save_dict, checkpoint_path)
- with open(os.path.join(self.checkpoint_dir, 'timestamp.txt'), 'a') as f:
- if is_best:
- f.write('**Best**: Step - {}, Loss - {:.3f} \n{}\n'.format(self.global_step, self.best_val_loss, loss_dict))
- else:
- f.write('Step - {}, \n{}\n'.format(self.global_step, loss_dict))
-
- def configure_optimizers(self):
- params = list(self.net.mapper.parameters())
- if self.opts.optim_name == 'adam':
- optimizer = torch.optim.Adam(params, lr=self.opts.learning_rate)
- else:
- optimizer = Ranger(params, lr=self.opts.learning_rate)
- return optimizer
-
- def configure_datasets(self):
- if self.opts.latents_train_path:
- train_latents = torch.load(self.opts.latents_train_path)
- else:
- train_latents_z = torch.randn(self.opts.train_dataset_size, 512).cuda()
- train_latents = []
- for b in range(self.opts.train_dataset_size // self.opts.batch_size):
- with torch.no_grad():
- _, train_latents_b = self.net.decoder([train_latents_z[b: b + self.opts.batch_size]],
- truncation=0.7, truncation_latent=self.net.latent_avg, return_latents=True)
- train_latents.append(train_latents_b)
- train_latents = torch.cat(train_latents)
-
- if self.opts.latents_test_path:
- test_latents = torch.load(self.opts.latents_test_path)
- else:
- test_latents_z = torch.randn(self.opts.train_dataset_size, 512).cuda()
- test_latents = []
- for b in range(self.opts.test_dataset_size // self.opts.test_batch_size):
- with torch.no_grad():
- _, test_latents_b = self.net.decoder([test_latents_z[b: b + self.opts.test_batch_size]],
- truncation=0.7, truncation_latent=self.net.latent_avg, return_latents=True)
- test_latents.append(test_latents_b)
- test_latents = torch.cat(test_latents)
-
- train_dataset_celeba = LatentsDataset(latents=train_latents.cpu(),
- opts=self.opts)
- test_dataset_celeba = LatentsDataset(latents=test_latents.cpu(),
- opts=self.opts)
- train_dataset = train_dataset_celeba
- test_dataset = test_dataset_celeba
- print("Number of training samples: {}".format(len(train_dataset)))
- print("Number of test samples: {}".format(len(test_dataset)))
- return train_dataset, test_dataset
-
- def calc_loss(self, w, x, w_hat, x_hat):
- loss_dict = {}
- loss = 0.0
- if self.opts.id_lambda > 0:
- loss_id, sim_improvement = self.id_loss(x_hat, x)
- loss_dict['loss_id'] = float(loss_id)
- loss_dict['id_improve'] = float(sim_improvement)
- loss = loss_id * self.opts.id_lambda
- if self.opts.clip_lambda > 0:
- loss_clip = self.clip_loss(x_hat, self.text_inputs).mean()
- loss_dict['loss_clip'] = float(loss_clip)
- loss += loss_clip * self.opts.clip_lambda
- if self.opts.latent_l2_lambda > 0:
- loss_l2_latent = self.latent_l2_loss(w_hat, w)
- loss_dict['loss_l2_latent'] = float(loss_l2_latent)
- loss += loss_l2_latent * self.opts.latent_l2_lambda
- loss_dict['loss'] = float(loss)
- return loss, loss_dict
-
- def log_metrics(self, metrics_dict, prefix):
- for key, value in metrics_dict.items():
- #pass
- print(f"step: {self.global_step} \t metric: {prefix}/{key} \t value: {value}")
- self.logger.add_scalar('{}/{}'.format(prefix, key), value, self.global_step)
-
- def print_metrics(self, metrics_dict, prefix):
- print('Metrics for {}, step {}'.format(prefix, self.global_step))
- for key, value in metrics_dict.items():
- print('\t{} = '.format(key), value)
-
- def parse_and_log_images(self, x, x_hat, title, index=None):
- if index is None:
- path = os.path.join(self.log_dir, title, f'{str(self.global_step).zfill(5)}.jpg')
- else:
- path = os.path.join(self.log_dir, title, f'{str(self.global_step).zfill(5)}_{str(index).zfill(5)}.jpg')
- os.makedirs(os.path.dirname(path), exist_ok=True)
- torchvision.utils.save_image(torch.cat([x.detach().cpu(), x_hat.detach().cpu()]), path,
- normalize=True, scale_each=True, range=(-1, 1), nrow=self.opts.batch_size)
-
- def __get_save_dict(self):
- save_dict = {
- 'state_dict': self.net.state_dict(),
- 'opts': vars(self.opts)
- }
- return save_dict
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/grid_sample_gradfix.py b/spaces/DragGan/DragGan-Inversion/torch_utils/ops/grid_sample_gradfix.py
deleted file mode 100644
index 5031a7720cbe25b68946b93928f41dc9fbeac0d5..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/grid_sample_gradfix.py
+++ /dev/null
@@ -1,84 +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.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-# ----------------------------------------------------------------------------
-
-enabled = False # Enable the custom op by setting this to true.
-
-# ----------------------------------------------------------------------------
-
-
-def grid_sample(input, grid):
- if _should_use_custom_op():
- return _GridSample2dForward.apply(input, grid)
- return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-# ----------------------------------------------------------------------------
-
-
-def _should_use_custom_op():
- return enabled
-
-# ----------------------------------------------------------------------------
-
-
-class _GridSample2dForward(torch.autograd.Function):
- @staticmethod
- def forward(ctx, input, grid):
- assert input.ndim == 4
- assert grid.ndim == 4
- output = torch.nn.functional.grid_sample(
- input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
- ctx.save_for_backward(input, grid)
- return output
-
- @staticmethod
- def backward(ctx, grad_output):
- input, grid = ctx.saved_tensors
- grad_input, grad_grid = _GridSample2dBackward.apply(
- grad_output, input, grid)
- return grad_input, grad_grid
-
-# ----------------------------------------------------------------------------
-
-
-class _GridSample2dBackward(torch.autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input, grid):
- op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
- grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
- ctx.save_for_backward(grid)
- return grad_input, grad_grid
-
- @staticmethod
- def backward(ctx, grad2_grad_input, grad2_grad_grid):
- _ = grad2_grad_grid # unused
- grid, = ctx.saved_tensors
- grad2_grad_output = None
- grad2_input = None
- grad2_grid = None
-
- if ctx.needs_input_grad[0]:
- grad2_grad_output = _GridSample2dForward.apply(
- grad2_grad_input, grid)
-
- assert not ctx.needs_input_grad[2]
- return grad2_grad_output, grad2_input, grad2_grid
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/kalmanFilter.cpp b/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/kalmanFilter.cpp
deleted file mode 100644
index 168432a46810d0c1296f4b17500d41f8b4f308b4..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/kalmanFilter.cpp
+++ /dev/null
@@ -1,152 +0,0 @@
-#include "kalmanFilter.h"
-#include
-
-namespace byte_kalman
-{
- const double KalmanFilter::chi2inv95[10] = {
- 0,
- 3.8415,
- 5.9915,
- 7.8147,
- 9.4877,
- 11.070,
- 12.592,
- 14.067,
- 15.507,
- 16.919
- };
- KalmanFilter::KalmanFilter()
- {
- int ndim = 4;
- double dt = 1.;
-
- _motion_mat = Eigen::MatrixXf::Identity(8, 8);
- for (int i = 0; i < ndim; i++) {
- _motion_mat(i, ndim + i) = dt;
- }
- _update_mat = Eigen::MatrixXf::Identity(4, 8);
-
- this->_std_weight_position = 1. / 20;
- this->_std_weight_velocity = 1. / 160;
- }
-
- KAL_DATA KalmanFilter::initiate(const DETECTBOX &measurement)
- {
- DETECTBOX mean_pos = measurement;
- DETECTBOX mean_vel;
- for (int i = 0; i < 4; i++) mean_vel(i) = 0;
-
- KAL_MEAN mean;
- for (int i = 0; i < 8; i++) {
- if (i < 4) mean(i) = mean_pos(i);
- else mean(i) = mean_vel(i - 4);
- }
-
- KAL_MEAN std;
- std(0) = 2 * _std_weight_position * measurement[3];
- std(1) = 2 * _std_weight_position * measurement[3];
- std(2) = 1e-2;
- std(3) = 2 * _std_weight_position * measurement[3];
- std(4) = 10 * _std_weight_velocity * measurement[3];
- std(5) = 10 * _std_weight_velocity * measurement[3];
- std(6) = 1e-5;
- std(7) = 10 * _std_weight_velocity * measurement[3];
-
- KAL_MEAN tmp = std.array().square();
- KAL_COVA var = tmp.asDiagonal();
- return std::make_pair(mean, var);
- }
-
- void KalmanFilter::predict(KAL_MEAN &mean, KAL_COVA &covariance)
- {
- //revise the data;
- DETECTBOX std_pos;
- std_pos << _std_weight_position * mean(3),
- _std_weight_position * mean(3),
- 1e-2,
- _std_weight_position * mean(3);
- DETECTBOX std_vel;
- std_vel << _std_weight_velocity * mean(3),
- _std_weight_velocity * mean(3),
- 1e-5,
- _std_weight_velocity * mean(3);
- KAL_MEAN tmp;
- tmp.block<1, 4>(0, 0) = std_pos;
- tmp.block<1, 4>(0, 4) = std_vel;
- tmp = tmp.array().square();
- KAL_COVA motion_cov = tmp.asDiagonal();
- KAL_MEAN mean1 = this->_motion_mat * mean.transpose();
- KAL_COVA covariance1 = this->_motion_mat * covariance *(_motion_mat.transpose());
- covariance1 += motion_cov;
-
- mean = mean1;
- covariance = covariance1;
- }
-
- KAL_HDATA KalmanFilter::project(const KAL_MEAN &mean, const KAL_COVA &covariance)
- {
- DETECTBOX std;
- std << _std_weight_position * mean(3), _std_weight_position * mean(3),
- 1e-1, _std_weight_position * mean(3);
- KAL_HMEAN mean1 = _update_mat * mean.transpose();
- KAL_HCOVA covariance1 = _update_mat * covariance * (_update_mat.transpose());
- Eigen::Matrix diag = std.asDiagonal();
- diag = diag.array().square().matrix();
- covariance1 += diag;
- // covariance1.diagonal() << diag;
- return std::make_pair(mean1, covariance1);
- }
-
- KAL_DATA
- KalmanFilter::update(
- const KAL_MEAN &mean,
- const KAL_COVA &covariance,
- const DETECTBOX &measurement)
- {
- KAL_HDATA pa = project(mean, covariance);
- KAL_HMEAN projected_mean = pa.first;
- KAL_HCOVA projected_cov = pa.second;
-
- //chol_factor, lower =
- //scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)
- //kalmain_gain =
- //scipy.linalg.cho_solve((cho_factor, lower),
- //np.dot(covariance, self._upadte_mat.T).T,
- //check_finite=False).T
- Eigen::Matrix B = (covariance * (_update_mat.transpose())).transpose();
- Eigen::Matrix kalman_gain = (projected_cov.llt().solve(B)).transpose(); // eg.8x4
- Eigen::Matrix innovation = measurement - projected_mean; //eg.1x4
- auto tmp = innovation * (kalman_gain.transpose());
- KAL_MEAN new_mean = (mean.array() + tmp.array()).matrix();
- KAL_COVA new_covariance = covariance - kalman_gain * projected_cov*(kalman_gain.transpose());
- return std::make_pair(new_mean, new_covariance);
- }
-
- Eigen::Matrix
- KalmanFilter::gating_distance(
- const KAL_MEAN &mean,
- const KAL_COVA &covariance,
- const std::vector &measurements,
- bool only_position)
- {
- KAL_HDATA pa = this->project(mean, covariance);
- if (only_position) {
- printf("not implement!");
- exit(0);
- }
- KAL_HMEAN mean1 = pa.first;
- KAL_HCOVA covariance1 = pa.second;
-
- // Eigen::Matrix d(size, 4);
- DETECTBOXSS d(measurements.size(), 4);
- int pos = 0;
- for (DETECTBOX box : measurements) {
- d.row(pos++) = box - mean1;
- }
- Eigen::Matrix factor = covariance1.llt().matrixL();
- Eigen::Matrix z = factor.triangularView().solve(d).transpose();
- auto zz = ((z.array())*(z.array())).matrix();
- auto square_maha = zz.colwise().sum();
- return square_maha;
- }
-}
\ No newline at end of file
diff --git a/spaces/ELITE-library/ELITE/README.md b/spaces/ELITE-library/ELITE/README.md
deleted file mode 100644
index 5b15308243ce0ddc657d00e4a1d50f5fef9fb8c5..0000000000000000000000000000000000000000
--- a/spaces/ELITE-library/ELITE/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ELITE
-emoji: 🚀
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-suggested_hardware: a10g-small
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Edward-Ji/essentials-of-microeconomics/essentials_of_microeconomics/settings.py b/spaces/Edward-Ji/essentials-of-microeconomics/essentials_of_microeconomics/settings.py
deleted file mode 100644
index c7c7d8fb564c61fed0d3610fd72c64d578e4f710..0000000000000000000000000000000000000000
--- a/spaces/Edward-Ji/essentials-of-microeconomics/essentials_of_microeconomics/settings.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from dataclasses import dataclass
-from shiny import module, reactive, ui
-
-from util import Approx
-
-
-@dataclass
-class Settings:
- approx: ...
- perc: ...
-
-
-@module.ui
-def settings_ui():
- header = (
- ui.h1("Settings", id="settings-modal-label", class_="modal-title fs-5"),
- ui.tags.button(type="button", class_="btn-close",
- data_bs_dismiss="modal", aria_label="Close settings")
- )
- body = (
- ui.h2("Evaluation"),
- ui.input_select("approx", "Numerical evaluation of rationals:",
- [e.value for e in Approx]),
- ui.panel_conditional(
- f"input.approx !== '{Approx.HIDE.value}'",
- ui.input_slider("perc", "Binary percision:", 0, 100, 15)),
- ui.h2("Input storage"),
- ui.p("""Automatically save inputs when they change or manually save and
- clear inputs. Clear and refresh to reset all inputs to
- default."""),
- ui.div(
- ui.tags.button("Auto",
- id="input-auto-btn",
- class_="btn btn-outline-secondary",
- data_bs_toggle="button"),
- ui.tags.button("Save",
- id="input-save-btn",
- class_="btn btn-outline-secondary"),
- ui.tags.button("Clear",
- id="input-clear-btn",
- class_="btn btn-outline-danger"),
- class_="btn-group",
- role="group",
- aria_label="Input storage button group")
-
- )
- return ui.div(
- ui.div(
- ui.div(
- ui.div(*header, class_="modal-header"),
- ui.div(*body, class_="modal-body"),
- class_="modal-content"),
- class_="modal-dialog"),
- id="settings-modal",
- class_="modal fade",
- tabindex="-1",
- aria_labelledby="settings-modal-label",
- aria_hidden="true"
- )
-
-
-@module.server
-def settings_server(input, output, session):
- @reactive.Calc
- def approx():
- return Approx(input.approx())
-
- @reactive.Calc
- def perc():
- return input.perc()
-
- return Settings(approx, perc)
diff --git a/spaces/Egrt/LicenseGAN/nets/esrgan.py b/spaces/Egrt/LicenseGAN/nets/esrgan.py
deleted file mode 100644
index 04a30cb8a1905a58a2af221cdf1a247b565d9a3e..0000000000000000000000000000000000000000
--- a/spaces/Egrt/LicenseGAN/nets/esrgan.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import math
-import torch
-from torch import nn
-
-
-class DenseResidualBlock(nn.Module):
- """
- 密集连接型残差网络
- """
-
- def __init__(self, filters, res_scale=0.2):
- super(DenseResidualBlock, self).__init__()
- self.res_scale = res_scale
-
- def block(in_features, non_linearity=True):
- layers = [nn.Conv2d(in_features, filters, 3, 1, 1, bias=True)]
- if non_linearity:
- layers += [nn.GELU()]
- return nn.Sequential(*layers)
-
- self.b1 = block(in_features=1 * filters)
- self.b2 = block(in_features=2 * filters)
- self.b3 = block(in_features=3 * filters)
- self.b4 = block(in_features=4 * filters)
- self.b5 = block(in_features=5 * filters, non_linearity=False)
- self.blocks = [self.b1, self.b2, self.b3, self.b4, self.b5]
-
- def forward(self, x):
- inputs = x
- for block in self.blocks:
- out = block(inputs)
- inputs = torch.cat([inputs, out], 1)
- return out.mul(self.res_scale) + x
-
-class ResidualInResidualDenseBlock(nn.Module):
- def __init__(self, filters, res_scale=0.2):
- super(ResidualInResidualDenseBlock, self).__init__()
- self.res_scale = res_scale
- self.dense_blocks = nn.Sequential(
- DenseResidualBlock(filters), DenseResidualBlock(filters), DenseResidualBlock(filters)
- )
-
- def forward(self, x):
- return self.dense_blocks(x).mul(self.res_scale) + x
-
-class UpsampleBLock(nn.Module):
- def __init__(self, in_channels, up_scale):
- super(UpsampleBLock, self).__init__()
- self.conv = nn.Conv2d(in_channels, in_channels * up_scale ** 2, kernel_size=3, padding=1)
- self.pixel_shuffle = nn.PixelShuffle(up_scale)
- self.gelu = nn.GELU()
-
- def forward(self, x):
- x = self.conv(x)
- x = self.pixel_shuffle(x)
- x = self.gelu(x)
- return x
-
-class Generator(nn.Module):
- def __init__(self, scale_factor, channels=3, filters=64, num_res_blocks=6):
- super(Generator, self).__init__()
- upsample_block_num = int(math.log(scale_factor, 2))
- # 第一个卷积层
- self.conv1 = nn.Conv2d(channels, filters, kernel_size=3, stride=1, padding=1)
- # 密集残差连接块
- self.res_blocks = nn.Sequential(*[ResidualInResidualDenseBlock(filters) for _ in range(num_res_blocks)])
- # 第二个卷积层
- self.conv2 = nn.Conv2d(filters, filters, kernel_size=3, stride=1, padding=1)
- self.upsample = [UpsampleBLock(filters, 2) for _ in range(upsample_block_num)]
- self.upsample = nn.Sequential(*self.upsample)
- # 输出卷积层
- self.conv3 = nn.Sequential(
- nn.Conv2d(filters, filters, kernel_size=3, stride=1, padding=1),
- nn.GELU(),
- nn.Conv2d(filters, channels, kernel_size=3, stride=1, padding=1)
- )
-
- def forward(self, x):
- out1 = self.conv1(x)
- out = self.res_blocks(out1)
- out2 = self.conv2(out)
- out = torch.add(out1, out2)
- upsample = self.upsample(out)
- out = self.conv3(upsample)
- return out
-
-
-class Discriminator(nn.Module):
- def __init__(self):
- super(Discriminator, self).__init__()
- self.net = nn.Sequential(
- nn.Conv2d(3, 64, kernel_size=3, padding=1),
- nn.GELU(),
-
- nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
- nn.BatchNorm2d(64),
- nn.GELU(),
-
- nn.Conv2d(64, 128, kernel_size=3, padding=1),
- nn.BatchNorm2d(128),
- nn.GELU(),
-
- nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1),
- nn.BatchNorm2d(128),
- nn.GELU(),
-
- nn.Conv2d(128, 256, kernel_size=3, padding=1),
- nn.BatchNorm2d(256),
- nn.GELU(),
-
- nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1),
- nn.BatchNorm2d(256),
- nn.GELU(),
-
- nn.Conv2d(256, 512, kernel_size=3, padding=1),
- nn.BatchNorm2d(512),
- nn.GELU(),
-
- nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
- nn.BatchNorm2d(512),
- nn.GELU(),
-
- nn.AdaptiveAvgPool2d(1),
- nn.Conv2d(512, 1024, kernel_size=1),
- nn.GELU(),
- nn.Conv2d(1024, 1, kernel_size=1)
- )
-
- def forward(self, x):
- batch_size = x.size(0)
- return torch.sigmoid(self.net(x).view(batch_size))
-
-if __name__ == "__main__":
- from torchsummary import summary
-
- # 需要使用device来指定网络在GPU还是CPU运行
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- model = Generator(8).to(device)
- summary(model, input_size=(3,12,24))
-
diff --git a/spaces/Ekimetrics/Biomap/biomap/helper.py b/spaces/Ekimetrics/Biomap/biomap/helper.py
deleted file mode 100644
index cbed847f7c9c41418bc7e07a8a0967ca1f3be3ad..0000000000000000000000000000000000000000
--- a/spaces/Ekimetrics/Biomap/biomap/helper.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import torch.multiprocessing
-import torchvision.transforms as T
-import numpy as np
-from utils import transform_to_pil, compute_biodiv_score, plot_imgs_labels, plot_image
-from utils_gee import get_image
-from dateutil.relativedelta import relativedelta
-
-from model import LitUnsupervisedSegmenter
-import datetime
-import matplotlib as mpl
-from joblib import Parallel, cpu_count, delayed
-import logging
-from inference import inference
-import streamlit as st
-import cv2
-
-@st.cache_data(hash_funcs={LitUnsupervisedSegmenter: lambda dt: dt.name})
-def inference_on_location(model, latitude=48.81, longitude=2.98, start_date=2020, end_date=2022, how="year"):
- """Performe an inference on the latitude and longitude between the start date and the end date
-
- Args:
- latitude (float): the latitude of the landscape
- longitude (float): the longitude of the landscape
- start_date (str): the start date for our inference
- end_date (str): the end date for our inference
- model (_type_, optional): _description_. Defaults to model.
-
- Returns:
- img, labeled_img,biodiv_score: the original landscape, the labeled landscape and the biodiversity score and the landscape
- """
- logging.info("Running Inference on location")
- logging.info(f"latitude : {latitude} & longitude : {longitude}")
- logging.info(f"start date : {start_date} & end_date : {end_date}")
- logging.info(f"Prediction on intervale : {how}")
- if how == "month":
- delta_month = 1
- elif how == "2months":
- delta_month = 2
- elif how == "year":
- delta_month = 11
- else:
- raise ValueError("Wrong interval")
-
- assert int(end_date) > int(start_date), "end date must be stricly higher than start date"
- location = [float(latitude), float(longitude)]
-
- # Extract img numpy from earth engine and transform it to PIL img
- dates = [datetime.datetime(start_date, 1, 1, 0, 0, 0)]
- while dates[-1] < datetime.datetime(int(end_date), 1, 1, 0, 0, 0):
- dates.append(dates[-1] + relativedelta(months=delta_month))
-
- dates = [d.strftime("%Y-%m-%d") for d in dates]
-
- n_jobs = min([12, len(dates)])
-
- all_image = Parallel(n_jobs=n_jobs, prefer="threads")(delayed(get_image)(location, d1,d2) for d1, d2 in zip(dates[:-1],dates[1:]))
- # all_image = [cv2.imread("output/img.png") for i in range(len(dates))]
- outputs = inference(np.array(all_image), model)
-
- logging.info("Calculating Biodiversity Scores...")
- scores, scores_details = map(list, zip(*[compute_biodiv_score(output["linear_preds"].detach().numpy()) for output in outputs]))
- logging.info(f"Calculated Biodiversity Score : {scores}")
-
- imgs, labels, labeled_imgs = map(list, zip(*[transform_to_pil(output) for output in outputs]))
-
- images = [np.asarray(img) for img in imgs]
- labeled_imgs = [np.asarray(img) for img in labeled_imgs]
- title=f"TimeLapse at location ({location[0]:.2f},{location[1]:.2f}) between {start_date} and {end_date}"
- fig = plot_imgs_labels(dates, images, labeled_imgs, scores_details, scores, title=title)
- # fig.save("test.png")
- return fig
-
-@st.cache_data(hash_funcs={LitUnsupervisedSegmenter: lambda dt: dt.name})
-def inference_on_location_and_month(model, latitude = 48.81, longitude = 2.98, start_date = '2020-03-20'):
- """Performe an inference on the latitude and longitude between the start date and the end date
-
- Args:
- latitude (float): the latitude of the landscape
- longitude (float): the longitude of the landscape
- start_date (str): the start date for our inference
- end_date (str): the end date for our inference
- model (_type_, optional): _description_. Defaults to model.
-
- Returns:
- img, labeled_img,biodiv_score: the original landscape, the labeled landscape and the biodiversity score and the landscape
- """
- logging.info("Running Inference on location and month")
- logging.info(f"latitude : {latitude} & longitude : {longitude}")
- location = [float(latitude), float(longitude)]
-
- # Extract img numpy from earth engine and transform it to PIL img
- end_date = datetime.datetime.strptime(start_date, "%Y-%m-%d") + relativedelta(months=1)
- end_date = datetime.datetime.strftime(end_date, "%Y-%m-%d")
-
- img_test = get_image(location, start_date, end_date)
- outputs = inference(np.array([img_test]), model)
-
- logging.info("Calculating Biodiversity Score...")
- score, score_details = compute_biodiv_score(outputs[0]["linear_preds"].detach().numpy())
- logging.info(f"Calculated Biodiversity Score : {score}")
- img, label, labeled_img = transform_to_pil(outputs[0])
-
- title=f"Prediction at location ({location[0]:.2f},{location[1]:.2f}) at {start_date}"
- fig = plot_image([start_date], [np.asarray(img)], [np.asarray(labeled_img)], [score_details], [score],title=title)
- return fig
-
-
-if __name__ == "__main__":
- import logging
- import hydra
- import sys
- from model import LitUnsupervisedSegmenter
- file_handler = logging.FileHandler(filename='biomap.log')
- stdout_handler = logging.StreamHandler(stream=sys.stdout)
- handlers = [file_handler, stdout_handler]
-
- logging.basicConfig(handlers=handlers, encoding='utf-8', level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
- # Initialize hydra with configs
- hydra.initialize(config_path="configs", job_name="corine")
- cfg = hydra.compose(config_name="my_train_config.yml")
- logging.info(f"config : {cfg}")
- # Load the model
-
- nbclasses = cfg.dir_dataset_n_classes
- model = LitUnsupervisedSegmenter(nbclasses, cfg)
- logging.info(f"Model Initialiazed")
-
- model_path = "biomap/checkpoint/model/model.pt"
- saved_state_dict = torch.load(model_path, map_location=torch.device("cpu"))
- logging.info(f"Model weights Loaded")
- model.load_state_dict(saved_state_dict)
-
- logging.info(f"Model Loaded")
- # inference_on_location_and_month(model)
- inference_on_location(model)
-
diff --git a/spaces/EronSamez/RVC_HFmeu/diffq/diffq.py b/spaces/EronSamez/RVC_HFmeu/diffq/diffq.py
deleted file mode 100644
index b475ec7f55227417b014c69b5cf55033182113e1..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/diffq/diffq.py
+++ /dev/null
@@ -1,286 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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.
-
-"""
-Differentiable quantizer based on scaled noise injection.
-"""
-from dataclasses import dataclass
-import math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer
-from .uniform import uniform_quantize, uniform_unquantize
-from .utils import simple_repr
-
-
-class DiffQuantizer(BaseQuantizer):
- @dataclass
- class _QuantizedParam(BaseQuantizer._QuantizedParam):
- logit: torch.nn.Parameter
-
- def __init__(self, model: torch.nn.Module, min_size: float = 0.01, float16: bool = False,
- group_size: int = 1, min_bits: float = 2, max_bits: float = 15,
- param="bits", noise="gaussian",
- init_bits: float = 8, extra_bits: float = 0, suffix: str = "_diffq",
- exclude: tp.List[str] = [], detect_bound: bool = True):
- """
- Differentiable quantizer based on scaled noise injection.
- For every parameter `p` in the model, this introduces a number of bits parameter
- `b` with the same dimensions (when group_size = 1).
- Before each forward, `p` is replaced by `p + U`
- with U uniform iid noise with range [-d/2, d/2], with `d` the uniform quantization
- step for `b` bits.
- This noise approximates the quantization noise in a differentiable manner, both
- with respect to the unquantized parameter `p` and the number of bits `b`.
-
- At eveluation (as detected with `model.eval()`), the model is replaced
- by its true quantized version, and restored when going back to training.
-
- When doing actual quantization (for serialization, or evaluation),
- the number of bits is rounded to the nearest integer, and needs to be stored along.
- This will cost a few bits per dimension. To reduce this cost, one can use `group_size`,
- which will use a single noise level for multiple weight entries.
-
- You can use the `DiffQuantizer.model_size` method to get a differentiable estimate of the
- model size in MB. You can then use this estimate as a penalty in your training loss.
-
- Args:
- model (torch.nn.Module): model to quantize
- min_size (float): minimum size in MB of a parameter to be quantized.
- float16 (bool): if a layer is smaller than min_size, should we still do float16?
- group_size (int): weight entries are groupped together to reduce the number
- of noise scales to store. This should divide the size of all parameters
- bigger than min_size.
- min_bits (float): minimal number of bits.
- max_bits (float): maximal number of bits.
- init_bits (float): initial number of bits.
- extra_bits (float): extra bits to add for actual quantization (before roundoff).
- suffix (str): suffix used for the name of the extra noise scale parameters.
- exclude (list[str]): list of patterns used to match parameters to exclude.
- For instance `['bias']` to exclude all bias terms.
- detect_bound (bool): if True, will detect bound parameters and reuse
- the same quantized tensor for both, as well as the same number of bits.
-
- ..Warning::
- You must call `model.training()` and `model.eval()` for `DiffQuantizer` work properly.
-
- """
- self.group_size = group_size
- self.min_bits = min_bits
- self.max_bits = max_bits
- self.init_bits = init_bits
- self.extra_bits = extra_bits
- self.suffix = suffix
- self.param = param
- self.noise = noise
- assert noise in ["gaussian", "uniform"]
- self._optimizer_setup = False
-
- self._min_noise = 1 / (2 ** self.max_bits - 1)
- self._max_noise = 1 / (2 ** self.min_bits - 1)
-
- assert group_size >= 0
- assert min_bits < init_bits < max_bits, \
- "init_bits must be between min_bits and max_bits excluded3"
-
- for name, _ in model.named_parameters():
- if name.endswith(suffix):
- raise RuntimeError("The model already has some noise scales parameters, "
- "maybe you used twice a DiffQuantizer on the same model?.")
-
- super().__init__(model, min_size, float16, exclude, detect_bound)
-
- def _get_bits(self, logit: torch.Tensor):
- if self.param == "noise":
- return torch.log2(1 + 1 / self._get_noise_scale(logit))
- else:
- t = torch.sigmoid(logit)
- return self.max_bits * t + (1 - t) * self.min_bits
-
- def _get_noise_scale(self, logit: torch.Tensor):
- if self.param == "noise":
- t = torch.sigmoid(logit)
- return torch.exp(t * math.log(self._min_noise) + (1 - t) * math.log(self._max_noise))
- else:
- return 1 / (2 ** self._get_bits(logit) - 1)
-
- def _register_param(self, name, param, module, other):
- if other is not None:
- return self.__class__._QuantizedParam(
- name=name, param=param, module=module, logit=other.logit, other=other)
- assert self.group_size == 0 or param.numel() % self.group_size == 0
- # we want the initial number of bits to be init_bits.
- if self.param == "noise":
- noise_scale = 1 / (2 ** self.init_bits - 1)
- t = (math.log(noise_scale) - math.log(self._max_noise)) / (
- math.log(self._min_noise) - math.log(self._max_noise))
- else:
- t = (self.init_bits - self.min_bits) / (self.max_bits - self.min_bits)
- assert 0 < t < 1
- logit = torch.logit(torch.tensor(float(t)))
- assert abs(self._get_bits(logit) - self.init_bits) < 1e-5
- if self.group_size > 0:
- nparam = param.numel() // self.group_size
- else:
- nparam = 1
- logit = torch.nn.Parameter(
- torch.full(
- (nparam,),
- logit,
- device=param.device))
- module.register_parameter(name + self.suffix, logit)
- return self.__class__._QuantizedParam(
- name=name, param=param, module=module, logit=logit, other=None)
-
- def clear_optimizer(self, optimizer: torch.optim.Optimizer):
- params = [qp.logit for qp in self._qparams]
-
- for group in optimizer.param_groups:
- new_params = []
- for q in list(group["params"]):
- matched = False
- for p in params:
- if p is q:
- matched = True
- if not matched:
- new_params.append(q)
- group["params"][:] = new_params
-
- def setup_optimizer(self, optimizer: torch.optim.Optimizer,
- lr: float = 1e-3, **kwargs):
- """
- Setup the optimizer to tune the number of bits. In particular, this will deactivate
- weight decay for the bits parameters.
-
- Args:
- optimizer (torch.Optimizer): optimizer to use.
- lr (float): specific learning rate for the bits parameters. 1e-3
- is perfect for Adam.,w
- kwargs (dict): overrides for other optimization parameters for the bits.
- """
- assert not self._optimizer_setup
- self._optimizer_setup = True
-
- params = [qp.logit for qp in self._qparams]
-
- for group in optimizer.param_groups:
- for q in list(group["params"]):
- for p in params:
- if p is q:
- raise RuntimeError("You should create the optimizer "
- "before the quantizer!")
-
- group = {"params": params, "lr": lr, "weight_decay": 0}
- group.update(kwargs)
- optimizer.add_param_group(group)
-
- def no_optimizer(self):
- """
- Call this if you do not want to use an optimizer.
- """
- self._optimizer_setup = True
-
- def check_unused(self):
- for qparam in self._qparams:
- if qparam.other is not None:
- continue
- grad = qparam.param.grad
- if grad is None or (grad == 0).all():
- if qparam.logit.grad is not None:
- qparam.logit.grad.data.zero_()
-
- def model_size(self, exact=False):
- """
- Differentiable estimate of the model size.
- The size is returned in MB.
-
- If `exact` is True, then the output is no longer differentiable but
- reflect exactly an achievable size, even without compression,
- i.e.same as returned by `naive_model_size()`.
- """
- total = super().model_size()
- subtotal = 0
- for qparam in self._qparams:
- # only count the first appearance of a Parameter
- if qparam.other is not None:
- continue
- bits = self.extra_bits + self._get_bits(qparam.logit)
- if exact:
- bits = bits.round().clamp(1, 15)
- if self.group_size == 0:
- group_size = qparam.param.numel()
- else:
- group_size = self.group_size
- subtotal += group_size * bits.sum()
- subtotal += 2 * 32 # param scale
-
- # Number of bits to represent each number of bits
- bits_bits = math.ceil(math.log2(1 + (bits.max().round().item() - self.min_bits)))
- subtotal += 8 # 8 bits for bits_bits
- subtotal += bits_bits * bits.numel()
-
- subtotal /= 2 ** 20 * 8 # bits -> MegaBytes
- return total + subtotal
-
- def true_model_size(self):
- """
- Naive model size without zlib compression.
- """
- return self.model_size(exact=True).item()
-
- def _pre_forward_train(self):
- if not self._optimizer_setup:
- raise RuntimeError("You must call `setup_optimizer()` on your optimizer "
- "before starting training.")
- for qparam in self._qparams:
- if qparam.other is not None:
- noisy = qparam.other.module._parameters[qparam.other.name]
- else:
- bits = self._get_bits(qparam.logit)[:, None]
- if self.group_size == 0:
- p_flat = qparam.param.view(-1)
- else:
- p_flat = qparam.param.view(-1, self.group_size)
- scale = p_flat.max() - p_flat.min()
- unit = 1 / (2**bits - 1)
- if self.noise == "uniform":
- noise_source = (torch.rand_like(p_flat) - 0.5)
- elif self.noise == "gaussian":
- noise_source = torch.randn_like(p_flat) / 2
- noise = scale * unit * noise_source
- noisy = p_flat + noise
- # We bypass the checks by PyTorch on parameters being leafs
- qparam.module._parameters[qparam.name] = noisy.view_as(qparam.param)
- return True
-
- def _post_forward_train(self):
- for qparam in self._qparams:
- qparam.module._parameters[qparam.name] = qparam.param
- return True
-
- def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any:
- bits = self.extra_bits + self._get_bits(qparam.logit)
- bits = bits.round().clamp(1, 15)[:, None].byte()
- if self.group_size == 0:
- p = qparam.param.data.view(-1)
- else:
- p = qparam.param.data.view(-1, self.group_size)
- levels, scales = uniform_quantize(p, bits)
- return levels, scales, bits
-
- def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
- levels, param_scale, bits = quantized
- return uniform_unquantize(levels, param_scale, bits).view_as(qparam.param.data)
-
- def detach(self):
- super().detach()
- for qparam in self._qparams:
- delattr(qparam.module, qparam.name + self.suffix)
-
- def __repr__(self):
- return simple_repr(self)
diff --git a/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/lib_v5/layers_537227KB.py b/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/lib_v5/layers_537227KB.py
deleted file mode 100644
index a38b7bb3ae3136b07eadfc2db445fef4c2de186b..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/lib_v5/layers_537227KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(Conv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nout,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- bias=False,
- ),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(SeperableConv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nin,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- groups=nin,
- bias=False,
- ),
- nn.Conv2d(nin, nout, kernel_size=1, bias=False),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class Encoder(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
- super(Encoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
- def __call__(self, x):
- skip = self.conv1(x)
- h = self.conv2(skip)
-
- return h, skip
-
-
-class Decoder(nn.Module):
- def __init__(
- self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
- ):
- super(Decoder, self).__init__()
- self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def __call__(self, x, skip=None):
- x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
- if skip is not None:
- skip = spec_utils.crop_center(skip, x)
- x = torch.cat([x, skip], dim=1)
- h = self.conv(x)
-
- if self.dropout is not None:
- h = self.dropout(h)
-
- return h
-
-
-class ASPPModule(nn.Module):
- def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
- super(ASPPModule, self).__init__()
- self.conv1 = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, None)),
- Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
- )
- self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
- self.conv3 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
- )
- self.conv4 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
- )
- self.conv5 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.conv6 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.conv7 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.bottleneck = nn.Sequential(
- Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
- )
-
- def forward(self, x):
- _, _, h, w = x.size()
- feat1 = F.interpolate(
- self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
- )
- feat2 = self.conv2(x)
- feat3 = self.conv3(x)
- feat4 = self.conv4(x)
- feat5 = self.conv5(x)
- feat6 = self.conv6(x)
- feat7 = self.conv7(x)
- out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
- bottle = self.bottleneck(out)
- return bottle
diff --git a/spaces/EsoCode/text-generation-webui/convert-to-safetensors.py b/spaces/EsoCode/text-generation-webui/convert-to-safetensors.py
deleted file mode 100644
index 3b721e7cd4d15cf7e5e03caaee57ef83a41553bc..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/convert-to-safetensors.py
+++ /dev/null
@@ -1,38 +0,0 @@
-'''
-
-Converts a transformers model to safetensors format and shards it.
-
-This makes it faster to load (because of safetensors) and lowers its RAM usage
-while loading (because of sharding).
-
-Based on the original script by 81300:
-
-https://gist.github.com/81300/fe5b08bff1cba45296a829b9d6b0f303
-
-'''
-
-import argparse
-from pathlib import Path
-
-import torch
-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.")
-parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).')
-parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).")
-parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
-args = parser.parse_args()
-
-if __name__ == '__main__':
- path = Path(args.MODEL)
- model_name = path.name
-
- print(f"Loading {model_name}...")
- model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch.float16)
- tokenizer = AutoTokenizer.from_pretrained(path)
-
- out_folder = args.output or Path(f"models/{model_name}_safetensors")
- print(f"Saving the converted model to {out_folder} with a maximum shard size of {args.max_shard_size}...")
- model.save_pretrained(out_folder, max_shard_size=args.max_shard_size, safe_serialization=True)
- tokenizer.save_pretrained(out_folder)
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/clickbait_classifier.py b/spaces/EuroPython2022/clickbaitonator/fudge/clickbait_classifier.py
deleted file mode 100644
index ad7358c0614291e53a62e313fd79ef8d097fb09c..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/clickbait_classifier.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import torch
-from transformers import BertModel, BertConfig, PretrainedConfig, PreTrainedModel, AutoModel, AutoConfig
-from typing import List, Optional, Tuple, Union
-from transformers.modeling_outputs import TokenClassifierOutput,SequenceClassifierOutput
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss, BCELoss
-import torch.nn as nn
-# from modeling_mpnet import MPNetModel, MPnetConfig
-
-class ClickbaitConfig(PretrainedConfig):
- def __init__(
- self,
- model_type: str = "bert",
- pretrained_model: str = "bert-base-uncased",
- num_labels: int = 1,
- dropout: float = 0.1,
- inner_dim1: int = 256,
- inner_dim2: int = 32,
- max_length: int = 512,
- load_pretrained: bool = True,
- freeze_bert: bool = True,
- **kwargs
- ):
- super(ClickbaitConfig, self).__init__(num_labels=num_labels, **kwargs)
- self.model_type = model_type
- self.pretrained_model = pretrained_model
- self.dropout = dropout
- self.inner_dim1 = inner_dim1
- self.inner_dim2 = inner_dim2
- self.max_length = max_length
- self.load_pretrained = load_pretrained
- self.freeze_bert = freeze_bert
-
-
-class BertClickbaitClassifier(PreTrainedModel):
- """
- Taken and extended from BertforSequenceClassification : https://github.com/huggingface/transformers/blob/v4.19.2/src/transformers/models/bert/modeling_bert.py#L1508
- """
- config_class = ClickbaitConfig
- def __init__(self, config: ClickbaitConfig):
- super(BertClickbaitClassifier, self).__init__(config)
- self.num_labels = config.num_labels
- self.config = config
- # self.bert_config = BertConfig.from_pretrained(config.pretrained_model)
- self.bert_config = AutoConfig.from_pretrained(config.pretrained_model)
-
- # self.bert = BertModel(self.bert_config)
- self.bert = AutoModel.from_pretrained(config.pretrained_model, config=self.bert_config)
- # self.bert = SentenceTransformer(config.pretrained_model, config=self.bert_config)
- # self.bert = MPNetModel(config.pretrained_model, config=self.bert_config)
- if config.load_pretrained:
- print("Load pretrained weights from {}".format(config.pretrained_model))
- self.bert = self.bert.from_pretrained(config.pretrained_model)
- if config.freeze_bert:
- print("Freeze weights in the BERT model. Just the classifier will be trained")
- for param in self.bert.parameters():
- param.requires_grad = False
-
- self.linear_1 = nn.Linear(self.bert.config.hidden_size, config.inner_dim1)
- self.dropout_1 = nn.Dropout(config.dropout)
- self.relu_1 = nn.ReLU()
- self.dropout_2 = nn.Dropout(config.dropout)
- self.linear_2 = nn.Linear(config.inner_dim1, config.inner_dim2)
- self.relu_2 = nn.ReLU()
- self.dropout_3 = nn.Dropout(config.dropout)
- self.classifier = nn.Linear(config.inner_dim2, config.num_labels)
- self.sigmoid = nn.Sigmoid()
-
-
- def forward(
- self,
- input_ids: Optional[torch.Tensor] = None,
- attention_mask: Optional[torch.Tensor] = None,
- token_type_ids: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.Tensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- inputs_embeds: Optional[torch.Tensor] = None,
- labels: Optional[torch.Tensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
- Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
- config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
- `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
- """
-
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- output = outputs[0][:,0,:]
-
- x = self.dropout_1(output)
- x = self.linear_1(x)
- x = self.relu_1(x)
- x = self.dropout_2(x)
- x = self.linear_2(x)
- x = self.relu_2(x)
- x = self.dropout_3(x)
-
- logits = self.classifier(x)
- logits = self.sigmoid(logits)
-
- loss = None
- if labels is not None:
- loss_fct = BCELoss(weight=WEIGHT)
- labels = 1.0*labels
- loss = loss_fct(logits.view(-1), labels.view(-1))
- if not return_dict:
- output = (logits,) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return SequenceClassifierOutput(
- loss=loss,
- logits=logits
- )
\ No newline at end of file
diff --git a/spaces/FKBaffour/Gradio_App_for_Sentiment_Analysis/app.py b/spaces/FKBaffour/Gradio_App_for_Sentiment_Analysis/app.py
deleted file mode 100644
index 75ab300afc2679c0c5f597dcc405d20679c14474..0000000000000000000000000000000000000000
--- a/spaces/FKBaffour/Gradio_App_for_Sentiment_Analysis/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoConfig
-import numpy as np
-from scipy.special import softmax
-import gradio as gr
-
-# Requirements
-model_path = f"FKBaffour/fine-tuned-roberta-base-model-for-sentiment-analysis"
-tokenizer = AutoTokenizer.from_pretrained('roberta-base')
-config = AutoConfig.from_pretrained(model_path)
-model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
-# Preprocess text (username and link placeholders)
-def preprocess(text):
- new_text = []
- for t in text.split(" "):
- t = '@user' if t.startswith('@') and len(t) > 1 else t
- t = 'http' if t.startswith('http') else t
- new_text.append(t)
- return " ".join(new_text)
-
-
-def sentiment_analysis(text):
- text = preprocess(text)
-
- # PyTorch-based models
- encoded_input = tokenizer(text, return_tensors='pt')
- output = model(**encoded_input)
- scores_ = output[0][0].detach().numpy()
- scores_ = softmax(scores_)
-
- # Format output dict of scores
- labels = ['Negative', 'Neutral', 'Positive']
- scores = {l:float(s) for (l,s) in zip(labels, scores_) }
-
- return scores
-
-demo = gr.Interface(
- fn=sentiment_analysis,
- inputs=gr.Textbox(placeholder="Copy and paste/Write a tweet here..."),
- outputs="label",
- interpretation="default",
- examples=[["What's up with the vaccine"]],
- title="Sentiment Analysis on Vaccinations",
- description="This Application assesses if a social media post relating to vaccination is positive, neutral, or negative.", )
-
-demo.launch()
-
diff --git a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.html b/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.html
deleted file mode 100644
index b1f2c057c0cb16aa13d7e2c658345ada50b6cfaa..0000000000000000000000000000000000000000
--- a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.html
+++ /dev/null
@@ -1,24 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/ForTheLoveOfML0/X-ray_Classifier/app.py b/spaces/ForTheLoveOfML0/X-ray_Classifier/app.py
deleted file mode 100644
index c3b9aa510ee32b4f8c6941004af6946b9fbe3f18..0000000000000000000000000000000000000000
--- a/spaces/ForTheLoveOfML0/X-ray_Classifier/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import torch
-import os
-import gradio as gr
-import numpy as np
-import matplotlib.pyplot as plt
-from PIL import Image
-from functools import partial
-
-import Utils.Pneumonia_Utils as PU
-import Utils.CT_Scan_Utils as CSU
-import Utils.Covid19_Utils as C19U
-import Utils.DR_Utils as DRU
-
-# Constants for model paths
-CANCER_MODEL_PATH = 'cs_models/EfficientNet_CT_Scans.pth.tar'
-DIABETIC_RETINOPATHY_MODEL_PATH = 'cs_models/model_DR_9.pth.tar'
-PNEUMONIA_MODEL_PATH = 'cs_models/DenseNet_Pneumonia.pth.tar'
-COVID_MODEL_PATH = 'cs_models/DenseNet_Covid.pth.tar'
-
-# Constants for class labels
-CANCER_CLASS_LABELS = ['adenocarcinoma','large.cell.carcinoma','normal','squamous.cell.carcinoma']
-DIABETIC_RETINOPATHY_CLASS_LABELS = ['No DR','Mild', 'Moderate', 'Severe', 'Proliferative DR']
-PNEUMONIA_CLASS_LABELS = ['Normal', 'Pneumonia']
-COVID_CLASS_LABELS = ['Normal','Covid19']
-
-if torch.cuda.is_available():
- device = torch.device("cuda")
-else:
- device = torch.device("cpu")
-
-
-def cancer_page(image, test_model):
- x_ray_image = CSU.transform_image(image, CSU.val_transform)
- x_ray_image = x_ray_image.to(device)
- grad_x_ray_image, pred_label, pred_conf = CSU.plot_grad_cam(test_model,
- x_ray_image,
- CANCER_CLASS_LABELS,
- normalized=True)
- grad_x_ray_image = np.clip(grad_x_ray_image, 0, 1)
- return grad_x_ray_image, pred_label, pred_conf
-
-
-def covid_page(image, test_model):
- x_ray_image = C19U.transform_image(image, C19U.val_transform)
- x_ray_image = x_ray_image.to(device)
- grad_x_ray_image, pred_label, pred_conf = C19U.plot_grad_cam(test_model,
- x_ray_image,
- COVID_CLASS_LABELS,
- normalized=True)
- grad_x_ray_image = np.clip(grad_x_ray_image, 0, 1)
- return grad_x_ray_image, pred_label, pred_conf
-
-
-def pneumonia_page(image, test_model):
- x_ray_image = PU.transform_image(image, PU.val_transform)
- x_ray_image = x_ray_image.to(device)
- grad_x_ray_image, pred_label, pred_conf = PU.plot_grad_cam(test_model,
- x_ray_image,
- PNEUMONIA_CLASS_LABELS,
- normalized=True)
- grad_x_ray_image = np.clip(grad_x_ray_image, 0, 1)
- return grad_x_ray_image, pred_label, pred_conf
-
-def diabetic_retinopathy_page(image_1, image_2, test_model):
- images = DRU.transform_image(image_1, image_2, DRU.val_transform)
- pred_label_1, pred_label_2 = DRU.Inf_predict_image(test_model,
- images,
- DIABETIC_RETINOPATHY_CLASS_LABELS)
- return pred_label_1, pred_label_2
-
-CSU_model = CSU.Efficient().to(device)
-CSU_model.load_state_dict(torch.load(CANCER_MODEL_PATH,map_location=torch.device('cpu')),strict=False)
-CSU_test_model = CSU.ModelGradCam(CSU_model).to(device)
-CSU_images_dir = "TESTS/CHEST_CT_SCANS"
-all_images = os.listdir(CSU_images_dir)
-CSU_examples = [[os.path.join(CSU_images_dir,image)] for image in np.random.choice(all_images, size=4, replace=False)]
-
-C19U_model = C19U.DenseNet().to(device)
-C19U_model.load_state_dict(torch.load(COVID_MODEL_PATH,map_location=torch.device('cpu')),strict=False)
-C19U_test_model = C19U.ModelGradCam(C19U_model).to(device)
-C19U_C19_images_dir = [[os.path.join("TESTS/COVID19",image)] for image in np.random.choice(os.listdir("TESTS/COVID19"), size=2, replace=False)]
-NORM_images_dir = [[os.path.join("TESTS/NORMAL",image)] for image in np.random.choice(os.listdir("TESTS/NORMAL"), size=2, replace=False)]
-C19U_examples = C19U_C19_images_dir + NORM_images_dir
-
-PU_model = PU.DenseNet.to(device)
-PU_model.load_state_dict(torch.load(PNEUMONIA_MODEL_PATH,map_location=torch.device('cpu')),strict=False)
-PU_test_model = PU.ModelGradCam(PU_model).to(device)
-PU_images_dir = [[os.path.join("TESTS/PNEUMONIA",image)] for image in np.random.choice(os.listdir("TESTS/PNEUMONIA"), size=2, replace=False)]
-NORM_images_dir = [[os.path.join("TESTS/NORMAL",image)] for image in np.random.choice(os.listdir("TESTS/NORMAL"), size=2, replace=False)]
-PU_examples = PU_images_dir + NORM_images_dir
-
-DRU_cnn_model = DRU.ConvolutionNeuralNetwork().to(device)
-DRU_eff_b3 = DRU.Efficient().to(device)
-DRU_ensemble = DRU.EnsembleModel(DRU_cnn_model, DRU_eff_b3).to(device)
-DRU_ensemble.load_state_dict(torch.load(DIABETIC_RETINOPATHY_MODEL_PATH,map_location=torch.device('cpu'))["state_dict"], strict=False)
-DRU_test_model = DRU_ensemble
-DRU_examples = [['TESTS/DR_1/10030_left._aug_0._aug_6.jpeg','TESTS/DR_0/10031_right._aug_17.jpeg']]
-
-demo = gr.Blocks(title="X-RAY_CLASSIFIER")
-
-with demo:
-
- gr.Markdown(
- """ # WELCOME, Try Out the X-ray_Classifier Below
- Try out the following classification models below."""
- )
-
- with gr.Tab("Chest Cancer"):
- with gr.Row():
- cancer_input = gr.Image(type="pil", label="Image")
- cancer_output1 = gr.Image(type="numpy", label="Heatmap Image")
- cancer_output2 = gr.Textbox(label="Labels Present")
- cancer_output3 = gr.Label(label="Probabilities", show_label=False)
- cancer_button = gr.Button("Predict")
- cancer_examples = gr.Examples(CSU_examples, inputs=[cancer_input])
-
- with gr.Tab("Covid19"):
- with gr.Row():
- covid_input = gr.Image(type="pil", label="Image")
- covid_output1 = gr.Image(type="numpy", label="Heatmap Image")
- covid_output2 = gr.Textbox(label="Labels Present")
- covid_output3 = gr.Label(label="Probabilities", show_label=False)
- covid_button = gr.Button("Predict")
- covid_examples = gr.Examples(C19U_examples, inputs=[covid_input])
-
- with gr.Tab("Pneumonia"):
- with gr.Row():
- pneumonia_input = gr.Image(type="pil", label="Image")
- pneumonia_output1 = gr.Image(type="numpy", label="Heatmap Image")
- pneumonia_output2 = gr.Textbox(label="Labels Present")
- pneumonia_output3 = gr.Label(label="Probabilities", show_label=False)
- pneumonia_button = gr.Button("Predict")
- pneumonia_examples = gr.Examples(PU_examples, inputs=[pneumonia_input])
-
- with gr.Tab("Diabetic Retinopathy"):
- with gr.Row():
- dr_input1 = gr.Image(type="pil", label="Image")
- dr_input2 = gr.Image(type="pil", label="Image")
- dr_output1 = gr.Textbox(label="Labels Present")
- dr_output2 = gr.Textbox(label="Labels Present")
- dr_button = gr.Button("Predict")
- dr_examples = gr.Examples(DRU_examples, inputs=[dr_input1, dr_input2])
-
- cancer_button.click(partial(cancer_page, test_model=CSU_test_model),
- inputs=cancer_input,
- outputs=[cancer_output1, cancer_output2, cancer_output3])
-
- covid_button.click(partial(covid_page, test_model=C19U_test_model),
- inputs=covid_input,
- outputs=[covid_output1, covid_output2, covid_output3])
-
- pneumonia_button.click(partial(pneumonia_page, test_model=PU_test_model),
- inputs=pneumonia_input,
- outputs=[pneumonia_output1, pneumonia_output2, pneumonia_output3])
-
- dr_button.click(partial(diabetic_retinopathy_page,
- test_model=DRU_test_model),
- inputs=[dr_input1, dr_input2],
- outputs=[dr_output1, dr_output2])
-
-
-if __name__ == "__main__":
-
- demo.launch()
diff --git a/spaces/Francesco/FairytaleDJ/utils.py b/spaces/Francesco/FairytaleDJ/utils.py
deleted file mode 100644
index e57d58aa6ce049deec031562c35a5aa4b977e641..0000000000000000000000000000000000000000
--- a/spaces/Francesco/FairytaleDJ/utils.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import numpy as np
-
-
-def weighted_random_sample(items: np.array, weights: np.array, n: int) -> np.array:
- """
- Does np.random.choice but ensuring we don't have duplicates in the final result
-
- Args:
- items (np.array): _description_
- weights (np.array): _description_
- n (int): _description_
-
- Returns:
- np.array: _description_
- """
- indices = np.arange(len(items))
- out_indices = []
-
- for _ in range(n):
- chosen_index = np.random.choice(indices, p=weights)
- out_indices.append(chosen_index)
-
- mask = indices != chosen_index
- indices = indices[mask]
- weights = weights[mask]
-
- if weights.sum() != 0:
- weights = weights / weights.sum()
-
- return items[out_indices]
diff --git a/spaces/FridaZuley/RVC_HFKawaii/julius/resample.py b/spaces/FridaZuley/RVC_HFKawaii/julius/resample.py
deleted file mode 100644
index fd3b9b547d4c33ec7136d32e5f086420d0a72e14..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/julius/resample.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-"""
-Differentiable, Pytorch based resampling.
-Implementation of Julius O. Smith algorithm for resampling.
-See https://ccrma.stanford.edu/~jos/resample/ for details.
-This implementation is specially optimized for when new_sr / old_sr is a fraction
-with a small numerator and denominator when removing the gcd (e.g. new_sr = 700, old_sr = 500).
-
-Very similar to [bmcfee/resampy](https://github.com/bmcfee/resampy) except this implementation
-is optimized for the case mentioned before, while resampy is slower but more general.
-
-"""
-
-import math
-from typing import Optional
-
-import torch
-from torch.nn import functional as F
-
-from .core import sinc
-from .utils import simple_repr
-
-
-class ResampleFrac(torch.nn.Module):
- """
- Resampling from the sample rate `old_sr` to `new_sr`.
- """
- def __init__(self, old_sr: int, new_sr: int, zeros: int = 24, rolloff: float = 0.945):
- """
- Args:
- old_sr (int): sample rate of the input signal x.
- new_sr (int): sample rate of the output.
- zeros (int): number of zero crossing to keep in the sinc filter.
- rolloff (float): use a lowpass filter that is `rolloff * new_sr / 2`,
- to ensure sufficient margin due to the imperfection of the FIR filter used.
- Lowering this value will reduce anti-aliasing, but will reduce some of the
- highest frequencies.
-
- Shape:
-
- - Input: `[*, T]`
- - Output: `[*, T']` with `T' = int(new_sr * T / old_sr)
-
-
- .. caution::
- After dividing `old_sr` and `new_sr` by their GCD, both should be small
- for this implementation to be fast.
-
- >>> import torch
- >>> resample = ResampleFrac(4, 5)
- >>> x = torch.randn(1000)
- >>> print(len(resample(x)))
- 1250
- """
- super().__init__()
- if not isinstance(old_sr, int) or not isinstance(new_sr, int):
- raise ValueError("old_sr and new_sr should be integers")
- gcd = math.gcd(old_sr, new_sr)
- self.old_sr = old_sr // gcd
- self.new_sr = new_sr // gcd
- self.zeros = zeros
- self.rolloff = rolloff
-
- self._init_kernels()
-
- def _init_kernels(self):
- if self.old_sr == self.new_sr:
- return
-
- kernels = []
- sr = min(self.new_sr, self.old_sr)
- # rolloff will perform antialiasing filtering by removing the highest frequencies.
- # At first I thought I only needed this when downsampling, but when upsampling
- # you will get edge artifacts without this, the edge is equivalent to zero padding,
- # which will add high freq artifacts.
- sr *= self.rolloff
-
- # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor)
- # using the sinc interpolation formula:
- # x(t) = sum_i x[i] sinc(pi * old_sr * (i / old_sr - t))
- # We can then sample the function x(t) with a different sample rate:
- # y[j] = x(j / new_sr)
- # or,
- # y[j] = sum_i x[i] sinc(pi * old_sr * (i / old_sr - j / new_sr))
-
- # We see here that y[j] is the convolution of x[i] with a specific filter, for which
- # we take an FIR approximation, stopping when we see at least `zeros` zeros crossing.
- # But y[j+1] is going to have a different set of weights and so on, until y[j + new_sr].
- # Indeed:
- # y[j + new_sr] = sum_i x[i] sinc(pi * old_sr * ((i / old_sr - (j + new_sr) / new_sr))
- # = sum_i x[i] sinc(pi * old_sr * ((i - old_sr) / old_sr - j / new_sr))
- # = sum_i x[i + old_sr] sinc(pi * old_sr * (i / old_sr - j / new_sr))
- # so y[j+new_sr] uses the same filter as y[j], but on a shifted version of x by `old_sr`.
- # This will explain the F.conv1d after, with a stride of old_sr.
- self._width = math.ceil(self.zeros * self.old_sr / sr)
- # If old_sr is still big after GCD reduction, most filters will be very unbalanced, i.e.,
- # they will have a lot of almost zero values to the left or to the right...
- # There is probably a way to evaluate those filters more efficiently, but this is kept for
- # future work.
- idx = torch.arange(-self._width, self._width + self.old_sr).float()
- for i in range(self.new_sr):
- t = (-i/self.new_sr + idx/self.old_sr) * sr
- t = t.clamp_(-self.zeros, self.zeros)
- t *= math.pi
- window = torch.cos(t/self.zeros/2)**2
- kernel = sinc(t) * window
- # Renormalize kernel to ensure a constant signal is preserved.
- kernel.div_(kernel.sum())
- kernels.append(kernel)
-
- self.register_buffer("kernel", torch.stack(kernels).view(self.new_sr, 1, -1))
-
- def forward(self, x: torch.Tensor, output_length: Optional[int] = None, full: bool = False):
- """
- Resample x.
- Args:
- x (Tensor): signal to resample, time should be the last dimension
- output_length (None or int): This can be set to the desired output length
- (last dimension). Allowed values are between 0 and
- ceil(length * new_sr / old_sr). When None (default) is specified, the
- floored output length will be used. In order to select the largest possible
- size, use the `full` argument.
- full (bool): return the longest possible output from the input. This can be useful
- if you chain resampling operations, and want to give the `output_length` only
- for the last one, while passing `full=True` to all the other ones.
- """
- if self.old_sr == self.new_sr:
- return x
- shape = x.shape
- length = x.shape[-1]
- x = x.reshape(-1, length)
- x = F.pad(x[:, None], (self._width, self._width + self.old_sr), mode='replicate')
- ys = F.conv1d(x, self.kernel, stride=self.old_sr) # type: ignore
- y = ys.transpose(1, 2).reshape(list(shape[:-1]) + [-1])
-
- float_output_length = self.new_sr * length / self.old_sr
- max_output_length = int(math.ceil(float_output_length))
- default_output_length = int(float_output_length)
- if output_length is None:
- output_length = max_output_length if full else default_output_length
- elif output_length < 0 or output_length > max_output_length:
- raise ValueError(f"output_length must be between 0 and {max_output_length}")
- else:
- if full:
- raise ValueError("You cannot pass both full=True and output_length")
- return y[..., :output_length]
-
- def __repr__(self):
- return simple_repr(self)
-
-
-def resample_frac(x: torch.Tensor, old_sr: int, new_sr: int,
- zeros: int = 24, rolloff: float = 0.945,
- output_length: Optional[int] = None, full: bool = False):
- """
- Functional version of `ResampleFrac`, refer to its documentation for more information.
-
- ..warning::
- If you call repeatidly this functions with the same sample rates, then the
- resampling kernel will be recomputed everytime. For best performance, you should use
- and cache an instance of `ResampleFrac`.
- """
- return ResampleFrac(old_sr, new_sr, zeros, rolloff).to(x)(x, output_length, full)
-
-
-# Easier implementations for downsampling and upsampling by a factor of 2
-# Kept for testing and reference
-
-def _kernel_upsample2_downsample2(zeros):
- # Kernel for upsampling and downsampling by a factor of 2. Interestingly,
- # it is the same kernel used for both.
- win = torch.hann_window(4 * zeros + 1, periodic=False)
- winodd = win[1::2]
- t = torch.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros)
- t *= math.pi
- kernel = (sinc(t) * winodd).view(1, 1, -1)
- return kernel
-
-
-def _upsample2(x, zeros=24):
- """
- Upsample x by a factor of two. The output will be exactly twice as long as the input.
- Args:
- x (Tensor): signal to upsample, time should be the last dimension
- zeros (int): number of zero crossing to keep in the sinc filter.
-
- This function is kept only for reference, you should use the more generic `resample_frac`
- one. This function does not perform anti-aliasing filtering.
- """
- *other, time = x.shape
- kernel = _kernel_upsample2_downsample2(zeros).to(x)
- out = F.conv1d(x.view(-1, 1, time), kernel, padding=zeros)[..., 1:].view(*other, time)
- y = torch.stack([x, out], dim=-1)
- return y.view(*other, -1)
-
-
-def _downsample2(x, zeros=24):
- """
- Downsample x by a factor of two. The output length is half of the input, ceiled.
- Args:
- x (Tensor): signal to downsample, time should be the last dimension
- zeros (int): number of zero crossing to keep in the sinc filter.
-
- This function is kept only for reference, you should use the more generic `resample_frac`
- one. This function does not perform anti-aliasing filtering.
- """
- if x.shape[-1] % 2 != 0:
- x = F.pad(x, (0, 1))
- xeven = x[..., ::2]
- xodd = x[..., 1::2]
- *other, time = xodd.shape
- kernel = _kernel_upsample2_downsample2(zeros).to(x)
- out = xeven + F.conv1d(xodd.view(-1, 1, time), kernel, padding=zeros)[..., :-1].view(
- *other, time)
- return out.view(*other, -1).mul(0.5)
diff --git a/spaces/GIZ/SDSN-demo/ver0.1 scripts/docPreprocessing.py b/spaces/GIZ/SDSN-demo/ver0.1 scripts/docPreprocessing.py
deleted file mode 100644
index 81b982e5a00cfe908b4b1e9d61278e53a28afba4..0000000000000000000000000000000000000000
--- a/spaces/GIZ/SDSN-demo/ver0.1 scripts/docPreprocessing.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from typing import Callable, Dict, List, Optional
-
-from pathlib import Path
-import re
-import logging
-import string
-import streamlit as st
-logger = logging.getLogger(__name__)
-
-import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-from haystack.utils import convert_files_to_docs, fetch_archive_from_http
-from haystack.nodes.file_converter import BaseConverter, DocxToTextConverter
-from haystack.nodes.file_converter import PDFToTextConverter, TextConverter
-from haystack.schema import Document
-import pdfplumber
-
-import pandas as pd
-
-import tempfile
-import sqlite3
-
-
-
-def load_document(
- file_path: str,
- file_name,
- encoding: Optional[str] = None,
- id_hash_keys: Optional[List[str]] = None,
-) -> List[Document]:
-
- """
- takes docx, txt and pdf files as input and \
- extracts text as well as the filename as metadata. \
- Since haystack does not take care of all pdf files, \
- pdfplumber is attached to the pipeline in case the pdf \
- extraction fails via Haystack.
-
- Returns a list of type haystack.schema.Document
- """
-
- if file_name.endswith('.pdf'):
- converter = PDFToTextConverter(remove_numeric_tables=True)
- if file_name.endswith('.txt'):
- converter = TextConverter()
- if file_name.endswith('.docx'):
- converter = DocxToTextConverter()
-
-
- documents = []
- logger.info("Converting {}".format(file_name))
- # PDFToTextConverter, TextConverter, and DocxToTextConverter
- # return a list containing a single Document
- document = converter.convert(
- file_path=file_path, meta=None,
- encoding=encoding, id_hash_keys=id_hash_keys
- )[0]
- text = document.content
- documents.append(Document(content=text,
- meta={"name": file_name},
- id_hash_keys=id_hash_keys))
-
- '''check if text is empty and apply different pdf processor. \
- This can happen whith certain pdf types.'''
- for i in documents:
- if i.content == "":
- with st.spinner("using pdfplumber"):
- text = []
- with pdfplumber.open(file_path) as pdf:
- for page in pdf.pages:
- text.append(page.extract_text())
- i.content = ' '.join([page for page in text])
-
- return documents
diff --git a/spaces/Gauri54damle/sdxl-lora-multi-object/README.md b/spaces/Gauri54damle/sdxl-lora-multi-object/README.md
deleted file mode 100644
index aaa9153343b8f8f3930227027b49e9f49dff95c3..0000000000000000000000000000000000000000
--- a/spaces/Gauri54damle/sdxl-lora-multi-object/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sdxl Lora Multi Object
-emoji: 🏢
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GeemiW/pdb_answers/README.md b/spaces/GeemiW/pdb_answers/README.md
deleted file mode 100644
index 35c4d939d766bfaaff76dff55488bdda33fd5157..0000000000000000000000000000000000000000
--- a/spaces/GeemiW/pdb_answers/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pdb Answers
-emoji: 📚
-colorFrom: green
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-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/Gen-Sim/Gen-Sim/cliport/tasks/generated_task.py b/spaces/Gen-Sim/Gen-Sim/cliport/tasks/generated_task.py
deleted file mode 100644
index 6f0f252e7def331252d76049410f75a3f54a5d45..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/generated_task.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import numpy as np
-
-from cliport.tasks import Task
-
-from cliport.tasks.grippers import Spatula
-import random
-
-from cliport.utils import utils
-
-import pybullet as p
-import numpy as np
-import os
-import pybullet as p
-import random
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import pybullet as p
-
-class GeneratedTask(Task):
- """Build a car using blocks."""
-
-
- """Stack 5 cylinders of different colors (red, blue, green, yellow, and orange) on top of each other in a pyramid shape on a table."""
-
- def __init__(self):
- super().__init__()
- self.max_steps = 20
- self.lang_template = "stack 5 cylinders of different colors (red, blue, green, yellow, and orange) on top of each other in a pyramid shape on a table"
- self.task_completed_desc = "done stacking cylinders."
- self.additional_reset()
-
- def reset(self, env):
- super().reset(env)
-
- # Add table.
- table_size = (0.4, 0.4, 0.02)
- table_urdf = 'table/table.urdf'
- table_pose = self.get_random_pose(env, table_size)
- env.add_object(table_urdf, table_pose, 'fixed')
-
- # Cylinder colors.
- colors = [utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'], utils.COLORS['yellow'], utils.COLORS['orange']]
-
- # Add cylinders.
- cylinder_size = (0.04, 0.04, 0.04)
- cylinder_urdf = 'cylinder/cylinder-template.urdf'
- cylinders = []
- for i in range(5):
- cylinder_pose = self.get_random_pose(env, cylinder_size)
- cylinder_id = env.add_object(cylinder_urdf, cylinder_pose, color=colors[i])
- cylinders.append(cylinder_id)
-
- # Associate placement locations for goals.
- place_pos = [(0, -0.1, 0.02), (0, 0.1, 0.02), (0, -0.05, 0.06), (0, 0.05, 0.06), (0, 0, 0.1)]
- targs = [(utils.apply(cylinder_pose, pos), cylinder_pose[1]) for pos in place_pos]
-
- # Goal: cylinders are stacked in a pyramid shape.
- self.add_goal(objs=cylinders, matches=np.ones((5, 5)), targ_poses=targs, replace=False,
- rotations=True, metric='pose', params=None, step_max_reward=1, symmetries=[np.pi/2]*5,
- language_goal=self.lang_template)
diff --git a/spaces/Gen-Sim/Gen-Sim/misc/generate_all_gif.py b/spaces/Gen-Sim/Gen-Sim/misc/generate_all_gif.py
deleted file mode 100644
index 8a7ff002fd08801f8b8e44b199d8932ce4c42281..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/misc/generate_all_gif.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-import json
-
-# generated_tasks = json.load(open('prompts/data/generated_tasks.json'))
-generated_tasks = json.load(open('prompts/data/base_tasks.json'))
-
-generated_tasks = ([g['task-name'] for k, g in generated_tasks.items()])
-
-# generated_tasks = [ 'build-house' ]
-output_directory = 'output/output_gifs'
-for data_folder in os.listdir('data'):
- folder = os.path.join('data', data_folder, 'videos')
- # print(data_folder)
- if data_folder[:-5] in generated_tasks:
- if os.path.exists(folder):
- try:
- input_file = os.path.join(folder, "input.txt")
- with open(input_file, "w") as file:
- video_files = [f for f in os.listdir(folder) if f.endswith(".mp4")]
- for video_file in video_files:
- file.write(f"file '{ video_file}'\n")
-
- # Concatenate the videos within the subfolder
- concatenated_file = os.path.join(output_directory, f"{data_folder}.mp4")
- ffmpeg_concat_command = f"ffmpeg -f concat -safe 0 -i {input_file} -c copy {concatenated_file}"
- os.system(f"rm {concatenated_file}")
- os.system(ffmpeg_concat_command)
-
- # Convert the concatenated video to a GIF
- gif_file = os.path.join(output_directory, f"{data_folder}.gif")
- os.system(f"rm {gif_file}")
- ffmpeg_gif_command = f"ffmpeg -i {concatenated_file} -frames:v 800 {gif_file}"
- os.system(ffmpeg_gif_command)
- # os.system(f"rm {concatenated_file}")
-
- except Exception as e:
- print(e)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/scripts/inference.py b/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/scripts/inference.py
deleted file mode 100644
index 185b9b34db85dcd97b9793bd5dbfc9d1ca046549..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/scripts/inference.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import argparse
-
-import torch
-import numpy as np
-import sys
-import os
-import dlib
-
-sys.path.append(".")
-sys.path.append("..")
-
-from configs import data_configs, paths_config
-from datasets.inference_dataset import InferenceDataset
-from torch.utils.data import DataLoader
-from utils.model_utils import setup_model
-from utils.common import tensor2im
-from utils.alignment import align_face
-from PIL import Image
-
-
-def main(args):
- net, opts = setup_model(args.ckpt, device)
- is_cars = 'cars_' in opts.dataset_type
- generator = net.decoder
- generator.eval()
- args, data_loader = setup_data_loader(args, opts)
-
- # Check if latents exist
- latents_file_path = os.path.join(args.save_dir, 'latents.pt')
- if os.path.exists(latents_file_path):
- latent_codes = torch.load(latents_file_path).to(device)
- else:
- latent_codes = get_all_latents(net, data_loader, args.n_sample, is_cars=is_cars)
- torch.save(latent_codes, latents_file_path)
-
- if not args.latents_only:
- generate_inversions(args, generator, latent_codes, is_cars=is_cars)
-
-
-def setup_data_loader(args, opts):
- dataset_args = data_configs.DATASETS[opts.dataset_type]
- transforms_dict = dataset_args['transforms'](opts).get_transforms()
- images_path = args.images_dir if args.images_dir is not None else dataset_args['test_source_root']
- print(f"images path: {images_path}")
- align_function = None
- if args.align:
- align_function = run_alignment
- test_dataset = InferenceDataset(root=images_path,
- transform=transforms_dict['transform_test'],
- preprocess=align_function,
- opts=opts)
-
- data_loader = DataLoader(test_dataset,
- batch_size=args.batch,
- shuffle=False,
- num_workers=2,
- drop_last=True)
-
- print(f'dataset length: {len(test_dataset)}')
-
- if args.n_sample is None:
- args.n_sample = len(test_dataset)
- return args, data_loader
-
-
-def get_latents(net, x, is_cars=False):
- codes = net.encoder(x)
- if net.opts.start_from_latent_avg:
- if codes.ndim == 2:
- codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]
- else:
- codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)
- if codes.shape[1] == 18 and is_cars:
- codes = codes[:, :16, :]
- return codes
-
-
-def get_all_latents(net, data_loader, n_images=None, is_cars=False):
- all_latents = []
- i = 0
- with torch.no_grad():
- for batch in data_loader:
- if n_images is not None and i > n_images:
- break
- x = batch
- inputs = x.to(device).float()
- latents = get_latents(net, inputs, is_cars)
- all_latents.append(latents)
- i += len(latents)
- return torch.cat(all_latents)
-
-
-def save_image(img, save_dir, idx):
- result = tensor2im(img)
- im_save_path = os.path.join(save_dir, f"{idx:05d}.jpg")
- Image.fromarray(np.array(result)).save(im_save_path)
-
-
-@torch.no_grad()
-def generate_inversions(args, g, latent_codes, is_cars):
- print('Saving inversion images')
- inversions_directory_path = os.path.join(args.save_dir, 'inversions')
- os.makedirs(inversions_directory_path, exist_ok=True)
- for i in range(args.n_sample):
- imgs, _ = g([latent_codes[i].unsqueeze(0)], input_is_latent=True, randomize_noise=False, return_latents=True)
- if is_cars:
- imgs = imgs[:, :, 64:448, :]
- save_image(imgs[0], inversions_directory_path, i + 1)
-
-
-def run_alignment(image_path):
- predictor = dlib.shape_predictor(paths_config.model_paths['shape_predictor'])
- aligned_image = align_face(filepath=image_path, predictor=predictor)
- print("Aligned image has shape: {}".format(aligned_image.size))
- return aligned_image
-
-
-if __name__ == "__main__":
- device = "cuda"
-
- parser = argparse.ArgumentParser(description="Inference")
- parser.add_argument("--images_dir", type=str, default=None,
- help="The directory of the images to be inverted")
- parser.add_argument("--save_dir", type=str, default=None,
- help="The directory to save the latent codes and inversion images. (default: images_dir")
- parser.add_argument("--batch", type=int, default=1, help="batch size for the generator")
- parser.add_argument("--n_sample", type=int, default=None, help="number of the samples to infer.")
- parser.add_argument("--latents_only", action="store_true", help="infer only the latent codes of the directory")
- parser.add_argument("--align", action="store_true", help="align face images before inference")
- parser.add_argument("ckpt", metavar="CHECKPOINT", help="path to generator checkpoint")
-
- args = parser.parse_args()
- main(args)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/iou_loss.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/iou_loss.py
deleted file mode 100644
index eba6f18b80981ca891c1add37007e6bf478c651f..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/iou_loss.py
+++ /dev/null
@@ -1,436 +0,0 @@
-import math
-
-import mmcv
-import torch
-import torch.nn as nn
-
-from mmdet.core import bbox_overlaps
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def iou_loss(pred, target, linear=False, eps=1e-6):
- """IoU loss.
-
- Computing the IoU loss between a set of predicted bboxes and target bboxes.
- The loss is calculated as negative log of IoU.
-
- Args:
- pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
- linear (bool, optional): If True, use linear scale of loss instead of
- log scale. Default: False.
- eps (float): Eps to avoid log(0).
-
- Return:
- torch.Tensor: Loss tensor.
- """
- ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
- if linear:
- loss = 1 - ious
- else:
- loss = -ious.log()
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
- """BIoULoss.
-
- This is an implementation of paper
- `Improving Object Localization with Fitness NMS and Bounded IoU Loss.
- `_.
-
- Args:
- pred (torch.Tensor): Predicted bboxes.
- target (torch.Tensor): Target bboxes.
- beta (float): beta parameter in smoothl1.
- eps (float): eps to avoid NaN.
- """
- pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5
- pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5
- pred_w = pred[:, 2] - pred[:, 0]
- pred_h = pred[:, 3] - pred[:, 1]
- with torch.no_grad():
- target_ctrx = (target[:, 0] + target[:, 2]) * 0.5
- target_ctry = (target[:, 1] + target[:, 3]) * 0.5
- target_w = target[:, 2] - target[:, 0]
- target_h = target[:, 3] - target[:, 1]
-
- dx = target_ctrx - pred_ctrx
- dy = target_ctry - pred_ctry
-
- loss_dx = 1 - torch.max(
- (target_w - 2 * dx.abs()) /
- (target_w + 2 * dx.abs() + eps), torch.zeros_like(dx))
- loss_dy = 1 - torch.max(
- (target_h - 2 * dy.abs()) /
- (target_h + 2 * dy.abs() + eps), torch.zeros_like(dy))
- loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w /
- (target_w + eps))
- loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h /
- (target_h + eps))
- loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh],
- dim=-1).view(loss_dx.size(0), -1)
-
- loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta,
- loss_comb - 0.5 * beta)
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def giou_loss(pred, target, eps=1e-7):
- r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
- Box Regression `_.
-
- Args:
- pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
- eps (float): Eps to avoid log(0).
-
- Return:
- Tensor: Loss tensor.
- """
- gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps)
- loss = 1 - gious
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def diou_loss(pred, target, eps=1e-7):
- r"""`Implementation of Distance-IoU Loss: Faster and Better
- Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.
-
- Code is modified from https://github.com/Zzh-tju/DIoU.
-
- Args:
- pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (Tensor): Corresponding gt bboxes, shape (n, 4).
- eps (float): Eps to avoid log(0).
- Return:
- Tensor: Loss tensor.
- """
- # overlap
- lt = torch.max(pred[:, :2], target[:, :2])
- rb = torch.min(pred[:, 2:], target[:, 2:])
- wh = (rb - lt).clamp(min=0)
- overlap = wh[:, 0] * wh[:, 1]
-
- # union
- ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
- ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
- union = ap + ag - overlap + eps
-
- # IoU
- ious = overlap / union
-
- # enclose area
- enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
- enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
- enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
-
- cw = enclose_wh[:, 0]
- ch = enclose_wh[:, 1]
-
- c2 = cw**2 + ch**2 + eps
-
- b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
- b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
- b2_x1, b2_y1 = target[:, 0], target[:, 1]
- b2_x2, b2_y2 = target[:, 2], target[:, 3]
-
- left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
- right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
- rho2 = left + right
-
- # DIoU
- dious = ious - rho2 / c2
- loss = 1 - dious
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def ciou_loss(pred, target, eps=1e-7):
- r"""`Implementation of paper `Enhancing Geometric Factors into
- Model Learning and Inference for Object Detection and Instance
- Segmentation `_.
-
- Code is modified from https://github.com/Zzh-tju/CIoU.
-
- Args:
- pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (Tensor): Corresponding gt bboxes, shape (n, 4).
- eps (float): Eps to avoid log(0).
- Return:
- Tensor: Loss tensor.
- """
- # overlap
- lt = torch.max(pred[:, :2], target[:, :2])
- rb = torch.min(pred[:, 2:], target[:, 2:])
- wh = (rb - lt).clamp(min=0)
- overlap = wh[:, 0] * wh[:, 1]
-
- # union
- ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
- ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
- union = ap + ag - overlap + eps
-
- # IoU
- ious = overlap / union
-
- # enclose area
- enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
- enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
- enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
-
- cw = enclose_wh[:, 0]
- ch = enclose_wh[:, 1]
-
- c2 = cw**2 + ch**2 + eps
-
- b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
- b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
- b2_x1, b2_y1 = target[:, 0], target[:, 1]
- b2_x2, b2_y2 = target[:, 2], target[:, 3]
-
- w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
- w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-
- left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
- right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
- rho2 = left + right
-
- factor = 4 / math.pi**2
- v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
-
- # CIoU
- cious = ious - (rho2 / c2 + v**2 / (1 - ious + v))
- loss = 1 - cious
- return loss
-
-
-@LOSSES.register_module()
-class IoULoss(nn.Module):
- """IoULoss.
-
- Computing the IoU loss between a set of predicted bboxes and target bboxes.
-
- Args:
- linear (bool): If True, use linear scale of loss instead of log scale.
- Default: False.
- eps (float): Eps to avoid log(0).
- reduction (str): Options are "none", "mean" and "sum".
- loss_weight (float): Weight of loss.
- """
-
- def __init__(self,
- linear=False,
- eps=1e-6,
- reduction='mean',
- loss_weight=1.0):
- super(IoULoss, self).__init__()
- self.linear = linear
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- """Forward function.
-
- Args:
- pred (torch.Tensor): The prediction.
- target (torch.Tensor): The learning target of the prediction.
- weight (torch.Tensor, optional): The weight of loss for each
- prediction. Defaults to None.
- avg_factor (int, optional): Average factor that is used to average
- the loss. Defaults to None.
- reduction_override (str, optional): The reduction method used to
- override the original reduction method of the loss.
- Defaults to None. Options are "none", "mean" and "sum".
- """
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if (weight is not None) and (not torch.any(weight > 0)) and (
- reduction != 'none'):
- return (pred * weight).sum() # 0
- if weight is not None and weight.dim() > 1:
- # TODO: remove this in the future
- # reduce the weight of shape (n, 4) to (n,) to match the
- # iou_loss of shape (n,)
- assert weight.shape == pred.shape
- weight = weight.mean(-1)
- loss = self.loss_weight * iou_loss(
- pred,
- target,
- weight,
- linear=self.linear,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
-
-
-@LOSSES.register_module()
-class BoundedIoULoss(nn.Module):
-
- def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1.0):
- super(BoundedIoULoss, self).__init__()
- self.beta = beta
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- if weight is not None and not torch.any(weight > 0):
- return (pred * weight).sum() # 0
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- loss = self.loss_weight * bounded_iou_loss(
- pred,
- target,
- weight,
- beta=self.beta,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
-
-
-@LOSSES.register_module()
-class GIoULoss(nn.Module):
-
- def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
- super(GIoULoss, self).__init__()
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- if weight is not None and not torch.any(weight > 0):
- return (pred * weight).sum() # 0
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if weight is not None and weight.dim() > 1:
- # TODO: remove this in the future
- # reduce the weight of shape (n, 4) to (n,) to match the
- # giou_loss of shape (n,)
- assert weight.shape == pred.shape
- weight = weight.mean(-1)
- loss = self.loss_weight * giou_loss(
- pred,
- target,
- weight,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
-
-
-@LOSSES.register_module()
-class DIoULoss(nn.Module):
-
- def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
- super(DIoULoss, self).__init__()
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- if weight is not None and not torch.any(weight > 0):
- return (pred * weight).sum() # 0
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if weight is not None and weight.dim() > 1:
- # TODO: remove this in the future
- # reduce the weight of shape (n, 4) to (n,) to match the
- # giou_loss of shape (n,)
- assert weight.shape == pred.shape
- weight = weight.mean(-1)
- loss = self.loss_weight * diou_loss(
- pred,
- target,
- weight,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
-
-
-@LOSSES.register_module()
-class CIoULoss(nn.Module):
-
- def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
- super(CIoULoss, self).__init__()
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- if weight is not None and not torch.any(weight > 0):
- return (pred * weight).sum() # 0
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if weight is not None and weight.dim() > 1:
- # TODO: remove this in the future
- # reduce the weight of shape (n, 4) to (n,) to match the
- # giou_loss of shape (n,)
- assert weight.shape == pred.shape
- weight = weight.mean(-1)
- loss = self.loss_weight * ciou_loss(
- pred,
- target,
- weight,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
deleted file mode 100644
index 8cec429c3e27ad2543b7e38fa206e6606fda4d5a..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
- '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
- decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/__init__.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/__init__.py
deleted file mode 100644
index 70643517cd1a8b4e712eca90e23411ae89937795..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/__init__.py
+++ /dev/null
@@ -1,6 +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.
-"""Dora Grids."""
diff --git a/spaces/Grezz/generate_human_motion/VQ-Trans/checkpoints/train_vq.py b/spaces/Grezz/generate_human_motion/VQ-Trans/checkpoints/train_vq.py
deleted file mode 100644
index d89b9930ba1262747542df3d5b2f03f8fab1b04a..0000000000000000000000000000000000000000
--- a/spaces/Grezz/generate_human_motion/VQ-Trans/checkpoints/train_vq.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import os
-import json
-
-import torch
-import torch.optim as optim
-from torch.utils.tensorboard import SummaryWriter
-
-import models.vqvae as vqvae
-import utils.losses as losses
-import options.option_vq as option_vq
-import utils.utils_model as utils_model
-from dataset import dataset_VQ, dataset_TM_eval
-import utils.eval_trans as eval_trans
-from options.get_eval_option import get_opt
-from models.evaluator_wrapper import EvaluatorModelWrapper
-import warnings
-warnings.filterwarnings('ignore')
-from utils.word_vectorizer import WordVectorizer
-
-def update_lr_warm_up(optimizer, nb_iter, warm_up_iter, lr):
-
- current_lr = lr * (nb_iter + 1) / (warm_up_iter + 1)
- for param_group in optimizer.param_groups:
- param_group["lr"] = current_lr
-
- return optimizer, current_lr
-
-##### ---- Exp dirs ---- #####
-args = option_vq.get_args_parser()
-torch.manual_seed(args.seed)
-
-args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
-os.makedirs(args.out_dir, exist_ok = True)
-
-##### ---- Logger ---- #####
-logger = utils_model.get_logger(args.out_dir)
-writer = SummaryWriter(args.out_dir)
-logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
-
-
-
-w_vectorizer = WordVectorizer('./glove', 'our_vab')
-
-if args.dataname == 'kit' :
- dataset_opt_path = 'checkpoints/kit/Comp_v6_KLD005/opt.txt'
- args.nb_joints = 21
-
-else :
- dataset_opt_path = 'checkpoints/t2m/Comp_v6_KLD005/opt.txt'
- args.nb_joints = 22
-
-logger.info(f'Training on {args.dataname}, motions are with {args.nb_joints} joints')
-
-wrapper_opt = get_opt(dataset_opt_path, torch.device('cuda'))
-eval_wrapper = EvaluatorModelWrapper(wrapper_opt)
-
-
-##### ---- Dataloader ---- #####
-train_loader = dataset_VQ.DATALoader(args.dataname,
- args.batch_size,
- window_size=args.window_size,
- unit_length=2**args.down_t)
-
-train_loader_iter = dataset_VQ.cycle(train_loader)
-
-val_loader = dataset_TM_eval.DATALoader(args.dataname, False,
- 32,
- w_vectorizer,
- unit_length=2**args.down_t)
-
-##### ---- Network ---- #####
-net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers
- args.nb_code,
- args.code_dim,
- args.output_emb_width,
- args.down_t,
- args.stride_t,
- args.width,
- args.depth,
- args.dilation_growth_rate,
- args.vq_act,
- args.vq_norm)
-
-
-if args.resume_pth :
- logger.info('loading checkpoint from {}'.format(args.resume_pth))
- ckpt = torch.load(args.resume_pth, map_location='cpu')
- net.load_state_dict(ckpt['net'], strict=True)
-net.train()
-net.cuda()
-
-##### ---- Optimizer & Scheduler ---- #####
-optimizer = optim.AdamW(net.parameters(), lr=args.lr, betas=(0.9, 0.99), weight_decay=args.weight_decay)
-scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_scheduler, gamma=args.gamma)
-
-
-Loss = losses.ReConsLoss(args.recons_loss, args.nb_joints)
-
-##### ------ warm-up ------- #####
-avg_recons, avg_perplexity, avg_commit = 0., 0., 0.
-
-for nb_iter in range(1, args.warm_up_iter):
-
- optimizer, current_lr = update_lr_warm_up(optimizer, nb_iter, args.warm_up_iter, args.lr)
-
- gt_motion = next(train_loader_iter)
- gt_motion = gt_motion.cuda().float() # (bs, 64, dim)
-
- pred_motion, loss_commit, perplexity = net(gt_motion)
- loss_motion = Loss(pred_motion, gt_motion)
- loss_vel = Loss.forward_vel(pred_motion, gt_motion)
-
- loss = loss_motion + args.commit * loss_commit + args.loss_vel * loss_vel
-
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
-
- avg_recons += loss_motion.item()
- avg_perplexity += perplexity.item()
- avg_commit += loss_commit.item()
-
- if nb_iter % args.print_iter == 0 :
- avg_recons /= args.print_iter
- avg_perplexity /= args.print_iter
- avg_commit /= args.print_iter
-
- logger.info(f"Warmup. Iter {nb_iter} : lr {current_lr:.5f} \t Commit. {avg_commit:.5f} \t PPL. {avg_perplexity:.2f} \t Recons. {avg_recons:.5f}")
-
- avg_recons, avg_perplexity, avg_commit = 0., 0., 0.
-
-##### ---- Training ---- #####
-avg_recons, avg_perplexity, avg_commit = 0., 0., 0.
-best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, writer, logger = eval_trans.evaluation_vqvae(args.out_dir, val_loader, net, logger, writer, 0, best_fid=1000, best_iter=0, best_div=100, best_top1=0, best_top2=0, best_top3=0, best_matching=100, eval_wrapper=eval_wrapper)
-
-for nb_iter in range(1, args.total_iter + 1):
-
- gt_motion = next(train_loader_iter)
- gt_motion = gt_motion.cuda().float() # bs, nb_joints, joints_dim, seq_len
-
- pred_motion, loss_commit, perplexity = net(gt_motion)
- loss_motion = Loss(pred_motion, gt_motion)
- loss_vel = Loss.forward_vel(pred_motion, gt_motion)
-
- loss = loss_motion + args.commit * loss_commit + args.loss_vel * loss_vel
-
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- scheduler.step()
-
- avg_recons += loss_motion.item()
- avg_perplexity += perplexity.item()
- avg_commit += loss_commit.item()
-
- if nb_iter % args.print_iter == 0 :
- avg_recons /= args.print_iter
- avg_perplexity /= args.print_iter
- avg_commit /= args.print_iter
-
- writer.add_scalar('./Train/L1', avg_recons, nb_iter)
- writer.add_scalar('./Train/PPL', avg_perplexity, nb_iter)
- writer.add_scalar('./Train/Commit', avg_commit, nb_iter)
-
- logger.info(f"Train. Iter {nb_iter} : \t Commit. {avg_commit:.5f} \t PPL. {avg_perplexity:.2f} \t Recons. {avg_recons:.5f}")
-
- avg_recons, avg_perplexity, avg_commit = 0., 0., 0.,
-
- if nb_iter % args.eval_iter==0 :
- best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, writer, logger = eval_trans.evaluation_vqvae(args.out_dir, val_loader, net, logger, writer, nb_iter, best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, eval_wrapper=eval_wrapper)
-
\ No newline at end of file
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/bart/modeling_bart.py b/spaces/HaloMaster/chinesesummary/fengshen/models/bart/modeling_bart.py
deleted file mode 100644
index f9a58ac8036fbc0bb9334b083b12a5599950d355..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/bart/modeling_bart.py
+++ /dev/null
@@ -1,423 +0,0 @@
-import warnings
-from pytorch_lightning import LightningModule
-from fengshen.models import transformer_utils
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-import torch.nn.functional as F
-
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-from transformers.file_utils import *
-from transformers.modeling_outputs import *
-from transformers.models.bart import *
-from transformers.models.bart.modeling_bart import BartClassificationHead
-
-
-_CONFIG_FOR_DOC = "BartConfig"
-
-
-# ------------------------ ZZ: CBart addition ------------------------
-
-
-def _reorder_buffer(attn_cache, new_order):
- for k, input_buffer_k in attn_cache.items():
- if input_buffer_k is not None:
- attn_cache[k] = input_buffer_k.index_select(0, new_order)
- return attn_cache
-
-
-def _make_linear_from_emb(emb):
- vocab_size, emb_size = emb.weight.shape
- lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
- lin_layer.weight.data = emb.weight.data
- return lin_layer
-
-
-BART_GENERATION_EXAMPLE = r"""
- Summarization example::
-
- >>> from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
-
- >>> model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
- >>> tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
-
- >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
- >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
-
- >>> # Generate Summary
- >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
- >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
-
- Mask filling example::
-
- >>> from transformers import BartTokenizer, BartForConditionalGeneration
- >>> tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
- >>> TXT = "My friends are but they eat too many carbs."
-
- >>> model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
- >>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
- >>> logits = model(input_ids).logits
-
- >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
- >>> probs = logits[0, masked_index].softmax(dim=0)
- >>> values, predictions = probs.topk(5)
-
- >>> tokenizer.decode(predictions).split()
-"""
-
-
-@dataclass
-class CBartLMOutput(ModelOutput):
- """
- Base class for CBart specific language models outputs.
-
- Args:
- ....
- """
- loss: Optional[torch.FloatTensor] = None
- encoder_loss: Optional[torch.FloatTensor] = None
- decoder_loss: Optional[torch.FloatTensor] = None
- encoder_logits: torch.FloatTensor = None
- logits: torch.FloatTensor = None
- past_key_values: Optional[Tuple[torch.FloatTensor]] = None
- decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
- decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
- encoder_last_hidden_state: Optional[torch.FloatTensor] = None
- encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
- encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-class BartForTextInfill(BartPretrainedModel):
- """
- this class is designed for text infilling.
- During training, the encoder is used to predict replace, insert,
- and the decoder is used to generate original input.
- Compared with BartForConditionalGeneration class,
- we add a module over the encoder and add a new loss for the encoder.
- """
- base_model_prefix = "model"
- authorized_missing_keys = [r"final_logits_bias",
- r"encoder\.version", r"decoder\.version"]
-
- def __init__(self, config: BartConfig):
- super().__init__(config)
- base_model = BartModel(config)
- self.model = base_model
- self.register_buffer("final_logits_bias", torch.zeros(
- (1, self.model.shared.num_embeddings)))
- # print( config.encoder_loss_type, config.num_labels)
-
- # add a new attribute into BartConfig class (revise BartConfig)
- self.encoder_loss_type = config.encoder_loss_type
- self.num_labels = config.num_labels
- if self.encoder_loss_type == 0: # 0 is classification loss, 1 is regression loss
- # add a classification module for the encoder
- self.classification_head = BartClassificationHead(
- config.d_model, config.d_model, config.num_labels, config.classif_dropout,
- )
- else:
- # add a regression module for the encoder
- self.classification_head = BartClassificationHead(
- config.d_model, config.d_model, 1, config.classif_dropout,
- )
-
- self.model._init_weights(self.classification_head.dense)
- self.model._init_weights(self.classification_head.out_proj)
- self.loss_weight = config.loss_weight
- self.register_buffer("label_weights", torch.zeros((self.num_labels)))
-
- def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
- old_num_tokens = self.model.shared.num_embeddings
- new_embeddings = super().resize_token_embeddings(new_num_tokens)
- self.model.shared = new_embeddings
- self._resize_final_logits_bias(new_num_tokens, old_num_tokens)
- return new_embeddings
-
- def _resize_final_logits_bias(self, new_num_tokens: int, old_num_tokens: int) -> None:
- if new_num_tokens <= old_num_tokens:
- new_bias = self.final_logits_bias[:, :new_num_tokens]
- else:
- extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens),
- device=self.final_logits_bias.device)
- new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
- self.register_buffer("final_logits_bias", new_bias)
-
- @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
- @add_end_docstrings(BART_GENERATION_EXAMPLE)
- def forward(
- self,
- input_ids,
- attention_mask=None,
- encoder_outputs=None,
- decoder_input_ids=None,
- decoder_attention_mask=None,
- past_key_values=None,
- encoder_labels=None,
- labels=None,
- use_cache=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=True,
- **unused,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
- Labels for computing the masked language modeling loss.
- Indices should either be in ``[0, ..., config.vocab_size]`` or -100 (see ``input_ids`` docstring).
- Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens
- with labels in ``[0, ..., config.vocab_size]``.
-
- Returns:
-
- Conditional generation example::
-
- # Mask filling only works for bart-large
- from transformers import BartTokenizer, BartForConditionalGeneration
- tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
- TXT = "My friends are but they eat too many carbs."
-
- model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
- input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
- logits = model(input_ids).logits
-
- masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
- probs = logits[0, masked_index].softmax(dim=0)
- values, predictions = probs.topk(5)
-
- tokenizer.decode(predictions).split()
- # ['good', 'great', 'all', 'really', 'very']
- """
- if "lm_labels" in unused:
- warnings.warn(
- "The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
- FutureWarning,
- )
- labels = unused.pop("lm_labels")
- if "decoder_cached_states" in unused:
- warnings.warn(
- "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `decoder_past_key_values` instead.",
- FutureWarning,
- )
- decoder_past_key_values = unused.pop("decoder_cached_states")
- return_dict = return_dict if return_dict is not None else False
-
- if labels is not None:
- use_cache = False
-
- outputs = self.model(
- input_ids,
- attention_mask=attention_mask,
- decoder_input_ids=decoder_input_ids,
- encoder_outputs=encoder_outputs,
- decoder_attention_mask=decoder_attention_mask,
- past_key_values=past_key_values,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- # logits and loss for the encoder
- # last hidden state
- encoder_last_hidden_state = outputs['encoder_last_hidden_state']
- # eos_mask = input_ids.eq(self.config.eos_token_id)
- # if len(torch.unique(eos_mask.sum(1))) > 1:
- # raise ValueError("All examples must have the same number of tokens.")
- # sentence_representation = x[eos_mask, :].view(x.size(0), -1, x.size(-1))[:, -1, :]
- encoder_logits = self.classification_head(encoder_last_hidden_state)
- encoder_loss = None
- if encoder_labels is not None:
- # classification loss
- if self.encoder_loss_type == 0:
- # ZZ: seems like MSE loss does not support weighting, so only CEL has weighting applied for now
- loss_fct = nn.CrossEntropyLoss(weight=self.label_weights)
- encoder_loss = loss_fct(
- encoder_logits.view(-1, self.config.num_labels), encoder_labels.view(-1))
- # regression loss
- else:
- encoder_logits = encoder_logits.view(
- encoder_logits.size(0), -1)
- encoder_logits = torch.sigmoid(
- encoder_logits) * self.num_labels - 0.5
- loss_fct = nn.MSELoss(reduction='none')
- _loss = loss_fct(encoder_logits, encoder_labels)
- encoder_loss = torch.mean(_loss[encoder_labels >= 0])
- # encoder_loss =_loss[encoder_labels>=0]
-
- # logits and loss for the decoder
- lm_logits = F.linear(
- outputs[0], self.model.shared.weight, bias=self.final_logits_bias)
- masked_lm_loss = None
- if labels is not None:
- loss_fct = nn.CrossEntropyLoss()
- # TODO(SS): do we need to ignore pad tokens in labels?
- masked_lm_loss = loss_fct(
- lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
-
- loss = None
- if masked_lm_loss is not None and encoder_loss is not None:
- loss = encoder_loss * self.loss_weight + masked_lm_loss
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return ((loss,) + output) if loss is not None else output
-
- return CBartLMOutput(
- loss=loss,
- encoder_loss=encoder_loss,
- decoder_loss=masked_lm_loss,
- encoder_logits=encoder_logits,
- logits=lm_logits,
- past_key_values=outputs.past_key_values,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
- def prepare_inputs_for_generation(self, decoder_input_ids, past, attention_mask, use_cache, **kwargs):
- assert past is not None, "past has to be defined for encoder_outputs"
-
- encoder_outputs, past_key_values = past
- return {
- "input_ids": None, # encoder_outputs is defined. input_ids not needed
- "encoder_outputs": encoder_outputs,
- "past_key_values": past_key_values,
- "decoder_input_ids": decoder_input_ids,
- "attention_mask": attention_mask,
- # change this to avoid caching (presumably for debugging)
- "use_cache": use_cache,
- }
-
- def adjust_logits_during_generation(self, logits, cur_len, max_length):
- if cur_len == 1:
- self._force_token_ids_generation(logits, self.config.bos_token_id)
- if cur_len == max_length - 1 and self.config.eos_token_id is not None:
- self._force_token_ids_generation(logits, self.config.eos_token_id)
- return logits
-
- def _force_token_ids_generation(self, scores, token_ids) -> None:
- """force one of token_ids to be generated by setting prob of all other tokens to 0"""
- if isinstance(token_ids, int):
- token_ids = [token_ids]
- all_but_token_ids_mask = torch.tensor(
- [x for x in range(self.config.vocab_size) if x not in token_ids],
- dtype=torch.long,
- device=next(self.parameters()).device,
- )
- assert len(
- scores.shape) == 2, "scores should be of rank 2 with shape: [batch_size, vocab_size]"
- scores[:, all_but_token_ids_mask] = -float("inf")
-
- @staticmethod
- def _reorder_cache(past, beam_idx):
- ((enc_out, enc_mask), past_key_values) = past
- reordered_past = []
- for layer_past in past_key_values:
- # get the correct batch idx from decoder layer's batch dim for cross and self-attn
- layer_past_new = {
- attn_key: _reorder_buffer(attn_cache, beam_idx) for attn_key, attn_cache in layer_past.items()
- }
- reordered_past.append(layer_past_new)
-
- new_enc_out = enc_out if enc_out is None else enc_out.index_select(
- 0, beam_idx)
- new_enc_mask = enc_mask if enc_mask is None else enc_mask.index_select(
- 0, beam_idx)
-
- past = ((new_enc_out, new_enc_mask), reordered_past)
- return past
-
- def get_encoder(self):
- return self.model.encoder
-
- def get_output_embeddings(self):
- return _make_linear_from_emb(self.model.shared) # make it on the fly
-
- def get_encoder_logits(self, input_ids, attention_mask=None):
- # print(input_ids, attention_mask)
- # encoder_outputs = self.model.get_encoder_outputs(
- # self,
- # input_ids,
- # attention_mask=attention_mask,
- # output_attentions=None,
- # output_hidden_states=None,
- # return_dict=None,
- # )
-
- encoder_outputs = self.model.encoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- return_dict=True
- )
- # logits and loss for the encoder
- # last hidden state
- encoder_last_hidden_state = encoder_outputs['last_hidden_state']
- encoder_logits = self.classification_head(encoder_last_hidden_state)
-
- # classification
- if self.encoder_loss_type == 0:
- # probs = torch.softmax(encoder_logits,dim=-1)
- pass
- # regression
- else:
- encoder_logits = encoder_logits.view(encoder_logits.size(0), -1)
- encoder_logits = torch.sigmoid(
- encoder_logits) * self.num_labels - 0.5
- return encoder_outputs, encoder_logits
-
-
-class CBartLightning(LightningModule):
- @staticmethod
- def add_module_specific_args(parent_args):
- parser = parent_args.add_argument_group("CBart specific parameters")
- parser.add_argument('--num_labels', type=int, default=3)
- parser.add_argument('--encoder_loss_type', type=int, default=0)
- parser.add_argument('--loss_weight', type=float, default=1.0)
- parser.add_argument('--label_weights', type=float, nargs='+', default=[1.0, 1.0, 1.0])
- parser.add_argument('--masked_lm', type=float, default=0)
- return parent_args
-
- def __init__(
- self,
- args,
- **kwargs,
- ):
- super().__init__()
- self.save_hyperparameters(args)
- self.model = BartForTextInfill.from_pretrained(args.model_path, num_labels=self.hparams.num_labels,
- encoder_loss_type=self.hparams.encoder_loss_type,
- loss_weight=self.hparams.loss_weight,)
- self.model.label_weights = torch.tensor(
- self.hparams.label_weights, dtype=torch.half)
-
- def forward(self, **inputs):
- return self.model(**inputs)
-
- def training_step(self, batch, batch_idx):
- outputs = self(**batch)
- return outputs
-
- def validation_step(self, batch, batch_idx, dataloader_idx=0):
- outputs = self(**batch)
- val_loss = outputs["loss"]
-
- return {"loss": val_loss}
-
- def setup(self, stage=None) -> None:
- if stage != "fit":
- return
- # Get dataloader by calling it - train_dataloader() is called after setup() by default
- train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
-
- # Calculate total steps
- tb_size = self.hparams.train_batchsize * max(1, self.trainer.gpus)
- ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs)
- self.total_steps = (len(train_loader.dataset) // tb_size) // ab_size
-
- def configure_optimizers(self):
- transformer_utils.configure_optimizers(self)
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/roformer/modeling_roformer.py b/spaces/HaloMaster/chinesesummary/fengshen/models/roformer/modeling_roformer.py
deleted file mode 100644
index 3f67d34c6484108890f21983a0924b3a748e97b4..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/roformer/modeling_roformer.py
+++ /dev/null
@@ -1,1954 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA 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.
-""" PyTorch RoFormer model. """
-
-import math
-import os
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
- ModelOutput,
- add_code_sample_docstrings,
- add_start_docstrings,
- add_start_docstrings_to_model_forward,
- replace_return_docstrings,
-)
-from transformers.modeling_outputs import (
- BaseModelOutputWithPastAndCrossAttentions,
- BaseModelOutputWithPoolingAndCrossAttentions,
- CausalLMOutputWithCrossAttentions,
- MaskedLMOutput,
- MultipleChoiceModelOutput,
- NextSentencePredictorOutput,
- QuestionAnsweringModelOutput,
- SequenceClassifierOutput,
- TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
- PreTrainedModel,
- apply_chunking_to_forward,
- find_pruneable_heads_and_indices,
- prune_linear_layer,
-)
-from transformers.utils import logging
-from .configuration_roformer import RoFormerConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "RoFormerConfig"
-_TOKENIZER_FOR_DOC = "BertTokenizer"
-_CHECKPOINT_FOR_DOC = "nvidia/megatron-bert-cased-345m"
-
-RoFormer_PRETRAINED_MODEL_ARCHIVE_LIST = [
- "nvidia/megatron-bert-cased-345m",
- # See all RoFormer models at https://huggingface.co/models?filter=RoFormer
-]
-
-
-def load_tf_weights_in_RoFormer(model, config, tf_checkpoint_path):
- """Load tf checkpoints in a pytorch model."""
- try:
- import re
-
- import numpy as np
- import tensorflow as tf
- except ImportError:
- logger.error(
- "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
- "https://www.tensorflow.org/install/ for installation instructions."
- )
- raise
- tf_path = os.path.abspath(tf_checkpoint_path)
- logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
- # Load weights from TF model
- init_vars = tf.train.list_variables(tf_path)
- names = []
- arrays = []
- for name, shape in init_vars:
- logger.info(f"Loading TF weight {name} with shape {shape}")
- array = tf.train.load_variable(tf_path, name)
- names.append(name)
- arrays.append(array)
-
- for name, array in zip(names, arrays):
- name = name.split("/")
- # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
- # which are not required for using pretrained model
- if any(
- n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer",
- "AdamWeightDecayOptimizer_1", "global_step"]
- for n in name
- ):
- logger.info(f"Skipping {'/'.join(name)}")
- continue
- pointer = model
- for m_name in name:
- if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
- scope_names = re.split(r"_(\d+)", m_name)
- else:
- scope_names = [m_name]
- if scope_names[0] == "kernel" or scope_names[0] == "gamma":
- pointer = getattr(pointer, "weight")
- elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
- pointer = getattr(pointer, "bias")
- elif scope_names[0] == "output_weights":
- pointer = getattr(pointer, "weight")
- elif scope_names[0] == "squad":
- pointer = getattr(pointer, "classifier")
- else:
- try:
- pointer = getattr(pointer, scope_names[0])
- except AttributeError:
- logger.info(f"Skipping {'/'.join(name)}")
- continue
- if len(scope_names) >= 2:
- num = int(scope_names[1])
- pointer = pointer[num]
- if m_name[-11:] == "_embeddings":
- pointer = getattr(pointer, "weight")
- elif m_name == "kernel":
- array = np.transpose(array)
- try:
- assert (
- pointer.shape == array.shape
- ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
- except AssertionError as e:
- e.args += (pointer.shape, array.shape)
- raise
- logger.info("Initialize PyTorch weight {}".format(name))
- pointer.data = torch.from_numpy(array)
- return model
-
-
-class RoFormerEmbeddings(nn.Module):
- """Construct the embeddings from word, position and token_type embeddings."""
-
- def __init__(self, config):
- super().__init__()
- self.word_embeddings = nn.Embedding(
- config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-
- # @IDEA modified -> roformer removed the position_embedding, and add the totary position embedding in the self_attention_layer
- # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
- self.token_type_embeddings = nn.Embedding(
- config.type_vocab_size, config.hidden_size)
-
- # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
- # any TensorFlow checkpoint file
-
- # In Megatron, layer-norm is applied after the 1st dropout.
- # self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
- # position_ids (1, len position emb) is contiguous in memory and exported when serialized
- self.register_buffer("position_ids", torch.arange(
- config.max_position_embeddings).expand((1, -1)))
- self.position_embedding_type = getattr(
- config, "position_embedding_type", "absolute")
-
- def forward(
- self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
- ):
- if input_ids is not None:
- input_shape = input_ids.size()
- else:
- input_shape = inputs_embeds.size()[:-1]
-
- seq_length = input_shape[1]
-
- if position_ids is None:
- position_ids = self.position_ids[:,
- past_key_values_length: seq_length + past_key_values_length]
-
- if token_type_ids is None:
- token_type_ids = torch.zeros(
- input_shape, dtype=torch.long, device=self.position_ids.device)
-
- if inputs_embeds is None:
- inputs_embeds = self.word_embeddings(input_ids)
- token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
- embeddings = inputs_embeds + token_type_embeddings
-
- # @IDEA modified -> roformer removed the position_embedding
- # if self.position_embedding_type == "absolute":
- # position_embeddings = self.position_embeddings(position_ids)
- # embeddings += position_embeddings
-
- # Megatron BERT moves that layer norm after the drop-out (and to each layer).
- # embeddings = self.LayerNorm(embeddings)
- embeddings = self.dropout(embeddings)
- return embeddings
-
-
-class RoPEmbedding(nn.Module):
- def __init__(self, d_model):
- super(RoPEmbedding, self).__init__()
- self.d_model = d_model
- div_term = torch.exp(torch.arange(
- 0, d_model, 2).float() * (-math.log(10000.0) / d_model))
- self.register_buffer('div_term', div_term)
-
- def forward(self, x, seq_dim=0):
- # x 是 [s, b, np, hn]，例如query和key
- x = x.permute(2, 1, 0, 3)
- t = torch.arange(x.size(seq_dim), device=x.device).type_as(
- self.div_term)
- sinusoid_inp = torch.outer(t, self.div_term)
- sin, cos = sinusoid_inp.sin(), sinusoid_inp.cos() # [s, hn]
- o_shape = (sin.size(0), 1, 1, sin.size(1))
- sin, cos = sin.view(*o_shape), cos.view(*o_shape) # [s, 1, 1, hn]
- sin = torch.repeat_interleave(sin, 2, dim=-1)
- cos = torch.repeat_interleave(cos, 2, dim=-1)
- x2 = torch.stack([-x[..., 1::2], x[..., ::2]], dim=-1).reshape_as(x)
- x = cos * x + sin * x2
- return x.permute(2, 1, 0, 3)
-
-
-# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->RoFormer
-class RoFormerSelfAttention(nn.Module):
- def __init__(self, config):
- super().__init__()
- if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
- raise ValueError(
- f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
- f"heads ({config.num_attention_heads})"
- )
-
- self.num_attention_heads = config.num_attention_heads
- self.attention_head_size = int(
- config.hidden_size / config.num_attention_heads)
- self.all_head_size = self.num_attention_heads * self.attention_head_size
-
- self.query = nn.Linear(config.hidden_size, self.all_head_size)
- self.key = nn.Linear(config.hidden_size, self.all_head_size)
- self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
- self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
- self.position_embedding_type = getattr(
- config, "position_embedding_type", "absolute")
- if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
- self.max_position_embeddings = config.max_position_embeddings
- self.distance_embedding = nn.Embedding(
- 2 * config.max_position_embeddings - 1, self.attention_head_size)
- # @IDEA modified -> add rope positional embedding
- self.rope_emb = RoPEmbedding(self.attention_head_size)
-
- self.is_decoder = config.is_decoder
-
- def transpose_for_scores(self, x):
- new_x_shape = x.size()[
- :-1] + (self.num_attention_heads, self.attention_head_size)
- x = x.view(*new_x_shape)
- return x.permute(0, 2, 1, 3)
-
- def forward(
- self,
- hidden_states,
- attention_mask=None,
- head_mask=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- past_key_value=None,
- output_attentions=False,
- ):
- mixed_query_layer = self.query(hidden_states)
-
- # If this is instantiated as a cross-attention module, the keys
- # and values come from an encoder; the attention mask needs to be
- # such that the encoder's padding tokens are not attended to.
- is_cross_attention = encoder_hidden_states is not None
-
- if is_cross_attention and past_key_value is not None:
- # reuse k,v, cross_attentions
- key_layer = past_key_value[0]
- value_layer = past_key_value[1]
- attention_mask = encoder_attention_mask
- elif is_cross_attention:
- key_layer = self.transpose_for_scores(
- self.key(encoder_hidden_states))
- value_layer = self.transpose_for_scores(
- self.value(encoder_hidden_states))
- attention_mask = encoder_attention_mask
- elif past_key_value is not None:
- key_layer = self.transpose_for_scores(self.key(hidden_states))
- value_layer = self.transpose_for_scores(self.value(hidden_states))
- key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
- value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
- else:
- key_layer = self.transpose_for_scores(self.key(hidden_states))
- value_layer = self.transpose_for_scores(self.value(hidden_states))
- query_layer = self.transpose_for_scores(mixed_query_layer)
-
- if self.is_decoder:
- # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
- # Further calls to cross_attention layer can then reuse all cross-attention
- # key/value_states (first "if" case)
- # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
- # all previous decoder key/value_states. Further calls to uni-directional self-attention
- # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
- # if encoder bi-directional self-attention `past_key_value` is always `None`
- past_key_value = (key_layer, value_layer)
-
- # Take the dot product between "query" and "key" to get the raw attention scores.
-
- # @IDEA modified -> add rope positional embedding
- # print('query_layer.shape')
- # print(query_layer.shape)
- # query_layer.hsape -> [batch_size,num_head,seq_len,per_head_hidden_size]
- query_layer = self.rope_emb(query_layer)
- key_layer = self.rope_emb(key_layer)
-
- attention_scores = torch.matmul(
- query_layer, key_layer.transpose(-1, -2))
-
- """ @IDEA modified -> removed the megatron positional
- if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
- seq_length = hidden_states.size()[1]
- position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
- position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
- distance = position_ids_l - position_ids_r
- positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
- positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
-
- if self.position_embedding_type == "relative_key":
- relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
- attention_scores = attention_scores + relative_position_scores
- elif self.position_embedding_type == "relative_key_query":
- relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
- relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
- attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
- """
-
- attention_scores = attention_scores / \
- math.sqrt(self.attention_head_size)
- if attention_mask is not None:
- # Apply the attention mask is (precomputed for all layers in RoFormerModel forward() function)
- attention_scores = attention_scores + attention_mask
-
- # Normalize the attention scores to probabilities.
- attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
- # This is actually dropping out entire tokens to attend to, which might
- # seem a bit unusual, but is taken from the original Transformer paper.
- attention_probs = self.dropout(attention_probs)
-
- # Mask heads if we want to
- if head_mask is not None:
- attention_probs = attention_probs * head_mask
-
- context_layer = torch.matmul(attention_probs, value_layer)
-
- context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
- new_context_layer_shape = context_layer.size()[
- :-2] + (self.all_head_size,)
- context_layer = context_layer.view(*new_context_layer_shape)
-
- outputs = (context_layer, attention_probs) if output_attentions else (
- context_layer,)
-
- if self.is_decoder:
- outputs = outputs + (past_key_value,)
- return outputs
-
-
-# Based transformers.models.bert.modeling_bert.BertSelfOutput. Moved LayerNorm to RoFormerAttention below.
-class RoFormerSelfOutput(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.dense = nn.Linear(config.hidden_size, config.hidden_size)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
- def forward(self, hidden_states, residual):
- hidden_states = self.dense(hidden_states)
- hidden_states = self.dropout(hidden_states)
- return residual + hidden_states
-
-
-# Based transformers.models.bert.modeling_bert.BertAttention. Added LayerNorm.
-class RoFormerAttention(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
- self.self = RoFormerSelfAttention(config)
- self.output = RoFormerSelfOutput(config)
- self.pruned_heads = set()
-
- def prune_heads(self, heads):
- if len(heads) == 0:
- return
- heads, index = find_pruneable_heads_and_indices(
- heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
- )
-
- # Prune linear layers
- self.self.query = prune_linear_layer(self.self.query, index)
- self.self.key = prune_linear_layer(self.self.key, index)
- self.self.value = prune_linear_layer(self.self.value, index)
- self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
- # Update hyper params and store pruned heads
- self.self.num_attention_heads = self.self.num_attention_heads - \
- len(heads)
- self.self.all_head_size = self.self.attention_head_size * \
- self.self.num_attention_heads
- self.pruned_heads = self.pruned_heads.union(heads)
-
- def forward(
- self,
- hidden_states,
- attention_mask=None,
- head_mask=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- past_key_value=None,
- output_attentions=False,
- ):
- ln_outputs = self.ln(hidden_states)
- self_outputs = self.self(
- ln_outputs,
- attention_mask,
- head_mask,
- encoder_hidden_states,
- encoder_attention_mask,
- past_key_value,
- output_attentions,
- )
- attention_output = self.output(self_outputs[0], hidden_states)
- # add attentions if we output them
- outputs = (attention_output,) + self_outputs[1:]
- return outputs
-
-
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->RoFormer
-class RoFormerIntermediate(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
- if isinstance(config.hidden_act, str):
- self.intermediate_act_fn = ACT2FN[config.hidden_act]
- else:
- self.intermediate_act_fn = config.hidden_act
-
- def forward(self, hidden_states):
- hidden_states = self.dense(hidden_states)
- hidden_states = self.intermediate_act_fn(hidden_states)
- return hidden_states
-
-
-# Based on transformers.models.bert.modeling_bert.BertOutput. Moved LayerNorm to RoFormerLayer below.
-class RoFormerOutput(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
- def forward(self, hidden_states, input_tensor):
- hidden_states = self.dense(hidden_states)
- hidden_states = self.dropout(hidden_states)
- return input_tensor + hidden_states
-
-
-# Based on transformers.models.bert.modeling_bert.BertLayer. Added LayerNorm.
-class RoFormerLayer(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.chunk_size_feed_forward = config.chunk_size_feed_forward
- self.seq_len_dim = 1
- self.attention = RoFormerAttention(config)
- self.is_decoder = config.is_decoder
- self.add_cross_attention = config.add_cross_attention
- if self.add_cross_attention:
- assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
- self.crossattention = RoFormerAttention(config)
- self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
- self.intermediate = RoFormerIntermediate(config)
- self.output = RoFormerOutput(config)
-
- def forward(
- self,
- hidden_states,
- attention_mask=None,
- head_mask=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- past_key_value=None,
- output_attentions=False,
- ):
- # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
- self_attn_past_key_value = past_key_value[:
- 2] if past_key_value is not None else None
- self_attention_outputs = self.attention(
- hidden_states,
- attention_mask,
- head_mask,
- output_attentions=output_attentions,
- past_key_value=self_attn_past_key_value,
- )
- attention_output = self_attention_outputs[0]
-
- # if decoder, the last output is tuple of self-attn cache
- if self.is_decoder:
- outputs = self_attention_outputs[1:-1]
- present_key_value = self_attention_outputs[-1]
- else:
- # add self attentions if we output attention weights
- outputs = self_attention_outputs[1:]
-
- cross_attn_present_key_value = None
- if self.is_decoder and encoder_hidden_states is not None:
- assert hasattr(
- self, "crossattention"
- ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
-
- # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
- cross_attn_past_key_value = past_key_value[-2:
- ] if past_key_value is not None else None
- cross_attention_outputs = self.crossattention(
- attention_output,
- attention_mask,
- head_mask,
- encoder_hidden_states,
- encoder_attention_mask,
- cross_attn_past_key_value,
- output_attentions,
- )
- attention_output = cross_attention_outputs[0]
- # add cross attentions if we output attention weights
- outputs = outputs + cross_attention_outputs[1:-1]
-
- # add cross-attn cache to positions 3,4 of present_key_value tuple
- cross_attn_present_key_value = cross_attention_outputs[-1]
- present_key_value = present_key_value + cross_attn_present_key_value
-
- layer_output = apply_chunking_to_forward(
- self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
- )
- outputs = (layer_output,) + outputs
-
- # if decoder, return the attn key/values as the last output
- if self.is_decoder:
- outputs = outputs + (present_key_value,)
-
- return outputs
-
- def feed_forward_chunk(self, attention_output):
- ln_output = self.ln(attention_output)
- intermediate_output = self.intermediate(ln_output)
- layer_output = self.output(intermediate_output, attention_output)
- return layer_output
-
-
-def roformer_extended_attention_mask(attention_mask, tokentype_ids):
- # copy from bert_model.py and
- # https://github.com/bojone/bert4keras/blob/8836dc01fa99aa54947a15db5aa60a0ab6c0c036/bert4keras/models.py#L382
- # We create a 3D attention mask from a 2D tensor mask.
- # [b, 1, s]
- attention_mask_b1s = attention_mask.unsqueeze(1)
- # [b, s, 1]
- attention_mask_bs1 = attention_mask.unsqueeze(2)
- # [b, s, s]
- padding_mask_bss = attention_mask_b1s * attention_mask_bs1
-
- # Convert attention mask to binary:
- padding_mask_bss = (padding_mask_bss < 0.5)
-
- # 根据tokentype_ids来获取相应的双向或者单向mask，注意
- # 这里改变了原本实现中的小于等于号，因为megatron中的mask
- # 中非mask部分为0，mask部分为1
- idx = torch.cumsum(tokentype_ids, dim=1)
- causal_mask = idx[:, None, :] > idx[:, :, None]
- # 合并两个mask
- mask = torch.logical_or(causal_mask, padding_mask_bss)
- mask = mask.unsqueeze(1) # [b, 1, s, s]
- return mask
-
-
-class RoFormerEncoder(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.config = config
- self.layer = nn.ModuleList([RoFormerLayer(config)
- for _ in range(config.num_hidden_layers)])
-
- # The final layer norm. We removed the 1st LN, moved LN to each hidden layer and this one
- # is simply the final LN (Transformer's BERT has it attached to each hidden layer).
- self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
- def forward(
- self,
- hidden_states,
- attention_mask=None,
- head_mask=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- past_key_values=None,
- use_cache=None,
- output_attentions=False,
- output_hidden_states=False,
- return_dict=True,
- ):
-
- all_hidden_states = () if output_hidden_states else None
- all_self_attentions = () if output_attentions else None
- all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-
- next_decoder_cache = () if use_cache else None
- for i, layer_module in enumerate(self.layer):
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- layer_head_mask = head_mask[i] if head_mask is not None else None
- past_key_value = past_key_values[i] if past_key_values is not None else None
-
- if getattr(self.config, "gradient_checkpointing", False) and self.training:
-
- if use_cache:
- logger.warn(
- "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
- "`use_cache=False`..."
- )
- use_cache = False
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- return module(*inputs, past_key_value, output_attentions)
-
- return custom_forward
-
- layer_outputs = torch.utils.checkpoint.checkpoint(
- create_custom_forward(layer_module),
- hidden_states,
- attention_mask,
- layer_head_mask,
- encoder_hidden_states,
- encoder_attention_mask,
- )
- else:
- layer_outputs = layer_module(
- hidden_states,
- attention_mask,
- layer_head_mask,
- encoder_hidden_states,
- encoder_attention_mask,
- past_key_value,
- output_attentions,
- )
-
- # Because we moved the layer-norm at the end of the hidden layer, we have non-normali-
- # zed data here. If that's really needed, we must apply LN to match Transformer's BERT.
-
- hidden_states = layer_outputs[0]
- if use_cache:
- next_decoder_cache += (layer_outputs[-1],)
- if output_attentions:
- all_self_attentions = all_self_attentions + (layer_outputs[1],)
- if self.config.add_cross_attention:
- all_cross_attentions = all_cross_attentions + \
- (layer_outputs[2],)
-
- # Finalize the hidden states.
- hidden_states = self.ln(hidden_states)
-
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- if not return_dict:
- return tuple(
- v
- for v in [
- hidden_states,
- next_decoder_cache,
- all_hidden_states,
- all_self_attentions,
- all_cross_attentions,
- ]
- if v is not None
- )
- return BaseModelOutputWithPastAndCrossAttentions(
- last_hidden_state=hidden_states,
- past_key_values=next_decoder_cache,
- hidden_states=all_hidden_states,
- attentions=all_self_attentions,
- cross_attentions=all_cross_attentions,
- )
-
-
-# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->RoFormer
-class RoFormerPooler(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.dense = nn.Linear(config.hidden_size, config.hidden_size)
- self.activation = nn.Tanh()
-
- def forward(self, hidden_states):
- # We "pool" the model by simply taking the hidden state corresponding
- # to the first token.
- first_token_tensor = hidden_states[:, 0]
- pooled_output = self.dense(first_token_tensor)
- pooled_output = self.activation(pooled_output)
- return pooled_output
-
-
-# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->RoFormer
-class RoFormerPredictionHeadTransform(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.dense = nn.Linear(config.hidden_size, config.hidden_size)
- if isinstance(config.hidden_act, str):
- self.transform_act_fn = ACT2FN[config.hidden_act]
- else:
- self.transform_act_fn = config.hidden_act
- self.LayerNorm = nn.LayerNorm(
- config.hidden_size, eps=config.layer_norm_eps)
-
- def forward(self, hidden_states):
- hidden_states = self.dense(hidden_states)
- hidden_states = self.transform_act_fn(hidden_states)
- hidden_states = self.LayerNorm(hidden_states)
- return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->RoFormer
-class RoFormerLMPredictionHead(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.transform = RoFormerPredictionHeadTransform(config)
-
- # The output weights are the same as the input embeddings, but there is
- # an output-only bias for each token.
- self.decoder = nn.Linear(
- config.hidden_size, config.vocab_size, bias=False)
- self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
- # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
- self.decoder.bias = self.bias
-
- def forward(self, hidden_states):
- hidden_states = self.transform(hidden_states)
- hidden_states = self.decoder(hidden_states)
- return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->RoFormer
-class RoFormerOnlyMLMHead(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.predictions = RoFormerLMPredictionHead(config)
-
- def forward(self, sequence_output):
- prediction_scores = self.predictions(sequence_output)
- return prediction_scores
-
-
-# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->RoFormer
-class RoFormerOnlyNSPHead(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
- def forward(self, pooled_output):
- seq_relationship_score = self.seq_relationship(pooled_output)
- return seq_relationship_score
-
-
-# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->RoFormer
-class RoFormerPreTrainingHeads(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.predictions = RoFormerLMPredictionHead(config)
- self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
- def forward(self, sequence_output, pooled_output):
- prediction_scores = self.predictions(sequence_output)
- seq_relationship_score = self.seq_relationship(pooled_output)
- return prediction_scores, seq_relationship_score
-
-
-class RoFormerPreTrainedModel(PreTrainedModel):
- """
- An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
- models.
- """
-
- config_class = RoFormerConfig
- load_tf_weights = load_tf_weights_in_RoFormer
- base_model_prefix = "bert"
- _keys_to_ignore_on_load_missing = [r"position_ids"]
-
- def _init_weights(self, module):
- """Initialize the weights"""
- if isinstance(module, (nn.Linear, nn.Embedding)):
- # Slightly different from the TF version which uses truncated_normal for initialization
- # cf https://github.com/pytorch/pytorch/pull/5617
- module.weight.data.normal_(
- mean=0.0, std=self.config.initializer_range)
- elif isinstance(module, nn.LayerNorm):
- module.bias.data.zero_()
- module.weight.data.fill_(1.0)
- if isinstance(module, nn.Linear) and module.bias is not None:
- module.bias.data.zero_()
-
-
-@dataclass
-# Copied from transformers.models.bert.modeling_bert.BertForPreTrainingOutput with Bert->RoFormer
-class RoFormerForPreTrainingOutput(ModelOutput):
- """
- Output type of :class:`~transformers.RoFormerForPreTraining`.
-
- Args:
- loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
- Total loss as the sum of the masked language modeling loss and the next sequence prediction
- (classification) loss.
- prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- seq_relationship_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
- Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
- before SoftMax).
- hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
- Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
- of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
- Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
- sequence_length, sequence_length)`.
-
- Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
- heads.
- """
-
- loss: Optional[torch.FloatTensor] = None
- prediction_logits: torch.FloatTensor = None
- seq_relationship_logits: torch.FloatTensor = None
- hidden_states: Optional[Tuple[torch.FloatTensor]] = None
- attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-RoFormer_START_DOCSTRING = r"""
-
- This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
- methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
- pruning heads etc.)
-
- This model is also a PyTorch `torch.nn.Module `__
- subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
- general usage and behavior.
-
- Parameters:
- config (:class:`~transformers.RoFormerConfig`): Model configuration class with all the parameters of the model.
- Initializing with a config file does not load the weights associated with the model, only the
- configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
- weights.
-"""
-
-RoFormer_INPUTS_DOCSTRING = r"""
- Args:
- input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
- Indices of input sequence tokens in the vocabulary.
-
- Indices can be obtained using :class:`~transformers.BertTokenizer`. See
- :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
- details.
-
- `What are input IDs? <../glossary.html#input-ids>`__
- attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
- Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
-
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
-
- `What are attention masks? <../glossary.html#attention-mask>`__
- token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
- Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
- 1]``:
-
- - 0 corresponds to a `sentence A` token,
- - 1 corresponds to a `sentence B` token.
-
- `What are token type IDs? <../glossary.html#token-type-ids>`_
- position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
- config.max_position_embeddings - 1]``.
-
- `What are position IDs? <../glossary.html#position-ids>`_
- head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
- Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
-
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
-
- inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
- Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
- This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
- vectors than the model's internal embedding lookup matrix.
- output_attentions (:obj:`bool`, `optional`):
- Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
- tensors for more detail.
- output_hidden_states (:obj:`bool`, `optional`):
- Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
- more detail.
- return_dict (:obj:`bool`, `optional`):
- Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
- "The bare RoFormer Model transformer outputting raw hidden-states without any specific head on top.",
- RoFormer_START_DOCSTRING,
-)
-class RoFormerModel(RoFormerPreTrainedModel):
- """
-
- The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
- cross-attention is added between the self-attention layers, following the architecture described in `Attention is
- all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
- Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-
- To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration
- set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
- argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
- input to the forward pass.
- """
-
- def __init__(self, config, add_pooling_layer=True):
- super().__init__(config)
- self.config = config
-
- self.embeddings = RoFormerEmbeddings(config)
- self.encoder = RoFormerEncoder(config)
-
- self.pooler = RoFormerPooler(config) if add_pooling_layer else None
-
- self.init_weights()
-
- def get_input_embeddings(self):
- return self.embeddings.word_embeddings
-
- def set_input_embeddings(self, value):
- self.embeddings.word_embeddings = value
-
- def _prune_heads(self, heads_to_prune):
- """
- Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
- class PreTrainedModel
- """
- for layer, heads in heads_to_prune.items():
- self.encoder.layer[layer].attention.prune_heads(heads)
-
- @add_start_docstrings_to_model_forward(RoFormer_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @add_code_sample_docstrings(
- processor_class=_TOKENIZER_FOR_DOC,
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=BaseModelOutputWithPoolingAndCrossAttentions,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- past_key_values=None,
- use_cache=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- r"""
- encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
- Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
- the model is configured as a decoder.
- encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
- Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
- the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
- past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
- Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
- If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
- (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
- instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
- use_cache (:obj:`bool`, `optional`):
- If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
- decoding (see :obj:`past_key_values`).
- """
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- if self.config.is_decoder:
- use_cache = use_cache if use_cache is not None else self.config.use_cache
- else:
- use_cache = False
-
- if input_ids is not None and inputs_embeds is not None:
- raise ValueError(
- "You cannot specify both input_ids and inputs_embeds at the same time")
- elif input_ids is not None:
- input_shape = input_ids.size()
- batch_size, seq_length = input_shape
- elif inputs_embeds is not None:
- input_shape = inputs_embeds.size()[:-1]
- batch_size, seq_length = input_shape
- else:
- raise ValueError(
- "You have to specify either input_ids or inputs_embeds")
-
- device = input_ids.device if input_ids is not None else inputs_embeds.device
-
- # past_key_values_length
- past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
- if attention_mask is None:
- attention_mask = torch.ones(
- ((batch_size, seq_length + past_key_values_length)), device=device)
- if token_type_ids is None:
- token_type_ids = torch.zeros(
- input_shape, dtype=torch.long, device=device)
-
- # @IDEA modified -> get_extended_attention_mask -> roformer_extended_attention_mask
- extended_attention_mask = roformer_extended_attention_mask(
- attention_mask, token_type_ids)
- """
- # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
- # ourselves in which case we just need to make it broadcastable to all heads.
- extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
- """
-
- # If a 2D or 3D attention mask is provided for the cross-attention
- # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
- if self.config.is_decoder and encoder_hidden_states is not None:
- encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
- encoder_hidden_shape = (
- encoder_batch_size, encoder_sequence_length)
- if encoder_attention_mask is None:
- encoder_attention_mask = torch.ones(
- encoder_hidden_shape, device=device)
- encoder_extended_attention_mask = self.invert_attention_mask(
- encoder_attention_mask)
- else:
- encoder_extended_attention_mask = None
-
- # Prepare head mask if needed
- # 1.0 in head_mask indicate we keep the head
- # attention_probs has shape bsz x n_heads x N x N
- # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
- # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
- head_mask = self.get_head_mask(
- head_mask, self.config.num_hidden_layers)
-
- embedding_output = self.embeddings(
- input_ids=input_ids,
- position_ids=position_ids,
- token_type_ids=token_type_ids,
- inputs_embeds=inputs_embeds,
- past_key_values_length=past_key_values_length,
- )
- encoder_outputs = self.encoder(
- embedding_output,
- attention_mask=extended_attention_mask,
- head_mask=head_mask,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_extended_attention_mask,
- past_key_values=past_key_values,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
- sequence_output = encoder_outputs[0]
- pooled_output = self.pooler(
- sequence_output) if self.pooler is not None else None
-
- if not return_dict:
- return (sequence_output, pooled_output) + encoder_outputs[1:]
-
- return BaseModelOutputWithPoolingAndCrossAttentions(
- last_hidden_state=sequence_output,
- pooler_output=pooled_output,
- past_key_values=encoder_outputs.past_key_values,
- hidden_states=encoder_outputs.hidden_states,
- attentions=encoder_outputs.attentions,
- cross_attentions=encoder_outputs.cross_attentions,
- )
-
-
-@add_start_docstrings(
- """
- RoFormer Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
- `next sentence prediction (classification)` head.
- """,
- RoFormer_START_DOCSTRING,
-)
-class RoFormerForPreTraining(RoFormerPreTrainedModel):
- def __init__(self, config, add_binary_head=True):
- super().__init__(config)
-
- self.bert = RoFormerModel(config)
- self.cls = RoFormerPreTrainingHeads(config)
-
- self.init_weights()
-
- def get_output_embeddings(self):
- return self.cls.predictions.decoder
-
- def set_output_embeddings(self, new_embeddings):
- self.cls.predictions.decoder = new_embeddings
-
- @add_start_docstrings_to_model_forward(RoFormer_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @replace_return_docstrings(output_type=RoFormerForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- labels=None,
- next_sentence_label=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape ``(batch_size, sequence_length)``, `optional`):
- Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
- config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
- (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
- next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
- Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
- (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:
-
- - 0 indicates sequence B is a continuation of sequence A,
- - 1 indicates sequence B is a random sequence.
- kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
- Used to hide legacy arguments that have been deprecated.
-
- Returns:
-
- Example::
-
- >>> from transformers import BertTokenizer, RoFormerForPreTraining
- >>> import torch
-
- >>> tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m')
- >>> model = RoFormerForPreTraining.from_pretrained('nvidia/megatron-bert-cased-345m')
-
- >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
- >>> outputs = model(**inputs)
-
- >>> prediction_logits = outputs.prediction_logits
- >>> seq_relationship_logits = outputs.seq_relationship_logits
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output, pooled_output = outputs[:2]
- prediction_scores, seq_relationship_score = self.cls(
- sequence_output, pooled_output)
-
- total_loss = None
- if labels is not None and next_sentence_label is not None:
- loss_fct = CrossEntropyLoss()
- masked_lm_loss = loss_fct(
- prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
- next_sentence_loss = loss_fct(
- seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
- total_loss = masked_lm_loss + next_sentence_loss
-
- if not return_dict:
- output = (prediction_scores, seq_relationship_score) + outputs[2:]
- return ((total_loss,) + output) if total_loss is not None else output
-
- return RoFormerForPreTrainingOutput(
- loss=total_loss,
- prediction_logits=prediction_scores,
- seq_relationship_logits=seq_relationship_score,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
-
-@add_start_docstrings(
- """RoFormer Model with a `language modeling` head on top for CLM fine-tuning. """,
- RoFormer_START_DOCSTRING,
-)
-class RoFormerForCausalLM(RoFormerPreTrainedModel):
-
- _keys_to_ignore_on_load_unexpected = [r"pooler"]
- _keys_to_ignore_on_load_missing = [
- r"position_ids", r"predictions.decoder.bias"]
-
- def __init__(self, config):
- super().__init__(config)
-
- if not config.is_decoder:
- logger.warning(
- "If you want to use `RoFormerForCausalLM` as a standalone, add `is_decoder=True.`")
-
- self.bert = RoFormerModel(config, add_pooling_layer=False)
- self.cls = RoFormerOnlyMLMHead(config)
-
- self.init_weights()
-
- def get_output_embeddings(self):
- return self.cls.predictions.decoder
-
- def set_output_embeddings(self, new_embeddings):
- self.cls.predictions.decoder = new_embeddings
-
- @add_start_docstrings_to_model_forward(RoFormer_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- labels=None,
- past_key_values=None,
- use_cache=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- r"""
- encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
- Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
- the model is configured as a decoder.
- encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
- Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
- the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
- Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
- ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
- ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
- past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
- Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
- If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
- (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
- instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
- use_cache (:obj:`bool`, `optional`):
- If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
- decoding (see :obj:`past_key_values`).
-
- Returns:
-
- Example::
-
- >>> from transformers import BertTokenizer, RoFormerForCausalLM, RoFormerConfig
- >>> import torch
-
- >>> tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m')
- >>> model = RoFormerLMHeadModel.from_pretrained('nvidia/megatron-bert-cased-345m', is_decoder=True)
-
- >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
- >>> outputs = model(**inputs)
-
- >>> prediction_logits = outputs.logits
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
- if labels is not None:
- use_cache = False
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_attention_mask,
- past_key_values=past_key_values,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = outputs[0]
- prediction_scores = self.cls(sequence_output)
-
- lm_loss = None
- if labels is not None:
- # we are doing next-token prediction; shift prediction scores and input ids by one
- shifted_prediction_scores = prediction_scores[:,
- :-1, :].contiguous()
- labels = labels[:, 1:].contiguous()
- loss_fct = CrossEntropyLoss()
- lm_loss = loss_fct(
- shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-
- if not return_dict:
- output = (prediction_scores,) + outputs[2:]
- return ((lm_loss,) + output) if lm_loss is not None else output
-
- return CausalLMOutputWithCrossAttentions(
- loss=lm_loss,
- logits=prediction_scores,
- past_key_values=outputs.past_key_values,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- cross_attentions=outputs.cross_attentions,
- )
-
- def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
- input_shape = input_ids.shape
- # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
- if attention_mask is None:
- attention_mask = input_ids.new_ones(input_shape)
-
- # cut decoder_input_ids if past is used
- if past is not None:
- input_ids = input_ids[:, -1:]
-
- return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
-
- def _reorder_cache(self, past, beam_idx):
- reordered_past = ()
- for layer_past in past:
- reordered_past += (tuple(past_state.index_select(0, beam_idx)
- for past_state in layer_past),)
- return reordered_past
-
-
-@add_start_docstrings("""RoFormer Model with a `language modeling` head on top. """, RoFormer_START_DOCSTRING)
-class RoFormerForMaskedLM(RoFormerPreTrainedModel):
-
- _keys_to_ignore_on_load_unexpected = [r"pooler", r"seq_relationship"]
- _keys_to_ignore_on_load_missing = [
- r"position_ids", r"predictions.decoder.bias"]
-
- def __init__(self, config):
- super().__init__(config)
-
- if config.is_decoder:
- logger.warning(
- "If you want to use `RoFormerForMaskedLM` make sure `config.is_decoder=False` for "
- "bi-directional self-attention."
- )
-
- self.bert = RoFormerModel(config, add_pooling_layer=False)
- self.cls = RoFormerOnlyMLMHead(config)
-
- self.init_weights()
-
- def get_output_embeddings(self):
- return self.cls.predictions.decoder
-
- def set_output_embeddings(self, new_embeddings):
- self.cls.predictions.decoder = new_embeddings
-
- @add_start_docstrings_to_model_forward(RoFormer_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @add_code_sample_docstrings(
- processor_class=_TOKENIZER_FOR_DOC,
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=MaskedLMOutput,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- labels=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
- Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
- config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
- (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
- """
-
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_attention_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = outputs[0]
- prediction_scores = self.cls(sequence_output)
-
- masked_lm_loss = None
- if labels is not None:
- loss_fct = CrossEntropyLoss() # -100 index = padding token
- masked_lm_loss = loss_fct(
- prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-
- if not return_dict:
- output = (prediction_scores,) + outputs[2:]
- return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
- return MaskedLMOutput(
- loss=masked_lm_loss,
- logits=prediction_scores,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
- def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
- input_shape = input_ids.shape
- effective_batch_size = input_shape[0]
-
- # add a dummy token
- assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
- attention_mask = torch.cat(
- [attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
- dummy_token = torch.full(
- (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
- )
- input_ids = torch.cat([input_ids, dummy_token], dim=1)
-
- return {"input_ids": input_ids, "attention_mask": attention_mask}
-
-
-@add_start_docstrings(
- """RoFormer Model with a `next sentence prediction (classification)` head on top. """,
- RoFormer_START_DOCSTRING,
-)
-class RoFormerForNextSentencePrediction(RoFormerPreTrainedModel):
-
- _keys_to_ignore_on_load_unexpected = [r"predictions"]
-
- def __init__(self, config):
- super().__init__(config)
-
- self.bert = RoFormerModel(config)
- self.cls = RoFormerOnlyNSPHead(config)
-
- self.init_weights()
-
- @add_start_docstrings_to_model_forward(RoFormer_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- labels=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- **kwargs
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
- Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
- (see ``input_ids`` docstring). Indices should be in ``[0, 1]``:
-
- - 0 indicates sequence B is a continuation of sequence A,
- - 1 indicates sequence B is a random sequence.
-
- Returns:
-
- Example::
-
- >>> from transformers import BertTokenizer, RoFormerForNextSentencePrediction
- >>> import torch
-
- >>> tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m')
- >>> model = RoFormerForNextSentencePrediction.from_pretrained('nvidia/megatron-bert-cased-345m')
-
- >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
- >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
- >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
-
- >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
- >>> logits = outputs.logits
- >>> assert logits[0, 0] < logits[0, 1] # next sentence was random
- """
-
- if "next_sentence_label" in kwargs:
- warnings.warn(
- "The `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.",
- FutureWarning,
- )
- labels = kwargs.pop("next_sentence_label")
-
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- pooled_output = outputs[1]
-
- seq_relationship_scores = self.cls(pooled_output)
-
- next_sentence_loss = None
- if labels is not None:
- loss_fct = CrossEntropyLoss()
- next_sentence_loss = loss_fct(
- seq_relationship_scores.view(-1, 2), labels.view(-1))
-
- if not return_dict:
- output = (seq_relationship_scores,) + outputs[2:]
- return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
-
- return NextSentencePredictorOutput(
- loss=next_sentence_loss,
- logits=seq_relationship_scores,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
-
-@add_start_docstrings(
- """
- RoFormer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
- pooled output) e.g. for GLUE tasks.
- """,
- RoFormer_START_DOCSTRING,
-)
-class RoFormerForSequenceClassification(RoFormerPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.num_labels = config.num_labels
-
- self.bert = RoFormerModel(config)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
- self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
- self.init_weights()
-
- @add_start_docstrings_to_model_forward(RoFormer_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @add_code_sample_docstrings(
- processor_class=_TOKENIZER_FOR_DOC,
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=SequenceClassifierOutput,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- labels=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
- Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
- config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
- If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- pooled_output = outputs[1]
-
- pooled_output = self.dropout(pooled_output)
- logits = self.classifier(pooled_output)
-
- loss = None
- if labels is not None:
- if self.num_labels == 1:
- # We are doing regression
- loss_fct = MSELoss()
- loss = loss_fct(logits.view(-1), labels.view(-1))
- else:
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(
- logits.view(-1, self.num_labels), labels.view(-1))
-
- if not return_dict:
- output = (logits,) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return SequenceClassifierOutput(
- loss=loss,
- logits=logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
-
-@add_start_docstrings(
- """
- RoFormer Model with a multiple choice classification head on top (a linear layer on top of the pooled output
- and a softmax) e.g. for RocStories/SWAG tasks.
- """,
- RoFormer_START_DOCSTRING,
-)
-class RoFormerForMultipleChoice(RoFormerPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
-
- self.bert = RoFormerModel(config)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
- self.classifier = nn.Linear(config.hidden_size, 1)
-
- self.init_weights()
-
- @add_start_docstrings_to_model_forward(
- RoFormer_INPUTS_DOCSTRING.format(
- "batch_size, num_choices, sequence_length")
- )
- @add_code_sample_docstrings(
- processor_class=_TOKENIZER_FOR_DOC,
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=MultipleChoiceModelOutput,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- labels=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
- Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
- num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
- :obj:`input_ids` above)
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
- num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
-
- input_ids = input_ids.view(-1, input_ids.size(-1)
- ) if input_ids is not None else None
- attention_mask = attention_mask.view(
- -1, attention_mask.size(-1)) if attention_mask is not None else None
- token_type_ids = token_type_ids.view(
- -1, token_type_ids.size(-1)) if token_type_ids is not None else None
- position_ids = position_ids.view(-1, position_ids.size(-1)
- ) if position_ids is not None else None
- inputs_embeds = (
- inputs_embeds.view(-1, inputs_embeds.size(-2),
- inputs_embeds.size(-1))
- if inputs_embeds is not None
- else None
- )
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- pooled_output = outputs[1]
-
- pooled_output = self.dropout(pooled_output)
- logits = self.classifier(pooled_output)
- reshaped_logits = logits.view(-1, num_choices)
-
- loss = None
- if labels is not None:
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(reshaped_logits, labels)
-
- if not return_dict:
- output = (reshaped_logits,) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return MultipleChoiceModelOutput(
- loss=loss,
- logits=reshaped_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
-
-@add_start_docstrings(
- """
- RoFormer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
- for Named-Entity-Recognition (NER) tasks.
- """,
- RoFormer_START_DOCSTRING,
-)
-class RoFormerForTokenClassification(RoFormerPreTrainedModel):
-
- _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
- def __init__(self, config):
- super().__init__(config)
- self.num_labels = config.num_labels
-
- self.bert = RoFormerModel(config, add_pooling_layer=False)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
- self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
- self.init_weights()
-
- @add_start_docstrings_to_model_forward(RoFormer_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @add_code_sample_docstrings(
- processor_class=_TOKENIZER_FOR_DOC,
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=TokenClassifierOutput,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- labels=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
- Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
- 1]``.
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = outputs[0]
-
- sequence_output = self.dropout(sequence_output)
- logits = self.classifier(sequence_output)
-
- loss = None
- if labels is not None:
- loss_fct = CrossEntropyLoss()
- # Only keep active parts of the loss
- if attention_mask is not None:
- active_loss = attention_mask.view(-1) == 1
- active_logits = logits.view(-1, self.num_labels)
- active_labels = torch.where(
- active_loss, labels.view(-1), torch.tensor(
- loss_fct.ignore_index).type_as(labels)
- )
- loss = loss_fct(active_logits, active_labels)
- else:
- loss = loss_fct(
- logits.view(-1, self.num_labels), labels.view(-1))
-
- if not return_dict:
- output = (logits,) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return TokenClassifierOutput(
- loss=loss,
- logits=logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
-
-@add_start_docstrings(
- """
- RoFormer Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
- linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
- """,
- RoFormer_START_DOCSTRING,
-)
-class RoFormerForQuestionAnswering(RoFormerPreTrainedModel):
-
- _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
- def __init__(self, config):
- super().__init__(config)
- self.num_labels = config.num_labels
-
- self.bert = RoFormerModel(config, add_pooling_layer=False)
- self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
- self.init_weights()
-
- @add_start_docstrings_to_model_forward(RoFormer_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @add_code_sample_docstrings(
- processor_class=_TOKENIZER_FOR_DOC,
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=QuestionAnsweringModelOutput,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- start_positions=None,
- end_positions=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- r"""
- start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
- Labels for position (index) of the start of the labelled span for computing the token classification loss.
- Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
- sequence are not taken into account for computing the loss.
- end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
- Labels for position (index) of the end of the labelled span for computing the token classification loss.
- Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
- sequence are not taken into account for computing the loss.
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = outputs[0]
-
- logits = self.qa_outputs(sequence_output)
- start_logits, end_logits = logits.split(1, dim=-1)
- start_logits = start_logits.squeeze(-1).contiguous()
- end_logits = end_logits.squeeze(-1).contiguous()
-
- total_loss = None
- if start_positions is not None and end_positions is not None:
- # If we are on multi-GPU, split add a dimension
- if len(start_positions.size()) > 1:
- start_positions = start_positions.squeeze(-1)
- if len(end_positions.size()) > 1:
- end_positions = end_positions.squeeze(-1)
- # sometimes the start/end positions are outside our model inputs, we ignore these terms
- ignored_index = start_logits.size(1)
- start_positions = start_positions.clamp(0, ignored_index)
- end_positions = end_positions.clamp(0, ignored_index)
-
- loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
- start_loss = loss_fct(start_logits, start_positions)
- end_loss = loss_fct(end_logits, end_positions)
- total_loss = (start_loss + end_loss) / 2
-
- if not return_dict:
- output = (start_logits, end_logits) + outputs[2:]
- return ((total_loss,) + output) if total_loss is not None else output
-
- return QuestionAnsweringModelOutput(
- loss=total_loss,
- start_logits=start_logits,
- end_logits=end_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
diff --git a/spaces/HaohuaLv/one-shot_object_detection/app.py b/spaces/HaohuaLv/one-shot_object_detection/app.py
deleted file mode 100644
index 952c290f29803a5e79b539df9453022e097494b3..0000000000000000000000000000000000000000
--- a/spaces/HaohuaLv/one-shot_object_detection/app.py
+++ /dev/null
@@ -1,254 +0,0 @@
-import gradio as gr
-from PIL import Image, ImageDraw
-import torch
-from transformers import OwlViTProcessor, OwlViTForObjectDetection, OwlViTModel, OwlViTImageProcessor
-from transformers.image_transforms import center_to_corners_format
-from transformers.models.owlvit.modeling_owlvit import box_iou
-from functools import partial
-import numpy as np
-
-# from utils import iou
-
-processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
-model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
-
-from transformers.models.owlvit.modeling_owlvit import OwlViTImageGuidedObjectDetectionOutput, OwlViTClassPredictionHead
-
-
-
-
-
-def classpredictionhead_box_forward(
- self,
- image_embeds,
- query_indice,
- query_mask,
-):
- image_class_embeds = self.dense0(image_embeds)
-
- # Normalize image and text features
- image_class_embeds = image_class_embeds / (torch.linalg.norm(image_class_embeds, dim=-1, keepdim=True) + 1e-6)
- query_embeds = image_class_embeds[0, query_indice].unsqueeze(0).unsqueeze(0)
- # query_embeds = query_embeds / (torch.linalg.norm(query_embeds, dim=-1, keepdim=True) + 1e-6)
-
- # Get class predictions
- pred_logits = torch.einsum("...pd,...qd->...pq", image_class_embeds, query_embeds)
-
- # Apply a learnable shift and scale to logits
- logit_shift = self.logit_shift(image_embeds)
- logit_scale = self.logit_scale(image_embeds)
- logit_scale = self.elu(logit_scale) + 1
- pred_logits = (pred_logits + logit_shift) * logit_scale
-
- if query_mask is not None:
- if query_mask.ndim > 1:
- query_mask = torch.unsqueeze(query_mask, dim=-2)
-
- pred_logits = pred_logits.to(torch.float64)
- pred_logits = torch.where(query_mask == 0, -1e6, pred_logits)
- pred_logits = pred_logits.to(torch.float32)
-
- return (pred_logits, image_class_embeds)
-
-
-
-def class_predictor(
- self,
- image_feats,
- query_indice=None,
- query_mask=None,
-):
-
- (pred_logits, image_class_embeds) = self.class_head.classpredictionhead_box_forward(image_feats, query_indice, query_mask)
-
- return (pred_logits, image_class_embeds)
-
-
-
-
-
-
-
-
-def get_max_iou_indice(target_pred_boxes, query_box, target_sizes):
- boxes = center_to_corners_format(target_pred_boxes)
- img_h, img_w = target_sizes.unbind(1)
- scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
- boxes = boxes * scale_fct[:, None, :]
-
- iou, _ = box_iou(boxes.squeeze(0), query_box)
-
- return iou.argmax()
-
-
-def box_guided_detection(
- self: OwlViTForObjectDetection,
- pixel_values,
- query_box=None,
- target_sizes=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
-):
-
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.return_dict
-
- # Compute feature maps for the input and query images
- # query_feature_map = self.image_embedder(pixel_values=query_pixel_values)[0]
- feature_map, vision_outputs = self.image_embedder(
- pixel_values=pixel_values,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- )
-
- batch_size, num_patches, num_patches, hidden_dim = feature_map.shape
- image_feats = torch.reshape(feature_map, (batch_size, num_patches * num_patches, hidden_dim))
-
- # batch_size, num_patches, num_patches, hidden_dim = query_feature_map.shape
- # query_image_feats = torch.reshape(query_feature_map, (batch_size, num_patches * num_patches, hidden_dim))
- # # Get top class embedding and best box index for each query image in batch
- # query_embeds, best_box_indices, query_pred_boxes = self.embed_image_query(query_image_feats, query_feature_map)
-
- # Predict object boxes
- target_pred_boxes = self.box_predictor(image_feats, feature_map)
-
- # Get MAX IOU box corresponding embedding
- query_indice = get_max_iou_indice(target_pred_boxes, query_box, target_sizes)
-
- # Predict object classes [batch_size, num_patches, num_queries+1]
- (pred_logits, class_embeds) = self.class_predictor(image_feats=image_feats, query_indice=query_indice)
-
-
-
-
-
- if not return_dict:
- output = (
- feature_map,
- # query_feature_map,
- target_pred_boxes,
- # query_pred_boxes,
- pred_logits,
- class_embeds,
- vision_outputs.to_tuple(),
- )
- output = tuple(x for x in output if x is not None)
- return output
-
- return OwlViTImageGuidedObjectDetectionOutput(
- image_embeds=feature_map,
- # query_image_embeds=query_feature_map,
- target_pred_boxes=target_pred_boxes,
- # query_pred_boxes=query_pred_boxes,
- logits=pred_logits,
- class_embeds=class_embeds,
- text_model_output=None,
- vision_model_output=vision_outputs,
- )
-
-
-model.box_guided_detection = partial(box_guided_detection, model)
-model.class_predictor = partial(class_predictor, model)
-model.class_head.classpredictionhead_box_forward = partial(classpredictionhead_box_forward, model.class_head)
-
-
-outputs = None
-def prepare_embedds(xmin, ymin, xmax, ymax, image):
- box = (int(xmin), int(ymin), int(xmax), int(ymax))
- return (image, [(box, "manul")])
-
-def manul_box_change(xmin, ymin, xmax, ymax, image):
- box = (int(xmin), int(ymin), int(xmax), int(ymax))
- return (image["image"], [(box, "manul")])
-
-def threshold_change(xmin, ymin, xmax, ymax, image, threshold, nms):
- manul_box = (int(xmin), int(ymin), int(xmax), int(ymax))
-
- global outputs
- target_sizes = torch.Tensor([image["image"].size[::-1]])
-
- results = processor.post_process_image_guided_detection(outputs=outputs, threshold=threshold, nms_threshold=nms, target_sizes=target_sizes)
-
- boxes = results[0]['boxes'].type(torch.int64).tolist()
- scores = results[0]['scores'].tolist()
- labels = list(zip(boxes, scores))
-
- cnt = len(boxes)
-
- return (image["image"], labels), cnt
-
-def one_shot_detect(xmin, ymin, xmax, ymax, image, threshold, nms):
- manul_box = (int(xmin), int(ymin), int(xmax), int(ymax))
-
- global outputs
- target_sizes = torch.Tensor([image["image"].size[::-1]])
- inputs = processor(images=image["image"].convert("RGB"), return_tensors="pt")
- outputs = model.box_guided_detection(**inputs, query_box=torch.Tensor([manul_box]), target_sizes=target_sizes)
-
- results = processor.post_process_image_guided_detection(outputs=outputs, threshold=threshold, nms_threshold=nms, target_sizes=target_sizes)
-
- boxes = results[0]['boxes'].type(torch.int64).tolist()
- scores = results[0]['scores'].tolist()
- labels = list(zip(boxes, scores))
-
- cnt = len(boxes)
-
- return (image["image"], labels), cnt
-
-def save_embedding(exam):
- print(exam)
- global outputs
- embedding = outputs["class_embeds"][0, outputs["logits"].argmax()]
- return embedding.detach().numpy()
-
-
-def sketch2box(sketch_box):
- mask = sketch_box["mask"].convert("L")
- mask = np.array(mask)
-
- masked_index = np.where(mask == 255)
- if len(masked_index[0]) == 0:
- return (sketch_box["image"], []), -1, -1, -1, -1
- xmin, ymin, xmax, ymax = masked_index[1].min(), masked_index[0].min(), masked_index[1].max(), masked_index[0].max()
- box = (xmin, ymin, xmax, ymax)
-
- return (sketch_box["image"], [(box, "manual")]), xmin, ymin, xmax, ymax
-
-
-with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- sketch_box = gr.Image(type="pil", source="upload", tool="sketch")
- box_preview = gr.AnnotatedImage(type="pil", interactive=False, height=256)
- threshold = gr.Number(0.95, label="threshold", step=0.01)
- nms = gr.Number(0.3, label="nms", step=0.01)
- cnt = gr.Number(0, label="count", interactive=False)
- with gr.Column():
- annotatedimage = gr.AnnotatedImage()
- with gr.Row():
- xmin = gr.Number(-1, label="xmin")
- ymin = gr.Number(-1, label="ymin")
- xmax = gr.Number(-1, label="xmax")
- ymax = gr.Number(-1, label="ymax")
- with gr.Row():
- run_button = gr.Button(variant="primary")
- # save_button = gr.Button("Save", variant="secondary")
-
-
- sketch_box.edit(sketch2box, [sketch_box], [box_preview, xmin, ymin, xmax, ymax])
- xmin.change(manul_box_change, [xmin, ymin, xmax, ymax, sketch_box], [box_preview])
- ymin.change(manul_box_change, [xmin, ymin, xmax, ymax, sketch_box], [box_preview])
- xmax.change(manul_box_change, [xmin, ymin, xmax, ymax, sketch_box], [box_preview])
- ymax.change(manul_box_change, [xmin, ymin, xmax, ymax, sketch_box], [box_preview])
- threshold.change(threshold_change, [xmin, ymin, xmax, ymax, sketch_box, threshold, nms], [annotatedimage, cnt])
- nms.change(threshold_change, [xmin, ymin, xmax, ymax, sketch_box, threshold, nms], [annotatedimage, cnt])
- run_button.click(one_shot_detect, [xmin, ymin, xmax, ymax, sketch_box, threshold, nms], [annotatedimage, cnt])
-
-
-
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/indexed_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/indexed_dataset.py
deleted file mode 100644
index 23afb43356557d65c0e8f441ff9cdc890136ddbf..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/indexed_dataset.py
+++ /dev/null
@@ -1,585 +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 shutil
-import struct
-from functools import lru_cache
-
-import numpy as np
-import torch
-from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
-from fairseq.data.fasta_dataset import FastaDataset
-from fairseq.file_io import PathManager
-from fairseq.data.huffman import HuffmanMMapIndexedDataset, HuffmanMMapIndex
-
-from . import FairseqDataset
-
-from typing import Union
-
-
-def best_fitting_int_dtype(
- max_int_to_represent,
-) -> Union[np.uint16, np.uint32, np.int64]:
-
- if max_int_to_represent is None:
- return np.uint32 # Safe guess
- elif max_int_to_represent < 65500:
- return np.uint16
- elif max_int_to_represent < 4294967295:
- return np.uint32
- else:
- return np.int64
- # we avoid np.uint64 because it doesn't save space and its type promotion behaves unexpectedly
- # https://github.com/numpy/numpy/issues/5745
-
-
-def get_available_dataset_impl():
- return list(map(str, DATASET_IMPL_CHOICES))
-
-
-def infer_dataset_impl(path):
- if IndexedRawTextDataset.exists(path):
- return "raw"
- elif IndexedDataset.exists(path):
- with open(index_file_path(path), "rb") as f:
- magic = f.read(8)
- if magic == IndexedDataset._HDR_MAGIC:
- return "cached"
- elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]:
- return "mmap"
- elif magic == HuffmanMMapIndex._HDR_MAGIC[:8]:
- return "huffman"
- else:
- return None
- elif FastaDataset.exists(path):
- return "fasta"
- else:
- return None
-
-
-def make_builder(out_file, impl, vocab_size=None):
- if impl == "mmap":
- return MMapIndexedDatasetBuilder(
- out_file, dtype=best_fitting_int_dtype(vocab_size)
- )
- elif impl == "fasta":
- raise NotImplementedError
- elif impl == "huffman":
- raise ValueError("Use HuffmanCodeBuilder directly as it has a different interface.")
- else:
- return IndexedDatasetBuilder(out_file)
-
-
-def make_dataset(path, impl, fix_lua_indexing=False, dictionary=None):
- if impl == "raw" and IndexedRawTextDataset.exists(path):
- assert dictionary is not None
- return IndexedRawTextDataset(path, dictionary)
- elif impl == "lazy" and IndexedDataset.exists(path):
- return IndexedDataset(path, fix_lua_indexing=fix_lua_indexing)
- elif impl == "cached" and IndexedDataset.exists(path):
- return IndexedCachedDataset(path, fix_lua_indexing=fix_lua_indexing)
- elif impl == "mmap" and MMapIndexedDataset.exists(path):
- return MMapIndexedDataset(path)
- elif impl == "fasta" and FastaDataset.exists(path):
- from fairseq.data.fasta_dataset import EncodedFastaDataset
-
- return EncodedFastaDataset(path, dictionary)
- elif impl == "huffman" and HuffmanMMapIndexedDataset.exists(path):
- return HuffmanMMapIndexedDataset(path)
- return None
-
-
-def dataset_exists(path, impl):
- if impl == "raw":
- return IndexedRawTextDataset.exists(path)
- elif impl == "mmap":
- return MMapIndexedDataset.exists(path)
- elif impl == "huffman":
- return HuffmanMMapIndexedDataset.exists(path)
- else:
- return IndexedDataset.exists(path)
-
-
-def read_longs(f, n):
- a = np.empty(n, dtype=np.int64)
- f.readinto(a)
- return a
-
-
-def write_longs(f, a):
- f.write(np.array(a, dtype=np.int64))
-
-
-_code_to_dtype = {
- 1: np.uint8,
- 2: np.int8,
- 3: np.int16,
- 4: np.int32,
- 5: np.int64,
- 6: np.float,
- 7: np.double,
- 8: np.uint16,
- 9: np.uint32,
- 10: np.uint64,
-}
-
-
-def _dtype_header_code(dtype) -> int:
- for k in _code_to_dtype.keys():
- if _code_to_dtype[k] == dtype:
- return k
- raise ValueError(dtype)
-
-
-def index_file_path(prefix_path):
- return prefix_path + ".idx"
-
-
-def data_file_path(prefix_path):
- return prefix_path + ".bin"
-
-
-class IndexedDataset(FairseqDataset):
- """Loader for TorchNet IndexedDataset"""
-
- _HDR_MAGIC = b"TNTIDX\x00\x00"
-
- def __init__(self, path, fix_lua_indexing=False):
- super().__init__()
- self.path = path
- self.fix_lua_indexing = fix_lua_indexing
- self.data_file = None
- self.read_index(path)
-
- def read_index(self, path):
- with open(index_file_path(path), "rb") as f:
- magic = f.read(8)
- assert magic == self._HDR_MAGIC, (
- "Index file doesn't match expected format. "
- "Make sure that --dataset-impl is configured properly."
- )
- version = f.read(8)
- assert struct.unpack("= self._len:
- raise IndexError("index out of range")
-
- def __del__(self):
- if self.data_file:
- self.data_file.close()
-
- @lru_cache(maxsize=8)
- def __getitem__(self, i) -> torch.Tensor:
- if not self.data_file:
- self.read_data(self.path)
- self.check_index(i)
- tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
- a = np.empty(tensor_size, dtype=self.dtype)
- self.data_file.seek(self.data_offsets[i] * self.element_size)
- self.data_file.readinto(a)
- item = torch.from_numpy(a).long()
- if self.fix_lua_indexing:
- item -= 1 # subtract 1 for 0-based indexing
- return item
-
- def __len__(self):
- return self._len
-
- def num_tokens(self, index):
- return self.sizes[index]
-
- def size(self, index):
- return self.sizes[index]
-
- @staticmethod
- def exists(path):
- return PathManager.exists(index_file_path(path)) and PathManager.exists(
- data_file_path(path)
- )
-
- @property
- def supports_prefetch(self):
- return False # avoid prefetching to save memory
-
-
-class IndexedCachedDataset(IndexedDataset):
- def __init__(self, path, fix_lua_indexing=False):
- super().__init__(path, fix_lua_indexing=fix_lua_indexing)
- self.cache = None
- self.cache_index = {}
-
- @property
- def supports_prefetch(self):
- return True
-
- def prefetch(self, indices):
- if all(i in self.cache_index for i in indices):
- return
- if not self.data_file:
- self.read_data(self.path)
- indices = sorted(set(indices))
- total_size = 0
- for i in indices:
- total_size += self.data_offsets[i + 1] - self.data_offsets[i]
- self.cache = np.empty(total_size, dtype=self.dtype)
- ptx = 0
- self.cache_index.clear()
- for i in indices:
- self.cache_index[i] = ptx
- size = self.data_offsets[i + 1] - self.data_offsets[i]
- a = self.cache[ptx : ptx + size]
- self.data_file.seek(self.data_offsets[i] * self.element_size)
- self.data_file.readinto(a)
- ptx += size
- if self.data_file:
- # close and delete data file after prefetch so we can pickle
- self.data_file.close()
- self.data_file = None
-
- @lru_cache(maxsize=8)
- def __getitem__(self, i):
- self.check_index(i)
- tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
- a = np.empty(tensor_size, dtype=self.dtype)
- ptx = self.cache_index[i]
- np.copyto(a, self.cache[ptx : ptx + a.size])
- item = torch.from_numpy(a).long()
- if self.fix_lua_indexing:
- item -= 1 # subtract 1 for 0-based indexing
- return item
-
-
-class IndexedRawTextDataset(FairseqDataset):
- """Takes a text file as input and binarizes it in memory at instantiation.
- Original lines are also kept in memory"""
-
- def __init__(self, path, dictionary, append_eos=True, reverse_order=False):
- self.tokens_list = []
- self.lines = []
- self.sizes = []
- self.append_eos = append_eos
- self.reverse_order = reverse_order
- self.read_data(path, dictionary)
- self.size = len(self.tokens_list)
-
- def read_data(self, path, dictionary):
- with open(path, "r", encoding="utf-8") as f:
- for line in f:
- self.lines.append(line.strip("\n"))
- tokens = dictionary.encode_line(
- line,
- add_if_not_exist=False,
- append_eos=self.append_eos,
- reverse_order=self.reverse_order,
- ).long()
- self.tokens_list.append(tokens)
- self.sizes.append(len(tokens))
- self.sizes = np.array(self.sizes)
-
- def check_index(self, i):
- if i < 0 or i >= self.size:
- raise IndexError("index out of range")
-
- @lru_cache(maxsize=8)
- def __getitem__(self, i):
- self.check_index(i)
- return self.tokens_list[i]
-
- def get_original_text(self, i):
- self.check_index(i)
- return self.lines[i]
-
- def __del__(self):
- pass
-
- def __len__(self):
- return self.size
-
- def num_tokens(self, index):
- return self.sizes[index]
-
- def size(self, index):
- return self.sizes[index]
-
- @staticmethod
- def exists(path):
- return PathManager.exists(path)
-
-
-class IndexedDatasetBuilder:
- element_sizes = {
- np.uint8: 1,
- np.int8: 1,
- np.int16: 2,
- np.int32: 4,
- np.int64: 8,
- np.float: 4,
- np.double: 8,
- }
-
- def __init__(self, out_file, dtype=np.int32):
- self.out_file = open(out_file, "wb")
- self.dtype = dtype
- self.data_offsets = [0]
- self.dim_offsets = [0]
- self.sizes = []
- self.element_size = self.element_sizes[self.dtype]
-
- def add_item(self, tensor):
- # +1 for Lua compatibility
- bytes = self.out_file.write(np.array(tensor.numpy() + 1, dtype=self.dtype))
- self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size)
- for s in tensor.size():
- self.sizes.append(s)
- self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size()))
-
- def merge_file_(self, another_file):
- index = IndexedDataset(another_file)
- assert index.dtype == self.dtype
-
- begin = self.data_offsets[-1]
- for offset in index.data_offsets[1:]:
- self.data_offsets.append(begin + offset)
- self.sizes.extend(index.sizes)
- begin = self.dim_offsets[-1]
- for dim_offset in index.dim_offsets[1:]:
- self.dim_offsets.append(begin + dim_offset)
-
- with open(data_file_path(another_file), "rb") as f:
- while True:
- data = f.read(1024)
- if data:
- self.out_file.write(data)
- else:
- break
-
- def finalize(self, index_file):
- self.out_file.close()
- index = open(index_file, "wb")
- index.write(b"TNTIDX\x00\x00")
- index.write(struct.pack(" str:
- local_index_path = PathManager.get_local_path(index_file_path(path))
- local_data_path = PathManager.get_local_path(data_file_path(path))
-
- assert local_index_path.endswith(".idx") and local_data_path.endswith(".bin"), (
- "PathManager.get_local_path does not return files with expected patterns: "
- f"{local_index_path} and {local_data_path}"
- )
-
- local_path = local_data_path[:-4] # stripping surfix ".bin"
- assert local_path == local_index_path[:-4] # stripping surfix ".idx"
- return local_path
-
-
-class MMapIndexedDatasetBuilder:
- def __init__(self, out_file, dtype=np.int64):
- self._data_file = open(out_file, "wb")
- self._dtype = dtype
- self._sizes = []
-
- def add_item(self, tensor):
- np_array = np.array(tensor.numpy(), dtype=self._dtype)
- self._data_file.write(np_array.tobytes(order="C"))
- self._sizes.append(np_array.size)
-
- def merge_file_(self, another_file):
- # Concatenate index
- index = MMapIndexedDataset.Index(index_file_path(another_file))
- assert index.dtype == self._dtype
-
- for size in index.sizes:
- self._sizes.append(size)
-
- # Concatenate data
- with open(data_file_path(another_file), "rb") as f:
- shutil.copyfileobj(f, self._data_file)
-
- def finalize(self, index_file):
- self._data_file.close()
-
- with MMapIndexedDataset.Index.writer(index_file, self._dtype) as index:
- index.write(self._sizes)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/utils/cider/pyciderevalcap/cider/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/utils/cider/pyciderevalcap/cider/__init__.py
deleted file mode 100644
index 3f7d85bba884ea8f83fc6ab2a1e6ade80d98d4d9..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/utils/cider/pyciderevalcap/cider/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__author__ = 'tylin'
diff --git a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/transliterate/unicode_transliterate.py b/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/transliterate/unicode_transliterate.py
deleted file mode 100644
index 9754b40821b519aeee669973156d970b18ef6f3b..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/transliterate/unicode_transliterate.py
+++ /dev/null
@@ -1,347 +0,0 @@
-#
-# Copyright (c) 2013-present, Anoop Kunchukuttan
-# All rights reserved.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-#
-
-#Program for text written in one Indic script to another based on Unicode mappings.
-#
-# @author Anoop Kunchukuttan
-#
-
-import sys, string, itertools, re, os
-from collections import defaultdict
-
-from indicnlp import common
-from indicnlp import langinfo
-from indicnlp.script import indic_scripts as isc
-from indicnlp.transliterate.sinhala_transliterator import SinhalaDevanagariTransliterator as sdt
-import pandas as pd
-
-OFFSET_TO_ITRANS={}
-ITRANS_TO_OFFSET=defaultdict(list)
-
-DUPLICATE_ITRANS_REPRESENTATIONS={}
-
-
-def init():
- """
- To be called by library loader, do not call it in your program
- """
-
- ### Load the ITRANS-script offset map. The map was initially generated using the snippet below (uses the old itrans transliterator)
- ### The map is modified as needed to accomodate extensions and corrections to the mappings
- #
- # base=0x900
- # l=[]
- # for i in range(0,0x80):
- # c=chr(base+i)
- # itrans=ItransTransliterator.to_itrans(c,'hi')
- # l.append((hex(i),c,itrans))
- # print(l)
- #
- # pd.DataFrame(l,columns=['offset_hex','devnag_char','itrans']).to_csv('offset_itrans_map.csv',index=False,encoding='utf-8')
-
- itrans_map_fname=os.path.join(common.get_resources_path(),'transliterate','offset_itrans_map.csv')
- #itrans_map_fname=r'D:\src\python_sandbox\src\offset_itrans_map.csv'
- itrans_df=pd.read_csv(itrans_map_fname,encoding='utf-8')
-
- global OFFSET_TO_ITRANS, ITRANS_TO_OFFSET, DUPLICATE_ITRANS_REPRESENTATIONS
-
- for r in itrans_df.iterrows():
- itrans=r[1]['itrans']
- o=int(r[1]['offset_hex'],base=16)
-
- OFFSET_TO_ITRANS[o]=itrans
-
- if langinfo.is_consonant_offset(o):
- ### for consonants, strip the schwa - add halant offset
- ITRANS_TO_OFFSET[itrans[:-1]].extend([o,0x4d])
- else:
- ### the append assumes that the maatra always comes after independent vowel in the df
- ITRANS_TO_OFFSET[itrans].append(o)
-
-
- DUPLICATE_ITRANS_REPRESENTATIONS = {
- 'A': 'aa',
- 'I': 'ii',
- 'U': 'uu',
- 'RRi': 'R^i',
- 'RRI': 'R^I',
- 'LLi': 'L^i',
- 'LLI': 'L^I',
- 'L': 'ld',
- 'w': 'v',
- 'x': 'kSh',
- 'gj': 'j~n',
- 'dny': 'j~n',
- '.n': '.m',
- 'M': '.m',
- 'OM': 'AUM'
- }
-
-class UnicodeIndicTransliterator(object):
- """
- Base class for rule-based transliteration among Indian languages.
-
- Script pair specific transliterators should derive from this class and override the transliterate() method.
- They can call the super class 'transliterate()' method to avail of the common transliteration
- """
-
- @staticmethod
- def _correct_tamil_mapping(offset):
- # handle missing unaspirated and voiced plosives in Tamil script
- # replace by unvoiced, unaspirated plosives
-
- # for first 4 consonant rows of varnamala
- # exception: ja has a mapping in Tamil
- if offset>=0x15 and offset<=0x28 and \
- offset!=0x1c and \
- not ( (offset-0x15)%5==0 or (offset-0x15)%5==4 ) :
- subst_char=(offset-0x15)//5
- offset=0x15+5*subst_char
-
- # for 5th consonant row of varnamala
- if offset in [ 0x2b, 0x2c, 0x2d]:
- offset=0x2a
-
- # 'sh' becomes 'Sh'
- if offset==0x36:
- offset=0x37
-
- return offset
-
- @staticmethod
- def transliterate(text,lang1_code,lang2_code):
- """
- convert the source language script (lang1) to target language script (lang2)
-
- text: text to transliterate
- lang1_code: language 1 code
- lang1_code: language 2 code
- """
- if lang1_code in langinfo.SCRIPT_RANGES and lang2_code in langinfo.SCRIPT_RANGES:
-
- # if Sinhala is source, do a mapping to Devanagari first
- if lang1_code=='si':
- text=sdt.sinhala_to_devanagari(text)
- lang1_code='hi'
-
- # if Sinhala is target, make Devanagiri the intermediate target
- org_lang2_code=''
- if lang2_code=='si':
- lang2_code='hi'
- org_lang2_code='si'
-
- trans_lit_text=[]
- for c in text:
- newc=c
- offset=ord(c)-langinfo.SCRIPT_RANGES[lang1_code][0]
- if offset >=langinfo.COORDINATED_RANGE_START_INCLUSIVE and offset <= langinfo.COORDINATED_RANGE_END_INCLUSIVE and c!='\u0964' and c!='\u0965':
- if lang2_code=='ta':
- # tamil exceptions
- offset=UnicodeIndicTransliterator._correct_tamil_mapping(offset)
- newc=chr(langinfo.SCRIPT_RANGES[lang2_code][0]+offset)
-
- trans_lit_text.append(newc)
-
- # if Sinhala is source, do a mapping to Devanagari first
- if org_lang2_code=='si':
- return sdt.devanagari_to_sinhala(''.join(trans_lit_text))
-
- return ''.join(trans_lit_text)
- else:
- return text
-
-class ItransTransliterator(object):
- """
- Transliterator between Indian scripts and ITRANS
- """
-
- @staticmethod
- def to_itrans(text,lang_code):
- if lang_code in langinfo.SCRIPT_RANGES:
- if lang_code=='ml':
- # Change from chillus characters to corresponding consonant+halant
- text=text.replace('\u0d7a','\u0d23\u0d4d')
- text=text.replace('\u0d7b','\u0d28\u0d4d')
- text=text.replace('\u0d7c','\u0d30\u0d4d')
- text=text.replace('\u0d7d','\u0d32\u0d4d')
- text=text.replace('\u0d7e','\u0d33\u0d4d')
- text=text.replace('\u0d7f','\u0d15\u0d4d')
-
- offsets = [ isc.get_offset(c,lang_code) for c in text ]
-
- ### naive lookup
- # itrans_l = [ OFFSET_TO_ITRANS.get(o, '-' ) for o in offsets ]
- itrans_l=[]
- for o in offsets:
- itrans=OFFSET_TO_ITRANS.get(o, chr(langinfo.SCRIPT_RANGES[lang_code][0]+o) )
- if langinfo.is_halanta_offset(o):
- itrans=''
- if len(itrans_l)>0:
- itrans_l.pop()
- elif langinfo.is_vowel_sign_offset(o) and len(itrans_l)>0:
- itrans_l.pop()
- itrans_l.extend(itrans)
-
- return ''.join(itrans_l)
-
- else:
- return text
-
- @staticmethod
- def from_itrans(text,lang):
- """
- TODO: Document this method properly
- TODO: A little hack is used to handle schwa: needs to be documented
- TODO: check for robustness
- """
-
- MAXCODE=4 ### TODO: Needs to be fixed
-
- ## handle_duplicate_itrans_representations
- for k, v in DUPLICATE_ITRANS_REPRESENTATIONS.items():
- if k in text:
- text=text.replace(k,v)
-
- start=0
- match=None
- solution=[]
-
- i=start+1
- while i<=len(text):
-
- itrans=text[start:i]
-
- # print('===')
- # print('i: {}'.format(i))
- # if i0 and langinfo.is_halanta(solution[-1],lang):
- offs=[offs[1]] ## dependent vowel
- else:
- offs=[offs[0]] ## independent vowel
-
- c=''.join([ langinfo.offset_to_char(x,lang) for x in offs ])
- match=(i,c)
-
- elif len(itrans)==1: ## unknown character
- match=(i,itrans)
- elif i")
- sys.exit(1)
-
- if sys.argv[1]=='transliterate':
-
- src_language=sys.argv[4]
- tgt_language=sys.argv[5]
-
- with open(sys.argv[2],'r', encoding='utf-8') as ifile:
- with open(sys.argv[3],'w', encoding='utf-8') as ofile:
- for line in ifile.readlines():
- transliterated_line=UnicodeIndicTransliterator.transliterate(line,src_language,tgt_language)
- ofile.write(transliterated_line)
-
- elif sys.argv[1]=='romanize':
-
- language=sys.argv[4]
-
- ### temp fix to replace anusvara with corresponding nasal
- #r1_nasal=re.compile(ur'\u0902([\u0915-\u0918])')
- #r2_nasal=re.compile(ur'\u0902([\u091a-\u091d])')
- #r3_nasal=re.compile(ur'\u0902([\u091f-\u0922])')
- #r4_nasal=re.compile(ur'\u0902([\u0924-\u0927])')
- #r5_nasal=re.compile(ur'\u0902([\u092a-\u092d])')
-
- with open(sys.argv[2],'r', encoding='utf-8') as ifile:
- with open(sys.argv[3],'w', encoding='utf-8') as ofile:
- for line in ifile.readlines():
- ### temp fix to replace anusvara with corresponding nasal
- #line=r1_nasal.sub(u'\u0919\u094D\\1',line)
- #line=r2_nasal.sub(u'\u091e\u094D\\1',line)
- #line=r3_nasal.sub(u'\u0923\u094D\\1',line)
- #line=r4_nasal.sub(u'\u0928\u094D\\1',line)
- #line=r5_nasal.sub(u'\u092e\u094D\\1',line)
-
- transliterated_line=ItransTransliterator.to_itrans(line,language)
-
- ## temp fix to replace 'ph' to 'F' to match with Urdu transliteration scheme
- transliterated_line=transliterated_line.replace('ph','f')
-
- ofile.write(transliterated_line)
-
- elif sys.argv[1]=='indicize':
-
- language=sys.argv[4]
-
- with open(sys.argv[2],'r', encoding='utf-8') as ifile:
- with open(sys.argv[3],'w', encoding='utf-8') as ofile:
- for line in ifile.readlines():
- transliterated_line=ItransTransliterator.from_itrans(line,language)
- ofile.write(transliterated_line)
-
diff --git a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/data_measurements/tokenize.py b/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/data_measurements/tokenize.py
deleted file mode 100644
index e956fc404a282f76733503301cc357e386c668c4..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/data_measurements/tokenize.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import pandas as pd
-import utils
-from sklearn.feature_extraction.text import CountVectorizer
-
-logs = utils.prepare_logging(__file__)
-
-TEXT = "text"
-TOKENIZED_TEXT = "tokenized_text"
-
-
-class Tokenize:
-
- def __init__(self, text_dset, feature=TEXT, tok_feature=TOKENIZED_TEXT,
- lowercase=True):
- self.text_dset = text_dset
- self.feature = feature
- self.tok_feature = tok_feature
- self.lowercase = lowercase
- # Pattern for tokenization
- self.cvec = CountVectorizer(token_pattern="(?u)\\b\\w+\\b",
- lowercase=lowercase)
- self.tokenized_dset = self.do_tokenization()
-
- def do_tokenization(self):
- """
- Tokenizes a Hugging Face dataset in the self.feature field.
- :return: Hugging Face Dataset with tokenized text in self.tok_feature.
- """
- sent_tokenizer = self.cvec.build_tokenizer()
-
- def tokenize_batch(examples):
- if self.lowercase:
- tok_sent = {
- self.tok_feature: [tuple(sent_tokenizer(text.lower())) for
- text in examples[self.feature]]}
- else:
- tok_sent = {
- self.tok_feature: [tuple(sent_tokenizer(text)) for text in
- examples[self.feature]]}
- return tok_sent
-
- tokenized_dset = self.text_dset.map(
- tokenize_batch,
- batched=True
- )
- logs.info("Tokenized the dataset.")
- return tokenized_dset
-
- def get(self):
- return self.tokenized_dset
-
- def get_df(self):
- return pd.DataFrame(self.tokenized_dset)
diff --git a/spaces/IDEA-Research/Grounded-SAM/segment_anything/linter.sh b/spaces/IDEA-Research/Grounded-SAM/segment_anything/linter.sh
deleted file mode 100644
index df2e17436d30e89ff1728109301599f425f1ad6b..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/segment_anything/linter.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-{
- black --version | grep -E "23\." > /dev/null
-} || {
- echo "Linter requires 'black==23.*' !"
- exit 1
-}
-
-ISORT_VERSION=$(isort --version-number)
-if [[ "$ISORT_VERSION" != 5.12* ]]; then
- echo "Linter requires isort==5.12.0 !"
- exit 1
-fi
-
-echo "Running isort ..."
-isort . --atomic
-
-echo "Running black ..."
-black -l 100 .
-
-echo "Running flake8 ..."
-if [ -x "$(command -v flake8)" ]; then
- flake8 .
-else
- python3 -m flake8 .
-fi
-
-echo "Running mypy..."
-
-mypy --exclude 'setup.py|notebooks' .
diff --git a/spaces/Iceclear/StableSR/StableSR/ldm/models/diffusion/ddpm.py b/spaces/Iceclear/StableSR/StableSR/ldm/models/diffusion/ddpm.py
deleted file mode 100644
index 8a0c83d9904e447bfe058c22e39a292509f7020d..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/ldm/models/diffusion/ddpm.py
+++ /dev/null
@@ -1,3234 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager
-from functools import partial
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.data.transforms import paired_random_crop, triplet_random_crop
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
-import random
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import make_ddim_timesteps
-import copy
-import os
-import cv2
-import matplotlib.pyplot as plt
-from sklearn.decomposition import PCA
-
-__conditioning_keys__ = {'concat': 'c_concat',
- 'crossattn': 'c_crossattn',
- 'adm': 'y'}
-
-def torch2img(input):
- input_ = input[0]
- input_ = input_.permute(1,2,0)
- input_ = input_.data.cpu().numpy()
- input_ = (input_ + 1.0) / 2
- cv2.imwrite('./test.png', input_[:,:,::-1]*255.0)
-
-def cal_pca_components(input, n_components=3):
- pca = PCA(n_components=n_components)
- c, h, w = input.size()
- pca_data = input.permute(1,2,0)
- pca_data = pca_data.reshape(h*w, c)
- pca_data = pca.fit_transform(pca_data.data.cpu().numpy())
- pca_data = pca_data.reshape((h, w, n_components))
- return pca_data
-
-def visualize_fea(save_path, fea_img):
- fig = plt.figure(figsize = (fea_img.shape[1]/10, fea_img.shape[0]/10)) # Your image (W)idth and (H)eight in inches
- plt.subplots_adjust(left = 0, right = 1.0, top = 1.0, bottom = 0)
- im = plt.imshow(fea_img, vmin=0.0, vmax=1.0, cmap='jet', aspect='auto') # Show the image
- plt.savefig(save_path)
- plt.clf()
-
-def calc_mean_std(feat, eps=1e-5):
- """Calculate mean and std for adaptive_instance_normalization.
- Args:
- feat (Tensor): 4D tensor.
- eps (float): A small value added to the variance to avoid
- divide-by-zero. Default: 1e-5.
- """
- size = feat.size()
- assert len(size) == 4, 'The input feature should be 4D tensor.'
- b, c = size[:2]
- feat_var = feat.view(b, c, -1).var(dim=2) + eps
- feat_std = feat_var.sqrt().view(b, c, 1, 1)
- feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
- return feat_mean, feat_std
-
-def adaptive_instance_normalization(content_feat, style_feat):
- """Adaptive instance normalization.
- Adjust the reference features to have the similar color and illuminations
- as those in the degradate features.
- Args:
- content_feat (Tensor): The reference feature.
- style_feat (Tensor): The degradate features.
- """
- size = content_feat.size()
- style_mean, style_std = calc_mean_std(style_feat)
- content_mean, content_std = calc_mean_std(content_feat)
- normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
- return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-def space_timesteps(num_timesteps, section_counts):
- """
- Create a list of timesteps to use from an original diffusion process,
- given the number of timesteps we want to take from equally-sized portions
- of the original process.
-
- For example, if there's 300 timesteps and the section counts are [10,15,20]
- then the first 100 timesteps are strided to be 10 timesteps, the second 100
- are strided to be 15 timesteps, and the final 100 are strided to be 20.
-
- If the stride is a string starting with "ddim", then the fixed striding
- from the DDIM paper is used, and only one section is allowed.
-
- :param num_timesteps: the number of diffusion steps in the original
- process to divide up.
- :param section_counts: either a list of numbers, or a string containing
- comma-separated numbers, indicating the step count
- per section. As a special case, use "ddimN" where N
- is a number of steps to use the striding from the
- DDIM paper.
- :return: a set of diffusion steps from the original process to use.
- """
- if isinstance(section_counts, str):
- if section_counts.startswith("ddim"):
- desired_count = int(section_counts[len("ddim"):])
- for i in range(1, num_timesteps):
- if len(range(0, num_timesteps, i)) == desired_count:
- return set(range(0, num_timesteps, i))
- raise ValueError(
- f"cannot create exactly {num_timesteps} steps with an integer stride"
- )
- section_counts = [int(x) for x in section_counts.split(",")] #[250,]
- size_per = num_timesteps // len(section_counts)
- extra = num_timesteps % len(section_counts)
- start_idx = 0
- all_steps = []
- for i, section_count in enumerate(section_counts):
- size = size_per + (1 if i < extra else 0)
- if size < section_count:
- raise ValueError(
- f"cannot divide section of {size} steps into {section_count}"
- )
- if section_count <= 1:
- frac_stride = 1
- else:
- frac_stride = (size - 1) / (section_count - 1)
- cur_idx = 0.0
- taken_steps = []
- for _ in range(section_count):
- taken_steps.append(start_idx + round(cur_idx))
- cur_idx += frac_stride
- all_steps += taken_steps
- start_idx += size
- return set(all_steps)
-
-def disabled_train(self, mode=True):
- """Overwrite model.train with this function to make sure train/eval mode
- does not change anymore."""
- return self
-
-def uniform_on_device(r1, r2, shape, device):
- return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-class DDPM(pl.LightningModule):
- # classic DDPM with Gaussian diffusion, in image space
- def __init__(self,
- unet_config,
- timesteps=1000,
- beta_schedule="linear",
- loss_type="l2",
- ckpt_path=None,
- ignore_keys=[],
- load_only_unet=False,
- monitor="val/loss",
- use_ema=True,
- first_stage_key="image",
- image_size=256,
- channels=3,
- log_every_t=100,
- clip_denoised=True,
- linear_start=1e-4,
- linear_end=2e-2,
- cosine_s=8e-3,
- given_betas=None,
- original_elbo_weight=0.,
- v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
- l_simple_weight=1.,
- conditioning_key=None,
- parameterization="eps", # all assuming fixed variance schedules
- scheduler_config=None,
- use_positional_encodings=False,
- learn_logvar=False,
- logvar_init=0.,
- ):
- super().__init__()
- assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
- self.parameterization = parameterization
- print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
- self.cond_stage_model = None
- self.clip_denoised = clip_denoised
- self.log_every_t = log_every_t
- self.first_stage_key = first_stage_key
- self.image_size = image_size # try conv?
- self.channels = channels
- self.use_positional_encodings = use_positional_encodings
- self.model = DiffusionWrapper(unet_config, conditioning_key)
- count_params(self.model, verbose=True)
- self.use_ema = use_ema
- if self.use_ema:
- self.model_ema = LitEma(self.model)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
- self.use_scheduler = scheduler_config is not None
- if self.use_scheduler:
- self.scheduler_config = scheduler_config
-
- self.v_posterior = v_posterior
- self.original_elbo_weight = original_elbo_weight
- self.l_simple_weight = l_simple_weight
-
- if monitor is not None:
- self.monitor = monitor
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-
- self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
- linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-
- self.loss_type = loss_type
-
- self.learn_logvar = learn_logvar
- self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
- if self.learn_logvar:
- self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
-
- def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
- linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
- if exists(given_betas):
- betas = given_betas
- else:
- betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
- cosine_s=cosine_s)
- alphas = 1. - betas
- alphas_cumprod = np.cumprod(alphas, axis=0)
- alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
- timesteps, = betas.shape
- self.num_timesteps = int(timesteps)
- self.linear_start = linear_start
- self.linear_end = linear_end
- assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
- to_torch = partial(torch.tensor, dtype=torch.float32)
-
- self.register_buffer('betas', to_torch(betas))
- self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
- self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
- # calculations for diffusion q(x_t | x_{t-1}) and others
- self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
- self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
- self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
- self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
- self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
- # calculations for posterior q(x_{t-1} | x_t, x_0)
- posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
- 1. - alphas_cumprod) + self.v_posterior * betas
- # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
- self.register_buffer('posterior_variance', to_torch(posterior_variance))
- # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
- self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
- self.register_buffer('posterior_mean_coef1', to_torch(
- betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
- self.register_buffer('posterior_mean_coef2', to_torch(
- (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
- if self.parameterization == "eps":
- lvlb_weights = self.betas ** 2 / (
- 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
- elif self.parameterization == "x0":
- lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
- elif self.parameterization == "v":
- lvlb_weights = torch.ones_like(self.betas ** 2 / (
- 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
- else:
- raise NotImplementedError("mu not supported")
- # TODO how to choose this term
- lvlb_weights[0] = lvlb_weights[1]
- self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
- assert not torch.isnan(self.lvlb_weights).all()
-
- @contextmanager
- def ema_scope(self, context=None):
- if self.use_ema:
- self.model_ema.store(self.model.parameters())
- self.model_ema.copy_to(self.model)
- if context is not None:
- print(f"{context}: Switched to EMA weights")
- try:
- yield None
- finally:
- if self.use_ema:
- self.model_ema.restore(self.model.parameters())
- if context is not None:
- print(f"{context}: Restored training weights")
-
- def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
- sd = torch.load(path, map_location="cpu")
- if "state_dict" in list(sd.keys()):
- sd = sd["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
- sd, strict=False)
- print('<<<<<<<<<<<<>>>>>>>>>>>>>>>')
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- if len(unexpected) > 0:
- print(f"Unexpected Keys: {unexpected}")
-
- def q_mean_variance(self, x_start, t):
- """
- Get the distribution q(x_t | x_0).
- :param x_start: the [N x C x ...] tensor of noiseless inputs.
- :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
- :return: A tuple (mean, variance, log_variance), all of x_start's shape.
- """
- mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
- variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
- log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
- return mean, variance, log_variance
-
- def predict_start_from_noise(self, x_t, t, noise):
- return (
- extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
- )
-
- def q_posterior(self, x_start, x_t, t):
- posterior_mean = (
- extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
- extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
- )
- posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
- posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
- return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
- def p_mean_variance(self, x, t, clip_denoised: bool):
- model_out = self.model(x, t)
- if self.parameterization == "eps":
- x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
- elif self.parameterization == "x0":
- x_recon = model_out
- elif self.parameterization == "v":
- x_recon = self.predict_start_from_z_and_v(x, model_out, t)
- if clip_denoised:
- x_recon.clamp_(-1., 1.)
-
- model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
- return model_mean, posterior_variance, posterior_log_variance
-
- @torch.no_grad()
- def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
- b, *_, device = *x.shape, x.device
- model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
- noise = noise_like(x.shape, device, repeat_noise)
- # no noise when t == 0
- nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
- @torch.no_grad()
- def p_sample_loop(self, shape, return_intermediates=False):
- device = self.betas.device
- b = shape[0]
- img = torch.randn(shape, device=device)
- intermediates = [img]
- for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
- img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
- clip_denoised=self.clip_denoised)
- if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
- intermediates.append(img)
- if return_intermediates:
- return img, intermediates
- return img
-
- @torch.no_grad()
- def sample(self, batch_size=16, return_intermediates=False):
- image_size = self.image_size
- channels = self.channels
- return self.p_sample_loop((batch_size, channels, image_size, image_size),
- return_intermediates=return_intermediates)
-
- def q_sample(self, x_start, t, noise=None):
- noise = default(noise, lambda: torch.randn_like(x_start))
- return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
- extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
- def q_sample_respace(self, x_start, t, sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod, noise=None):
- noise = default(noise, lambda: torch.randn_like(x_start))
- return (extract_into_tensor(sqrt_alphas_cumprod.to(noise.device), t, x_start.shape) * x_start +
- extract_into_tensor(sqrt_one_minus_alphas_cumprod.to(noise.device), t, x_start.shape) * noise)
-
- def get_v(self, x, noise, t):
- return (
- extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
- extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
- )
-
- def predict_start_from_z_and_v(self, x, v, t):
- return (
- extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * x -
- extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * v
- )
-
- def get_loss(self, pred, target, mean=True):
- if self.loss_type == 'l1':
- loss = (target - pred).abs()
- if mean:
- loss = loss.mean()
- elif self.loss_type == 'l2':
- if mean:
- loss = torch.nn.functional.mse_loss(target, pred)
- else:
- loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
- else:
- raise NotImplementedError("unknown loss type '{loss_type}'")
-
- return loss
-
- def p_losses(self, x_start, t, noise=None):
- noise = default(noise, lambda: torch.randn_like(x_start))
- x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
- model_out = self.model(x_noisy, t)
-
- loss_dict = {}
- if self.parameterization == "eps":
- target = noise
- elif self.parameterization == "x0":
- target = x_start
- elif self.parameterization == "v":
- target = self.get_v(x_start, noise, t)
- else:
- raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
-
- loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
-
- log_prefix = 'train' if self.training else 'val'
-
- loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
- loss_simple = loss.mean() * self.l_simple_weight
-
- loss_vlb = (self.lvlb_weights[t] * loss).mean()
- loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
-
- loss = loss_simple + self.original_elbo_weight * loss_vlb
-
- loss_dict.update({f'{log_prefix}/loss': loss})
-
- return loss, loss_dict
-
- def forward(self, x, *args, **kwargs):
- # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
- # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
- t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
- return self.p_losses(x, t, *args, **kwargs)
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = rearrange(x, 'b h w c -> b c h w')
- x = x.to(memory_format=torch.contiguous_format).float()
- return x
-
- def shared_step(self, batch):
- x = self.get_input(batch, self.first_stage_key)
- loss, loss_dict = self(x)
- return loss, loss_dict
-
- def training_step(self, batch, batch_idx):
- loss, loss_dict = self.shared_step(batch)
-
- self.log_dict(loss_dict, prog_bar=True,
- logger=True, on_step=True, on_epoch=True)
-
- self.log("global_step", self.global_step,
- prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
- if self.use_scheduler:
- lr = self.optimizers().param_groups[0]['lr']
- self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
- return loss
-
- @torch.no_grad()
- def validation_step(self, batch, batch_idx):
- _, loss_dict_no_ema = self.shared_step(batch)
- with self.ema_scope():
- _, loss_dict_ema = self.shared_step(batch)
- loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
- self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
- self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-
- def on_train_batch_end(self, *args, **kwargs):
- if self.use_ema:
- self.model_ema(self.model)
-
- def _get_rows_from_list(self, samples):
- n_imgs_per_row = len(samples)
- denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
- denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
- denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
- return denoise_grid
-
- @torch.no_grad()
- def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
- log = dict()
- x = self.get_input(batch, self.first_stage_key)
- N = min(x.shape[0], N)
- n_row = min(x.shape[0], n_row)
- x = x.to(self.device)[:N]
- log["inputs"] = x
-
- # get diffusion row
- diffusion_row = list()
- x_start = x[:n_row]
-
- for t in range(self.num_timesteps):
- if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
- t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
- t = t.to(self.device).long()
- noise = torch.randn_like(x_start)
- x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
- diffusion_row.append(x_noisy)
-
- log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
-
- if sample:
- # get denoise row
- with self.ema_scope("Plotting"):
- samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
-
- log["samples"] = samples
- log["denoise_row"] = self._get_rows_from_list(denoise_row)
-
- if return_keys:
- if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
- return log
- else:
- return {key: log[key] for key in return_keys}
- return log
-
- def configure_optimizers(self):
- lr = self.learning_rate
- params = list(self.model.parameters())
- if self.learn_logvar:
- params = params + [self.logvar]
- opt = torch.optim.AdamW(params, lr=lr)
- return opt
-
-class LatentDiffusion(DDPM):
- """main class"""
- def __init__(self,
- first_stage_config,
- cond_stage_config,
- num_timesteps_cond=None,
- cond_stage_key="image",
- cond_stage_trainable=False,
- concat_mode=True,
- cond_stage_forward=None,
- conditioning_key=None,
- scale_factor=1.0,
- scale_by_std=False,
- *args, **kwargs):
- self.num_timesteps_cond = default(num_timesteps_cond, 1)
- self.scale_by_std = scale_by_std
- assert self.num_timesteps_cond <= kwargs['timesteps']
- # for backwards compatibility after implementation of DiffusionWrapper
- if conditioning_key is None:
- conditioning_key = 'concat' if concat_mode else 'crossattn'
- if cond_stage_config == '__is_unconditional__':
- conditioning_key = None
- ckpt_path = kwargs.pop("ckpt_path", None)
- ignore_keys = kwargs.pop("ignore_keys", [])
- super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
- self.concat_mode = concat_mode
- self.cond_stage_trainable = cond_stage_trainable
- self.cond_stage_key = cond_stage_key
- try:
- self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
- except:
- self.num_downs = 0
- if not scale_by_std:
- self.scale_factor = scale_factor
- else:
- self.register_buffer('scale_factor', torch.tensor(scale_factor))
- self.instantiate_first_stage(first_stage_config)
- self.instantiate_cond_stage(cond_stage_config)
- self.cond_stage_forward = cond_stage_forward
- self.clip_denoised = False
- self.bbox_tokenizer = None
-
- self.restarted_from_ckpt = False
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys)
- self.restarted_from_ckpt = True
-
- # self.model.eval()
- # self.model.train = disabled_train
- # for param in self.model.parameters():
- # param.requires_grad = False
-
- def make_cond_schedule(self, ):
- self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
- ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
- self.cond_ids[:self.num_timesteps_cond] = ids
-
- @rank_zero_only
- @torch.no_grad()
- def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
- # only for very first batch
- if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
- assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
- # set rescale weight to 1./std of encodings
- print("### USING STD-RESCALING ###")
- x = super().get_input(batch, self.first_stage_key)
- x = x.to(self.device)
- encoder_posterior = self.encode_first_stage(x)
- z = self.get_first_stage_encoding(encoder_posterior).detach()
- del self.scale_factor
- self.register_buffer('scale_factor', 1. / z.flatten().std())
- print(f"setting self.scale_factor to {self.scale_factor}")
- print("### USING STD-RESCALING ###")
-
- def register_schedule(self,
- given_betas=None, beta_schedule="linear", timesteps=1000,
- linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
- super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
- self.shorten_cond_schedule = self.num_timesteps_cond > 1
- if self.shorten_cond_schedule:
- self.make_cond_schedule()
-
- def instantiate_first_stage(self, config):
- model = instantiate_from_config(config)
- self.first_stage_model = model.eval()
- self.first_stage_model.train = disabled_train
- for param in self.first_stage_model.parameters():
- param.requires_grad = False
-
- def instantiate_cond_stage(self, config):
- if not self.cond_stage_trainable:
- if config == "__is_first_stage__":
- print("Using first stage also as cond stage.")
- self.cond_stage_model = self.first_stage_model
- elif config == "__is_unconditional__":
- print(f"Training {self.__class__.__name__} as an unconditional model.")
- self.cond_stage_model = None
- # self.be_unconditional = True
- else:
- model = instantiate_from_config(config)
- self.cond_stage_model = model.eval()
- self.cond_stage_model.train = disabled_train
- for param in self.cond_stage_model.parameters():
- param.requires_grad = False
- else:
- assert config != '__is_first_stage__'
- assert config != '__is_unconditional__'
- model = instantiate_from_config(config)
- self.cond_stage_model = model
-
- def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
- denoise_row = []
- for zd in tqdm(samples, desc=desc):
- denoise_row.append(self.decode_first_stage(zd.to(self.device),
- force_not_quantize=force_no_decoder_quantization))
- n_imgs_per_row = len(denoise_row)
- denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
- denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
- denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
- denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
- return denoise_grid
-
- def get_first_stage_encoding(self, encoder_posterior):
- if isinstance(encoder_posterior, DiagonalGaussianDistribution):
- z = encoder_posterior.sample()
- elif isinstance(encoder_posterior, torch.Tensor):
- z = encoder_posterior
- else:
- raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
- return self.scale_factor * z
-
- def get_learned_conditioning(self, c):
- if self.cond_stage_forward is None:
- if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
- c = self.cond_stage_model.encode(c)
- if isinstance(c, DiagonalGaussianDistribution):
- c = c.mode()
- else:
- c = self.cond_stage_model(c)
- else:
- assert hasattr(self.cond_stage_model, self.cond_stage_forward)
- c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
- return c
-
- def meshgrid(self, h, w):
- y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
- x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
- arr = torch.cat([y, x], dim=-1)
- return arr
-
- def delta_border(self, h, w):
- """
- :param h: height
- :param w: width
- :return: normalized distance to image border,
- wtith min distance = 0 at border and max dist = 0.5 at image center
- """
- lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
- arr = self.meshgrid(h, w) / lower_right_corner
- dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
- dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
- edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
- return edge_dist
-
- def get_weighting(self, h, w, Ly, Lx, device):
- weighting = self.delta_border(h, w)
- weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
- self.split_input_params["clip_max_weight"], )
- weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
- if self.split_input_params["tie_braker"]:
- L_weighting = self.delta_border(Ly, Lx)
- L_weighting = torch.clip(L_weighting,
- self.split_input_params["clip_min_tie_weight"],
- self.split_input_params["clip_max_tie_weight"])
-
- L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
- weighting = weighting * L_weighting
- return weighting
-
- def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
- """
- :param x: img of size (bs, c, h, w)
- :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
- """
- bs, nc, h, w = x.shape
-
- # number of crops in image
- Ly = (h - kernel_size[0]) // stride[0] + 1
- Lx = (w - kernel_size[1]) // stride[1] + 1
-
- if uf == 1 and df == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
- unfold = torch.nn.Unfold(**fold_params)
-
- fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
- weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
- elif uf > 1 and df == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
- unfold = torch.nn.Unfold(**fold_params)
-
- fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
- dilation=1, padding=0,
- stride=(stride[0] * uf, stride[1] * uf))
- fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
- weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
- elif df > 1 and uf == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
- unfold = torch.nn.Unfold(**fold_params)
-
- fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
- dilation=1, padding=0,
- stride=(stride[0] // df, stride[1] // df))
- fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
- weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
- else:
- raise NotImplementedError
-
- return fold, unfold, normalization, weighting
-
- @torch.no_grad()
- def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
- cond_key=None, return_original_cond=False, bs=None):
- x = batch[k]
-
- x = F.interpolate(
- x,
- size=(self.image_size,
- self.image_size),
- mode='bicubic',
- )
-
- if len(x.shape) == 3:
- x = x[..., None]
- # x = rearrange(x, 'b h w c -> b c h w')
- x = x.to(memory_format=torch.contiguous_format).float()
-
- if bs is not None:
- x = x[:bs]
- x = x.to(self.device)
- encoder_posterior = self.encode_first_stage(x)
- z = self.get_first_stage_encoding(encoder_posterior).detach()
-
- if self.model.conditioning_key is not None:
- if cond_key is None:
- cond_key = self.cond_stage_key
- if cond_key != self.first_stage_key:
- if cond_key in ['caption', 'coordinates_bbox']:
- # xc = batch[cond_key]
- xc = ['']*x.size(0)
- elif cond_key == 'class_label':
- xc = batch
- else:
- xc = super().get_input(batch, cond_key).to(self.device)
- else:
- xc = x
- if not self.cond_stage_trainable or force_c_encode:
- if isinstance(xc, dict) or isinstance(xc, list):
- # import pudb; pudb.set_trace()
- c = self.get_learned_conditioning(xc)
- else:
- c = self.get_learned_conditioning(xc.to(self.device))
- else:
- c = xc
-
- if bs is not None:
- c = c[:bs]
-
- if self.use_positional_encodings:
- pos_x, pos_y = self.compute_latent_shifts(batch)
- ckey = __conditioning_keys__[self.model.conditioning_key]
- c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
- else:
- c = None
- xc = None
- if self.use_positional_encodings:
- pos_x, pos_y = self.compute_latent_shifts(batch)
- c = {'pos_x': pos_x, 'pos_y': pos_y}
- out = [z, c]
- if return_first_stage_outputs:
- xrec = self.decode_first_stage(z)
- out.extend([x, xrec])
- if return_original_cond:
- out.append(xc)
- return out
-
- @torch.no_grad()
- def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
- if predict_cids:
- if z.dim() == 4:
- z = torch.argmax(z.exp(), dim=1).long()
- z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
- z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
- z = 1. / self.scale_factor * z
-
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- uf = self.split_input_params["vqf"]
- bs, nc, h, w = z.shape
- if ks[0] > h or ks[1] > w:
- ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
-
- if stride[0] > h or stride[1] > w:
- stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
-
- fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
- z = unfold(z) # (bn, nc * prod(**ks), L)
- # 1. Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
-
- # 2. apply model loop over last dim
- if isinstance(self.first_stage_model, VQModelInterface):
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
- force_not_quantize=predict_cids or force_not_quantize)
- for i in range(z.shape[-1])]
- else:
-
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
-
- o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
- o = o * weighting
- # Reverse 1. reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
- # stitch crops together
- decoded = fold(o)
- decoded = decoded / normalization # norm is shape (1, 1, h, w)
- return decoded
- else:
- if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
- else:
- return self.first_stage_model.decode(z)
-
- else:
- if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
- else:
- return self.first_stage_model.decode(z)
-
- # same as above but without decorator
- def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
- if predict_cids:
- if z.dim() == 4:
- z = torch.argmax(z.exp(), dim=1).long()
- z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
- z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
- z = 1. / self.scale_factor * z
-
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- uf = self.split_input_params["vqf"]
- bs, nc, h, w = z.shape
- if ks[0] > h or ks[1] > w:
- ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
-
- if stride[0] > h or stride[1] > w:
- stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
-
- fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
- z = unfold(z) # (bn, nc * prod(**ks), L)
- # 1. Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
-
- # 2. apply model loop over last dim
- if isinstance(self.first_stage_model, VQModelInterface):
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
- force_not_quantize=predict_cids or force_not_quantize)
- for i in range(z.shape[-1])]
- else:
-
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
-
- o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
- o = o * weighting
- # Reverse 1. reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
- # stitch crops together
- decoded = fold(o)
- decoded = decoded / normalization # norm is shape (1, 1, h, w)
- return decoded
- else:
- if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
- else:
- return self.first_stage_model.decode(z)
-
- else:
- if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
- else:
- return self.first_stage_model.decode(z)
-
- @torch.no_grad()
- def encode_first_stage(self, x):
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- df = self.split_input_params["vqf"]
- self.split_input_params['original_image_size'] = x.shape[-2:]
- bs, nc, h, w = x.shape
- if ks[0] > h or ks[1] > w:
- ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
-
- if stride[0] > h or stride[1] > w:
- stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
-
- fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
- z = unfold(x) # (bn, nc * prod(**ks), L)
- # Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
-
- output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
-
- o = torch.stack(output_list, axis=-1)
- o = o * weighting
-
- # Reverse reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
- # stitch crops together
- decoded = fold(o)
- decoded = decoded / normalization
- return decoded
-
- else:
- return self.first_stage_model.encode(x)
- else:
- return self.first_stage_model.encode(x)
-
- def shared_step(self, batch, **kwargs):
- x, c = self.get_input(batch, self.first_stage_key)
- loss = self(x, c)
- return loss
-
- def forward(self, x, c, *args, **kwargs):
- t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
- if self.model.conditioning_key is not None:
- assert c is not None
- if self.cond_stage_trainable:
- c = self.get_learned_conditioning(c)
- if self.shorten_cond_schedule: # TODO: drop this option
- tc = self.cond_ids[t].to(self.device)
- c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
- return self.p_losses(x, c, t, *args, **kwargs)
-
- def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
- def rescale_bbox(bbox):
- x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
- y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
- w = min(bbox[2] / crop_coordinates[2], 1 - x0)
- h = min(bbox[3] / crop_coordinates[3], 1 - y0)
- return x0, y0, w, h
-
- return [rescale_bbox(b) for b in bboxes]
-
- def apply_model(self, x_noisy, t, cond, return_ids=False):
-
- if isinstance(cond, dict):
- # hybrid case, cond is exptected to be a dict
- pass
- else:
- if not isinstance(cond, list):
- cond = [cond]
- key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
- cond = {key: cond}
-
- if hasattr(self, "split_input_params"):
- assert len(cond) == 1 # todo can only deal with one conditioning atm
- assert not return_ids
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
-
- h, w = x_noisy.shape[-2:]
-
- fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
-
- z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
- # Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
- z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
-
- if self.cond_stage_key in ["image", "LR_image", "segmentation",
- 'bbox_img'] and self.model.conditioning_key: # todo check for completeness
- c_key = next(iter(cond.keys())) # get key
- c = next(iter(cond.values())) # get value
- assert (len(c) == 1) # todo extend to list with more than one elem
- c = c[0] # get element
-
- c = unfold(c)
- c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
-
- cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
-
- elif self.cond_stage_key == 'coordinates_bbox':
- assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
-
- # assuming padding of unfold is always 0 and its dilation is always 1
- n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
- full_img_h, full_img_w = self.split_input_params['original_image_size']
- # as we are operating on latents, we need the factor from the original image size to the
- # spatial latent size to properly rescale the crops for regenerating the bbox annotations
- num_downs = self.first_stage_model.encoder.num_resolutions - 1
- rescale_latent = 2 ** (num_downs)
-
- # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
- # need to rescale the tl patch coordinates to be in between (0,1)
- tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
- rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
- for patch_nr in range(z.shape[-1])]
-
- # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
- patch_limits = [(x_tl, y_tl,
- rescale_latent * ks[0] / full_img_w,
- rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
- # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
-
- # tokenize crop coordinates for the bounding boxes of the respective patches
- patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
- for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
- print(patch_limits_tknzd[0].shape)
- # cut tknzd crop position from conditioning
- assert isinstance(cond, dict), 'cond must be dict to be fed into model'
- cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
- print(cut_cond.shape)
-
- adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
- adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
- print(adapted_cond.shape)
- adapted_cond = self.get_learned_conditioning(adapted_cond)
- print(adapted_cond.shape)
- adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
- print(adapted_cond.shape)
-
- cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
-
- else:
- cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
-
- # apply model by loop over crops
- output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
- assert not isinstance(output_list[0],
- tuple) # todo cant deal with multiple model outputs check this never happens
-
- o = torch.stack(output_list, axis=-1)
- o = o * weighting
- # Reverse reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
- # stitch crops together
- x_recon = fold(o) / normalization
-
- else:
- x_recon = self.model(x_noisy, t, **cond)
-
- if isinstance(x_recon, tuple) and not return_ids:
- return x_recon[0]
- else:
- return x_recon
-
- def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
- return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
- def _prior_bpd(self, x_start):
- """
- Get the prior KL term for the variational lower-bound, measured in
- bits-per-dim.
- This term can't be optimized, as it only depends on the encoder.
- :param x_start: the [N x C x ...] tensor of inputs.
- :return: a batch of [N] KL values (in bits), one per batch element.
- """
- batch_size = x_start.shape[0]
- t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
- qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
- kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
- return mean_flat(kl_prior) / np.log(2.0)
-
- def p_losses(self, x_start, cond, t, noise=None):
- noise = default(noise, lambda: torch.randn_like(x_start))
- x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
- model_output = self.apply_model(x_noisy, t, cond)
-
- loss_dict = {}
- prefix = 'train' if self.training else 'val'
-
- if self.parameterization == "x0":
- target = x_start
- elif self.parameterization == "eps":
- target = noise
- elif self.parameterization == "v":
- target = self.get_v(x_start, noise, t)
- else:
- raise NotImplementedError()
-
- loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
- loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
- logvar_t = self.logvar[t].to(self.device)
- loss = loss_simple / torch.exp(logvar_t) + logvar_t
- # loss = loss_simple / torch.exp(self.logvar) + self.logvar
- if self.learn_logvar:
- loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
- loss_dict.update({'logvar': self.logvar.data.mean()})
-
- loss = self.l_simple_weight * loss.mean()
-
- loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
- loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
- loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
- loss += (self.original_elbo_weight * loss_vlb)
- loss_dict.update({f'{prefix}/loss': loss})
-
- return loss, loss_dict
-
- def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
- return_x0=False, score_corrector=None, corrector_kwargs=None):
- t_in = t
- model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
- if score_corrector is not None:
- assert self.parameterization == "eps"
- model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
- if return_codebook_ids:
- model_out, logits = model_out
-
- if self.parameterization == "eps":
- x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
- elif self.parameterization == "x0":
- x_recon = model_out
- elif self.parameterization == "v":
- x_recon = self.predict_start_from_z_and_v(x, model_out, t)
- else:
- raise NotImplementedError()
-
- if clip_denoised:
- x_recon.clamp_(-1., 1.)
- if quantize_denoised:
- x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
- model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
- if return_codebook_ids:
- return model_mean, posterior_variance, posterior_log_variance, logits
- elif return_x0:
- return model_mean, posterior_variance, posterior_log_variance, x_recon
- else:
- return model_mean, posterior_variance, posterior_log_variance
-
- @torch.no_grad()
- def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
- return_codebook_ids=False, quantize_denoised=False, return_x0=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
- b, *_, device = *x.shape, x.device
- outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
- return_codebook_ids=return_codebook_ids,
- quantize_denoised=quantize_denoised,
- return_x0=return_x0,
- score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
- if return_codebook_ids:
- raise DeprecationWarning("Support dropped.")
- model_mean, _, model_log_variance, logits = outputs
- elif return_x0:
- model_mean, _, model_log_variance, x0 = outputs
- else:
- model_mean, _, model_log_variance = outputs
-
- noise = noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
- noise = torch.nn.functional.dropout(noise, p=noise_dropout)
- # no noise when t == 0
- nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
- if return_codebook_ids:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
- if return_x0:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
- else:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
- @torch.no_grad()
- def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
- img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
- score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
- log_every_t=None):
- if not log_every_t:
- log_every_t = self.log_every_t
- timesteps = self.num_timesteps
- if batch_size is not None:
- b = batch_size if batch_size is not None else shape[0]
- shape = [batch_size] + list(shape)
- else:
- b = batch_size = shape[0]
- if x_T is None:
- img = torch.randn(shape, device=self.device)
- else:
- img = x_T
- intermediates = []
- if cond is not None:
- if isinstance(cond, dict):
- cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
- list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
- else:
- cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
- if start_T is not None:
- timesteps = min(timesteps, start_T)
- iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
- total=timesteps) if verbose else reversed(
- range(0, timesteps))
- if type(temperature) == float:
- temperature = [temperature] * timesteps
-
- for i in iterator:
- ts = torch.full((b,), i, device=self.device, dtype=torch.long)
- if self.shorten_cond_schedule:
- assert self.model.conditioning_key != 'hybrid'
- tc = self.cond_ids[ts].to(cond.device)
- cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
- img, x0_partial = self.p_sample(img, cond, ts,
- clip_denoised=self.clip_denoised,
- quantize_denoised=quantize_denoised, return_x0=True,
- temperature=temperature[i], noise_dropout=noise_dropout,
- score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
- if mask is not None:
- assert x0 is not None
- img_orig = self.q_sample(x0, ts)
- img = img_orig * mask + (1. - mask) * img
-
- if i % log_every_t == 0 or i == timesteps - 1:
- intermediates.append(x0_partial)
- if callback: callback(i)
- if img_callback: img_callback(img, i)
- return img, intermediates
-
- @torch.no_grad()
- def p_sample_loop(self, cond, shape, return_intermediates=False,
- x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, start_T=None,
- log_every_t=None):
-
- if not log_every_t:
- log_every_t = self.log_every_t
- device = self.betas.device
- b = shape[0]
- if x_T is None:
- img = torch.randn(shape, device=device)
- else:
- img = x_T
-
- intermediates = [img]
- if timesteps is None:
- timesteps = self.num_timesteps
-
- if start_T is not None:
- timesteps = min(timesteps, start_T)
- iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
- range(0, timesteps))
-
- if mask is not None:
- assert x0 is not None
- assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
-
- for i in iterator:
- ts = torch.full((b,), i, device=device, dtype=torch.long)
- if self.shorten_cond_schedule:
- assert self.model.conditioning_key != 'hybrid'
- tc = self.cond_ids[ts].to(cond.device)
- cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
- img = self.p_sample(img, cond, ts,
- clip_denoised=self.clip_denoised,
- quantize_denoised=quantize_denoised)
- if mask is not None:
- img_orig = self.q_sample(x0, ts)
- img = img_orig * mask + (1. - mask) * img
-
- if i % log_every_t == 0 or i == timesteps - 1:
- intermediates.append(img)
- if callback: callback(i)
- if img_callback: img_callback(img, i)
-
- if return_intermediates:
- return img, intermediates
- return img
-
- @torch.no_grad()
- def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
- verbose=True, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, shape=None,**kwargs):
- if shape is None:
- shape = (batch_size, self.channels, self.image_size//8, self.image_size//8)
- if cond is not None:
- if isinstance(cond, dict):
- cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
- list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
- else:
- cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
- return self.p_sample_loop(cond,
- shape,
- return_intermediates=return_intermediates, x_T=x_T,
- verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
- mask=mask, x0=x0)
-
- @torch.no_grad()
- def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
-
- if ddim:
- ddim_sampler = DDIMSampler(self)
- shape = (self.channels, self.image_size, self.image_size)
- samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
- shape,cond,verbose=False,**kwargs)
-
- else:
- samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
- return_intermediates=True,**kwargs)
-
- return samples, intermediates
-
-
- @torch.no_grad()
- def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
- quantize_denoised=True, inpaint=False, plot_denoise_rows=False, plot_progressive_rows=True,
- plot_diffusion_rows=True, **kwargs):
-
- use_ddim = ddim_steps is not None
-
- log = dict()
- z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
- return_first_stage_outputs=True,
- force_c_encode=True,
- return_original_cond=True,
- bs=N)
- N = min(x.shape[0], N)
- n_row = min(x.shape[0], n_row)
- log["inputs"] = x
- log["reconstruction"] = xrec
-
- # print(z.size())
- # print(x.size())
- # if self.model.conditioning_key is not None:
- # if hasattr(self.cond_stage_model, "decode"):
- # xc = self.cond_stage_model.decode(c)
- # log["conditioning"] = xc
- # elif self.cond_stage_key in ["caption"]:
- # xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
- # log["conditioning"] = xc
- # elif self.cond_stage_key == 'class_label':
- # xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
- # log['conditioning'] = xc
- # elif isimage(xc):
- # log["conditioning"] = xc
- # if ismap(xc):
- # log["original_conditioning"] = self.to_rgb(xc)
-
- if plot_diffusion_rows:
- # get diffusion row
- diffusion_row = list()
- z_start = z[:n_row]
- for t in range(self.num_timesteps):
- if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
- t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
- t = t.to(self.device).long()
- noise = torch.randn_like(z_start)
- z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
- diffusion_row.append(self.decode_first_stage(z_noisy))
-
- diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
- diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
- diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
- diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
- log["diffusion_row"] = diffusion_grid
-
- if sample:
- # get denoise row
- with self.ema_scope("Plotting"):
- samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
- ddim_steps=ddim_steps,eta=ddim_eta)
- # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
- x_samples = self.decode_first_stage(samples)
- log["samples"] = x_samples
- if plot_denoise_rows:
- denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
- log["denoise_row"] = denoise_grid
-
- if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
- self.first_stage_model, IdentityFirstStage):
- # also display when quantizing x0 while sampling
- with self.ema_scope("Plotting Quantized Denoised"):
- samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
- ddim_steps=ddim_steps,eta=ddim_eta,
- quantize_denoised=True)
- # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
- # quantize_denoised=True)
- x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_x0_quantized"] = x_samples
-
- if inpaint:
- # make a simple center square
- b, h, w = z.shape[0], z.shape[2], z.shape[3]
- mask = torch.ones(N, h, w).to(self.device)
- # zeros will be filled in
- mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
- mask = mask[:, None, ...]
- with self.ema_scope("Plotting Inpaint"):
-
- samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
- ddim_steps=ddim_steps, x0=z[:N], mask=mask)
- x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_inpainting"] = x_samples
- log["mask"] = mask
-
- # outpaint
- with self.ema_scope("Plotting Outpaint"):
- samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
- ddim_steps=ddim_steps, x0=z[:N], mask=mask)
- x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_outpainting"] = x_samples
-
- if plot_progressive_rows:
- with self.ema_scope("Plotting Progressives"):
- img, progressives = self.progressive_denoising(c,
- shape=(self.channels, self.image_size, self.image_size),
- batch_size=N)
- prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
- log["progressive_row"] = prog_row
-
- if return_keys:
- if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
- return log
- else:
- return {key: log[key] for key in return_keys}
- return log
-
- def configure_optimizers(self):
- lr = self.learning_rate
- params = list(self.model.parameters())
- if self.cond_stage_trainable:
- print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
- params = params + list(self.cond_stage_model.parameters())
- # params = list(self.cond_stage_model.parameters())
- if self.learn_logvar:
- print('Diffusion model optimizing logvar')
- params.append(self.logvar)
- opt = torch.optim.AdamW(params, lr=lr)
- if self.use_scheduler:
- assert 'target' in self.scheduler_config
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- }]
- return [opt], scheduler
- return opt
-
- @torch.no_grad()
- def to_rgb(self, x):
- x = x.float()
- if not hasattr(self, "colorize"):
- self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
- x = nn.functional.conv2d(x, weight=self.colorize)
- x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
- return x
-
-class LatentDiffusionSRTextWT(DDPM):
- """main class"""
- def __init__(self,
- first_stage_config,
- cond_stage_config,
- structcond_stage_config,
- num_timesteps_cond=None,
- cond_stage_key="image",
- cond_stage_trainable=False,
- concat_mode=True,
- cond_stage_forward=None,
- conditioning_key=None,
- scale_factor=1.0,
- scale_by_std=False,
- unfrozen_diff=False,
- random_size=False,
- test_gt=False,
- p2_gamma=None,
- p2_k=None,
- time_replace=None,
- use_usm=False,
- mix_ratio=0.0,
- *args, **kwargs):
- # put this in your init
- self.num_timesteps_cond = default(num_timesteps_cond, 1)
- self.scale_by_std = scale_by_std
- self.unfrozen_diff = unfrozen_diff
- self.random_size = random_size
- self.test_gt = test_gt
- self.time_replace = time_replace
- self.use_usm = use_usm
- self.mix_ratio = mix_ratio
- assert self.num_timesteps_cond <= kwargs['timesteps']
- # for backwards compatibility after implementation of DiffusionWrapper
- if conditioning_key is None:
- conditioning_key = 'concat' if concat_mode else 'crossattn'
- if cond_stage_config == '__is_unconditional__':
- conditioning_key = None
- ckpt_path = kwargs.pop("ckpt_path", None)
- ignore_keys = kwargs.pop("ignore_keys", [])
- super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
- self.concat_mode = concat_mode
- self.cond_stage_trainable = cond_stage_trainable
- self.cond_stage_key = cond_stage_key
- try:
- self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
- except:
- self.num_downs = 0
- if not scale_by_std:
- self.scale_factor = scale_factor
- else:
- self.register_buffer('scale_factor', torch.tensor(scale_factor))
- self.instantiate_first_stage(first_stage_config)
- self.instantiate_cond_stage(cond_stage_config)
- self.instantiate_structcond_stage(structcond_stage_config)
- self.cond_stage_forward = cond_stage_forward
- self.clip_denoised = False
- self.bbox_tokenizer = None
-
- self.restarted_from_ckpt = False
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys)
- self.restarted_from_ckpt = True
-
- if not self.unfrozen_diff:
- self.model.eval()
- # self.model.train = disabled_train
- for name, param in self.model.named_parameters():
- if 'spade' not in name:
- param.requires_grad = False
- else:
- param.requires_grad = True
-
- print('>>>>>>>>>>>>>>>>model>>>>>>>>>>>>>>>>>>>>')
- param_list = []
- for name, params in self.model.named_parameters():
- if params.requires_grad:
- param_list.append(name)
- print(param_list)
- param_list = []
- print('>>>>>>>>>>>>>>>>>cond_stage_model>>>>>>>>>>>>>>>>>>>')
- for name, params in self.cond_stage_model.named_parameters():
- if params.requires_grad:
- param_list.append(name)
- print(param_list)
- param_list = []
- print('>>>>>>>>>>>>>>>>structcond_stage_model>>>>>>>>>>>>>>>>>>>>')
- for name, params in self.structcond_stage_model.named_parameters():
- if params.requires_grad:
- param_list.append(name)
- print(param_list)
-
- # P2 weighting: https://github.com/jychoi118/P2-weighting
- if p2_gamma is not None:
- assert p2_k is not None
- self.p2_gamma = p2_gamma
- self.p2_k = p2_k
- self.snr = 1.0 / (1 - self.alphas_cumprod) - 1
- else:
- self.snr = None
-
- # Support time respacing during training
- if self.time_replace is None:
- self.time_replace = kwargs['timesteps']
- use_timesteps = set(space_timesteps(kwargs['timesteps'], [self.time_replace]))
- last_alpha_cumprod = 1.0
- new_betas = []
- timestep_map = []
- for i, alpha_cumprod in enumerate(self.alphas_cumprod):
- if i in use_timesteps:
- new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
- last_alpha_cumprod = alpha_cumprod
- timestep_map.append(i)
- new_betas = [beta.data.cpu().numpy() for beta in new_betas]
- self.register_schedule(given_betas=np.array(new_betas), timesteps=len(new_betas), linear_start=kwargs['linear_start'], linear_end=kwargs['linear_end'])
- self.ori_timesteps = list(use_timesteps)
- self.ori_timesteps.sort()
-
- def make_cond_schedule(self, ):
- self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
- ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
- self.cond_ids[:self.num_timesteps_cond] = ids
-
- @rank_zero_only
- @torch.no_grad()
- def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
- # only for very first batch
- if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
- assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
- # set rescale weight to 1./std of encodings
- print("### USING STD-RESCALING ###")
- x = super().get_input(batch, self.first_stage_key)
- x = x.to(self.device)
- encoder_posterior = self.encode_first_stage(x)
- z = self.get_first_stage_encoding(encoder_posterior).detach()
- del self.scale_factor
- self.register_buffer('scale_factor', 1. / z.flatten().std())
- print(f"setting self.scale_factor to {self.scale_factor}")
- print("### USING STD-RESCALING ###")
-
- def register_schedule(self,
- given_betas=None, beta_schedule="linear", timesteps=1000,
- linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
- super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
- self.shorten_cond_schedule = self.num_timesteps_cond > 1
- if self.shorten_cond_schedule:
- self.make_cond_schedule()
-
- def instantiate_first_stage(self, config):
- model = instantiate_from_config(config)
- self.first_stage_model = model.eval()
- self.first_stage_model.train = disabled_train
- for param in self.first_stage_model.parameters():
- param.requires_grad = False
-
- def instantiate_cond_stage(self, config):
- if not self.cond_stage_trainable:
- if config == "__is_first_stage__":
- print("Using first stage also as cond stage.")
- self.cond_stage_model = self.first_stage_model
- elif config == "__is_unconditional__":
- print(f"Training {self.__class__.__name__} as an unconditional model.")
- self.cond_stage_model = None
- # self.be_unconditional = True
- else:
- model = instantiate_from_config(config)
- self.cond_stage_model = model.eval()
- # self.cond_stage_model.train = disabled_train
- for name, param in self.cond_stage_model.named_parameters():
- if 'final_projector' not in name:
- param.requires_grad = False
- else:
- assert config != '__is_first_stage__'
- assert config != '__is_unconditional__'
- model = instantiate_from_config(config)
- self.cond_stage_model = model
- self.cond_stage_model.train()
-
- def instantiate_structcond_stage(self, config):
- model = instantiate_from_config(config)
- self.structcond_stage_model = model
- self.structcond_stage_model.train()
-
- def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
- denoise_row = []
- for zd in tqdm(samples, desc=desc):
- denoise_row.append(self.decode_first_stage(zd.to(self.device),
- force_not_quantize=force_no_decoder_quantization))
- n_imgs_per_row = len(denoise_row)
- denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
- denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
- denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
- denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
- return denoise_grid
-
- def get_first_stage_encoding(self, encoder_posterior):
- if isinstance(encoder_posterior, DiagonalGaussianDistribution):
- z = encoder_posterior.sample()
- elif isinstance(encoder_posterior, torch.Tensor):
- z = encoder_posterior
- else:
- raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
- return self.scale_factor * z
-
- def get_learned_conditioning(self, c):
- if self.cond_stage_forward is None:
- if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
- c = self.cond_stage_model.encode(c)
- if isinstance(c, DiagonalGaussianDistribution):
- c = c.mode()
- else:
- c = self.cond_stage_model(c)
- else:
- assert hasattr(self.cond_stage_model, self.cond_stage_forward)
- c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
- return c
-
- def meshgrid(self, h, w):
- y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
- x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
- arr = torch.cat([y, x], dim=-1)
- return arr
-
- def delta_border(self, h, w):
- """
- :param h: height
- :param w: width
- :return: normalized distance to image border,
- wtith min distance = 0 at border and max dist = 0.5 at image center
- """
- lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
- arr = self.meshgrid(h, w) / lower_right_corner
- dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
- dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
- edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
- return edge_dist
-
- def get_weighting(self, h, w, Ly, Lx, device):
- weighting = self.delta_border(h, w)
- weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
- self.split_input_params["clip_max_weight"], )
- weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
- if self.split_input_params["tie_braker"]:
- L_weighting = self.delta_border(Ly, Lx)
- L_weighting = torch.clip(L_weighting,
- self.split_input_params["clip_min_tie_weight"],
- self.split_input_params["clip_max_tie_weight"])
-
- L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
- weighting = weighting * L_weighting
- return weighting
-
- def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
- """
- :param x: img of size (bs, c, h, w)
- :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
- """
- bs, nc, h, w = x.shape
-
- # number of crops in image
- Ly = (h - kernel_size[0]) // stride[0] + 1
- Lx = (w - kernel_size[1]) // stride[1] + 1
-
- if uf == 1 and df == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
- unfold = torch.nn.Unfold(**fold_params)
-
- fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
- weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
- elif uf > 1 and df == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
- unfold = torch.nn.Unfold(**fold_params)
-
- fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
- dilation=1, padding=0,
- stride=(stride[0] * uf, stride[1] * uf))
- fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
- weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
- elif df > 1 and uf == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
- unfold = torch.nn.Unfold(**fold_params)
-
- fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
- dilation=1, padding=0,
- stride=(stride[0] // df, stride[1] // df))
- fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
- weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
- else:
- raise NotImplementedError
-
- return fold, unfold, normalization, weighting
-
- @torch.no_grad()
- def _dequeue_and_enqueue(self):
- """It is the training pair pool for increasing the diversity in a batch, taken from Real-ESRGAN:
- https://github.com/xinntao/Real-ESRGAN
-
- Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
- batch could not have different resize scaling factors. Therefore, we employ this training pair pool
- to increase the degradation diversity in a batch.
- """
- # initialize
- b, c, h, w = self.lq.size()
- if b == self.configs.data.params.batch_size:
- if not hasattr(self, 'queue_size'):
- self.queue_size = self.configs.data.params.train.params.get('queue_size', b*50)
- if not hasattr(self, 'queue_lr'):
- assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
- self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
- _, c, h, w = self.gt.size()
- self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
- self.queue_ptr = 0
- if self.queue_ptr == self.queue_size: # the pool is full
- # do dequeue and enqueue
- # shuffle
- idx = torch.randperm(self.queue_size)
- self.queue_lr = self.queue_lr[idx]
- self.queue_gt = self.queue_gt[idx]
- # get first b samples
- lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
- gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
- # update the queue
- self.queue_lr[0:b, :, :, :] = self.lq.clone()
- self.queue_gt[0:b, :, :, :] = self.gt.clone()
-
- self.lq = lq_dequeue
- self.gt = gt_dequeue
- else:
- # only do enqueue
- self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
- self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
- self.queue_ptr = self.queue_ptr + b
-
- def randn_cropinput(self, lq, gt, base_size=[64, 128, 256, 512]):
- cur_size_h = random.choice(base_size)
- cur_size_w = random.choice(base_size)
- init_h = lq.size(-2)//2
- init_w = lq.size(-1)//2
- lq = lq[:, :, init_h-cur_size_h//2:init_h+cur_size_h//2, init_w-cur_size_w//2:init_w+cur_size_w//2]
- gt = gt[:, :, init_h-cur_size_h//2:init_h+cur_size_h//2, init_w-cur_size_w//2:init_w+cur_size_w//2]
- assert lq.size(-1)>=64
- assert lq.size(-2)>=64
- return [lq, gt]
-
- @torch.no_grad()
- def get_input(self, batch, k=None, return_first_stage_outputs=False, force_c_encode=False,
- cond_key=None, return_original_cond=False, bs=None, val=False, text_cond=[''], return_gt=False, resize_lq=True):
-
- """Degradation pipeline, modified from Real-ESRGAN:
- https://github.com/xinntao/Real-ESRGAN
- """
-
- if not hasattr(self, 'jpeger'):
- jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts
- if not hasattr(self, 'usm_sharpener'):
- usm_sharpener = USMSharp().cuda() # do usm sharpening
-
- im_gt = batch['gt'].cuda()
- if self.use_usm:
- im_gt = usm_sharpener(im_gt)
- im_gt = im_gt.to(memory_format=torch.contiguous_format).float()
- kernel1 = batch['kernel1'].cuda()
- kernel2 = batch['kernel2'].cuda()
- sinc_kernel = batch['sinc_kernel'].cuda()
-
- ori_h, ori_w = im_gt.size()[2:4]
-
- # ----------------------- The first degradation process ----------------------- #
- # blur
- out = filter2D(im_gt, kernel1)
- # random resize
- updown_type = random.choices(
- ['up', 'down', 'keep'],
- self.configs.degradation['resize_prob'],
- )[0]
- if updown_type == 'up':
- scale = random.uniform(1, self.configs.degradation['resize_range'][1])
- elif updown_type == 'down':
- scale = random.uniform(self.configs.degradation['resize_range'][0], 1)
- else:
- scale = 1
- mode = random.choice(['area', 'bilinear', 'bicubic'])
- out = F.interpolate(out, scale_factor=scale, mode=mode)
- # add noise
- gray_noise_prob = self.configs.degradation['gray_noise_prob']
- if random.random() < self.configs.degradation['gaussian_noise_prob']:
- out = random_add_gaussian_noise_pt(
- out,
- sigma_range=self.configs.degradation['noise_range'],
- clip=True,
- rounds=False,
- gray_prob=gray_noise_prob,
- )
- else:
- out = random_add_poisson_noise_pt(
- out,
- scale_range=self.configs.degradation['poisson_scale_range'],
- gray_prob=gray_noise_prob,
- clip=True,
- rounds=False)
- # JPEG compression
- jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.configs.degradation['jpeg_range'])
- out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
- out = jpeger(out, quality=jpeg_p)
-
- # ----------------------- The second degradation process ----------------------- #
- # blur
- if random.random() < self.configs.degradation['second_blur_prob']:
- out = filter2D(out, kernel2)
- # random resize
- updown_type = random.choices(
- ['up', 'down', 'keep'],
- self.configs.degradation['resize_prob2'],
- )[0]
- if updown_type == 'up':
- scale = random.uniform(1, self.configs.degradation['resize_range2'][1])
- elif updown_type == 'down':
- scale = random.uniform(self.configs.degradation['resize_range2'][0], 1)
- else:
- scale = 1
- mode = random.choice(['area', 'bilinear', 'bicubic'])
- out = F.interpolate(
- out,
- size=(int(ori_h / self.configs.sf * scale),
- int(ori_w / self.configs.sf * scale)),
- mode=mode,
- )
- # add noise
- gray_noise_prob = self.configs.degradation['gray_noise_prob2']
- if random.random() < self.configs.degradation['gaussian_noise_prob2']:
- out = random_add_gaussian_noise_pt(
- out,
- sigma_range=self.configs.degradation['noise_range2'],
- clip=True,
- rounds=False,
- gray_prob=gray_noise_prob,
- )
- else:
- out = random_add_poisson_noise_pt(
- out,
- scale_range=self.configs.degradation['poisson_scale_range2'],
- gray_prob=gray_noise_prob,
- clip=True,
- rounds=False,
- )
-
- # JPEG compression + the final sinc filter
- # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
- # as one operation.
- # We consider two orders:
- # 1. [resize back + sinc filter] + JPEG compression
- # 2. JPEG compression + [resize back + sinc filter]
- # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
- if random.random() < 0.5:
- # resize back + the final sinc filter
- mode = random.choice(['area', 'bilinear', 'bicubic'])
- out = F.interpolate(
- out,
- size=(ori_h // self.configs.sf,
- ori_w // self.configs.sf),
- mode=mode,
- )
- out = filter2D(out, sinc_kernel)
- # JPEG compression
- jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.configs.degradation['jpeg_range2'])
- out = torch.clamp(out, 0, 1)
- out = jpeger(out, quality=jpeg_p)
- else:
- # JPEG compression
- jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.configs.degradation['jpeg_range2'])
- out = torch.clamp(out, 0, 1)
- out = jpeger(out, quality=jpeg_p)
- # resize back + the final sinc filter
- mode = random.choice(['area', 'bilinear', 'bicubic'])
- out = F.interpolate(
- out,
- size=(ori_h // self.configs.sf,
- ori_w // self.configs.sf),
- mode=mode,
- )
- out = filter2D(out, sinc_kernel)
-
- # clamp and round
- im_lq = torch.clamp(out, 0, 1.0)
-
- # random crop
- gt_size = self.configs.degradation['gt_size']
- im_gt, im_lq = paired_random_crop(im_gt, im_lq, gt_size, self.configs.sf)
- self.lq, self.gt = im_lq, im_gt
-
- if resize_lq:
- self.lq = F.interpolate(
- self.lq,
- size=(self.gt.size(-2),
- self.gt.size(-1)),
- mode='bicubic',
- )
-
- if random.random() < self.configs.degradation['no_degradation_prob'] or torch.isnan(self.lq).any():
- self.lq = self.gt
-
- # training pair pool
- if not val and not self.random_size:
- self._dequeue_and_enqueue()
- # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
- self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract
- self.lq = self.lq*2 - 1.0
- self.gt = self.gt*2 - 1.0
-
- if self.random_size:
- self.lq, self.gt = self.randn_cropinput(self.lq, self.gt)
-
- self.lq = torch.clamp(self.lq, -1.0, 1.0)
-
- x = self.lq
- y = self.gt
- if bs is not None:
- x = x[:bs]
- y = y[:bs]
- x = x.to(self.device)
- y = y.to(self.device)
- encoder_posterior = self.encode_first_stage(x)
- z = self.get_first_stage_encoding(encoder_posterior).detach()
-
- encoder_posterior_y = self.encode_first_stage(y)
- z_gt = self.get_first_stage_encoding(encoder_posterior_y).detach()
-
- xc = None
- if self.use_positional_encodings:
- assert NotImplementedError
- pos_x, pos_y = self.compute_latent_shifts(batch)
- c = {'pos_x': pos_x, 'pos_y': pos_y}
-
- while len(text_cond) < z.size(0):
- text_cond.append(text_cond[-1])
- if len(text_cond) > z.size(0):
- text_cond = text_cond[:z.size(0)]
- assert len(text_cond) == z.size(0)
-
- out = [z, text_cond]
- out.append(z_gt)
-
- if return_first_stage_outputs:
- xrec = self.decode_first_stage(z_gt)
- out.extend([x, self.gt, xrec])
- if return_original_cond:
- out.append(xc)
-
- return out
-
- @torch.no_grad()
- def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
- if predict_cids:
- if z.dim() == 4:
- z = torch.argmax(z.exp(), dim=1).long()
- z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
- z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
- z = 1. / self.scale_factor * z
-
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- uf = self.split_input_params["vqf"]
- bs, nc, h, w = z.shape
- if ks[0] > h or ks[1] > w:
- ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
-
- if stride[0] > h or stride[1] > w:
- stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
-
- fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
- z = unfold(z) # (bn, nc * prod(**ks), L)
- # 1. Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
-
- # 2. apply model loop over last dim
- if isinstance(self.first_stage_model, VQModelInterface):
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
- force_not_quantize=predict_cids or force_not_quantize)
- for i in range(z.shape[-1])]
- else:
-
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
-
- o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
- o = o * weighting
- # Reverse 1. reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
- # stitch crops together
- decoded = fold(o)
- decoded = decoded / normalization # norm is shape (1, 1, h, w)
- return decoded
- else:
- if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
- else:
- return self.first_stage_model.decode(z)
-
- else:
- if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
- else:
- return self.first_stage_model.decode(z)
-
-
- # same as above but without decorator
- def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
- if predict_cids:
- if z.dim() == 4:
- z = torch.argmax(z.exp(), dim=1).long()
- z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
- z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
- z = 1. / self.scale_factor * z
-
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- uf = self.split_input_params["vqf"]
- bs, nc, h, w = z.shape
- if ks[0] > h or ks[1] > w:
- ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
-
- if stride[0] > h or stride[1] > w:
- stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
-
- fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
- z = unfold(z) # (bn, nc * prod(**ks), L)
- # 1. Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
-
- # 2. apply model loop over last dim
- if isinstance(self.first_stage_model, VQModelInterface):
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
- force_not_quantize=predict_cids or force_not_quantize)
- for i in range(z.shape[-1])]
- else:
-
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
-
- o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
- o = o * weighting
- # Reverse 1. reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
- # stitch crops together
- decoded = fold(o)
- decoded = decoded / normalization # norm is shape (1, 1, h, w)
- return decoded
- else:
- if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
- else:
- return self.first_stage_model.decode(z)
-
- else:
- if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
- else:
- return self.first_stage_model.decode(z)
-
- @torch.no_grad()
- def encode_first_stage(self, x):
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- df = self.split_input_params["vqf"]
- self.split_input_params['original_image_size'] = x.shape[-2:]
- bs, nc, h, w = x.shape
- if ks[0] > h or ks[1] > w:
- ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
-
- if stride[0] > h or stride[1] > w:
- stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
-
- fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
- z = unfold(x) # (bn, nc * prod(**ks), L)
- # Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
-
- output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
-
- o = torch.stack(output_list, axis=-1)
- o = o * weighting
-
- # Reverse reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
- # stitch crops together
- decoded = fold(o)
- decoded = decoded / normalization
- return decoded
-
- else:
- return self.first_stage_model.encode(x)
- else:
- return self.first_stage_model.encode(x)
-
- def shared_step(self, batch, **kwargs):
- x, c, gt = self.get_input(batch, self.first_stage_key)
- loss = self(x, c, gt)
- return loss
-
- def forward(self, x, c, gt, *args, **kwargs):
- index = np.random.randint(0, self.num_timesteps, size=x.size(0))
- t = torch.from_numpy(index)
- t = t.to(self.device).long()
-
- t_ori = torch.tensor([self.ori_timesteps[index_i] for index_i in index])
- t_ori = t_ori.long().to(x.device)
-
- if self.model.conditioning_key is not None:
- assert c is not None
- if self.cond_stage_trainable:
- c = self.get_learned_conditioning(c)
- else:
- c = self.cond_stage_model(c)
- if self.shorten_cond_schedule: # TODO: drop this option
- print(s)
- tc = self.cond_ids[t].to(self.device)
- c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
- if self.test_gt:
- struc_c = self.structcond_stage_model(gt, t_ori)
- else:
- struc_c = self.structcond_stage_model(x, t_ori)
- return self.p_losses(gt, c, struc_c, t, t_ori, x, *args, **kwargs)
-
- def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
- def rescale_bbox(bbox):
- x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
- y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
- w = min(bbox[2] / crop_coordinates[2], 1 - x0)
- h = min(bbox[3] / crop_coordinates[3], 1 - y0)
- return x0, y0, w, h
-
- return [rescale_bbox(b) for b in bboxes]
-
- def apply_model(self, x_noisy, t, cond, struct_cond, return_ids=False):
-
- if isinstance(cond, dict):
- # hybrid case, cond is exptected to be a dict
- pass
- else:
- if not isinstance(cond, list):
- cond = [cond]
- key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
- cond = {key: cond}
-
- if hasattr(self, "split_input_params"):
- assert len(cond) == 1 # todo can only deal with one conditioning atm
- assert not return_ids
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
-
- h, w = x_noisy.shape[-2:]
-
- fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
-
- z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
- # Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
- z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
-
- if self.cond_stage_key in ["image", "LR_image", "segmentation",
- 'bbox_img'] and self.model.conditioning_key: # todo check for completeness
- c_key = next(iter(cond.keys())) # get key
- c = next(iter(cond.values())) # get value
- assert (len(c) == 1) # todo extend to list with more than one elem
- c = c[0] # get element
-
- c = unfold(c)
- c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
-
- cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
-
- elif self.cond_stage_key == 'coordinates_bbox':
- assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
-
- # assuming padding of unfold is always 0 and its dilation is always 1
- n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
- full_img_h, full_img_w = self.split_input_params['original_image_size']
- # as we are operating on latents, we need the factor from the original image size to the
- # spatial latent size to properly rescale the crops for regenerating the bbox annotations
- num_downs = self.first_stage_model.encoder.num_resolutions - 1
- rescale_latent = 2 ** (num_downs)
-
- # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
- # need to rescale the tl patch coordinates to be in between (0,1)
- tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
- rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
- for patch_nr in range(z.shape[-1])]
-
- # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
- patch_limits = [(x_tl, y_tl,
- rescale_latent * ks[0] / full_img_w,
- rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
- # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
-
- # tokenize crop coordinates for the bounding boxes of the respective patches
- patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
- for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
- print(patch_limits_tknzd[0].shape)
- # cut tknzd crop position from conditioning
- assert isinstance(cond, dict), 'cond must be dict to be fed into model'
- cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
- print(cut_cond.shape)
-
- adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
- adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
- print(adapted_cond.shape)
- adapted_cond = self.get_learned_conditioning(adapted_cond)
- print(adapted_cond.shape)
- adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
- print(adapted_cond.shape)
-
- cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
-
- else:
- cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
-
- # apply model by loop over crops
- output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
- assert not isinstance(output_list[0],
- tuple) # todo cant deal with multiple model outputs check this never happens
-
- o = torch.stack(output_list, axis=-1)
- o = o * weighting
- # Reverse reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
- # stitch crops together
- x_recon = fold(o) / normalization
-
- else:
- cond['struct_cond'] = struct_cond
- x_recon = self.model(x_noisy, t, **cond)
-
- if isinstance(x_recon, tuple) and not return_ids:
- return x_recon[0]
- else:
- return x_recon
-
- def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
- return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
- def _prior_bpd(self, x_start):
- """
- Get the prior KL term for the variational lower-bound, measured in
- bits-per-dim.
- This term can't be optimized, as it only depends on the encoder.
- :param x_start: the [N x C x ...] tensor of inputs.
- :return: a batch of [N] KL values (in bits), one per batch element.
- """
- batch_size = x_start.shape[0]
- t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
- qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
- kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
- return mean_flat(kl_prior) / np.log(2.0)
-
- def p_losses(self, x_start, cond, struct_cond, t, t_ori, z_gt, noise=None):
- noise = default(noise, lambda: torch.randn_like(x_start))
- x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-
- if self.mix_ratio > 0:
- if random.random() < self.mix_ratio:
- noise_new = default(noise, lambda: torch.randn_like(x_start))
- noise = noise_new * 0.5 + noise * 0.5
- x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-
- model_output = self.apply_model(x_noisy, t_ori, cond, struct_cond)
-
- loss_dict = {}
- prefix = 'train' if self.training else 'val'
-
- if self.parameterization == "x0":
- target = x_start
- elif self.parameterization == "eps":
- target = noise
- elif self.parameterization == "v":
- target = self.get_v(x_start, noise, t)
- else:
- raise NotImplementedError()
-
- model_output_ = model_output
-
- loss_simple = self.get_loss(model_output_, target, mean=False).mean([1, 2, 3])
- loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
- #P2 weighting
- if self.snr is not None:
- self.snr = self.snr.to(loss_simple.device)
- weight = extract_into_tensor(1 / (self.p2_k + self.snr)**self.p2_gamma, t, target.shape)
- loss_simple = weight * loss_simple
-
- logvar_t = self.logvar[t.cpu()].to(self.device)
- loss = loss_simple / torch.exp(logvar_t) + logvar_t
- # loss = loss_simple / torch.exp(self.logvar) + self.logvar
- if self.learn_logvar:
- loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
- loss_dict.update({'logvar': self.logvar.data.mean()})
-
- loss = self.l_simple_weight * loss.mean()
-
- loss_vlb = self.get_loss(model_output_, target, mean=False).mean(dim=(1, 2, 3))
- loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
- loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
- loss += (self.original_elbo_weight * loss_vlb)
- loss_dict.update({f'{prefix}/loss': loss})
-
- return loss, loss_dict
-
- def p_mean_variance(self, x, c, struct_cond, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
- return_x0=False, score_corrector=None, corrector_kwargs=None, t_replace=None):
- if t_replace is None:
- t_in = t
- else:
- t_in = t_replace
- model_out = self.apply_model(x, t_in, c, struct_cond, return_ids=return_codebook_ids)
-
- if score_corrector is not None:
- assert self.parameterization == "eps"
- model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
- if return_codebook_ids:
- model_out, logits = model_out
-
- if self.parameterization == "eps":
- x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
- elif self.parameterization == "x0":
- x_recon = model_out
- elif self.parameterization == "v":
- x_recon = self.predict_start_from_z_and_v(x, model_out, t)
- else:
- raise NotImplementedError()
-
- if clip_denoised:
- x_recon.clamp_(-1., 1.)
- if quantize_denoised:
- x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
- model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
- if return_codebook_ids:
- return model_mean, posterior_variance, posterior_log_variance, logits
- elif return_x0:
- return model_mean, posterior_variance, posterior_log_variance, x_recon
- else:
- return model_mean, posterior_variance, posterior_log_variance
-
- def p_mean_variance_canvas(self, x, c, struct_cond, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
- return_x0=False, score_corrector=None, corrector_kwargs=None, t_replace=None, tile_size=64, tile_overlap=32, batch_size=4, tile_weights=None):
- """
- Aggregation Sampling strategy for arbitrary-size image super-resolution
- """
- assert tile_weights is not None
-
- if t_replace is None:
- t_in = t
- else:
- t_in = t_replace
-
- _, _, h, w = x.size()
-
- grid_rows = 0
- cur_x = 0
- while cur_x < x.size(-1):
- cur_x = max(grid_rows * tile_size-tile_overlap * grid_rows, 0)+tile_size
- grid_rows += 1
-
- grid_cols = 0
- cur_y = 0
- while cur_y < x.size(-2):
- cur_y = max(grid_cols * tile_size-tile_overlap * grid_cols, 0)+tile_size
- grid_cols += 1
-
- input_list = []
- cond_list = []
- noise_preds = []
- for row in range(grid_rows):
- noise_preds_row = []
- for col in range(grid_cols):
- if col < grid_cols-1 or row < grid_rows-1:
- # extract tile from input image
- ofs_x = max(row * tile_size-tile_overlap * row, 0)
- ofs_y = max(col * tile_size-tile_overlap * col, 0)
- # input tile area on total image
- if row == grid_rows-1:
- ofs_x = w - tile_size
- if col == grid_cols-1:
- ofs_y = h - tile_size
-
- input_start_x = ofs_x
- input_end_x = ofs_x + tile_size
- input_start_y = ofs_y
- input_end_y = ofs_y + tile_size
-
- # print('input_start_x', input_start_x)
- # print('input_end_x', input_end_x)
- # print('input_start_y', input_start_y)
- # print('input_end_y', input_end_y)
-
- # input tile dimensions
- input_tile_width = input_end_x - input_start_x
- input_tile_height = input_end_y - input_start_y
- input_tile = x[:, :, input_start_y:input_end_y, input_start_x:input_end_x]
- input_list.append(input_tile)
- cond_tile = struct_cond[:, :, input_start_y:input_end_y, input_start_x:input_end_x]
- cond_list.append(cond_tile)
-
- if len(input_list) == batch_size or col == grid_cols-1:
- input_list = torch.cat(input_list, dim=0)
- cond_list = torch.cat(cond_list, dim=0)
-
- struct_cond_input = self.structcond_stage_model(cond_list, t_in[:input_list.size(0)])
- model_out = self.apply_model(input_list, t_in[:input_list.size(0)], c[:input_list.size(0)], struct_cond_input, return_ids=return_codebook_ids)
-
- if score_corrector is not None:
- assert self.parameterization == "eps"
- model_out = score_corrector.modify_score(self, model_out, input_list, t[:input_list.size(0)], c[:input_list.size(0)], **corrector_kwargs)
-
- if return_codebook_ids:
- model_out, logits = model_out
-
- for sample_i in range(model_out.size(0)):
- noise_preds_row.append(model_out[sample_i].unsqueeze(0))
- input_list = []
- cond_list = []
-
- noise_preds.append(noise_preds_row)
-
- # Stitch noise predictions for all tiles
- noise_pred = torch.zeros(x.shape, device=x.device)
- contributors = torch.zeros(x.shape, device=x.device)
- # Add each tile contribution to overall latents
- for row in range(grid_rows):
- for col in range(grid_cols):
- if col < grid_cols-1 or row < grid_rows-1:
- # extract tile from input image
- ofs_x = max(row * tile_size-tile_overlap * row, 0)
- ofs_y = max(col * tile_size-tile_overlap * col, 0)
- # input tile area on total image
- if row == grid_rows-1:
- ofs_x = w - tile_size
- if col == grid_cols-1:
- ofs_y = h - tile_size
-
- input_start_x = ofs_x
- input_end_x = ofs_x + tile_size
- input_start_y = ofs_y
- input_end_y = ofs_y + tile_size
- # print(noise_preds[row][col].size())
- # print(tile_weights.size())
- # print(noise_pred.size())
- noise_pred[:, :, input_start_y:input_end_y, input_start_x:input_end_x] += noise_preds[row][col] * tile_weights
- contributors[:, :, input_start_y:input_end_y, input_start_x:input_end_x] += tile_weights
- # Average overlapping areas with more than 1 contributor
- noise_pred /= contributors
- # noise_pred /= torch.sqrt(contributors)
- model_out = noise_pred
-
- if self.parameterization == "eps":
- x_recon = self.predict_start_from_noise(x, t=t[:model_out.size(0)], noise=model_out)
- elif self.parameterization == "x0":
- x_recon = model_out
- elif self.parameterization == "v":
- x_recon = self.predict_start_from_z_and_v(x, model_out, t[:model_out.size(0)])
- else:
- raise NotImplementedError()
-
- if clip_denoised:
- x_recon.clamp_(-1., 1.)
- if quantize_denoised:
- x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-
- model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t[:x_recon.size(0)])
- if return_codebook_ids:
- return model_mean, posterior_variance, posterior_log_variance, logits
- elif return_x0:
- return model_mean, posterior_variance, posterior_log_variance, x_recon
- else:
- return model_mean, posterior_variance, posterior_log_variance
-
- @torch.no_grad()
- def p_sample(self, x, c, struct_cond, t, clip_denoised=False, repeat_noise=False,
- return_codebook_ids=False, quantize_denoised=False, return_x0=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, t_replace=None):
- b, *_, device = *x.shape, x.device
- outputs = self.p_mean_variance(x=x, c=c, struct_cond=struct_cond, t=t, clip_denoised=clip_denoised,
- return_codebook_ids=return_codebook_ids,
- quantize_denoised=quantize_denoised,
- return_x0=return_x0,
- score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, t_replace=t_replace)
- if return_codebook_ids:
- raise DeprecationWarning("Support dropped.")
- model_mean, _, model_log_variance, logits = outputs
- elif return_x0:
- model_mean, _, model_log_variance, x0 = outputs
- else:
- model_mean, _, model_log_variance = outputs
-
- noise = noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
- noise = torch.nn.functional.dropout(noise, p=noise_dropout)
- # no noise when t == 0
- nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
- if return_codebook_ids:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
- if return_x0:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
- else:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
- @torch.no_grad()
- def p_sample_canvas(self, x, c, struct_cond, t, clip_denoised=False, repeat_noise=False,
- return_codebook_ids=False, quantize_denoised=False, return_x0=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, t_replace=None,
- tile_size=64, tile_overlap=32, batch_size=4, tile_weights=None):
- b, *_, device = *x.shape, x.device
- outputs = self.p_mean_variance_canvas(x=x, c=c, struct_cond=struct_cond, t=t, clip_denoised=clip_denoised,
- return_codebook_ids=return_codebook_ids,
- quantize_denoised=quantize_denoised,
- return_x0=return_x0,
- score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, t_replace=t_replace,
- tile_size=tile_size, tile_overlap=tile_overlap, batch_size=batch_size, tile_weights=tile_weights)
- if return_codebook_ids:
- raise DeprecationWarning("Support dropped.")
- model_mean, _, model_log_variance, logits = outputs
- elif return_x0:
- model_mean, _, model_log_variance, x0 = outputs
- else:
- model_mean, _, model_log_variance = outputs
-
- noise = noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
- noise = torch.nn.functional.dropout(noise, p=noise_dropout)
- # no noise when t == 0
- nonzero_mask = (1 - (t[:b] == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
- if return_codebook_ids:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
- if return_x0:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
- else:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
- @torch.no_grad()
- def progressive_denoising(self, cond, struct_cond, shape, verbose=True, callback=None, quantize_denoised=False,
- img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
- score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
- log_every_t=None):
- if not log_every_t:
- log_every_t = self.log_every_t
- timesteps = self.num_timesteps
- if batch_size is not None:
- b = batch_size if batch_size is not None else shape[0]
- shape = [batch_size] + list(shape)
- else:
- b = batch_size = shape[0]
- if x_T is None:
- img = torch.randn(shape, device=self.device)
- else:
- img = x_T
- intermediates = []
- if cond is not None:
- if isinstance(cond, dict):
- cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
- list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
- else:
- cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
- if start_T is not None:
- timesteps = min(timesteps, start_T)
- iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
- total=timesteps) if verbose else reversed(
- range(0, timesteps))
- if type(temperature) == float:
- temperature = [temperature] * timesteps
-
- for i in iterator:
- ts = torch.full((b,), i, device=self.device, dtype=torch.long)
- if self.shorten_cond_schedule:
- assert self.model.conditioning_key != 'hybrid'
- tc = self.cond_ids[ts].to(cond.device)
- cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
- img, x0_partial = self.p_sample(img, cond, struct_cond, ts,
- clip_denoised=self.clip_denoised,
- quantize_denoised=quantize_denoised, return_x0=True,
- temperature=temperature[i], noise_dropout=noise_dropout,
- score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
- if mask is not None:
- assert x0 is not None
- img_orig = self.q_sample(x0, ts)
- img = img_orig * mask + (1. - mask) * img
-
- if i % log_every_t == 0 or i == timesteps - 1:
- intermediates.append(x0_partial)
- if callback: callback(i)
- if img_callback: img_callback(img, i)
- return img, intermediates
-
- @torch.no_grad()
- def p_sample_loop(self, cond, struct_cond, shape, return_intermediates=False,
- x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, start_T=None,
- log_every_t=None, time_replace=None, adain_fea=None, interfea_path=None):
-
- if not log_every_t:
- log_every_t = self.log_every_t
- device = self.betas.device
- b = shape[0]
- if x_T is None:
- img = torch.randn(shape, device=device)
- else:
- img = x_T
-
- intermediates = [img]
- if timesteps is None:
- timesteps = self.num_timesteps
-
- iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
- range(0, timesteps))
-
- if mask is not None:
- assert x0 is not None
- assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
-
- batch_list = []
- for i in iterator:
- if time_replace is None or time_replace == 1000:
- ts = torch.full((b,), i, device=device, dtype=torch.long)
- t_replace=None
- else:
- ts = torch.full((b,), i, device=device, dtype=torch.long)
- t_replace = repeat(torch.tensor([self.ori_timesteps[i]]), '1 -> b', b=img.size(0))
- t_replace = t_replace.long().to(device)
- if self.shorten_cond_schedule:
- assert self.model.conditioning_key != 'hybrid'
- tc = self.cond_ids[ts].to(cond.device)
- cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
- if t_replace is not None:
- if start_T is not None:
- if self.ori_timesteps[i] > start_T:
- continue
- struct_cond_input = self.structcond_stage_model(struct_cond, t_replace)
- else:
- if start_T is not None:
- if i > start_T:
- continue
- struct_cond_input = self.structcond_stage_model(struct_cond, ts)
-
- if interfea_path is not None:
- batch_list.append(struct_cond_input)
-
- img = self.p_sample(img, cond, struct_cond_input, ts,
- clip_denoised=self.clip_denoised,
- quantize_denoised=quantize_denoised, t_replace=t_replace)
-
- if adain_fea is not None:
- if i < 1:
- img = adaptive_instance_normalization(img, adain_fea)
- if mask is not None:
- img_orig = self.q_sample(x0, ts)
- img = img_orig * mask + (1. - mask) * img
-
- if i % log_every_t == 0 or i == timesteps - 1:
- intermediates.append(img)
- if callback: callback(i)
- if img_callback: img_callback(img, i)
- if len(batch_list) > 0:
- num_batch = batch_list[0]['64'].size(0)
- for batch_i in range(num_batch):
- batch64_list = []
- batch32_list = []
- for num_i in range(len(batch_list)):
- batch64_list.append(cal_pca_components(batch_list[num_i]['64'][batch_i], 3))
- batch32_list.append(cal_pca_components(batch_list[num_i]['32'][batch_i], 3))
- batch64_list = np.array(batch64_list)
- batch32_list = np.array(batch32_list)
-
- batch64_list = batch64_list - np.min(batch64_list)
- batch64_list = batch64_list / np.max(batch64_list)
- batch32_list = batch32_list - np.min(batch32_list)
- batch32_list = batch32_list / np.max(batch32_list)
-
- total_num = batch64_list.shape[0]
-
- for index in range(total_num):
- os.makedirs(os.path.join(interfea_path, 'fea_'+str(batch_i)+'_64'), exist_ok=True)
- cur_path = os.path.join(interfea_path, 'fea_'+str(batch_i)+'_64', 'step_'+str(total_num-index)+'.png')
- visualize_fea(cur_path, batch64_list[index])
- os.makedirs(os.path.join(interfea_path, 'fea_'+str(batch_i)+'_32'), exist_ok=True)
- cur_path = os.path.join(interfea_path, 'fea_'+str(batch_i)+'_32', 'step_'+str(total_num-index)+'.png')
- visualize_fea(cur_path, batch32_list[index])
-
- if return_intermediates:
- return img, intermediates
- return img
-
- def _gaussian_weights(self, tile_width, tile_height, nbatches):
- """Generates a gaussian mask of weights for tile contributions"""
- from numpy import pi, exp, sqrt
- import numpy as np
-
- latent_width = tile_width
- latent_height = tile_height
-
- var = 0.01
- midpoint = (latent_width - 1) / 2 # -1 because index goes from 0 to latent_width - 1
- x_probs = [exp(-(x-midpoint)*(x-midpoint)/(latent_width*latent_width)/(2*var)) / sqrt(2*pi*var) for x in range(latent_width)]
- midpoint = latent_height / 2
- y_probs = [exp(-(y-midpoint)*(y-midpoint)/(latent_height*latent_height)/(2*var)) / sqrt(2*pi*var) for y in range(latent_height)]
-
- weights = np.outer(y_probs, x_probs)
- return torch.tile(torch.tensor(weights, device=self.betas.device), (nbatches, self.configs.model.params.channels, 1, 1))
-
- @torch.no_grad()
- def p_sample_loop_canvas(self, cond, struct_cond, shape, return_intermediates=False,
- x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, start_T=None,
- log_every_t=None, time_replace=None, adain_fea=None, interfea_path=None, tile_size=64, tile_overlap=32, batch_size=4):
-
- assert tile_size is not None
-
- if not log_every_t:
- log_every_t = self.log_every_t
- device = self.betas.device
- b = batch_size
- if x_T is None:
- img = torch.randn(shape, device=device)
- else:
- img = x_T
-
- intermediates = [img]
- if timesteps is None:
- timesteps = self.num_timesteps
-
- if start_T is not None:
- timesteps = min(timesteps, start_T)
- iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
- range(0, timesteps))
-
- if mask is not None:
- assert x0 is not None
- assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
-
- tile_weights = self._gaussian_weights(tile_size, tile_size, 1)
-
- for i in iterator:
- if time_replace is None or time_replace == 1000:
- ts = torch.full((b,), i, device=device, dtype=torch.long)
- t_replace=None
- else:
- ts = torch.full((b,), i, device=device, dtype=torch.long)
- t_replace = repeat(torch.tensor([self.ori_timesteps[i]]), '1 -> b', b=batch_size)
- t_replace = t_replace.long().to(device)
- if self.shorten_cond_schedule:
- assert self.model.conditioning_key != 'hybrid'
- tc = self.cond_ids[ts].to(cond.device)
- cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
- if interfea_path is not None:
- for batch_i in range(struct_cond_input['64'].size(0)):
- os.makedirs(os.path.join(interfea_path, 'fea_'+str(batch_i)+'_64'), exist_ok=True)
- cur_path = os.path.join(interfea_path, 'fea_'+str(batch_i)+'_64', 'step_'+str(i)+'.png')
- visualize_fea(cur_path, struct_cond_input['64'][batch_i, 0])
- os.makedirs(os.path.join(interfea_path, 'fea_'+str(batch_i)+'_32'), exist_ok=True)
- cur_path = os.path.join(interfea_path, 'fea_'+str(batch_i)+'_32', 'step_'+str(i)+'.png')
- visualize_fea(cur_path, struct_cond_input['32'][batch_i, 0])
-
- img = self.p_sample_canvas(img, cond, struct_cond, ts,
- clip_denoised=self.clip_denoised,
- quantize_denoised=quantize_denoised, t_replace=t_replace,
- tile_size=tile_size, tile_overlap=tile_overlap, batch_size=batch_size, tile_weights=tile_weights)
-
- if adain_fea is not None:
- if i < 1:
- img = adaptive_instance_normalization(img, adain_fea)
- if mask is not None:
- img_orig = self.q_sample(x0, ts)
- img = img_orig * mask + (1. - mask) * img
-
- if i % log_every_t == 0 or i == timesteps - 1:
- intermediates.append(img)
- if callback: callback(i)
- if img_callback: img_callback(img, i)
-
- if return_intermediates:
- return img, intermediates
- return img
-
- @torch.no_grad()
- def sample(self, cond, struct_cond, batch_size=16, return_intermediates=False, x_T=None,
- verbose=True, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, shape=None, time_replace=None, adain_fea=None, interfea_path=None, start_T=None, **kwargs):
-
- if shape is None:
- shape = (batch_size, self.channels, self.image_size//8, self.image_size//8)
- if cond is not None:
- if isinstance(cond, dict):
- cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
- list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
- else:
- cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
- return self.p_sample_loop(cond,
- struct_cond,
- shape,
- return_intermediates=return_intermediates, x_T=x_T,
- verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
- mask=mask, x0=x0, time_replace=time_replace, adain_fea=adain_fea, interfea_path=interfea_path, start_T=start_T)
-
- @torch.no_grad()
- def sample_canvas(self, cond, struct_cond, batch_size=16, return_intermediates=False, x_T=None,
- verbose=True, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, shape=None, time_replace=None, adain_fea=None, interfea_path=None, tile_size=64, tile_overlap=32, batch_size_sample=4, log_every_t=None, **kwargs):
-
- if shape is None:
- shape = (batch_size, self.channels, self.image_size//8, self.image_size//8)
- if cond is not None:
- if isinstance(cond, dict):
- cond = {key: cond[key] if not isinstance(cond[key], list) else
- list(map(lambda x: x, cond[key])) for key in cond}
- else:
- cond = [c for c in cond] if isinstance(cond, list) else cond
- return self.p_sample_loop_canvas(cond,
- struct_cond,
- shape,
- return_intermediates=return_intermediates, x_T=x_T,
- verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
- mask=mask, x0=x0, time_replace=time_replace, adain_fea=adain_fea, interfea_path=interfea_path, tile_size=tile_size, tile_overlap=tile_overlap, batch_size=batch_size_sample, log_every_t=log_every_t)
-
- @torch.no_grad()
- def sample_log(self,cond,struct_cond,batch_size,ddim, ddim_steps,**kwargs):
-
- if ddim:
- raise NotImplementedError
- ddim_sampler = DDIMSampler(self)
- shape = (self.channels, self.image_size//8, self.image_size//8)
- samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
- shape,cond,verbose=False,**kwargs)
-
- else:
- samples, intermediates = self.sample(cond=cond, struct_cond=struct_cond, batch_size=batch_size,
- return_intermediates=True,**kwargs)
-
- return samples, intermediates
-
-
- @torch.no_grad()
- def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
- quantize_denoised=True, inpaint=False, plot_denoise_rows=False, plot_progressive_rows=False,
- plot_diffusion_rows=False, **kwargs):
-
- use_ddim = ddim_steps is not None
-
- log = dict()
- z, c_lq, z_gt, x, gt, yrec, xc = self.get_input(batch, self.first_stage_key,
- return_first_stage_outputs=True,
- force_c_encode=True,
- return_original_cond=True,
- bs=N, val=True)
- N = min(x.shape[0], N)
- n_row = min(x.shape[0], n_row)
- if self.test_gt:
- log["gt"] = gt
- else:
- log["inputs"] = x
- log["reconstruction"] = gt
- log["recon_lq"] = self.decode_first_stage(z)
-
- c = self.cond_stage_model(c_lq)
- if self.test_gt:
- struct_cond = z_gt
- else:
- struct_cond = z
-
- if plot_diffusion_rows:
- # get diffusion row
- diffusion_row = list()
- z_start = z[:n_row]
- for t in range(self.num_timesteps):
- if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
- t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
- t = t.to(self.device).long()
- noise = torch.randn_like(z_start)
- z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
- diffusion_row.append(self.decode_first_stage(z_noisy))
-
- diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
- diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
- diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
- diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
- log["diffusion_row"] = diffusion_grid
-
- if sample:
- # get denoise row
- noise = torch.randn_like(z)
- ddim_sampler = DDIMSampler(self)
- with self.ema_scope("Plotting"):
- if self.time_replace is not None:
- cur_time_step=self.time_replace
- else:
- cur_time_step = 1000
-
- samples, z_denoise_row = self.sample(cond=c, struct_cond=struct_cond, batch_size=N, timesteps=cur_time_step, return_intermediates=True, time_replace=self.time_replace)
- x_samples = self.decode_first_stage(samples)
- log["samples"] = x_samples
- if plot_denoise_rows:
- denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
- log["denoise_row"] = denoise_grid
-
- if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
- self.first_stage_model, IdentityFirstStage):
- with self.ema_scope("Plotting Quantized Denoised"):
- samples, z_denoise_row = self.sample_log(cond=c,struct_cond=struct_cond,batch_size=N,ddim=use_ddim,
- ddim_steps=ddim_steps,eta=ddim_eta,
- quantize_denoised=True, x_T=x_T)
- x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_x0_quantized"] = x_samples
-
- if inpaint:
- assert NotImplementedError
- # make a simple center square
- b, h, w = z.shape[0], z.shape[2], z.shape[3]
- mask = torch.ones(N, h, w).to(self.device)
- # zeros will be filled in
- mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
- mask = mask[:, None, ...]
- with self.ema_scope("Plotting Inpaint"):
-
- samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
- ddim_steps=ddim_steps, x0=z[:N], mask=mask)
- x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_inpainting"] = x_samples
- log["mask"] = mask
-
- # outpaint
- with self.ema_scope("Plotting Outpaint"):
- samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
- ddim_steps=ddim_steps, x0=z[:N], mask=mask)
- x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_outpainting"] = x_samples
-
- if plot_progressive_rows:
- with self.ema_scope("Plotting Progressives"):
- img, progressives = self.progressive_denoising(c, struct_cond=struct_cond,
- shape=(self.channels, self.image_size//8, self.image_size//8),
- batch_size=N)
- prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
- log["progressive_row"] = prog_row
-
- if return_keys:
- if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
- return log
- else:
- return {key: log[key] for key in return_keys}
- return log
-
- def configure_optimizers(self):
- lr = self.learning_rate
- params = list(self.model.parameters())
- params = params + list(self.cond_stage_model.parameters())
- params = params + list(self.structcond_stage_model.parameters())
- if self.learn_logvar:
- assert not self.learn_logvar
- print('Diffusion model optimizing logvar')
- params.append(self.logvar)
- opt = torch.optim.AdamW(params, lr=lr)
- if self.use_scheduler:
- assert 'target' in self.scheduler_config
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- }]
- return [opt], scheduler
- return opt
-
- @torch.no_grad()
- def to_rgb(self, x):
- x = x.float()
- if not hasattr(self, "colorize"):
- self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
- x = nn.functional.conv2d(x, weight=self.colorize)
- x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
- return x
-
-class DiffusionWrapper(pl.LightningModule):
- def __init__(self, diff_model_config, conditioning_key):
- super().__init__()
- self.diffusion_model = instantiate_from_config(diff_model_config)
- self.conditioning_key = conditioning_key
- assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
-
- def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, struct_cond=None, seg_cond=None):
- if self.conditioning_key is None:
- out = self.diffusion_model(x, t)
- elif self.conditioning_key == 'concat':
- xc = torch.cat([x] + c_concat, dim=1)
- out = self.diffusion_model(xc, t)
- elif self.conditioning_key == 'crossattn':
- cc = torch.cat(c_crossattn, 1)
- if seg_cond is None:
- out = self.diffusion_model(x, t, context=cc, struct_cond=struct_cond)
- else:
- out = self.diffusion_model(x, t, context=cc, struct_cond=struct_cond, seg_cond=seg_cond)
- elif self.conditioning_key == 'hybrid':
- xc = torch.cat([x] + c_concat, dim=1)
- cc = torch.cat(c_crossattn, 1)
- out = self.diffusion_model(xc, t, context=cc)
- elif self.conditioning_key == 'adm':
- cc = c_crossattn[0]
- out = self.diffusion_model(x, t, y=cc)
- else:
- raise NotImplementedError()
-
- return out
-
-class Layout2ImgDiffusion(LatentDiffusion):
- # TODO: move all layout-specific hacks to this class
- def __init__(self, cond_stage_key, *args, **kwargs):
- assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
- super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
-
- def log_images(self, batch, N=8, *args, **kwargs):
- logs = super().log_images(batch=batch, N=N, *args, **kwargs)
-
- key = 'train' if self.training else 'validation'
- dset = self.trainer.datamodule.datasets[key]
- mapper = dset.conditional_builders[self.cond_stage_key]
-
- bbox_imgs = []
- map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
- for tknzd_bbox in batch[self.cond_stage_key][:N]:
- bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
- bbox_imgs.append(bboximg)
-
- cond_img = torch.stack(bbox_imgs, dim=0)
- logs['bbox_image'] = cond_img
- return logs
diff --git a/spaces/Illumotion/Koboldcpp/common/grammar-parser.h b/spaces/Illumotion/Koboldcpp/common/grammar-parser.h
deleted file mode 100644
index 9037d72728a42ed772f384f3d7ddcef01d0d15f5..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/common/grammar-parser.h
+++ /dev/null
@@ -1,29 +0,0 @@
-// Implements a parser for an extended Backus-Naur form (BNF), producing the
-// binary context-free grammar format specified by llama.h. Supports character
-// ranges, grouping, and repetition operators. As an example, a grammar for
-// arithmetic might look like:
-//
-// root ::= expr
-// expr ::= term ([-+*/] term)*
-// term ::= num | "(" space expr ")" space
-// num ::= [0-9]+ space
-// space ::= [ \t\n]*
-
-#pragma once
-#include "llama.h"
-#include
-#include
-
Conclusion
-
Mod Frozen City is a modified version of the original game Frozen City, which is a city-building simulation game set in an ice and snow apocalypse. In this game, you have to build your own city from scratch, manage your resources, recruit survivors, upgrade your buildings, and survive the harsh weather and enemies. Mod Frozen City offers unlimited money, coins, gems, and other resources, a menu mod that allows you to customize your gameplay settings, no ads, no root required, and more variety and content than the original game.
-
In this article, we have shown you how to download Mod Frozen City for your Android device, how to play it, how to uninstall it, and what are the pros and cons of this modded game. We hope you have found this article useful and informative. If you have any questions or feedback about Mod Frozen City, feel free to leave a comment below. Thank you for reading!
-
FAQ
-
Here are some frequently asked questions about Mod Frozen City that you may want to know:
-
Is Mod Frozen City safe to download and play?
-
Mod Frozen City is safe to download and play, as long as you download it from a reliable source and follow the instructions that we have provided above. However, you should be aware of the potential risks or issues that may arise from using a modded game, such as viruses, malware, bugs, crashes, bans, etc. You should also scan the APK file with an antivirus software before installing it on your device.
-
Is Mod Frozen City compatible with other devices or platforms?
-
Mod Frozen City is compatible with most Android devices that run on Android 4.4 or higher. However, it may not be compatible with some devices or Android versions that have different specifications or features. It may also not be compatible with other platforms, such as iOS, Windows, Mac, etc.
-
Can I play Mod Frozen City offline?
-
Mod Frozen City requires an internet connection to download the APK file and play the game online. You cannot play Mod Frozen City offline, as it needs to connect to the server and sync your data and progress. You also need an internet connection to access some features of the game, such as trading, chatting, ranking, etc.
-
Can I play Mod Frozen City with my friends or other players?
-
Mod Frozen City allows you to play with your friends or other players online. You can join or create a clan with your friends or other players and cooperate or compete with them in various modes and events. You can also chat with them in real-time and share your experience and feedback.
-
Can I update Mod Frozen City to the latest version?
-
Mod Frozen City may not be updated regularly or frequently, as it is a modified version of the original game. The game may not have the latest features or bug fixes that are available in the original game. However, you can check for updates from the source that you downloaded the APK file from or from this link: Mod Frozen City Update. You can also follow us on our social media channels to get notified of any updates.
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/sqc1729/bingi/src/lib/bots/bing/utils.ts b/spaces/sqc1729/bingi/src/lib/bots/bing/utils.ts
deleted file mode 100644
index 64b4b96452d125346b0fc4436b5f7c18c962df0b..0000000000000000000000000000000000000000
--- a/spaces/sqc1729/bingi/src/lib/bots/bing/utils.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-import { ChatResponseMessage, BingChatResponse } from './types'
-
-export function convertMessageToMarkdown(message: ChatResponseMessage): string {
- if (message.messageType === 'InternalSearchQuery') {
- return message.text
- }
- for (const card of message.adaptiveCards??[]) {
- for (const block of card.body) {
- if (block.type === 'TextBlock') {
- return block.text
- }
- }
- }
- return ''
-}
-
-const RecordSeparator = String.fromCharCode(30)
-
-export const websocketUtils = {
- packMessage(data: any) {
- return `${JSON.stringify(data)}${RecordSeparator}`
- },
- unpackMessage(data: string | ArrayBuffer | Blob) {
- if (!data) return {}
- return data
- .toString()
- .split(RecordSeparator)
- .filter(Boolean)
- .map((s) => {
- try {
- return JSON.parse(s)
- } catch (e) {
- return {}
- }
- })
- },
-}
-
-export async function createImage(prompt: string, id: string, headers: HeadersInit): Promise {
- const { headers: responseHeaders } = await fetch(`https://www.bing.com/images/create?partner=sydney&re=1&showselective=1&sude=1&kseed=7000&SFX=&q=${encodeURIComponent(prompt)}&iframeid=${id}`,
- {
- method: 'HEAD',
- headers,
- redirect: 'manual'
- },
- );
-
- if (!/&id=([^&]+)$/.test(responseHeaders.get('location') || '')) {
- throw new Error('请求异常，请检查 cookie 是否有效')
- }
-
- const resultId = RegExp.$1;
- let count = 0
- const imageThumbUrl = `https://www.bing.com/images/create/async/results/${resultId}?q=${encodeURIComponent(prompt)}&partner=sydney&showselective=1&IID=images.as`;
-
- do {
- await sleep(3000);
- const content = await fetch(imageThumbUrl, { headers, method: 'GET' })
-
- // @ts-ignore
- if (content.headers.get('content-length') > 1) {
- const text = await content.text()
- return (text?.match(/ target?.split('src="').pop()?.replace(/&/g, '&'))
- .map(img => ``).join(' ')
- }
- } while(count ++ < 10);
-}
-
-
-export async function* streamAsyncIterable(stream: ReadableStream) {
- const reader = stream.getReader()
- try {
- while (true) {
- const { done, value } = await reader.read()
- if (done) {
- return
- }
- yield value
- }
- } finally {
- reader.releaseLock()
- }
-}
-
-export const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms))
-
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/roberta/wsc/wsc_task.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/roberta/wsc/wsc_task.py
deleted file mode 100644
index 602ea737ed75a33fddf44dd859e999ecfce2730d..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/roberta/wsc/wsc_task.py
+++ /dev/null
@@ -1,401 +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 json
-import os
-import tempfile
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.data import (
- Dictionary,
- IdDataset,
- ListDataset,
- NestedDictionaryDataset,
- NumelDataset,
- NumSamplesDataset,
- PadDataset,
- SortDataset,
- data_utils,
- encoders,
-)
-from fairseq.tasks import LegacyFairseqTask, register_task
-
-from . import wsc_utils
-
-
-@register_task("wsc")
-class WSCTask(LegacyFairseqTask):
- """Task to finetune RoBERTa for Winograd Schemas."""
-
- @staticmethod
- def add_args(parser):
- """Add task-specific arguments to the parser."""
- parser.add_argument(
- "data", metavar="DIR", help="path to data directory; we load .jsonl"
- )
- parser.add_argument(
- "--init-token",
- type=int,
- default=None,
- help="add token at the beginning of each batch item",
- )
-
- def __init__(self, args, vocab):
- super().__init__(args)
- self.vocab = vocab
- self.mask = vocab.add_symbol("")
-
- self.bpe = encoders.build_bpe(args)
- self.tokenizer = encoders.build_tokenizer(args)
-
- # hack to handle GPT-2 BPE, which includes leading spaces
- if args.bpe == "gpt2":
- self.leading_space = True
- self.trailing_space = False
- else:
- self.leading_space = False
- self.trailing_space = True
-
- @classmethod
- def load_dictionary(cls, filename):
- """Load the dictionary from the filename
-
- Args:
- filename (str): the filename
- """
- dictionary = Dictionary.load(filename)
- dictionary.add_symbol("")
- return dictionary
-
- @classmethod
- def setup_task(cls, args, **kwargs):
- assert args.criterion == "wsc", "Must set --criterion=wsc"
-
- # load data and label dictionaries
- vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt"))
- print("| dictionary: {} types".format(len(vocab)))
-
- return cls(args, vocab)
-
- def binarize(self, s: str, append_eos: bool = False):
- if self.tokenizer is not None:
- s = self.tokenizer.encode(s)
- if self.bpe is not None:
- s = self.bpe.encode(s)
- tokens = self.vocab.encode_line(
- s,
- append_eos=append_eos,
- add_if_not_exist=False,
- ).long()
- if self.args.init_token is not None:
- tokens = torch.cat([tokens.new([self.args.init_token]), tokens])
- return tokens
-
- def binarize_with_mask(self, txt, prefix, suffix, leading_space, trailing_space):
- toks = self.binarize(
- prefix + leading_space + txt + trailing_space + suffix,
- append_eos=True,
- )
- mask = torch.zeros_like(toks, dtype=torch.bool)
- mask_start = len(self.binarize(prefix))
- mask_size = len(self.binarize(leading_space + txt))
- mask[mask_start : mask_start + mask_size] = 1
- return toks, mask
-
- def load_dataset(
- self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs
- ):
- """Load a given dataset split.
-
- Args:
- split (str): name of the split (e.g., train, valid, test)
- """
- if data_path is None:
- data_path = os.path.join(self.args.data, split + ".jsonl")
- if not os.path.exists(data_path):
- raise FileNotFoundError("Cannot find data: {}".format(data_path))
-
- query_tokens = []
- query_masks = []
- query_lengths = []
- candidate_tokens = []
- candidate_masks = []
- candidate_lengths = []
- labels = []
-
- for sentence, pronoun_span, query, label in wsc_utils.jsonl_iterator(data_path):
- prefix = sentence[: pronoun_span.start].text
- suffix = sentence[pronoun_span.end :].text_with_ws
-
- # spaCy spans include trailing spaces, but we need to know about
- # leading spaces for the GPT-2 BPE
- leading_space = (
- " " if sentence[: pronoun_span.start].text_with_ws.endswith(" ") else ""
- )
- trailing_space = " " if pronoun_span.text_with_ws.endswith(" ") else ""
-
- # get noun phrases, excluding pronouns and anything overlapping with the query
- cand_spans = wsc_utils.filter_noun_chunks(
- wsc_utils.extended_noun_chunks(sentence),
- exclude_pronouns=True,
- exclude_query=query,
- exact_match=False,
- )
-
- if query is not None:
- query_toks, query_mask = self.binarize_with_mask(
- query, prefix, suffix, leading_space, trailing_space
- )
- query_len = len(query_toks)
- else:
- query_toks, query_mask, query_len = None, None, 0
-
- query_tokens.append(query_toks)
- query_masks.append(query_mask)
- query_lengths.append(query_len)
-
- cand_toks, cand_masks = [], []
- for cand_span in cand_spans:
- toks, mask = self.binarize_with_mask(
- cand_span.text,
- prefix,
- suffix,
- leading_space,
- trailing_space,
- )
- cand_toks.append(toks)
- cand_masks.append(mask)
-
- # collate candidates
- cand_toks = data_utils.collate_tokens(cand_toks, pad_idx=self.vocab.pad())
- cand_masks = data_utils.collate_tokens(cand_masks, pad_idx=0)
- assert cand_toks.size() == cand_masks.size()
-
- candidate_tokens.append(cand_toks)
- candidate_masks.append(cand_masks)
- candidate_lengths.append(cand_toks.size(1))
-
- labels.append(label)
-
- query_lengths = np.array(query_lengths)
- query_tokens = ListDataset(query_tokens, query_lengths)
- query_masks = ListDataset(query_masks, query_lengths)
-
- candidate_lengths = np.array(candidate_lengths)
- candidate_tokens = ListDataset(candidate_tokens, candidate_lengths)
- candidate_masks = ListDataset(candidate_masks, candidate_lengths)
-
- labels = ListDataset(labels, [1] * len(labels))
-
- dataset = {
- "id": IdDataset(),
- "query_tokens": query_tokens,
- "query_masks": query_masks,
- "candidate_tokens": candidate_tokens,
- "candidate_masks": candidate_masks,
- "labels": labels,
- "nsentences": NumSamplesDataset(),
- "ntokens": NumelDataset(query_tokens, reduce=True),
- }
-
- nested_dataset = NestedDictionaryDataset(
- dataset,
- sizes=[query_lengths],
- )
-
- with data_utils.numpy_seed(self.args.seed):
- shuffle = np.random.permutation(len(query_tokens))
- dataset = SortDataset(
- nested_dataset,
- # shuffle
- sort_order=[shuffle],
- )
-
- if return_only:
- return dataset
-
- self.datasets[split] = dataset
- return self.datasets[split]
-
- def build_dataset_for_inference(self, sample_json):
- with tempfile.NamedTemporaryFile(buffering=0) as h:
- h.write((json.dumps(sample_json) + "\n").encode("utf-8"))
- dataset = self.load_dataset(
- "disambiguate_pronoun",
- data_path=h.name,
- return_only=True,
- )
- return dataset
-
- def disambiguate_pronoun(self, model, sentence, use_cuda=False):
- sample_json = wsc_utils.convert_sentence_to_json(sentence)
- dataset = self.build_dataset_for_inference(sample_json)
- sample = dataset.collater([dataset[0]])
- if use_cuda:
- sample = utils.move_to_cuda(sample)
-
- def get_masked_input(tokens, mask):
- masked_tokens = tokens.clone()
- masked_tokens[mask.bool()] = self.mask
- return masked_tokens
-
- def get_lprobs(tokens, mask):
- logits, _ = model(src_tokens=get_masked_input(tokens, mask))
- lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float)
- scores = lprobs.gather(2, tokens.unsqueeze(-1)).squeeze(-1)
- mask = mask.type_as(scores)
- scores = (scores * mask).sum(dim=-1) / mask.sum(dim=-1)
- return scores
-
- cand_lprobs = get_lprobs(
- sample["candidate_tokens"][0],
- sample["candidate_masks"][0],
- )
- if sample["query_tokens"][0] is not None:
- query_lprobs = get_lprobs(
- sample["query_tokens"][0].unsqueeze(0),
- sample["query_masks"][0].unsqueeze(0),
- )
- return (query_lprobs >= cand_lprobs).all().item() == 1
- else:
- best_idx = cand_lprobs.argmax().item()
- full_cand = sample["candidate_tokens"][0][best_idx]
- mask = sample["candidate_masks"][0][best_idx]
- toks = full_cand[mask.bool()]
- return self.bpe.decode(self.source_dictionary.string(toks)).strip()
-
- @property
- def source_dictionary(self):
- return self.vocab
-
- @property
- def target_dictionary(self):
- return self.vocab
-
-
-@register_task("winogrande")
-class WinograndeTask(WSCTask):
- """
- Task for WinoGrande dataset. Efficient implementation for Winograd schema
- tasks with exactly two candidates, one of which is correct.
- """
-
- @classmethod
- def setup_task(cls, args, **kwargs):
- assert args.criterion == "winogrande", "Must set --criterion=winogrande"
-
- # load data and label dictionaries
- vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt"))
- print("| dictionary: {} types".format(len(vocab)))
-
- return cls(args, vocab)
-
- def load_dataset(
- self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs
- ):
- """Load a given dataset split.
-
- Args:
- split (str): name of the split (e.g., train, valid, test)
- """
- if data_path is None:
- data_path = os.path.join(self.args.data, split + ".jsonl")
- if not os.path.exists(data_path):
- raise FileNotFoundError("Cannot find data: {}".format(data_path))
-
- query_tokens = []
- query_masks = []
- query_lengths = []
- candidate_tokens = []
- candidate_masks = []
- candidate_lengths = []
-
- itr = wsc_utils.winogrande_jsonl_iterator(data_path, eval=(split == "test"))
-
- for sample in itr:
- sentence, pronoun_span, query, cand_text = sample
- prefix = sentence[: pronoun_span[0]].rstrip()
- suffix = sentence[pronoun_span[1] :]
-
- leading_space = " " if sentence[: pronoun_span[0]].endswith(" ") else ""
- trailing_space = ""
-
- if query is not None:
- query_toks, query_mask = self.binarize_with_mask(
- query,
- prefix,
- suffix,
- leading_space,
- trailing_space,
- )
- query_len = len(query_toks)
- else:
- query_toks, query_mask, query_len = None, None, 0
-
- query_tokens.append(query_toks)
- query_masks.append(query_mask)
- query_lengths.append(query_len)
-
- cand_toks, cand_mask = self.binarize_with_mask(
- cand_text,
- prefix,
- suffix,
- leading_space,
- trailing_space,
- )
-
- candidate_tokens.append(cand_toks)
- candidate_masks.append(cand_mask)
- candidate_lengths.append(cand_toks.size(0))
-
- query_lengths = np.array(query_lengths)
-
- def get_pad_dataset_fn(tokens, length, pad_idx):
- return PadDataset(
- ListDataset(tokens, length),
- pad_idx=pad_idx,
- left_pad=False,
- )
-
- query_tokens = get_pad_dataset_fn(query_tokens, query_lengths, self.vocab.pad())
- query_masks = get_pad_dataset_fn(query_masks, query_lengths, 0)
-
- candidate_lengths = np.array(candidate_lengths)
- candidate_tokens = get_pad_dataset_fn(
- candidate_tokens, candidate_lengths, self.vocab.pad()
- )
- candidate_masks = get_pad_dataset_fn(candidate_masks, candidate_lengths, 0)
-
- dataset = {
- "id": IdDataset(),
- "query_tokens": query_tokens,
- "query_masks": query_masks,
- "candidate_tokens": candidate_tokens,
- "candidate_masks": candidate_masks,
- "nsentences": NumSamplesDataset(),
- "ntokens": NumelDataset(query_tokens, reduce=True),
- }
-
- nested_dataset = NestedDictionaryDataset(
- dataset,
- sizes=[query_lengths],
- )
-
- with data_utils.numpy_seed(self.args.seed):
- shuffle = np.random.permutation(len(query_tokens))
- dataset = SortDataset(
- nested_dataset,
- # shuffle
- sort_order=[shuffle],
- )
-
- if return_only:
- return dataset
-
- self.datasets[split] = dataset
- return self.datasets[split]
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py
deleted file mode 100644
index 62bfef745c30a56f7b6605d9e3becfbc40edb50d..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py
+++ /dev/null
@@ -1,65 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-import re
-
-
-valid_symbols = [
- 'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
- 'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
- 'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY',
- 'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1',
- 'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0',
- 'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW',
- 'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH'
-]
-
-_valid_symbol_set = set(valid_symbols)
-
-
-class CMUDict:
- '''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict'''
- def __init__(self, file_or_path, keep_ambiguous=True):
- if isinstance(file_or_path, str):
- with open(file_or_path, encoding='latin-1') as f:
- entries = _parse_cmudict(f)
- else:
- entries = _parse_cmudict(file_or_path)
- if not keep_ambiguous:
- entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
- self._entries = entries
-
-
- def __len__(self):
- return len(self._entries)
-
-
- def lookup(self, word):
- '''Returns list of ARPAbet pronunciations of the given word.'''
- return self._entries.get(word.upper())
-
-
-
-_alt_re = re.compile(r'\([0-9]+\)')
-
-
-def _parse_cmudict(file):
- cmudict = {}
- for line in file:
- if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"):
- parts = line.split(' ')
- word = re.sub(_alt_re, '', parts[0])
- pronunciation = _get_pronunciation(parts[1])
- if pronunciation:
- if word in cmudict:
- cmudict[word].append(pronunciation)
- else:
- cmudict[word] = [pronunciation]
- return cmudict
-
-
-def _get_pronunciation(s):
- parts = s.strip().split(' ')
- for part in parts:
- if part not in _valid_symbol_set:
- return None
- return ' '.join(parts)
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/prepare_text.sh b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/prepare_text.sh
deleted file mode 100644
index 1caf13cb6a2a0bd84e5322c92124b2fa37368f9a..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/prepare_text.sh
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/usr/bin/env zsh
-# 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.
-
-lg=$1
-text_path=$2
-target_dir=$3
-min_phones=$4
-phonemizer=$5
-lid_path=$6
-
-if [ -z "$lid_path" ]; then
- lid_path="lid.187.bin"
-fi
-
-ph_lg=${lg:l}
-if test "$lg" = 'fr'; then
- ph_lg='fr-fr'
-elif test "$lg" = 'en'; then
- ph_lg='en-us'
-elif test "$lg" = 'pt'; then
- ph_lg='pt-br'
-fi
-
-ESPEAK_PATH=''
-if test "$phonemizer" = 'espeak'; then
- ESPEAK_PATH=$(which espeak)
-elif test "$phonemizer" = 'espeak-ng'; then
- ESPEAK_PATH=$(which espeak-ng)
-elif test "$phonemizer" = 'G2P'; then
- ESPEAK_PATH=''
-else
- echo "Unknown phonemizer $phonemizer. Valid options are espeak, espean-ng and G2P"
- exit 1
-fi
-
-echo $lg
-echo $ph_lg
-echo $text_path
-echo $target_dir
-echo "min phone seen threshold is $min_phones"
-
-mkdir -p $target_dir
-python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py --lang $lg --fasttext-model $lid_path < $text_path | grep -v '\-\-\-' >! $target_dir/lm.upper.lid.txt
-python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/lm.upper.lid.txt --only-source --destdir $target_dir --thresholdsrc 2 --padding-factor 1 --dict-only
-cut -f1 -d' ' $target_dir/dict.txt | grep -v -x '[[:punct:]]*' | grep -Pv '\d\d\d\d\d+' >! $target_dir/words.txt
-
-
-if [ -z "$ESPEAK_PATH" ]; then
- python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py --compact < $target_dir/words.txt > $target_dir/phones.txt
-else
- # echoing 1 into corpus will prevent the mismatch lines between lexicon and phones in case the phonemizer fails
- one=$(echo "1" | PHONEMIZER_ESPEAK_PATH=$ESPEAK_PATH phonemize -p ' ' -w '' -l $ph_lg --language-switch remove-flags)
- sed 's/$/ 1/' $target_dir/words.txt | PHONEMIZER_ESPEAK_PATH=$ESPEAK_PATH phonemize -o $target_dir/phones.txt -p ' ' -w '' -l $ph_lg -j 70 --language-switch remove-flags
- echo "one is ${one}"
- sed -i "s/${one}$//" $target_dir/phones.txt
-fi
-
-paste $target_dir/words.txt $target_dir/phones.txt >! $target_dir/lexicon.lst
-
-python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/phones.txt --only-source --destdir $target_dir/phones --thresholdsrc $min_phones --padding-factor 1 --dict-only
-
-python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/filter_lexicon.py -d $target_dir/phones/dict.txt < $target_dir/lexicon.lst >! $target_dir/lexicon_filtered.lst
-python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py -s 0.25 --surround --lexicon $target_dir/lexicon_filtered.lst < $target_dir/lm.upper.lid.txt >! $target_dir/phones/lm.phones.filtered.txt
-cp $target_dir/phones/dict.txt $target_dir/phones/dict.phn.txt
-echo " 0" >> $target_dir/phones/dict.phn.txt
-python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/phones/lm.phones.filtered.txt --workers 70 --only-source --destdir $target_dir/phones --srcdict $target_dir/phones/dict.phn.txt
-
-$KENLM_ROOT/lmplz -o 4 < $target_dir/lm.upper.lid.txt --discount_fallback --prune 0 0 0 3 >! $target_dir/kenlm.wrd.o40003.arpa
-$KENLM_ROOT/build_binary $target_dir/kenlm.wrd.o40003.arpa $target_dir/kenlm.wrd.o40003.bin
-
-lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_words_sil lm_arpa=$target_dir/kenlm.wrd.o40003.arpa wav2letter_lexicon=$target_dir/lexicon_filtered.lst data_dir=$target_dir/phones in_labels=phn "blank_symbol=''"
-lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_words lm_arpa=$target_dir/kenlm.wrd.o40003.arpa wav2letter_lexicon=$target_dir/lexicon_filtered.lst data_dir=$target_dir/phones in_labels=phn
-
-$KENLM_ROOT/lmplz -o 4 < $target_dir/phones/lm.phones.filtered.txt --discount_fallback >! $target_dir/phones/lm.phones.filtered.04.arpa
-$KENLM_ROOT/build_binary $target_dir/phones/lm.phones.filtered.04.arpa $target_dir/phones/lm.phones.filtered.04.bin
-$KENLM_ROOT/lmplz -o 6 < $target_dir/phones/lm.phones.filtered.txt --discount_fallback >! $target_dir/phones/lm.phones.filtered.06.arpa
-$KENLM_ROOT/build_binary $target_dir/phones/lm.phones.filtered.06.arpa $target_dir/phones/lm.phones.filtered.06.bin
-
-lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_phn_sil lm_arpa=$target_dir/phones/lm.phones.filtered.06.arpa data_dir=$target_dir/phones in_labels=phn "blank_symbol=''"
diff --git a/spaces/stanfordnlp/Backpack-Demo/senses/use_senses.py b/spaces/stanfordnlp/Backpack-Demo/senses/use_senses.py
deleted file mode 100644
index bcb6fd0ce9a6acd70d242662c6f677e0fa87a4cc..0000000000000000000000000000000000000000
--- a/spaces/stanfordnlp/Backpack-Demo/senses/use_senses.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Visualize some sense vectors"""
-
-import torch
-import argparse
-
-import transformers
-
-def visualize_word(word, tokenizer, vecs, lm_head, count=20, contents=None):
- """
- Prints out the top-scoring words (and lowest-scoring words) for each sense.
-
- """
- if contents is None:
- print(word)
- token_id = tokenizer(word)['input_ids'][0]
- contents = vecs[token_id] # torch.Size([16, 768])
-
- for i in range(contents.shape[0]):
- print('~~~~~~~~~~~~~~~~~~~~~~~{}~~~~~~~~~~~~~~~~~~~~~~~~'.format(i))
- logits = contents[i,:] @ lm_head.t() # (vocab,) [768] @ [768, 50257] -> [50257]
- sorted_logits, sorted_indices = torch.sort(logits, descending=True)
- print('~~~Positive~~~')
- for j in range(count):
- print(tokenizer.decode(sorted_indices[j]), '\t','{:.2f}'.format(sorted_logits[j].item()))
- print('~~~Negative~~~')
- for j in range(count):
- print(tokenizer.decode(sorted_indices[-j-1]), '\t','{:.2f}'.format(sorted_logits[-j-1].item()))
- return contents
- print()
- print()
- print()
-
-argp = argparse.ArgumentParser()
-argp.add_argument('vecs_path')
-argp.add_argument('lm_head_path')
-args = argp.parse_args()
-
-# Load tokenizer and parameters
-tokenizer = transformers.AutoTokenizer.from_pretrained('gpt2')
-vecs = torch.load(args.vecs_path)
-lm_head = torch.load(args.lm_head_path)
-
-visualize_word(input('Enter a word:'), tokenizer, vecs, lm_head, count=5)
-
diff --git a/spaces/subhajitmaji/MusicGen/tests/common_utils/__init__.py b/spaces/subhajitmaji/MusicGen/tests/common_utils/__init__.py
deleted file mode 100644
index 74ffcfef96fec35c99b2a1a053a61f44f7a8bbe9..0000000000000000000000000000000000000000
--- a/spaces/subhajitmaji/MusicGen/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/subhc/Guess-What-Moves/mask_former/modeling/__init__.py b/spaces/subhc/Guess-What-Moves/mask_former/modeling/__init__.py
deleted file mode 100644
index 6dfafc0bea5858ddb8de852e0f938282ee970bd3..0000000000000000000000000000000000000000
--- a/spaces/subhc/Guess-What-Moves/mask_former/modeling/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .backbone.swin import D2SwinTransformer
-from .backbone.vit import D2ViTTransformer
-from .heads.mask_former_head import MaskFormerHead
-from .heads.per_pixel_baseline import PerPixelBaselineHead, PerPixelBaselinePlusHead
-from .heads.pixel_decoder import BasePixelDecoder
-from .heads.big_pixel_decoder import BigPixelDecoder
-from .heads.mega_big_pixel_decoder import MegaBigPixelDecoder
-from .heads.mask_former_head_baseline import MaskFormerBaselineHead
diff --git a/spaces/supertori/files/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py b/spaces/supertori/files/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py
deleted file mode 100644
index e912c6402bc80faa797cf2e95183101fb9a10286..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import os
-from modules import paths
-
-
-def preload(parser):
- parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR'))
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Neo Soul Keys Vst Crack Site.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Neo Soul Keys Vst Crack Site.md
deleted file mode 100644
index 839270575e2a8a046d4781b802e82a8e5600d5d4..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Neo Soul Keys Vst Crack Site.md
+++ /dev/null
@@ -1,8 +0,0 @@
-
-
however, neo-soul keys studio 2 is available for all major platforms. so, if youre looking for electric piano sounds, neo-soul keys 2 is the way to go. consequently, neo-soul keys studio 2 is a must have!
on top of that, neo-soul keys studio 2 is also available for all major platforms. so, if youre looking for electric piano sounds, neo-soul keys 2 is the way to go. consequently, neo-soul keys studio 2 is a must have!
-
the sounds of the cp70 are perfectly reproduced in each of the four stages. the stage, stage v1, stage v2, and stage v3 emulations are completely compatible with the stage and stage v1 of the original. plus, the other two stages, stage v1, stage v2, and stage v3, are an improvement of an original version of neo-soul keys. in addition, neo-soul keys 3x represents an improvement of an original version of neo-soul keys. moreover, enjoy these sounds and more in neo soul keys studio. with such great detail, your electrical piano can be very sterile, lifeless, and without personality. in fact, you can also download protastructure suite enterprise 2018 free.
-
in general, otherwise, do not worry our eps can sound as clear and clean as a buffer, but youre always welcome to dial up as much grime as you would like. especially, see what the late-great george duke has said concerning neo-soul keys and why he chose to use it reside, instead of a real piano. in case, we pride ourselves on the intangibles. in addition, neo-soul keys studio are some of the most realistic, natural, and hot sounding electric pianos ever created. the sounds of the cp70 are perfectly reproduced in each of the four stages. the stage, stage v1, stage v2, and stage v3 emulations are completely compatible with the stage and stage v1 of the original. in addition, the other two stages, stage v1, stage v2, and stage v3, are an improvement of an original version of neo-soul keys. for all these reasons, you can also download mytopc handycam pro
899543212b
-
-
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/The Man Masaan Hindi Dubbed 720p UPD.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/The Man Masaan Hindi Dubbed 720p UPD.md
deleted file mode 100644
index ca059cafbce7da89b1cf4d82329ab1cb37263ec0..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/The Man Masaan Hindi Dubbed 720p UPD.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Watch your favourite movies online for free in all languages and genres like Hindi, Tamil, Telugu, Action, Romance and Comedy. HD Quality. Low Data Usage. 4d29de3e1b
-
-
-
diff --git a/spaces/t13718236382/web-ui/404.html b/spaces/t13718236382/web-ui/404.html
deleted file mode 100644
index ffb373d061ee3f950f0952435efd1ee567baa02f..0000000000000000000000000000000000000000
--- a/spaces/t13718236382/web-ui/404.html
+++ /dev/null
@@ -1 +0,0 @@
-404: This page could not be found
404
This page could not be found.
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Chataro Collection- .zip.md b/spaces/terfces0erbo/CollegeProjectV2/Chataro Collection- .zip.md
deleted file mode 100644
index 2163864e9a86150e0bcd556bb44adb8a6d5b83d3..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Chataro Collection- .zip.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- 3cee63e6c2
-
-
-
diff --git a/spaces/thiago-osorio/track-search-engine/README.md b/spaces/thiago-osorio/track-search-engine/README.md
deleted file mode 100644
index 35b8160c556588e05c45b082f8603c0b04193155..0000000000000000000000000000000000000000
--- a/spaces/thiago-osorio/track-search-engine/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Track Search Engine
-emoji: 💻
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.46.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Abakada Unang Hakbang Sa Pagbasa A Classic Book for Filipino Learners.md b/spaces/tialenAdioni/chat-gpt-api/logs/Abakada Unang Hakbang Sa Pagbasa A Classic Book for Filipino Learners.md
deleted file mode 100644
index cce9269c83a9d3c9df907c15275ff19a76eaa1a7..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Abakada Unang Hakbang Sa Pagbasa A Classic Book for Filipino Learners.md
+++ /dev/null
@@ -1,133 +0,0 @@
-
-
Abakada Unang Hakbang Sa Pagbasa Book: A Guide for Filipino Parents and Teachers
-
If you are a parent or a teacher who wants to teach your child or student how to read in Filipino, you might have heard of Abakada Unang Hakbang Sa Pagbasa Book. This book is a popular and effective tool for introducing young learners to the Filipino alphabet and language. But what exactly is this book, and why is it important for Filipino children? In this article, we will answer these questions and more. We will also give you some tips on how to use the book effectively, where to buy it, and what other resources you can use to enhance your child's or student's learning experience.
-
What is Abakada Unang Hakbang Sa Pagbasa Book?
-
Abakada Unang Hakbang Sa Pagbasa Book, which means "Abakada First Step in Reading Book" in English, is a book that teaches children how to read in Filipino using the Abakada alphabet. The Abakada alphabet is a simplified version of the original Baybayin script that was used by Filipinos before the Spanish colonization. It consists of 20 letters that represent the basic sounds of the Filipino language. The book uses colorful illustrations, simple words, and fun activities to help children recognize, pronounce, and write these letters.
The book was first published in 1978 by Liwayway A. Arceo, a renowned Filipino writer and educator. She wrote the book as a response to the need for a standardized and systematic way of teaching Filipino to young learners. She also wanted to preserve and promote the Filipino language and culture among the new generation. The book has since been revised and updated several times to reflect the changes and developments in the Filipino language and society.
-
The features and benefits of the book
-
The book has several features and benefits that make it an ideal choice for parents and teachers who want to teach their children or students how to read in Filipino. Some of these are:
-
-
The book is divided into four parts: Part 1 introduces the vowels (a, e, i, o, u), Part 2 introduces the consonants (b, k, d, g, h, l, m, n, ng, p, r, s, t, w, y), Part 3 introduces the syllables (ba, be, bi, bo, bu), and Part 4 introduces the words (aba, abe, abi).
-
The book uses a progressive and cumulative approach that builds on the previous lessons. Each part starts with a review of the previous letters or syllables before introducing new ones. Each letter or syllable is presented with an illustration that shows its shape and sound. Each word is presented with an illustration that shows its meaning and usage.
-
The book provides various exercises and activities that reinforce learning. These include tracing, copying, matching, coloring, writing, reading aloud, identifying pictures or words that start with or contain a certain letter or syllable.
-
The book also includes songs, poems, stories, games, puzzles, riddles, jokes, proverbs, idioms, trivia, facts, tips, advice that enrich learning. These add fun and variety to the lessons while also exposing children to different aspects of Filipino culture and literature.
-
-
How to use the book effectively
-
To use the book effectively, parents and teachers should follow these steps:
-
-
Read the instructions carefully before starting each lesson. Make sure you understand what each letter or syllable sounds like and how it is written.
-
Show your child or student how to trace or copy each letter or syllable using a pencil or crayon. Encourage them to practice until they can write it correctly.
-
Show your child or student how to pronounce each letter or syllable using an example word or picture. Encourage them to repeat after you until they can say it clearly.
-
Show your child or student how to read each word using an example sentence or picture. Encourage them to read along with you until they can read it fluently.
-
Guide your child or student through each exercise or activity using positive feedback and praise. Help them correct any mistakes they make along the way.
-
Review each lesson regularly using flashcards or quizzes. Reinforce what they have learned by asking them questions or giving them examples.
-
Supplement each lesson with additional materials such as books, videos, songs that use the same letters or syllables. Expose them to different contexts where they can apply what they have learned.
-
-
Why is Abakada Unang Hakbang Sa Pagbasa Book important for Filipino children?
-
Abakada Unang Hakbang Sa Pagbasa Book is not only a useful tool for teaching children how to read in Filipino but also a valuable resource for developing their cognitive, linguistic, and cultural skills. Here are some reasons why this book is important for Filipino children:
-
Abakada book for beginners in reading Filipino
-How to teach Abakada to kids using Unang Hakbang Sa Pagbasa
-Abakada Unang Hakbang Sa Pagbasa review and feedback
-Where to buy Abakada Unang Hakbang Sa Pagbasa book online
-Abakada Unang Hakbang Sa Pagbasa book pdf download
-Abakada Unang Hakbang Sa Pagbasa book price and availability
-Abakada Unang Hakbang Sa Pagbasa book sample pages and contents
-Abakada Unang Hakbang Sa Pagbasa book author and publisher
-Abakada Unang Hakbang Sa Pagbasa book benefits and features
-Abakada Unang Hakbang Sa Pagbasa book vs other Filipino reading books
-Abakada Unang Hakbang Sa Pagbasa book testimonials and ratings
-Abakada Unang Hakbang Sa Pagbasa book exercises and activities
-Abakada Unang Hakbang Sa Pagbasa book level and difficulty
-Abakada Unang Hakbang Sa Pagbasa book edition and year of publication
-Abakada Unang Hakbang Sa Pagbasa book format and size
-Abakada Unang Hakbang Sa Pagbasa book illustrations and design
-Abakada Unang Hakbang Sa Pagbasa book awards and recognition
-Abakada Unang Hakbang Sa Pagbasa book curriculum and standards
-Abakada Unang Hakbang Sa Pagbasa book for homeschooling and tutoring
-Abakada Unang Hakbang Sa Pagbasa book for preschool and kindergarten
-Abakada Unang Hakbang Sa Pagbasa book for grade 1 and grade 2
-Abakada Unang Hakbang Sa Pagbasa book for special education and dyslexia
-Abakada Unang Hakbang Sa Pagbasa book for bilingual and multilingual learners
-Abakada Unang Hakbang Sa Pagbasa book for Filipino heritage and culture
-Abakada Unang Hakbang Sa Pagbasa book for fun and enjoyment
-How to use Abakada Unang Hakbang Sa Pagbasa book effectively
-How to supplement Abakada Unang Hakbang Sa Pagbasa book with other resources
-How to track progress and improvement with Abakada Unang Hakbang Sa Pagbasa book
-How to motivate and encourage children with Abakada Unang Hakbang Sa Pagbasa book
-How to customize and personalize Abakada Unang Hakbang Sa Pagbasa book for different learners
-How to pronounce and write the letters in Abakada Unang Hakbang Sa Pagbasa book
-How to learn the sounds and syllables in Abakada Unang Hakbang Sa Pagbasa book
-How to read the words and sentences in Abakada Unang Hakbang Sa Pagbasa book
-How to understand the meaning and context in Abakada Unang Hakbang Sa Pagbasa book
-How to expand vocabulary and grammar with Abakada Unang Hakbang Sa Pagbasa book
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-How to build confidence and self-esteem with Abakada Unang Hakbang Sa Pagbasa book
-How to inspire love for reading and learning with Abakada Unang Hakbang Sa Pagbasa book
-The history and origin of the Abakada alphabet in the Philippines
-The significance and relevance of the Abakada alphabet in the Filipino language
-The challenges and opportunities of the Abakada alphabet in the modern world
-The comparison and contrast of the Abakada alphabet with other alphabets in the world
-The evolution and development of the Abakada alphabet over time
-The variations and adaptations of the Abakada alphabet in different regions and dialects in the Philippines
-The influence and impact of the Abakada alphabet on Filipino literature, art, music, culture, identity, etc.
-The future and prospects of the Abakada alphabet in the global society
-
The advantages of learning Filipino as a first language
-
Learning Filipino as a first language has many advantages for children's cognitive development. According to research, learning one's native language can enhance one's memory, creativity, problem-solving, and critical thinking skills. It can also improve one's communication, comprehension, and expression skills. Moreover, learning one's native language can foster one's sense of identity, belonging, and pride. It can also help one appreciate one's heritage, traditions, and values.
-
The challenges and opportunities of bilingual education in the Philippines
-
The Philippines is a multilingual country with more than 170 languages spoken by different ethnic groups. The official languages are Filipino (based on Tagalog) and English, which are used as mediums of instruction in schools. However, many children also speak their regional languages at home, such as Cebuano, Ilocano, Hiligaynon, Bicolano, and others. This poses some challenges for bilingual education in terms of curriculum design, teacher training, material development, and assessment methods. However, it also offers some opportunities for promoting linguistic diversity, cultural awareness, and social integration. Abakada Unang Hakbang Sa Pagbasa Book can help address these challenges and opportunities by providing a common foundation for learning Filipino as a national language while also respecting and supporting other regional languages.
-
The role of Abakada Unang Hakbang Sa Pagbasa Book in promoting Filipino culture and identity
-
Abakada Unang Hakbang Sa Pagbasa Book can also play a significant role in promoting Filipino culture and identity among children. The book introduces children to various aspects of Filipino culture such as literature, art, music, history, geography, science, religion, sports, and cuisine. It also exposes them to different values such as respect, honesty, hard work, cooperation, and patriotism. By doing so, the book helps children develop a positive attitude towards their own culture while also appreciating other cultures. It also helps them form a strong sense of identity as Filipinos who are proud of their roots but also open to new ideas.
-
Where can you buy Abakada Unang Hakbang Sa Pagbasa Book?
-
If you are interested in buying Abakada Unang Hakbang Sa Pagbasa Book for your child or student, you have several options available online and offline. Here are some of them:
-
The availability and price of the book online and offline
-
You can buy Abakada Unang Hakbang Sa Pagbasa Book online from various websites such as Lazada, Shopee, Amazon, and others. the seller, the edition, and the condition of the book. You can also avail of discounts, free shipping, and cash on delivery options from some online sellers. You can also buy Abakada Unang Hakbang Sa Pagbasa Book offline from various bookstores such as National Bookstore, Fully Booked, Booksale, and others. The price is similar to the online price, but you might have to pay extra for transportation and handling fees. You can also check the availability and quality of the book before buying it.
-
The reviews and testimonials of the book from customers and experts
-
Abakada Unang Hakbang Sa Pagbasa Book has received many positive reviews and testimonials from customers and experts who have used or recommended the book. Some of these are:
-
-
"I bought this book for my daughter who is in kindergarten. She loves it! She learned how to read in Filipino in just a few weeks. The book is very easy to follow and has many fun activities. She also enjoys the songs and stories in the book. I highly recommend this book to all parents who want to teach their children how to read in Filipino."
-- Maria, a mother from Quezon City
-
-
-
"I use this book as a reference for teaching Filipino to my students who are non-native speakers. The book is very helpful and effective in introducing the basic sounds and words of Filipino. The book also has a lot of cultural information that enriches the learning experience. My students love the book and have improved their Filipino skills a lot."
-- John, a teacher from Cebu
-
-
-
"This book is a classic and a must-have for every Filipino child. The book is not only a tool for teaching how to read in Filipino but also a treasure for preserving and promoting Filipino culture and identity. The book is well-written, well-illustrated, and well-designed. It is suitable for children of different ages and levels."
-- Dr. Jose, a linguist and educator from Manila
-
-
The alternatives and supplements to the book for further learning
-
Abakada Unang Hakbang Sa Pagbasa Book is not the only resource you can use to teach your child or student how to read in Filipino. There are also other alternatives and supplements that you can use to enhance your learning experience. Some of these are:
-
-
Abakada Alphabet Flashcards: These are cards that show each letter of the Abakada alphabet with an illustration and a word that starts with or contains that letter. You can use these cards to review, quiz, or play games with your child or student.
-
Abakada Alphabet Songs: These are songs that sing each letter of the Abakada alphabet with an example word that starts with or contains that letter. You can use these songs to introduce, practice, or reinforce each letter with your child or student.
-
Abakada Alphabet Videos: These are videos that show each letter of the Abakada alphabet with an animation and a narration that explains its shape and sound. You can use these videos to demonstrate, visualize, or listen to each letter with your child or student.
-
Abakada Alphabet Worksheets: These are worksheets that provide various exercises and activities that test your child's or student's knowledge and skills on each letter of the Abakada alphabet. You can use these worksheets to assess, monitor, or challenge your child or student.
-
Abakada Alphabet Books: These are books that feature stories or topics that use words that start with or contain each letter of the Abakada alphabet. You can use these books to expose, expand, or explore your child's or student's vocabulary and comprehension in Filipino.
-
-
Conclusion
-
Abakada Unang Hakbang Sa Pagbasa Book is a great resource for teaching children how to read in Filipino using the Abakada alphabet. The book has a history and purpose that make it relevant and meaningful for Filipino children. The book has features and benefits that make it easy and enjoyable for parents and teachers to use. The book has an importance that make it valuable and essential for children's cognitive, linguistic, and cultural development. The book has an availability and price that make it accessible and affordable for everyone. The book has reviews and testimonials that make it reliable and trustworthy for customers and experts. The book has alternatives and supplements that make it flexible and adaptable for different needs and preferences.
-
If you are looking for a way to teach your child or student how to read in Filipino, you should definitely consider buying Abakada Unang Hakbang Sa Pagbasa Book. It will not only help them learn how to read but also help them appreciate their own language, culture, and identity as Filipinos. So what are you waiting for? Order your copy today!
-
FAQs
-
Here are some frequently asked questions about Abakada Unang Hakbang Sa Pagbasa Book:
-
-
Q: What age group is Abakada Unang Hakbang Sa Pagbasa Book suitable for?
-
A: Abakada Unang Hakbang Sa Pagbasa Book is suitable for children aged 4 to 8 years old who are beginners or intermediate learners of Filipino.
-
Q: How long does it take to finish Abakada Unang Hakbang Sa Pagbasa Book?
-
A: It depends on your child's or student's pace and progress, but generally, it takes about 6 months to finish Abakada Unang Hakbang Sa Pagbasa Book if you follow one lesson per day.
-
Q: How can I contact the author or publisher of Abakada Unang Hakbang Sa Pagbasa Book?
-
A: You can contact the author or publisher of Abakada Unang Hakbang Sa Pagbasa Book by sending an email to abakadabook@gmail.com or calling (02) 123-4567.
-
Q: Where can I find more information about Abakada Unang Hakbang Sa Pagbasa Book?
-
A: You can find more information about Abakada Unang Hakbang Sa Pagbasa Book by visiting its official website at www.abakadabook.com.ph or following its social media accounts at Facebook, Twitter, and Instagram.
-
Q: What other books can I read after finishing Abakada Unang Hakbang Sa Pagbasa Book?
-
A: After finishing Abakada Unang Hakbang Sa Pagbasa Book, you can read other books that teach you more advanced skills in Filipino such as grammar, spelling, writing, and reading comprehension. Some examples are:
-
Abakada Patuloy Na Pagbasa Book: This book teaches you how to read longer words, sentences, and paragraphs in Filipino using different strategies such as syllabication, context clues, and word analysis.
-
Abakada Masining Na Pagsulat Book: This book teaches you how to write different types of texts such as letters, stories, essays, and reports in Filipino using different techniques such as brainstorming, outlining, drafting, and revising.
-
Abakada Matalinong Pag-unawa Book: This book teaches you how to understand different types of texts such as narratives, expositions, arguments, and instructions in Filipino using different skills such as skimming, scanning, summarizing, and evaluating.
-
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/CA ARCserve Backup R16 LuLZiSO.rar Download and Install Guide.md b/spaces/tialenAdioni/chat-gpt-api/logs/CA ARCserve Backup R16 LuLZiSO.rar Download and Install Guide.md
deleted file mode 100644
index f04e165642f0426d0be2bac2b09998dfd2e917c3..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/CA ARCserve Backup R16 LuLZiSO.rar Download and Install Guide.md
+++ /dev/null
@@ -1,127 +0,0 @@
-
-
CA ARCserve Backup R16 LuLZiSO.rar: A Complete Review
-
If you are looking for a reliable and efficient backup solution for your data, you might want to consider CA ARCserve Backup R16 LuLZiSO.rar. This is a compressed file that contains the installation files for CA ARCserve Backup R16, a powerful software that can protect your data from any disaster. In this article, we will review the features, benefits and installation steps of CA ARCserve Backup R16 LuLZiSO.rar.
CA ARCserve Backup R16 LuLZiSO.rar is a file that you can download from the internet for free. It contains the setup files for CA ARCserve Backup R16, a software that can backup and restore your data across different platforms and devices. CA ARCserve Backup R16 is developed by Arcserve, a leading provider of data protection solutions. The software has been tested and verified by LuLZiSO, a group of hackers who crack and distribute software for free.
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What are the features of CA ARCserve Backup R16 LuLZiSO.rar?
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CA ARCserve Backup R16 LuLZiSO.rar has many features that make it a superior backup solution for your data. Some of the features are:
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It supports multiple backup types, such as full, incremental, differential and synthetic.
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It supports multiple backup destinations, such as local disks, network shares, tape drives and cloud storage.
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It supports multiple backup modes, such as online, offline and snapshot.
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CA ARCserve Backup R16 LuLZiSO.rar is not a perfect backup solution for your data. It has some drawbacks that you should be aware of before using it. Some of the drawbacks are:
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You can use a manual backup method like copying or burning your data to an external device. You can backup and restore your data offline with simple steps and tools.
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How to use CA ARCserve Backup R16 LuLZiSO.rar?
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After installing CA ARCserve Backup R16 LuLZiSO.rar, you can use it to backup and restore your data easily and efficiently. Here are some steps to use the software:
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Select the backup source and destination from the left pane.
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Select the backup type, mode, encryption, compression, schedule, notification and recovery options from the right pane.
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Click on the Backup button to start the backup process.
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Monitor the backup progress and status from the dashboard, reports and logs.
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Click on the Restore button to start the restore process.
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Select the backup file and destination from the left pane.
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Select the restore type, mode and options from the right pane.
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Click on the Restore button to start the restore process.
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Monitor the restore progress and status from the dashboard, reports and logs.
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Conclusion
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CA ARCserve Backup R16 LuLZiSO.rar is a file that contains the installation files for CA ARCserve Backup R16, a software that can backup and restore your data across different platforms and devices. It has many features and benefits that make it a superior backup solution for your data. However, it also has some drawbacks that you should be aware of before using it. It is illegal, risky, unsupported and unethical to use CA ARCserve Backup R16 LuLZiSO.rar without a valid license from Arcserve. You might want to consider some alternatives to CA ARCserve Backup R16 LuLZiSO.rar if you are looking for a different backup solution for your data. If you decide to use CA ARCserve Backup R16 LuLZiSO.rar, you can follow some steps to install and use it easily and efficiently. We hope this article has helped you understand more about CA ARCserve Backup R16 LuLZiSO.rar and how to use it for your data protection needs.
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Conclusion
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CA ARCserve Backup R16 LuLZiSO.rar is a file that contains the installation files for CA ARCserve Backup R16, a software that can backup and restore your data across different platforms and devices. It has many features and benefits that make it a superior backup solution for your data. However, it also has some drawbacks that you should be aware of before using it. It is illegal, risky, unsupported and unethical to use CA ARCserve Backup R16 LuLZiSO.rar without a valid license from Arcserve. You might want to consider some alternatives to CA ARCserve Backup R16 LuLZiSO.rar if you are looking for a different backup solution for your data. If you decide to use CA ARCserve Backup R16 LuLZiSO.rar, you can follow some steps to install and use it easily and efficiently. We hope this article has helped you understand more about CA ARCserve Backup R16 LuLZiSO.rar and how to use it for your data protection needs.
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/07 Man Oyunu - Manlarla Elencenin Adresi.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/07 Man Oyunu - Manlarla Elencenin Adresi.md
deleted file mode 100644
index e044823f20ed1e600d381712ef732c938735a47c..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/07 Man Oyunu - Manlarla Elencenin Adresi.md
+++ /dev/null
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07 Masin Oyunu Indir: How to Download and Play the Popular Car Racing Game
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If you are a fan of car racing games, you might have heard of 07 Masin Oyunu, a popular game that lets you drive various models of VAZ cars on different tracks and terrains. In this article, we will tell you everything you need to know about this game, including how to download it for free, how to play it online or offline, how to customize and upgrade your car, and how to master the skills and tricks of the game.
-
What is 07 Masin Oyunu?
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The origin and features of the game
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07 Masin Oyunu is a car racing game that was developed by a Turkish company called Seze Oyun. The game was released in 2019 and has gained a lot of popularity among car enthusiasts, especially in Azerbaijan and Turkey. The game features realistic graphics, physics, and sounds, as well as various modes, tracks, cars, and customization options.
The gameplay of 07 Masin Oyunu is simple and fun. You can choose from different models of VAZ cars, such as VAZ 2107, VAZ 2105, VAZ 2106, VAZ 21011, VAZ 2101, and Lada Niva. You can also modify your car's appearance, color, wheels, spoilers, stickers, and more. You can then select from different tracks and terrains, such as city streets, highways, dirt roads, mountains, deserts, snowfields, etc. You can also choose from different weather conditions, such as sunny, rainy, foggy, snowy, etc.
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The game has two main modes: online multiplayer mode and offline single-player mode. In online multiplayer mode, you can join or create a server and race against other players from around the world. You can chat with them, challenge them, or join their teams. You can also participate in tournaments and events that are organized by the game developers or the community. In offline single-player mode, you can race against the computer or practice your skills in free roam mode.
-
How to Download 07 Masin Oyunu for Free?
-
The official website and links of the game
-
If you want to download 07 Masin Oyunu for free, you can visit the official website of the game at . There you can find all the information about the game, such as its features, screenshots, videos, reviews, updates, etc. You can also find the links to download the game for Windows PC or Android devices. The game is compatible with Windows XP/Vista/7/8/10 and Android 4.0 or higher.
-
The system requirements and installation process of the game
-
Before you download 07 Masin Oyunu, you should make sure that your device meets the minimum system requirements of the game. Here are the system requirements for Windows PC and Android devices:
-
-
-
Device
-
Minimum System Requirements
-
-
-
Windows PC
-
-
-
CPU: Intel Core 2 Duo or AMD Athlon X2 64 2.4 GHz
-
RAM: 2 GB
-
GPU: NVIDIA GeForce 8600 GT or AMD Radeon HD 2600 XT
Once you have downloaded the game file from the official website, you can follow these steps to install the game on your device:
-
-
For Windows PC, extract the zip file and run the setup.exe file. Follow the instructions on the screen to complete the installation.
-
For Android devices, enable the installation of apps from unknown sources in your device settings. Then, tap on the apk file and install the game.
-
-
How to Play 07 Masin Oyunu Online or Offline?
-
The online multiplayer mode and servers of the game
-
If you want to play 07 Masin Oyunu online with other players, you need to have an internet connection and a registered account on the game website. You can create an account for free by entering your username, email, and password. Once you have an account, you can log in to the game and select the online multiplayer mode. You can then join or create a server and choose a car, a track, and a weather condition. You can also chat with other players, invite them to your team, or challenge them to a race.
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The game has several servers that are hosted by the game developers or the community. You can find a list of servers on the game website or in the game menu. You can also create your own server by selecting the create server option and setting up your own rules and settings. You can then invite other players to join your server or make it public for anyone to join.
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The offline single-player mode and options of the game
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If you want to play 07 Masin Oyunu offline without an internet connection, you can select the offline single-player mode. You can then choose a car, a track, and a weather condition. You can also adjust the difficulty level and the number of opponents. You can then start the race and compete against the computer or practice your skills in free roam mode.
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The offline single-player mode also has some options that you can access from the pause menu. You can change your car, track, or weather condition at any time during the game. You can also restart or quit the race, save or load your progress, adjust the sound and graphics settings, or view your statistics and achievements.
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How to Customize and Upgrade Your Car in 07 Masin Oyunu?
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The car selection and modification features of the game
-
One of the most fun aspects of 07 Masin Oyunu is that you can customize and upgrade your car in various ways. You can choose from different models of VAZ cars, such as VAZ 2107, VAZ 2105, VAZ 2106, VAZ 21011, VAZ 2101, and Lada Niva. Each car has its own characteristics and performance, such as speed, acceleration, handling, braking, etc.
-
You can also modify your car's appearance by changing its color, wheels, spoilers, stickers, lights, mirrors, windows, etc. You can also add some accessories to your car, such as flags, horns, sirens, etc. You can access the car selection and modification features from the main menu or the pause menu of the game.
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The car performance and tuning features of the game
-
Besides modifying your car's appearance, you can also upgrade your car's performance by changing its engine, transmission, suspension, brakes, tires, etc. You can also tune your car's settings, such as the gear ratio, the camber angle, the tire pressure, etc. You can access the car performance and tuning features from the garage menu of the game.
-
By customizing and upgrading your car, you can improve its speed, acceleration, handling, braking, etc. You can also make your car more suitable for different tracks and terrains. However, you should also be careful not to overdo it or damage your car. You can check your car's condition and repair it from the garage menu of the game.
-
How to Master the Skills and Tricks of 07 Masin Oyunu?
-
The basic controls and tips of the game
-
Before you start racing in 07 Masin Oyunu, you should familiarize yourself with the basic controls and tips of the game. Here are the default controls for Windows PC and Android devices:
-
-
-
Device
-
Default Controls
-
-
-
Windows PC
-
-
-
WASD or arrow keys: move forward/backward/left/right
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Space: handbrake
-
Shift: nitro boost
-
C: change camera view
-
R: reset car position
-
Esc: pause menu
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Enter: chat or confirm
-
Tab: scoreboard
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-
-
-
-
Android Device
-
-
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Tilt device or touch left/right buttons: steer left/right
-
Touch gas/brake buttons: accelerate/brake
-
Touch handbrake button: handbrake
-
Touch nitro button: nitro boost
-
Touch camera button: change camera view
-
Touch reset button: reset car position
-
Touch pause button: pause menu
-
Touch chat button: chat or confirm
-
Touch scoreboard button: scoreboard
-
-
-
-
-
You can also customize your controls from the settings menu of the game. Here are some tips to help you drive better in 07 Masin Oyunu:
-
-
Use the handbrake to drift around corners and gain more speed.
-
Use the nitro boost to overtake your opponents or escape from them.
-
Change your camera view to suit your preference and visibility.
-
Avoid crashing into obstacles or other cars, as this will damage your car and slow you down.
-
Follow the arrows and signs on the track to avoid getting lost or going out of bounds.
-
Collect coins and bonuses on the track to earn more money and rewards.
-
-
The advanced techniques and strategies of the game
-
If you want to master the skills and tricks of 07 Masin Oyunu, you should practice a lot and learn from your mistakes. You should also try to learn some advanced techniques and strategies that can give you an edge over your opponents. Here are some examples of advanced techniques and strategies that you can use in 07 Masin Oyunu:
-
-
Overtaking: There are different ways to overtake your opponents in 07 Masin Oyunu, such as slipstreaming, drafting, blocking, cutting corners, etc. You should try to use these techniques wisely and avoid being overtaken by others.
-
Nitro management: Nitro is a powerful tool that can help you win races in 07 Masin Oyunu, but it is also limited and needs to be recharged. You should try to manage your nitro wisely and use it at the right time and place. For example, you can use it on straight roads, uphill slopes, or before finishing lines.
-
Tuning: Tuning is a feature that allows you to adjust your car's settings according to your preference and needs. You should try to tune your car for different tracks and terrains, as this can affect your car's performance and handling. For example, you can lower your gear ratio for more acceleration, increase your camber angle for more grip, or reduce your tire pressure for more speed.
-
Drifting: Drifting is a technique that involves sliding your car sideways around corners and curves. Drifting can help you gain more speed, control, and style in 07 Masin Oyunu, but it also requires skill and practice. You should try to drift by using the handbrake, the gas, and the steering at the right time and angle. You should also try to avoid oversteering or understeering, as this can make you lose balance and speed.
-
Stunts: Stunts are tricks that involve performing jumps, flips, spins, or other maneuvers with your car. Stunts can help you earn more points, money, and rewards in 07 Masin Oyunu, but they also involve risk and challenge. You should try to perform stunts by using ramps, hills, bridges, or other obstacles on the track. You should also try to land safely and smoothly, as crashing or flipping can damage your car and slow you down.
-
-
Conclusion
-
07 Masin Oyunu is a fun and exciting car racing game that you can download and play for free on your Windows PC or Android device. You can choose from different models of VAZ cars, customize and upgrade them, and race on various tracks and terrains. You can also play online with other players or offline with the computer. You can also learn and master the skills and tricks of the game, such as overtaking, nitro management, tuning, drifting, and stunts. If you are looking for a realistic, challenging, and entertaining car racing game, you should definitely try 07 Masin Oyunu.
-
FAQs
-
Q: Is 07 Masin Oyunu safe to download and play?
-
A: Yes, 07 Masin Oyunu is safe to download and play. The game does not contain any viruses, malware, or spyware. The game also does not require any personal information or permissions from your device. However, you should always download the game from the official website or a trusted source to avoid any problems or risks.
-
Q: How can I update 07 Masin Oyunu to the latest version?
-
A: You can update 07 Masin Oyunu to the latest version by visiting the official website of the game at . There you can find the latest updates and patches for the game. You can also check for updates from the game menu or settings. You should always update the game to enjoy the new features, improvements, and bug fixes.
-
Q: How can I contact the developers or the community of 07 Masin Oyunu?
-
A: You can contact the developers or the community of 07 Masin Oyunu by visiting their social media pages or forums. You can find their links on the official website of the game at . There you can ask questions, give feedback, report issues, suggest ideas, or join discussions about the game.
-
Q: How can I support 07 Masin Oyunu financially?
-
A: You can support 07 Masin Oyunu financially by making a donation to the developers or buying some in-game items or features. You can find their donation link or their in-game store on the official website of the game at . There you can choose how much you want to donate or what you want to buy. Your support will help them maintain and improve the game.
-
Q: How can I share my screenshots or videos of 07 Masin Oyunu?
-
A: You can share your screenshots or videos of 07 Masin Oyunu by using the built-in screenshot or video capture feature of your device or by using a third-party app or software. You can then upload your screenshots or videos to your social media pages or platforms, such as Facebook, Instagram, YouTube, etc. You can also tag the developers or the community of 07 Masin Oyunu to show them your achievements or creations.
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/AR Rahman 90s Interlude Collection - Tamil Songs from Roja to Mudhalvan.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/AR Rahman 90s Interlude Collection - Tamil Songs from Roja to Mudhalvan.md
deleted file mode 100644
index 3d19eb120638a7cb4946f7eb1dee87edd563142b..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/AR Rahman 90s Interlude Collection - Tamil Songs from Roja to Mudhalvan.md
+++ /dev/null
@@ -1,55 +0,0 @@
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AR Rahman 90s BGM Download: How to Enjoy the Music of the Mozart of Madras
If you are a fan of Indian cinema, you must have heard of AR Rahman, the legendary composer who has won numerous awards and accolades for his musical scores. AR Rahman, whose full name is Allah Rakha Rahman, was born as A.S. Dileep Kumar in Chennai, India, on January 6, 1967. He started his career as a keyboard player and jingle composer before making his debut as a film composer with Roja in 1992. Since then, he has composed music for over 100 films in various languages, including Tamil, Hindi, Telugu, Malayalam, English, and Chinese.
AR Rahman's musical style is a fusion of Indian classical, folk, pop, rock, electronic, and world music. He is known for his innovative use of instruments, sounds, rhythms, and melodies. He is also famous for his background music (BGM), which enhances the mood and emotion of the scenes in the films. His BGMs are often catchy, memorable, and inspiring.
In this article, we will explore some of his best BGMs from the 90s and how you can download them to enjoy them anytime and anywhere.
Some of the Best AR Rahman BGMs from the 90s
The 90s was a golden era for AR Rahman's music. He collaborated with some of the finest directors and actors in Indian cinema and created some of his most iconic soundtracks. Here are some of his best BGMs from the 90s that you should not miss:
Bombay (1995): This film directed by Mani Ratnam is a love story set against the backdrop of the communal riots in Bombay in 1992-93. The BGM by AR Rahman is a masterpiece that captures the romance, tension, violence, and tragedy of the film. The theme song "Bombay Theme" is one of his most popular and recognizable tunes.
Dil Se (1998): This film directed by Mani Ratnam is a romantic thriller that follows a radio journalist who falls in love with a mysterious woman who is involved in a terrorist plot. The BGM by AR Rahman is a blend of haunting melodies and pulsating beats that reflect the passion and danger of the film. The song "Chaiyya Chaiyya" which features on top of a moving train is one of his most celebrated compositions.
Raja Hindustani (1996): This film directed by Dharmesh Darshan is a remake of the 1965 film Jab Jab Phool Khile. It is a love story between a taxi driver and a rich girl who elope and face opposition from their families. The BGM by AR Rahman is a mix of folk, classical, and pop elements that complement the film's mood and setting. The song "Pardesi Pardesi" is one of his most popular songs.
Kadhalan (1994): This film directed by Shankar is a romantic comedy that revolves around a college student who falls in love with the daughter of the governor and gets involved in a terrorist plot. The BGM by AR Rahman is a fusion of techno, rap, and folk music that adds to the fun and thrill of the film. The song "Mukkabla" which features a dance-off between Prabhu Deva and Nagma is one of his most iconic songs.
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-
-
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Armedia Plugin For Autodesk 3ds Max Crack Download !FULL!.md b/spaces/tioseFevbu/cartoon-converter/scripts/Armedia Plugin For Autodesk 3ds Max Crack Download !FULL!.md
deleted file mode 100644
index cd2b9a2cb0fd7efdf099f9a957ec2153b62bbb47..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Armedia Plugin For Autodesk 3ds Max Crack Download !FULL!.md
+++ /dev/null
@@ -1,43 +0,0 @@
-
-
How to Use AR-media Plugin for Autodesk 3ds Max to Create Augmented and Virtual Reality Experiences
-
AR-media Plugin for Autodesk 3ds Max is a tool that allows you to export your 3D models from 3ds Max to the AR-media Studio platform, where you can create immersive and interactive experiences for Augmented and Virtual Reality. In this article, we will show you how to install and use the plugin, as well as some examples of what you can achieve with it.
-
Installation
-
To install the AR-media Plugin for 3ds Max, you need to download it from the Autodesk App Store[^1^]. The plugin installs itself as a script utility, meaning that it will be available through the 3ds Max's Utility panel. To start the plugin:
-
Armedia Plugin For Autodesk 3ds Max Crack Download
Click the AR-media Plugin entry in the dropdown list
-
-
The plugin's interface will be displayed as a rollout and related toolbar.
-
Usage
-
When you install the plugin, you will see the AR-media Plugin's toolbar which is your quick access point to the main functionalities:
-
-
Using the toolbar you can:
-
-
Export the whole scene
-
Export selected models only
-
Go to your AR-media Studio account
-
-
You usually have to go to your AR-media Studio account to:
-
-
Retrieve your personal token (or a specific model token as described below)
-
Verify, manage and try the exported models
-
-
Before any export, you need to set your personal plugin token (that you retrieve from your personal account data) to bind the AR-media Extension to your space and resources available on the AR-media Studio platform. When you set a token you can check the "Remember me" option to make the token available among different sessions. Un-check it if you are working on a shared computer and do not want to allow other users to upload models to your account thus consuming your personal storage and limits.
-
-
Then to actually export a new model, you need to choose a name for your model. If you want to update a previously exported model instead, you must also specify a model token. The model token is automatically generated each time you export a model and is made available to let you keep working on the very same model asset instead of creating a new asset each time you export your content (this will also help you keeping the platform usage as low as possible).
-
Upon a successful export your model will appear in the AR-media Studio Asset Manager with the chosen model name.
-
Note: Model names have not to be unique even though you'd better choose unique names to distinguish them later on.
-
You set the plugin token, the model name (and optionally the model token) using the plugin's rollout:
-
-
To export the whole scene click the All button, whereas if you want to export only the current selection then click the Selected button. By clicking the Account button you will be able to login into your account to retrieve the plugin token, a model token (if required), or to verify, manage and test exported models. The Toolbar button will show the plugin's toolbar in case it has been closed by the user.
-
When exporting models from 3ds Max you can choose to include animations and eventually bake animations if you are experiencing problems with the default export method. To this regard you can rely on the Export Animations and Bake Animations checkboxes respectively.
-
Examples
-
Here are some examples of what you can do with AR-media Plugin for 3ds Max and AR-media Studio:
-
-
Make a building stand out of a blueprint or see it in the real environment where it is meant to be built
-
See your best designs coming to life on your desk or in the workshop
-
Create and deploy engaging experiences for your clients e93f5a0c3f
-
-
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Matru Ki Bijlee Ka Mandola Telugu Movie Dubbed In Hindi Free !LINK! Download.md b/spaces/tioseFevbu/cartoon-converter/scripts/Matru Ki Bijlee Ka Mandola Telugu Movie Dubbed In Hindi Free !LINK! Download.md
deleted file mode 100644
index 77f1442e0f46256240a0a296919cdd4380064f73..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Matru Ki Bijlee Ka Mandola Telugu Movie Dubbed In Hindi Free !LINK! Download.md
+++ /dev/null
@@ -1,25 +0,0 @@
-
-
How to Watch Matru Ki Bijlee Ka Mandola Telugu Movie Dubbed in Hindi for Free
-
Matru Ki Bijlee Ka Mandola is a 2013 Bollywood comedy-drama film directed by Vishal Bhardwaj and starring Imran Khan, Anushka Sharma and Pankaj Kapur. The film revolves around the lives of three characters: Matru, a village boy who works for a rich landlord; Bijlee, the landlord's daughter who is in love with Matru; and Mandola, the landlord who is addicted to alcohol and wants to sell his land to a corrupt politician.
-
Matru Ki Bijlee Ka Mandola telugu movie dubbed in hindi free download
If you are a fan of this film and want to watch it in Telugu language with Hindi subtitles, you might be wondering how to do that without paying any money. Well, you are in luck because there is a website that offers this service for free. The website is called MovierulzHD and it has a huge collection of multi-audio movies in various languages. You can watch Matru Ki Bijlee Ka Mandola Telugu movie dubbed in Hindi for free on this website.
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Here are the steps to watch Matru Ki Bijlee Ka Mandola Telugu movie dubbed in Hindi for free on MovierulzHD:
Select the quality of the video you want to watch. You can choose from 1080p, 720p or 480p.
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Select the audio language you want to hear. You can choose from Hindi, Telugu, Tamil, Malayalam, Kannada, Bengali, Marathi or Gujarati.
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Select the subtitle language you want to see. You can choose from Hindi, English or none.
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Click on the play button and enjoy the movie.
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MovierulzHD is a reliable and fast streaming service that lets you watch multi-audio movies for free. You can also download the movies if you want to watch them offline. However, you should be aware that watching or downloading pirated movies is illegal and may have legal consequences. We do not endorse or promote piracy in any way. This article is for informational purposes only.
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Baahubali Telugu movie dubbed in Hindi: This is a 2015 Indian epic action film directed by S.S. Rajamouli and starring Prabhas, Rana Daggubati, Anushka Shetty and Tamannaah. The film is set in a fictional kingdom and tells the story of two brothers who fight for the throne.
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Dangal Telugu movie dubbed in Hindi: This is a 2016 Bollywood biographical sports drama film directed by Nitesh Tiwari and starring Aamir Khan, Fatima Sana Shaikh, Sanya Malhotra and Sakshi Tanwar. The film is based on the true story of Mahavir Singh Phogat, a former wrestler who trains his daughters to become world-class wrestlers.
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-
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/metadata/_json.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/metadata/_json.py
deleted file mode 100644
index 336b52f1efddbcaeb6716583fc2f043699e278fa..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/metadata/_json.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Extracted from https://github.com/pfmoore/pkg_metadata
-
-from email.header import Header, decode_header, make_header
-from email.message import Message
-from typing import Any, Dict, List, Union
-
-METADATA_FIELDS = [
- # Name, Multiple-Use
- ("Metadata-Version", False),
- ("Name", False),
- ("Version", False),
- ("Dynamic", True),
- ("Platform", True),
- ("Supported-Platform", True),
- ("Summary", False),
- ("Description", False),
- ("Description-Content-Type", False),
- ("Keywords", False),
- ("Home-page", False),
- ("Download-URL", False),
- ("Author", False),
- ("Author-email", False),
- ("Maintainer", False),
- ("Maintainer-email", False),
- ("License", False),
- ("Classifier", True),
- ("Requires-Dist", True),
- ("Requires-Python", False),
- ("Requires-External", True),
- ("Project-URL", True),
- ("Provides-Extra", True),
- ("Provides-Dist", True),
- ("Obsoletes-Dist", True),
-]
-
-
-def json_name(field: str) -> str:
- return field.lower().replace("-", "_")
-
-
-def msg_to_json(msg: Message) -> Dict[str, Any]:
- """Convert a Message object into a JSON-compatible dictionary."""
-
- def sanitise_header(h: Union[Header, str]) -> str:
- if isinstance(h, Header):
- chunks = []
- for bytes, encoding in decode_header(h):
- if encoding == "unknown-8bit":
- try:
- # See if UTF-8 works
- bytes.decode("utf-8")
- encoding = "utf-8"
- except UnicodeDecodeError:
- # If not, latin1 at least won't fail
- encoding = "latin1"
- chunks.append((bytes, encoding))
- return str(make_header(chunks))
- return str(h)
-
- result = {}
- for field, multi in METADATA_FIELDS:
- if field not in msg:
- continue
- key = json_name(field)
- if multi:
- value: Union[str, List[str]] = [
- sanitise_header(v) for v in msg.get_all(field)
- ]
- else:
- value = sanitise_header(msg.get(field))
- if key == "keywords":
- # Accept both comma-separated and space-separated
- # forms, for better compatibility with old data.
- if "," in value:
- value = [v.strip() for v in value.split(",")]
- else:
- value = value.split()
- result[key] = value
-
- payload = msg.get_payload()
- if payload:
- result["description"] = payload
-
- return result
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/charsetgroupprober.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/charsetgroupprober.py
deleted file mode 100644
index 778ff332bbdeb0402acda7866851679ba6b28ee4..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/charsetgroupprober.py
+++ /dev/null
@@ -1,109 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Communicator client 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 .charsetprober import CharSetProber
-from .enums import ProbingState
-
-
-class CharSetGroupProber(CharSetProber):
- def __init__(self, lang_filter=None):
- super().__init__(lang_filter=lang_filter)
- self._active_num = 0
- self.probers = []
- self._best_guess_prober = None
-
- def reset(self):
- super().reset()
- self._active_num = 0
- for prober in self.probers:
- if prober:
- prober.reset()
- prober.active = True
- self._active_num += 1
- self._best_guess_prober = None
-
- @property
- def charset_name(self):
- if not self._best_guess_prober:
- self.get_confidence()
- if not self._best_guess_prober:
- return None
- return self._best_guess_prober.charset_name
-
- @property
- def language(self):
- if not self._best_guess_prober:
- self.get_confidence()
- if not self._best_guess_prober:
- return None
- return self._best_guess_prober.language
-
- def feed(self, byte_str):
- for prober in self.probers:
- if not prober:
- continue
- if not prober.active:
- continue
- state = prober.feed(byte_str)
- if not state:
- continue
- if state == ProbingState.FOUND_IT:
- self._best_guess_prober = prober
- self._state = ProbingState.FOUND_IT
- return self.state
- if state == ProbingState.NOT_ME:
- prober.active = False
- self._active_num -= 1
- if self._active_num <= 0:
- self._state = ProbingState.NOT_ME
- return self.state
- return self.state
-
- def get_confidence(self):
- state = self.state
- if state == ProbingState.FOUND_IT:
- return 0.99
- if state == ProbingState.NOT_ME:
- return 0.01
- best_conf = 0.0
- self._best_guess_prober = None
- for prober in self.probers:
- if not prober:
- continue
- if not prober.active:
- self.logger.debug("%s not active", prober.charset_name)
- continue
- conf = prober.get_confidence()
- self.logger.debug(
- "%s %s confidence = %s", prober.charset_name, prober.language, conf
- )
- if best_conf < conf:
- best_conf = conf
- self._best_guess_prober = prober
- if not self._best_guess_prober:
- return 0.0
- return best_conf
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_roi_heads/test_roi_extractor.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_roi_heads/test_roi_extractor.py
deleted file mode 100644
index 22743f2d3be3b4be82bc46699b76fecf7af60020..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_roi_heads/test_roi_extractor.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import pytest
-import torch
-
-from mmdet.models.roi_heads.roi_extractors import GenericRoIExtractor
-
-
-def test_groie():
- # test with pre/post
- cfg = dict(
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32],
- pre_cfg=dict(
- type='ConvModule',
- in_channels=256,
- out_channels=256,
- kernel_size=5,
- padding=2,
- inplace=False,
- ),
- post_cfg=dict(
- type='ConvModule',
- in_channels=256,
- out_channels=256,
- kernel_size=5,
- padding=2,
- inplace=False))
-
- groie = GenericRoIExtractor(**cfg)
-
- feats = (
- torch.rand((1, 256, 200, 336)),
- torch.rand((1, 256, 100, 168)),
- torch.rand((1, 256, 50, 84)),
- torch.rand((1, 256, 25, 42)),
- )
-
- rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
-
- res = groie(feats, rois)
- assert res.shape == torch.Size([1, 256, 7, 7])
-
- # test w.o. pre/post
- cfg = dict(
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32])
-
- groie = GenericRoIExtractor(**cfg)
-
- feats = (
- torch.rand((1, 256, 200, 336)),
- torch.rand((1, 256, 100, 168)),
- torch.rand((1, 256, 50, 84)),
- torch.rand((1, 256, 25, 42)),
- )
-
- rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
-
- res = groie(feats, rois)
- assert res.shape == torch.Size([1, 256, 7, 7])
-
- # test w.o. pre/post concat
- cfg = dict(
- aggregation='concat',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
- out_channels=256 * 4,
- featmap_strides=[4, 8, 16, 32])
-
- groie = GenericRoIExtractor(**cfg)
-
- feats = (
- torch.rand((1, 256, 200, 336)),
- torch.rand((1, 256, 100, 168)),
- torch.rand((1, 256, 50, 84)),
- torch.rand((1, 256, 25, 42)),
- )
-
- rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
-
- res = groie(feats, rois)
- assert res.shape == torch.Size([1, 1024, 7, 7])
-
- # test not supported aggregate method
- with pytest.raises(AssertionError):
- cfg = dict(
- aggregation='not support',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
- out_channels=1024,
- featmap_strides=[4, 8, 16, 32])
- _ = GenericRoIExtractor(**cfg)
-
- # test concat channels number
- cfg = dict(
- aggregation='concat',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
- out_channels=256 * 5, # 256*5 != 256*4
- featmap_strides=[4, 8, 16, 32])
-
- groie = GenericRoIExtractor(**cfg)
-
- feats = (
- torch.rand((1, 256, 200, 336)),
- torch.rand((1, 256, 100, 168)),
- torch.rand((1, 256, 50, 84)),
- torch.rand((1, 256, 25, 42)),
- )
-
- rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
-
- # out_channels does not sum of feat channels
- with pytest.raises(AssertionError):
- _ = groie(feats, rois)
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Chupulu kalisina subhavela telugu serial The best scenes and episodes of the show.md b/spaces/usbethFlerru/sovits-modelsV2/example/Chupulu kalisina subhavela telugu serial The best scenes and episodes of the show.md
deleted file mode 100644
index df1457fd5c237f1ea0d8fd162edc9efee3a58c67..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Chupulu kalisina subhavela telugu serial The best scenes and episodes of the show.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
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Chupulu kalisina subhavela serial is a dubbed version of the Hindi television serial, ... Watch Chupulu Kalisina Shubhavela Serial by MAA TV - All Episodes Online at ... Gossips, MP3 songs, film news, Andhra Pradesh News, Youtube Movies.
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-
-
\ No newline at end of file
diff --git a/spaces/uwnlp/guanaco-playground-tgi/dialogue.py b/spaces/uwnlp/guanaco-playground-tgi/dialogue.py
deleted file mode 100644
index 6ca26d2d887c6b683c5bd6240a09f9a55a046a3a..0000000000000000000000000000000000000000
--- a/spaces/uwnlp/guanaco-playground-tgi/dialogue.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-from dataclasses import asdict, dataclass
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Type, TypeVar, Union
-
-from huggingface_hub import ModelHubMixin, hf_hub_download
-
-# Generic variable that is either ModelHubMixin or a subclass thereof
-T = TypeVar("T", bound="ModelHubMixin")
-
-TEMPLATE_FILENAME = "dialogue_template.json"
-IGNORE_INDEX = -100
-
-
-@dataclass
-class DialogueTemplate(ModelHubMixin):
- """Converts all turns of a dialogue between a user and assistant to a standardized format.
- Adapted from OpenAI's ChatML (https://github.com/openai/openai-python/blob/main/chatml.md) and Vicuna (https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py)
- """
-
- system: str
- messages: List[Dict[str, str]] = None
- system_token: str = "<|system|>"
- user_token: str = "<|user|>"
- assistant_token: str = "<|assistant|>"
- end_token: str = "<|end|>"
-
- def get_training_prompt(self) -> str:
- prompt = self.system_token + "\n" + self.system + self.end_token + "\n"
- if self.messages is None:
- raise ValueError("Dialogue template must have at least one message.")
- for message in self.messages:
- if message["role"] == "user":
- prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n"
- else:
- prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n"
- return prompt
-
- def get_inference_prompt(self) -> str:
- prompt = self.system_token + "\n" + self.system + self.end_token + "\n"
- if self.messages is None:
- raise ValueError("Dialogue template must have at least one message.")
- for message in self.messages:
- if message["role"] == "user":
- prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n"
- else:
- prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n"
- prompt += self.assistant_token
- return prompt
-
- def get_dialogue(self):
- """Helper function to format the messages as an easy-to-read dialogue."""
- prompt = ""
- if self.messages is None:
- raise ValueError("Dialogue template must have at least one message.")
- for message in self.messages:
- if message["role"] == "user":
- prompt += "\n\nHuman: " + message["content"]
- else:
- prompt += "\n\nAssistant: " + message["content"]
- return prompt
-
- def get_special_tokens(self) -> List[str]:
- return [self.system_token, self.user_token, self.assistant_token, self.end_token]
-
- def copy(self):
- return DialogueTemplate(
- system=self.system,
- messages=self.messages,
- system_token=self.system_token,
- user_token=self.user_token,
- assistant_token=self.assistant_token,
- end_token=self.end_token,
- )
-
- def to_dict(self) -> Dict[str, Any]:
- return {k: v for k, v in asdict(self).items()}
-
- @classmethod
- def from_dict(cls, data):
- return DialogueTemplate(
- system=data["system"] if "system" in data else "",
- messages=data["messages"] if "messages" in data else None,
- system_token=data["system_token"] if "system_token" in data else "<|system|>",
- user_token=data["user_token"] if "user_token" in data else "<|user|>",
- assistant_token=data["assistant_token"] if "assistant_token" in data else "<|assistant|>",
- end_token=data["end_token"] if "end_token" in data else "<|end|>",
- )
-
- def _save_pretrained(self, save_directory: Union[str, Path]) -> None:
- save_directory = Path(save_directory)
- save_directory.mkdir(exist_ok=True)
- with open(save_directory / "dialogue_template.json", "w") as f:
- json.dump(self.to_dict(), f, indent=2)
-
- @classmethod
- def _from_pretrained(
- cls: Type[T],
- *,
- model_id: str,
- revision: Optional[str],
- cache_dir: Optional[Union[str, Path]],
- force_download: bool,
- proxies: Optional[Dict],
- resume_download: bool,
- local_files_only: bool,
- token: Optional[Union[str, bool]],
- **model_kwargs,
- ) -> T:
- """Loads the dialogue template from a local directory or the Huggingface Hub.
- Args:
- model_id (`str`):
- ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`).
- revision (`str`, *optional*):
- Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
- latest commit on `main` branch.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
- the existing cache.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint (e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`).
- token (`str` or `bool`, *optional*):
- The token to use as HTTP bearer authorization for remote files. By default, it will use the token
- cached when running `huggingface-cli login`.
- cache_dir (`str`, `Path`, *optional*):
- Path to the folder where cached files are stored.
- local_files_only (`bool`, *optional*, defaults to `False`):
- If `True`, avoid downloading the file and return the path to the local cached file if it exists.
- model_kwargs:
- Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method.
- """
- if os.path.isdir(model_id): # Can either be a local directory
- print("Loading dialogue template from local directory")
- template_file = os.path.join(model_id, TEMPLATE_FILENAME)
- else: # Or a template on the Hub
- template_file = hf_hub_download( # Download from the hub, passing same input args
- repo_id=model_id,
- filename=TEMPLATE_FILENAME,
- revision=revision,
- cache_dir=cache_dir,
- force_download=force_download,
- proxies=proxies,
- resume_download=resume_download,
- token=token,
- local_files_only=local_files_only,
- )
-
- # Load template
- with open(template_file, "r") as f:
- data = json.load(f)
- return cls.from_dict(data=data)
-
-
-# A shortened version of the system message in Anthropic's HHH prompt: https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt
-default_template = DialogueTemplate(
- system="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.",
-)
-
-# OpenAI and OpenAssistant train on few to no system messages.
-# TODO: consider defining this as the `default` template
-no_system_template = DialogueTemplate(
- system="",
-)
-
-alpaca_template = DialogueTemplate(
- system="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.",
- user_token="### Instruction:",
- assistant_token="### Response:",
-)
-
-SUPPORTED_DIALOGUE_TEMPLATES = {
- "default": default_template,
- "no_system": no_system_template,
- "alpaca": alpaca_template,
-}
-
-
-def get_dialogue_template(template: str) -> DialogueTemplate:
- if template not in SUPPORTED_DIALOGUE_TEMPLATES.keys():
- raise ValueError(f"Template {template} is not supported!")
- return SUPPORTED_DIALOGUE_TEMPLATES[template].copy()
-
-
-def prepare_dialogue(example, dialogue_template, is_train=True):
- """Format example to single- or multi-turn dialogue."""
- # TODO: make this simpler by just ensuring every dataset has a messages column
- if "messages" in example.keys() and example["messages"] is not None:
- dialogue_template.messages = example["messages"]
- elif all(k in example.keys() for k in ("prompt", "completion")):
- # Construct single-turn dialogue from prompt and completion
- dialogue_template.messages = [
- {"role": "user", "content": example["prompt"]},
- {"role": "assistant", "content": example["completion"]},
- ]
- elif "prompt" in example.keys():
- # Construct single-turn dialogue from prompt (inference only)
- dialogue_template.messages = [
- {"role": "user", "content": example["prompt"]},
- ]
- else:
- raise ValueError(
- f"Could not format example as dialogue! Require either `messages` or `[prompt, completion]` or `[prompt]` keys but found {list(example.keys())}"
- )
- if is_train:
- example["text"] = dialogue_template.get_training_prompt()
- else:
- example["text"] = dialogue_template.get_inference_prompt()
- return example
-
-
-def mask_user_labels(tokenizer, dialogue_template, labels):
- """Masks the user turns of a dialogue from the loss"""
- user_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.user_token)
- assistant_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.assistant_token)
- for idx, label_id in enumerate(labels):
- if label_id == user_token_id:
- current_idx = idx
- while labels[current_idx] != assistant_token_id and current_idx < len(labels):
- labels[current_idx] = IGNORE_INDEX
- current_idx += 1
\ No newline at end of file
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/nn/modules/conv.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/nn/modules/conv.py
deleted file mode 100644
index 38ee3f5f3f40618e5fc0f7f836a3ecc595496bec..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/nn/modules/conv.py
+++ /dev/null
@@ -1,297 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-"""
-Convolution modules
-"""
-
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-__all__ = ('Conv', 'LightConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv',
- 'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'RepConv')
-
-
-def autopad(k, p=None, d=1): # kernel, padding, dilation
- """Pad to 'same' shape outputs."""
- if d > 1:
- k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size
- if p is None:
- p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
- return p
-
-
-class Conv(nn.Module):
- """Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""
- default_act = nn.SiLU() # default activation
-
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
- """Initialize Conv layer with given arguments including activation."""
- super().__init__()
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
- self.bn = nn.BatchNorm2d(c2)
- self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
-
- def forward(self, x):
- """Apply convolution, batch normalization and activation to input tensor."""
- return self.act(self.bn(self.conv(x)))
-
- def forward_fuse(self, x):
- """Perform transposed convolution of 2D data."""
- return self.act(self.conv(x))
-
-
-class Conv2(Conv):
- """Simplified RepConv module with Conv fusing."""
-
- def __init__(self, c1, c2, k=3, s=1, p=None, g=1, d=1, act=True):
- """Initialize Conv layer with given arguments including activation."""
- super().__init__(c1, c2, k, s, p, g=g, d=d, act=act)
- self.cv2 = nn.Conv2d(c1, c2, 1, s, autopad(1, p, d), groups=g, dilation=d, bias=False) # add 1x1 conv
-
- def forward(self, x):
- """Apply convolution, batch normalization and activation to input tensor."""
- return self.act(self.bn(self.conv(x) + self.cv2(x)))
-
- def fuse_convs(self):
- """Fuse parallel convolutions."""
- w = torch.zeros_like(self.conv.weight.data)
- i = [x // 2 for x in w.shape[2:]]
- w[:, :, i[0]:i[0] + 1, i[1]:i[1] + 1] = self.cv2.weight.data.clone()
- self.conv.weight.data += w
- self.__delattr__('cv2')
-
-
-class LightConv(nn.Module):
- """Light convolution with args(ch_in, ch_out, kernel).
- https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
- """
-
- def __init__(self, c1, c2, k=1, act=nn.ReLU()):
- """Initialize Conv layer with given arguments including activation."""
- super().__init__()
- self.conv1 = Conv(c1, c2, 1, act=False)
- self.conv2 = DWConv(c2, c2, k, act=act)
-
- def forward(self, x):
- """Apply 2 convolutions to input tensor."""
- return self.conv2(self.conv1(x))
-
-
-class DWConv(Conv):
- """Depth-wise convolution."""
-
- def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation
- super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
-
-
-class DWConvTranspose2d(nn.ConvTranspose2d):
- """Depth-wise transpose convolution."""
-
- def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out
- super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
-
-
-class ConvTranspose(nn.Module):
- """Convolution transpose 2d layer."""
- default_act = nn.SiLU() # default activation
-
- def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True):
- """Initialize ConvTranspose2d layer with batch normalization and activation function."""
- super().__init__()
- self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn)
- self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity()
- self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
-
- def forward(self, x):
- """Applies transposed convolutions, batch normalization and activation to input."""
- return self.act(self.bn(self.conv_transpose(x)))
-
- def forward_fuse(self, x):
- """Applies activation and convolution transpose operation to input."""
- return self.act(self.conv_transpose(x))
-
-
-class Focus(nn.Module):
- """Focus wh information into c-space."""
-
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
- # self.contract = Contract(gain=2)
-
- def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
- return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
- # return self.conv(self.contract(x))
-
-
-class GhostConv(nn.Module):
- """Ghost Convolution https://github.com/huawei-noah/ghostnet."""
-
- def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
- super().__init__()
- c_ = c2 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
- self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)
-
- def forward(self, x):
- """Forward propagation through a Ghost Bottleneck layer with skip connection."""
- y = self.cv1(x)
- return torch.cat((y, self.cv2(y)), 1)
-
-
-class RepConv(nn.Module):
- """RepConv is a basic rep-style block, including training and deploy status
- This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
- """
- default_act = nn.SiLU() # default activation
-
- def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False):
- super().__init__()
- assert k == 3 and p == 1
- self.g = g
- self.c1 = c1
- self.c2 = c2
- self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
-
- self.bn = nn.BatchNorm2d(num_features=c1) if bn and c2 == c1 and s == 1 else None
- self.conv1 = Conv(c1, c2, k, s, p=p, g=g, act=False)
- self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False)
-
- def forward_fuse(self, x):
- """Forward process"""
- return self.act(self.conv(x))
-
- def forward(self, x):
- """Forward process"""
- id_out = 0 if self.bn is None else self.bn(x)
- return self.act(self.conv1(x) + self.conv2(x) + id_out)
-
- def get_equivalent_kernel_bias(self):
- kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
- kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2)
- kernelid, biasid = self._fuse_bn_tensor(self.bn)
- return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
-
- def _avg_to_3x3_tensor(self, avgp):
- channels = self.c1
- groups = self.g
- kernel_size = avgp.kernel_size
- input_dim = channels // groups
- k = torch.zeros((channels, input_dim, kernel_size, kernel_size))
- k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2
- return k
-
- def _pad_1x1_to_3x3_tensor(self, kernel1x1):
- if kernel1x1 is None:
- return 0
- else:
- return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
-
- def _fuse_bn_tensor(self, branch):
- if branch is None:
- return 0, 0
- if isinstance(branch, Conv):
- kernel = branch.conv.weight
- running_mean = branch.bn.running_mean
- running_var = branch.bn.running_var
- gamma = branch.bn.weight
- beta = branch.bn.bias
- eps = branch.bn.eps
- elif isinstance(branch, nn.BatchNorm2d):
- if not hasattr(self, 'id_tensor'):
- input_dim = self.c1 // self.g
- kernel_value = np.zeros((self.c1, input_dim, 3, 3), dtype=np.float32)
- for i in range(self.c1):
- kernel_value[i, i % input_dim, 1, 1] = 1
- self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
- kernel = self.id_tensor
- running_mean = branch.running_mean
- running_var = branch.running_var
- gamma = branch.weight
- beta = branch.bias
- eps = branch.eps
- std = (running_var + eps).sqrt()
- t = (gamma / std).reshape(-1, 1, 1, 1)
- return kernel * t, beta - running_mean * gamma / std
-
- def fuse_convs(self):
- if hasattr(self, 'conv'):
- return
- kernel, bias = self.get_equivalent_kernel_bias()
- self.conv = nn.Conv2d(in_channels=self.conv1.conv.in_channels,
- out_channels=self.conv1.conv.out_channels,
- kernel_size=self.conv1.conv.kernel_size,
- stride=self.conv1.conv.stride,
- padding=self.conv1.conv.padding,
- dilation=self.conv1.conv.dilation,
- groups=self.conv1.conv.groups,
- bias=True).requires_grad_(False)
- self.conv.weight.data = kernel
- self.conv.bias.data = bias
- for para in self.parameters():
- para.detach_()
- self.__delattr__('conv1')
- self.__delattr__('conv2')
- if hasattr(self, 'nm'):
- self.__delattr__('nm')
- if hasattr(self, 'bn'):
- self.__delattr__('bn')
- if hasattr(self, 'id_tensor'):
- self.__delattr__('id_tensor')
-
-
-class ChannelAttention(nn.Module):
- """Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""
-
- def __init__(self, channels: int) -> None:
- super().__init__()
- self.pool = nn.AdaptiveAvgPool2d(1)
- self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True)
- self.act = nn.Sigmoid()
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return x * self.act(self.fc(self.pool(x)))
-
-
-class SpatialAttention(nn.Module):
- """Spatial-attention module."""
-
- def __init__(self, kernel_size=7):
- """Initialize Spatial-attention module with kernel size argument."""
- super().__init__()
- assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
- padding = 3 if kernel_size == 7 else 1
- self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
- self.act = nn.Sigmoid()
-
- def forward(self, x):
- """Apply channel and spatial attention on input for feature recalibration."""
- return x * self.act(self.cv1(torch.cat([torch.mean(x, 1, keepdim=True), torch.max(x, 1, keepdim=True)[0]], 1)))
-
-
-class CBAM(nn.Module):
- """Convolutional Block Attention Module."""
-
- def __init__(self, c1, kernel_size=7): # ch_in, kernels
- super().__init__()
- self.channel_attention = ChannelAttention(c1)
- self.spatial_attention = SpatialAttention(kernel_size)
-
- def forward(self, x):
- """Applies the forward pass through C1 module."""
- return self.spatial_attention(self.channel_attention(x))
-
-
-class Concat(nn.Module):
- """Concatenate a list of tensors along dimension."""
-
- def __init__(self, dimension=1):
- """Concatenates a list of tensors along a specified dimension."""
- super().__init__()
- self.d = dimension
-
- def forward(self, x):
- """Forward pass for the YOLOv8 mask Proto module."""
- return torch.cat(x, self.d)
diff --git a/spaces/vinayarukala31/mygenAiAvatarspeech/app.py b/spaces/vinayarukala31/mygenAiAvatarspeech/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/vinayarukala31/mygenAiAvatarspeech/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/volen/nft-search/app.py b/spaces/volen/nft-search/app.py
deleted file mode 100644
index 6c95a95cc764f5b602d466d4a88195ee160f088a..0000000000000000000000000000000000000000
--- a/spaces/volen/nft-search/app.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import streamlit as st
-import numpy as np
-import torch
-
-from datasets import load_dataset
-from html import escape
-from transformers import RobertaModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained('volen/nft-text', use_auth_token=st.secrets["access_token"])
-text_encoder = RobertaModel.from_pretrained('volen/nft-text', use_auth_token=st.secrets["access_token"]).eval()
-image_embeddings = torch.load('image_embeddings.pt', map_location=torch.device('cpu'))
-links = np.load('image_links.npy', allow_pickle=True)
-
-
-@st.experimental_memo
-def image_search(query, top_k=10):
- with torch.no_grad():
- text_embedding = text_encoder(**tokenizer(query, return_tensors='pt')).pooler_output
- _, indices = torch.cosine_similarity(image_embeddings, text_embedding).sort(descending=True)
- return [links[i] for i in indices[:top_k]]
-
-
-def get_html(url_list):
- html = "
"
- for url in url_list:
- html2 = f""
- html = html + html2
- html += "
"
- return html
-
-
-description = '''
-# nft search
-- Enter your search and hit enter
-- Note: So far we only support BAYC, cool cats, doodles and MAYC
-'''
-
-
-def main():
- st.markdown('''
- ''',
-
- unsafe_allow_html=True)
-
-
- st.sidebar.markdown(description)
- _, c, _ = st.columns((1, 3, 1))
- query = c.text_input('search box', value='cat beanie')
- c.text("It'll take a few secs to load new images")
- if len(query) > 0:
- results = image_search(query)
- st.markdown(get_html(results), unsafe_allow_html=True)
-
-
-if __name__ == '__main__':
- main()
\ No newline at end of file
diff --git a/spaces/vonewman/mon-application-de-traduction-de-text/README.md b/spaces/vonewman/mon-application-de-traduction-de-text/README.md
deleted file mode 100644
index 415e9bfb1f939583d2177e8e923e8af851e788be..0000000000000000000000000000000000000000
--- a/spaces/vonewman/mon-application-de-traduction-de-text/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Mon Application De Traduction De Text
-emoji: 🐠
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.4
-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/wyysf/GenMM/GPS.py b/spaces/wyysf/GenMM/GPS.py
deleted file mode 100644
index fd9f56fae76c041ebfe615413ffcfdc13e7276b3..0000000000000000000000000000000000000000
--- a/spaces/wyysf/GenMM/GPS.py
+++ /dev/null
@@ -1,324 +0,0 @@
-import os
-import os.path as osp
-import torch
-import torch.nn.functional as F
-import numpy as np
-import itertools
-from tensorboardX import SummaryWriter
-
-from NN.losses import make_criteria
-from utils.base import logger
-
-class GPS:
- def __init__(self,
- init_mode: str = 'random_synthesis',
- noise_sigma: float = 1.0,
- coarse_ratio: float = 0.2,
- coarse_ratio_factor: float = 6,
- pyr_factor: float = 0.75,
- num_stages_limit: int = -1,
- device: str = 'cuda:0',
- silent: bool = False
- ):
- '''
- Args:
- init_mode:
- - 'random_synthesis': init with random seed
- - 'random': init with random seed
- noise_sigma: float = 1.0, random noise.
- coarse_ratio: float = 0.2, ratio at the coarse level.
- pyr_factor: float = 0.75, pyramid factor.
- num_stages_limit: int = -1, no limit.
- device: str = 'cuda:0', default device.
- silent: bool = False, mute the output.
- '''
- self.init_mode = init_mode
- self.noise_sigma = noise_sigma
- self.coarse_ratio = coarse_ratio
- self.coarse_ratio_factor = coarse_ratio_factor
- self.pyr_factor = pyr_factor
- self.num_stages_limit = num_stages_limit
- self.device = torch.device(device)
- self.silent = silent
-
- def _get_pyramid_lengths(self, dest, ext=None):
- """Get a list of pyramid lengths"""
- if self.coarse_ratio == -1:
- self.coarse_ratio = np.around(ext['criteria']['patch_size'] * self.coarse_ratio_factor / dest, 2)
-
- lengths = [int(np.round(dest * self.coarse_ratio))]
- while lengths[-1] < dest:
- lengths.append(int(np.round(lengths[-1] / self.pyr_factor)))
- if lengths[-1] == lengths[-2]:
- lengths[-1] += 1
- lengths[-1] = dest
-
- return lengths
-
- def _get_target_pyramid(self, target, ext=None):
- """Reads a target motion(s) and create a pyraimd out of it. Ordered in increatorch.sing size"""
- self._num_target = len(target)
- lengths = []
- min_len = 10000
- for i in range(len(target)):
- new_length = self._get_pyramid_lengths(len(target[i]), ext)
- min_len = min(min_len, len(new_length))
- if self.num_stages_limit != -1:
- new_length = new_length[:self.num_stages_limit]
- lengths.append(new_length)
- for i in range(len(target)):
- lengths[i] = lengths[i][-min_len:]
- self.pyraimd_lengths = lengths
-
- target_pyramid = [[] for _ in range(len(lengths[0]))]
- for step in range(len(lengths[0])):
- for i in range(len(target)):
- length = lengths[i][step]
- motion = target[i]
- target_pyramid[step].append(motion.sample(size=length).to(self.device))
- # target_pyramid[step].append(motion.pos2velo(motion.sample(size=length)))
- # motion.motion_data = motion.pos2velo(motion.motion_data)
- # target_pyramid[step].append(motion.sample(size=length))
- # motion.motion_data = motion.velo2pos(motion.motion_data)
-
- if not self.silent:
- print('Levels:', lengths)
- for i in range(len(target_pyramid)):
- print(f'Number of clips in target pyramid {i} is {len(target_pyramid[i])}: {[[tgt.min(), tgt.max()] for tgt in target_pyramid[i]]}')
-
- return target_pyramid
-
- def _get_initial_motion(self):
- """Prepare the initial motion for optimization"""
- if 'random_synthesis' in str(self.init_mode):
- m = self.init_mode.split('/')[-1]
- if m =='random_synthesis':
- final_length = sum([i[-1] for i in self.pyraimd_lengths])
- elif 'x' in m:
- final_length = int(m.replace('x', '')) * sum([i[-1] for i in self.pyraimd_lengths])
- elif (self.init_mode.split('/')[-1]).isdigit():
- final_length = int(self.init_mode.split('/')[-1])
- else:
- raise ValueError(f'incorrect init_mode: {self.init_mode}')
-
- self.synthesized_lengths = self._get_pyramid_lengths(final_length)
-
- else:
- raise ValueError(f'Unsupported init_mode {self.init_mode}')
-
- initial_motion = F.interpolate(torch.cat([self.target_pyramid[0][i] for i in range(self._num_target)], dim=-1),
- size=self.synthesized_lengths[0], mode='linear', align_corners=True)
- if self.noise_sigma > 0:
- initial_motion_w_noise = initial_motion + torch.randn_like(initial_motion) * self.noise_sigma
- initial_motion_w_noise = torch.fmod(initial_motion_w_noise, 1.0)
- else:
- initial_motion_w_noise = initial_motion
-
- if not self.silent:
- print('Synthesized lengths:', self.synthesized_lengths)
- print('Initial motion:', initial_motion.min(), initial_motion.max())
- print('Initial motion with noise:', initial_motion_w_noise.min(), initial_motion_w_noise.max())
-
- return initial_motion_w_noise
-
- def run(self, target, mode="backpropagate", ext=None, debug_dir=None):
- '''
- Run the patch-based motion synthesis.
-
- Args:
- target (torch.Tensor): Target data.
- mode (str): Optimization mode. Support ['backpropagate', 'match_and_blend']
- ext (dict): extra data or constrain.
- debug_dir (str): Debug directory.
- '''
- # preprare data
- self.target_pyramid = self._get_target_pyramid(target, ext)
- self.synthesized = self._get_initial_motion()
- if debug_dir is not None:
- writer = SummaryWriter(log_dir=debug_dir)
-
- # prepare configuration
- if mode == "backpropagate":
- self.synthesized.requires_grad_(True)
- assert 'criteria' in ext.keys(), 'Please specify a criteria for synthsis.'
- criteria = make_criteria(ext['criteria']).to(self.device)
- elif mode == "match_and_blend":
- self.synthesized.requires_grad_(False)
- assert 'criteria' in ext.keys(), 'Please specify a criteria for synthsis.'
- criteria = make_criteria(ext['criteria']).to(self.device)
- else:
- raise ValueError(f'Unsupported mode: {mode}')
-
- # perform synthsis
- self.pbar = logger(ext['num_itrs'], len(self.target_pyramid))
- ext['pbar'] = self.pbar
- for lvl, lvl_target in enumerate(self.target_pyramid):
- self.pbar.new_lvl()
- if lvl > 0:
- with torch.no_grad():
- self.synthesized = F.interpolate(self.synthesized.detach(), size=self.synthesized_lengths[lvl], mode='linear')
- if mode == "backpropagate":
- self.synthesized.requires_grad_(True)
-
- if mode == "backpropagate": # direct optimize the synthesized motion
- self.synthesized, losses = GPS.backpropagate(self.synthesized, lvl_target, criteria, ext=ext)
- elif mode == "match_and_blend":
- self.synthesized, losses = GPS.match_and_blend(self.synthesized, lvl_target, criteria, ext=ext)
-
- criteria.clean_cache()
- if debug_dir:
- for itr in range(len(losses)):
- writer.add_scalar(f'optimize/losses_lvl{lvl}', losses[itr], itr)
- self.pbar.pbar.close()
-
-
- return self.synthesized.detach()
-
- @staticmethod
- def backpropagate(synthesized, targets, criteria=None, ext=None):
- """
- Minimizes criteria(synthesized, target) for num_steps SGD steps
- Args:
- targets (torch.Tensor): Target data.
- ext (dict): extra configurations.
- """
- if criteria is None:
- assert 'criteria' in ext.keys(), 'Criteria is not set'
- criteria = make_criteria(ext['criteria']).to(synthesized.device)
-
- optim = None
- if 'optimizer' in ext.keys():
- if ext['optimizer'] == 'Adam':
- optim = torch.optim.Adam([synthesized], lr=ext['lr'])
- elif ext['optimizer'] == 'SGD':
- optim = torch.optim.SGD([synthesized], lr=ext['lr'])
- elif ext['optimizer'] == 'RMSprop':
- optim = torch.optim.RMSprop([synthesized], lr=ext['lr'])
- else:
- print(f'use default RMSprop optimizer')
- optim = torch.optim.RMSprop([synthesized], lr=ext['lr']) if optim is None else optim
- # optim = torch.optim.Adam([synthesized], lr=ext['lr']) if optim is None else optim
- lr_decay = np.exp(np.log(0.333) / ext['num_itrs'])
-
- # other constraints
- trajectory = ext['trajectory'] if 'trajectory' in ext.keys() else None
-
- losses = []
- for _i in range(ext['num_itrs']):
- optim.zero_grad()
-
- loss = criteria(synthesized, targets)
-
- if trajectory is not None: ## velo constrain
- target_traj = F.interpolate(trajectory, size=synthesized.shape[-1], mode='linear')
- # target_traj = F.interpolate(trajectory, size=synthesized.shape[-1], mode='linear', align_corners=False)
- target_velo = ext['pos2velo'](target_traj)
-
- velo_mask = [-3, -1]
- loss += 1 * F.l1_loss(synthesized[:, velo_mask, :], target_velo[:, velo_mask, :])
-
- loss.backward()
- optim.step()
-
- # Update staus
- losses.append(loss.item())
- if 'pbar' in ext.keys():
- ext['pbar'].step()
- ext['pbar'].print()
-
- return synthesized, losses
-
- @staticmethod
- @torch.no_grad()
- def match_and_blend(synthesized, targets, criteria, ext):
- """
- Minimizes criteria(synthesized, target)
- Args:
- targets (torch.Tensor): Target data.
- ext (dict): extra configurations.
- """
- losses = []
- for _i in range(ext['num_itrs']):
- if 'parts_list' in ext.keys():
- def extract_part_motions(motion, parts_list):
- part_motions = []
- n_frames = motion.shape[-1]
- rot, pos = motion[:, :-3, :].reshape(-1, 6, n_frames), motion[:, -3:, :]
-
- for part in parts_list:
- # part -= 1
- part = [i -1 for i in part]
-
- # print(part)
- if 0 in part:
- part_motions += [torch.cat([rot[part].view(1, -1, n_frames), pos.view(1, -1, n_frames)], dim=1)]
- else:
- part_motions += [rot[part].view(1, -1, n_frames)]
-
- return part_motions
- def combine_part_motions(part_motions, parts_list):
- assert len(part_motions) == len(parts_list)
- n_frames = part_motions[0].shape[-1]
- l = max(list(itertools.chain(*parts_list)))
- # print(l, n_frames)
- # motion = torch.zeros((1, (l+1)*6 + 3, n_frames), device=part_motions[0].device)
- rot = torch.zeros(((l+1), 6, n_frames), device=part_motions[0].device)
- pos = torch.zeros((1, 3, n_frames), device=part_motions[0].device)
- div_rot = torch.zeros((l+1), device=part_motions[0].device)
- div_pos = torch.zeros(1, device=part_motions[0].device)
-
- for part_motion, part in zip(part_motions, parts_list):
- part = [i -1 for i in part]
-
- if 0 in part:
- # print(part_motion.shape)
- pos += part_motion[:, -3:, :]
- div_pos += 1
- rot[part] += part_motion[:, :-3, :].view(-1, 6, n_frames)
- div_rot[part] += 1
- else:
- rot[part] += part_motion.view(-1, 6, n_frames)
- div_rot[part] += 1
-
- # print(div_rot, div_pos)
- # print(rot.shape)
- rot = (rot.permute(1, 2, 0) / div_rot).permute(2, 0, 1)
- pos = pos / div_pos
-
- return torch.cat([rot.view(1, -1, n_frames), pos.view(1, 3, n_frames)], dim=1)
-
- # raw_synthesized = synthesized
- # print(synthesized, synthesized.shape)
- synthesized_part_motions = extract_part_motions(synthesized, ext['parts_list'])
- targets_part_motions = [extract_part_motions(target, ext['parts_list']) for target in targets]
-
- synthesized = []
- for _j in range(len(synthesized_part_motions)):
- synthesized_part_motion = synthesized_part_motions[_j]
- # synthesized += [synthesized_part_motion]
- targets_part_motion = [target[_j] for target in targets_part_motions]
- # # print(synthesized_part_motion.shape, targets_part_motion[0].shape)
- synthesized += [criteria(synthesized_part_motion, targets_part_motion, ext=ext, return_blended_results=True)[0]]
-
- # print(len(synthesized))
-
- synthesized = combine_part_motions(synthesized, ext['parts_list'])
- # print(synthesized, synthesized.shape)
- # print((raw_synthesized-synthesized > 0.00001).sum())
- # exit()
- # print(synthesized.shape)
- losses = 0
-
- # exit()
-
- else:
- synthesized, loss = criteria(synthesized, targets, ext=ext, return_blended_results=True)
-
- # Update staus
- losses.append(loss.item())
- if 'pbar' in ext.keys():
- ext['pbar'].step()
- ext['pbar'].print()
-
- return synthesized, losses
-
diff --git a/spaces/xiang-wuu/yolov5/utils/loggers/wandb/sweep.py b/spaces/xiang-wuu/yolov5/utils/loggers/wandb/sweep.py
deleted file mode 100644
index d49ea6f2778b2e87d0f535c2b3595ccceebab459..0000000000000000000000000000000000000000
--- a/spaces/xiang-wuu/yolov5/utils/loggers/wandb/sweep.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import sys
-from pathlib import Path
-
-import wandb
-
-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
-
-from train import parse_opt, train
-from utils.callbacks import Callbacks
-from utils.general import increment_path
-from utils.torch_utils import select_device
-
-
-def sweep():
- wandb.init()
- # Get hyp dict from sweep agent. Copy because train() modifies parameters which confused wandb.
- hyp_dict = vars(wandb.config).get("_items").copy()
-
- # Workaround: get necessary opt args
- opt = parse_opt(known=True)
- opt.batch_size = hyp_dict.get("batch_size")
- opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
- opt.epochs = hyp_dict.get("epochs")
- opt.nosave = True
- opt.data = hyp_dict.get("data")
- opt.weights = str(opt.weights)
- opt.cfg = str(opt.cfg)
- opt.data = str(opt.data)
- opt.hyp = str(opt.hyp)
- opt.project = str(opt.project)
- device = select_device(opt.device, batch_size=opt.batch_size)
-
- # train
- train(hyp_dict, opt, device, callbacks=Callbacks())
-
-
-if __name__ == "__main__":
- sweep()
diff --git a/spaces/xiaoweigo/White-box-Cartoonization/wbc/cartoonize.py b/spaces/xiaoweigo/White-box-Cartoonization/wbc/cartoonize.py
deleted file mode 100644
index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000
--- a/spaces/xiaoweigo/White-box-Cartoonization/wbc/cartoonize.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-import wbc.network as network
-import wbc.guided_filter as guided_filter
-from tqdm import tqdm
-
-
-def resize_crop(image):
- h, w, c = np.shape(image)
- if min(h, w) > 720:
- if h > w:
- h, w = int(720 * h / w), 720
- else:
- h, w = 720, int(720 * w / h)
- image = cv2.resize(image, (w, h),
- interpolation=cv2.INTER_AREA)
- h, w = (h // 8) * 8, (w // 8) * 8
- image = image[:h, :w, :]
- return image
-
-
-def cartoonize(load_folder, save_folder, model_path):
- print(model_path)
- input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(input_photo)
- final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- sess = tf.Session(config=config)
-
- sess.run(tf.global_variables_initializer())
- saver.restore(sess, tf.train.latest_checkpoint(model_path))
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = sess.run(final_out, feed_dict={input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-class Cartoonize:
- def __init__(self, model_path):
- print(model_path)
- self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(self.input_photo)
- self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- self.sess = tf.Session(config=config)
-
- self.sess.run(tf.global_variables_initializer())
- saver.restore(self.sess, tf.train.latest_checkpoint(model_path))
-
- def run(self, load_folder, save_folder):
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
- def run_sigle(self, load_path, save_path):
- try:
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-if __name__ == '__main__':
- model_path = 'saved_models'
- load_folder = 'test_images'
- save_folder = 'cartoonized_images'
- if not os.path.exists(save_folder):
- os.mkdir(save_folder)
- cartoonize(load_folder, save_folder, model_path)
diff --git a/spaces/xillegas/duolingo-bot/main.rb b/spaces/xillegas/duolingo-bot/main.rb
deleted file mode 100644
index f4c590e7f8240ab277043e8ba79168f5502f157a..0000000000000000000000000000000000000000
--- a/spaces/xillegas/duolingo-bot/main.rb
+++ /dev/null
@@ -1,55 +0,0 @@
-# Duolingo bot for Telegram
-require_relative 'user'
-
-require 'telegram/bot'
-require 'open-uri'
-require 'json'
-require 'dotenv'
-
-Dotenv.load('.env')
-
-token = ENV['TOKEN_TELEGRAM']
-
-def fetch_user(username_recieved)
- url = "https://www.duolingo.com/2017-06-30/users?username=#{username_recieved}"
- duohash_serialized = URI.open(url).read
- duohash = JSON.parse(duohash_serialized)
- User.new(duohash['users'][0]) unless duohash['users'].empty?
-end
-
-puts "Duolingo Bot for Telegram"
-puts "Version 1.0"
-puts "Developed by Xillegas"
-
-Telegram::Bot::Client.run(token) do |bot|
- bot.listen do |message|
- # puts "ok"
- if message.instance_of?(Telegram::Bot::Types::Message) && message.text.instance_of?(String)
- attempt = message.text.split
- if attempt[0] == '/starts'
- bot.api.send_message(chat_id: message.chat.id, text: "Hello, #{message.from.first_name}.")
- elsif attempt[0] == '/xp' && /^[a-zA-Z0-9._-]{3,16}$/.match?(attempt[1])
- duo_user = fetch_user(attempt[1])
- unless duo_user.nil?
- msg = "Hello #{message.from.first_name}, the xp of #{duo_user.username} is #{duo_user.total_xp}"
- bot.api.send_message(chat_id: message.chat.id, text: msg)
- end
- elsif attempt[0] == '/streak' && /^[a-zA-Z0-9._-]{3,16}$/.match?(attempt[1])
- duo_user = fetch_user(attempt[1])
- unless duo_user.nil?
- msg = "Hello #{message.from.first_name}, the streak of #{duo_user.username} is: 🔥#{duo_user.streak} days"
- bot.api.send_message(chat_id: message.chat.id, text: msg)
- end
- elsif attempt[0] == '/crowns' && /^[a-zA-Z0-9._-]{3,16}$/.match?(attempt[1])
- bot.api.send_chat_action(chat_id: message.chat.id, action: 'typing')
- duo_user = fetch_user(attempt[1])
- unless duo_user.nil?
- msg = "Hi #{message.from.first_name}, the user #{duo_user.username} has: 👑#{duo_user.crowns} crowns"
- bot.api.send_message(chat_id: message.chat.id, text: msg)
- end
- elsif attempt[0] == '/stoped'
- bot.api.send_message(chat_id: message.chat.id, text: "Bye, #{message.from.first_name}")
- end
- end
- end
-end
diff --git a/spaces/xizhongluomu/Real-CUGAN/README.md b/spaces/xizhongluomu/Real-CUGAN/README.md
deleted file mode 100644
index d673114edadba73e80f33a3c71bc0dbee8758cc8..0000000000000000000000000000000000000000
--- a/spaces/xizhongluomu/Real-CUGAN/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Real CUGAN
-emoji: 🐢
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: DianXian/Real-CUGAN
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/xp3857/Image_Restoration_Colorization/Global/data/Create_Bigfile.py b/spaces/xp3857/Image_Restoration_Colorization/Global/data/Create_Bigfile.py
deleted file mode 100644
index 2df6ef3ceec4a80903410901fd9656d9707af84e..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/Global/data/Create_Bigfile.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import os
-import struct
-from PIL import Image
-
-IMG_EXTENSIONS = [
- '.jpg', '.JPG', '.jpeg', '.JPEG',
- '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
-]
-
-
-def is_image_file(filename):
- return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def make_dataset(dir):
- images = []
- assert os.path.isdir(dir), '%s is not a valid directory' % dir
-
- for root, _, fnames in sorted(os.walk(dir)):
- for fname in fnames:
- if is_image_file(fname):
- #print(fname)
- path = os.path.join(root, fname)
- images.append(path)
-
- return images
-
-### Modify these 3 lines in your own environment
-indir="/home/ziyuwan/workspace/data/temp_old"
-target_folders=['VOC','Real_L_old','Real_RGB_old']
-out_dir ="/home/ziyuwan/workspace/data/temp_old"
-###
-
-if os.path.exists(out_dir) is False:
- os.makedirs(out_dir)
-
-#
-for target_folder in target_folders:
- curr_indir = os.path.join(indir, target_folder)
- curr_out_file = os.path.join(os.path.join(out_dir, '%s.bigfile'%(target_folder)))
- image_lists = make_dataset(curr_indir)
- image_lists.sort()
- with open(curr_out_file, 'wb') as wfid:
- # write total image number
- wfid.write(struct.pack('i', len(image_lists)))
- for i, img_path in enumerate(image_lists):
- # write file name first
- img_name = os.path.basename(img_path)
- img_name_bytes = img_name.encode('utf-8')
- wfid.write(struct.pack('i', len(img_name_bytes)))
- wfid.write(img_name_bytes)
- #
- # # write image data in
- with open(img_path, 'rb') as img_fid:
- img_bytes = img_fid.read()
- wfid.write(struct.pack('i', len(img_bytes)))
- wfid.write(img_bytes)
-
- if i % 1000 == 0:
- print('write %d images done' % i)
\ No newline at end of file
diff --git a/spaces/xszqxszq/sovits-svc-mix/data_utils.py b/spaces/xszqxszq/sovits-svc-mix/data_utils.py
deleted file mode 100644
index f6fbe86c95d5eaa14cbade0336d1843dfe543b75..0000000000000000000000000000000000000000
--- a/spaces/xszqxszq/sovits-svc-mix/data_utils.py
+++ /dev/null
@@ -1,411 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-import torch.utils.data
-from mel_processing import spectrogram_torch
-
-from utils import load_wav_to_torch, load_filepaths_and_text
-
-
-def dropout1d(myarray, ratio=0.5):
- indices = np.random.choice(np.arange(myarray.size), replace=False,
- size=int(myarray.size * ratio))
- myarray[indices] = 0
- return myarray
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths_and_text, hparams):
- self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
- self.text_cleaners = hparams.text_cleaners
- 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.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", 190)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_and_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
- lengths = []
- for audiopath, text, pitch in self.audiopaths_and_text:
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- self.lengths = lengths
-
- def get_audio_text_pair(self, audiopath_and_text):
- # separate filename and text
- audiopath, text, pitch = audiopath_and_text[0], audiopath_and_text[1], audiopath_and_text[2]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- pitch = self.get_pitch(pitch)
- return text, spec, wav, pitch
-
- def get_pitch(self, pitch):
-
- return torch.LongTensor(np.load(pitch))
-
- 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 os.path.exists(spec_filename):
- spec = torch.load(spec_filename)
- 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):
- soft = np.load(text)
- text_norm = torch.FloatTensor(soft)
- return text_norm
-
- def __getitem__(self, index):
- return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
- def __len__(self):
- return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate:
- """ 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 and aduio
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized]
- """
- # 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])
- max_pitch_len = max([x[3].shape[0] for x in batch])
- # print(batch)
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
-
- text_padded = torch.FloatTensor(len(batch), max_text_len, 256)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- pitch_padded = torch.LongTensor(len(batch), max_pitch_len)
-
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- pitch_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)
-
- pitch = row[3]
- pitch_padded[i, :pitch.size(0)] = pitch
-
- if self.return_ids:
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing, pitch_padded
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, pitch_padded
-
-
-"""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.text_cleaners = hparams.text_cleaners
- 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.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", 190)
-
- 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
-
- lengths = []
- for audiopath, sid, text, pitch in self.audiopaths_sid_text:
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- # separate filename, speaker_id and text
- audiopath, sid, text, pitch = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2], \
- audiopath_sid_text[3]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- sid = self.get_sid(sid)
- pitch = self.get_pitch(pitch)
-
- return text, spec, wav, pitch, sid
-
- 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 os.path.exists(spec_filename):
- spec = torch.load(spec_filename)
- 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):
- soft = np.load(text)
- text_norm = torch.FloatTensor(soft)
- return text_norm
-
- def get_pitch(self, pitch):
- return torch.LongTensor(np.load(pitch))
-
- 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])
- max_pitch_len = max([x[3].shape[0] 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.FloatTensor(len(batch), max_text_len, 256)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- pitch_padded = torch.LongTensor(len(batch), max_pitch_len)
-
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- pitch_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)
-
- pitch = row[3]
- pitch_padded[i, :pitch.size(0)] = pitch
-
- sid[i] = row[4]
-
- if self.return_ids:
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, pitch_padded, sid, ids_sorted_decreasing
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, pitch_padded, sid
-
-
-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)
- 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 <= 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/yderre-aubay/midi-player-demo/src/common/midi/midiConversion.ts b/spaces/yderre-aubay/midi-player-demo/src/common/midi/midiConversion.ts
deleted file mode 100644
index 2f1770ba2f06cf89bbc5b45a8aa1b4796f376d27..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/common/midi/midiConversion.ts
+++ /dev/null
@@ -1,170 +0,0 @@
-import { partition } from "lodash"
-import groupBy from "lodash/groupBy"
-import {
- AnyEvent,
- EndOfTrackEvent,
- MidiFile,
- read,
- StreamSource,
- write as writeMidiFile,
-} from "midifile-ts"
-import { toJS } from "mobx"
-import { isNotNull } from "../helpers/array"
-import { downloadBlob } from "../helpers/Downloader"
-import { addDeltaTime, toRawEvents } from "../helpers/toRawEvents"
-import {
- addTick,
- tickedEventsToTrackEvents,
- toTrackEvents,
-} from "../helpers/toTrackEvents"
-import Song from "../song"
-import Track, { AnyEventFeature } from "../track"
-
-const trackFromMidiEvents = (events: AnyEvent[]): Track => {
- const track = new Track()
-
- const channel = findChannel(events)
- if (channel !== undefined) {
- track.channel = channel
- }
- track.addEvents(toTrackEvents(events))
-
- return track
-}
-
-const tracksFromFormat0Events = (events: AnyEvent[]): Track[] => {
- const tickedEvents = addTick(events)
- const eventsPerChannel = groupBy(tickedEvents, (e) => {
- if ("channel" in e) {
- return e.channel + 1
- }
- return 0 // conductor track
- })
- const tracks: Track[] = []
- for (const channel of Object.keys(eventsPerChannel)) {
- const events = eventsPerChannel[channel]
- const ch = parseInt(channel)
- while (tracks.length <= ch) {
- const track = new Track()
- track.channel = ch > 0 ? ch - 1 : undefined
- tracks.push(track)
- }
- const track = tracks[ch]
- const trackEvents = tickedEventsToTrackEvents(events)
- track.addEvents(trackEvents)
- }
- return tracks
-}
-
-const findChannel = (events: AnyEvent[]) => {
- const chEvent = events.find((e) => {
- return e.type === "channel"
- })
- if (chEvent !== undefined && "channel" in chEvent) {
- return chEvent.channel
- }
- return undefined
-}
-
-const isConductorTrack = (track: AnyEvent[]) => findChannel(track) === undefined
-
-const isConductorEvent = (e: AnyEventFeature) =>
- "subtype" in e && (e.subtype === "timeSignature" || e.subtype === "setTempo")
-
-export const createConductorTrackIfNeeded = (
- tracks: AnyEvent[][],
-): AnyEvent[][] => {
- // Find conductor track
- let [conductorTracks, normalTracks] = partition(tracks, isConductorTrack)
-
- // Create a conductor track if there is no conductor track
- if (conductorTracks.length === 0) {
- conductorTracks.push([])
- }
-
- const [conductorTrack, ...restTracks] = [
- ...conductorTracks,
- ...normalTracks,
- ].map(addTick)
-
- const newTracks = restTracks.map((track) =>
- track
- .map((e) => {
- // Collect all conductor events
- if (isConductorEvent(e)) {
- conductorTrack.push(e)
- return null
- }
- return e
- })
- .filter(isNotNull),
- )
-
- return [conductorTrack, ...newTracks].map(addDeltaTime)
-}
-
-const getTracks = (midi: MidiFile): Track[] => {
- switch (midi.header.formatType) {
- case 0:
- return tracksFromFormat0Events(midi.tracks[0])
- case 1:
- return createConductorTrackIfNeeded(midi.tracks).map(trackFromMidiEvents)
- default:
- throw new Error(`Unsupported midi format ${midi.header.formatType}`)
- }
-}
-
-export function songFromMidi(data: StreamSource) {
- const song = new Song()
- const midi = read(data)
-
- getTracks(midi).forEach((t) => song.addTrack(t))
-
- if (midi.header.formatType === 1 && song.tracks.length > 0) {
- // Use the first track name as the song title
- const name = song.tracks[0].name
- if (name !== undefined) {
- song.name = name
- }
- }
-
- song.timebase = midi.header.ticksPerBeat
-
- return song
-}
-
-const setChannel =
- (channel: number) =>
- (e: AnyEvent): AnyEvent => {
- if (e.type === "channel") {
- return { ...e, channel }
- }
- return e
- }
-
-export function songToMidiEvents(song: Song): AnyEvent[][] {
- const tracks = toJS(song.tracks)
- return tracks.map((t) => {
- const endOfTrack: EndOfTrackEvent = {
- deltaTime: 0,
- type: "meta",
- subtype: "endOfTrack",
- }
- const rawEvents = [...toRawEvents(t.events), endOfTrack]
- if (t.channel !== undefined) {
- return rawEvents.map(setChannel(t.channel))
- }
- return rawEvents
- })
-}
-
-export function songToMidi(song: Song) {
- const rawTracks = songToMidiEvents(song)
- return writeMidiFile(rawTracks, song.timebase)
-}
-
-export function downloadSongAsMidi(song: Song) {
- const bytes = songToMidi(song)
- const blob = new Blob([bytes], { type: "application/octet-stream" })
- downloadBlob(blob, song.filepath.length > 0 ? song.filepath : "no name.mid")
-}
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/Toolbar/AutoScrollButton.tsx b/spaces/yderre-aubay/midi-player-demo/src/main/components/Toolbar/AutoScrollButton.tsx
deleted file mode 100644
index 82e99c38db1fecb841ac43adfb1fb8f8aafe561f..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/Toolbar/AutoScrollButton.tsx
+++ /dev/null
@@ -1,27 +0,0 @@
-import styled from "@emotion/styled"
-import KeyboardTab from "mdi-react/KeyboardTabIcon"
-import { FC } from "react"
-import { Localized } from "../../../components/Localized"
-import { Tooltip } from "../../../components/Tooltip"
-import { ToolbarButton } from "./ToolbarButton"
-
-const AutoScrollIcon = styled(KeyboardTab)`
- height: 2rem;
- font-size: 1.3rem;
-`
-
-export interface AutoScrollButtonProps {
- onClick: () => void
- selected: boolean
-}
-
-export const AutoScrollButton: FC = ({
- onClick,
- selected,
-}) => (
- auto-scroll}>
-
-
-
-
-)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/donut/processing_donut.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/donut/processing_donut.py
deleted file mode 100644
index f797aec18ed42230dbd81780242f63a2228a9b23..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/donut/processing_donut.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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.
-"""
-Processor class for Donut.
-"""
-import re
-import warnings
-from contextlib import contextmanager
-
-from ...processing_utils import ProcessorMixin
-
-
-class DonutProcessor(ProcessorMixin):
- r"""
- Constructs a Donut processor which wraps a Donut image processor and an XLMRoBERTa tokenizer into a single
- processor.
-
- [`DonutProcessor`] offers all the functionalities of [`DonutImageProcessor`] and
- [`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]. See the [`~DonutProcessor.__call__`] and
- [`~DonutProcessor.decode`] for more information.
-
- Args:
- image_processor ([`DonutImageProcessor`], *optional*):
- An instance of [`DonutImageProcessor`]. The image processor is a required input.
- tokenizer ([`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`], *optional*):
- An instance of [`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]. The tokenizer is a required input.
- """
- attributes = ["image_processor", "tokenizer"]
- image_processor_class = "AutoImageProcessor"
- tokenizer_class = "AutoTokenizer"
-
- def __init__(self, image_processor=None, tokenizer=None, **kwargs):
- feature_extractor = None
- if "feature_extractor" in kwargs:
- warnings.warn(
- "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
- " instead.",
- FutureWarning,
- )
- feature_extractor = kwargs.pop("feature_extractor")
-
- image_processor = image_processor if image_processor is not None else feature_extractor
- if image_processor is None:
- raise ValueError("You need to specify an `image_processor`.")
- if tokenizer is None:
- raise ValueError("You need to specify a `tokenizer`.")
-
- super().__init__(image_processor, tokenizer)
- self.current_processor = self.image_processor
- self._in_target_context_manager = False
-
- def __call__(self, *args, **kwargs):
- """
- When used in normal mode, this method forwards all its arguments to AutoImageProcessor's
- [`~AutoImageProcessor.__call__`] and returns its output. If used in the context
- [`~DonutProcessor.as_target_processor`] this method forwards all its arguments to DonutTokenizer's
- [`~DonutTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information.
- """
- # For backward compatibility
- if self._in_target_context_manager:
- return self.current_processor(*args, **kwargs)
-
- images = kwargs.pop("images", None)
- text = kwargs.pop("text", None)
- if len(args) > 0:
- images = args[0]
- args = args[1:]
-
- if images is None and text is None:
- raise ValueError("You need to specify either an `images` or `text` input to process.")
-
- if images is not None:
- inputs = self.image_processor(images, *args, **kwargs)
- if text is not None:
- encodings = self.tokenizer(text, **kwargs)
-
- if text is None:
- return inputs
- elif images is None:
- return encodings
- else:
- inputs["labels"] = encodings["input_ids"]
- return inputs
-
- def batch_decode(self, *args, **kwargs):
- """
- This method forwards all its arguments to DonutTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
- to the docstring of this method for more information.
- """
- return self.tokenizer.batch_decode(*args, **kwargs)
-
- def decode(self, *args, **kwargs):
- """
- This method forwards all its arguments to DonutTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
- docstring of this method for more information.
- """
- return self.tokenizer.decode(*args, **kwargs)
-
- @contextmanager
- def as_target_processor(self):
- """
- Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning TrOCR.
- """
- warnings.warn(
- "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
- "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
- "your images inputs, or in a separate call."
- )
- self._in_target_context_manager = True
- self.current_processor = self.tokenizer
- yield
- self.current_processor = self.image_processor
- self._in_target_context_manager = False
-
- def token2json(self, tokens, is_inner_value=False, added_vocab=None):
- """
- Convert a (generated) token sequence into an ordered JSON format.
- """
- if added_vocab is None:
- added_vocab = self.tokenizer.get_added_vocab()
-
- output = {}
-
- while tokens:
- start_token = re.search(r"", tokens, re.IGNORECASE)
- if start_token is None:
- break
- key = start_token.group(1)
- key_escaped = re.escape(key)
-
- end_token = re.search(rf"", tokens, re.IGNORECASE)
- start_token = start_token.group()
- if end_token is None:
- tokens = tokens.replace(start_token, "")
- else:
- end_token = end_token.group()
- start_token_escaped = re.escape(start_token)
- end_token_escaped = re.escape(end_token)
- content = re.search(f"{start_token_escaped}(.*?){end_token_escaped}", tokens, re.IGNORECASE)
- if content is not None:
- content = content.group(1).strip()
- if r""):
- leaf = leaf.strip()
- if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
- leaf = leaf[1:-2] # for categorical special tokens
- output[key].append(leaf)
- if len(output[key]) == 1:
- output[key] = output[key][0]
-
- tokens = tokens[tokens.find(end_token) + len(end_token) :].strip()
- if tokens[:6] == r"": # non-leaf nodes
- return [output] + self.token2json(tokens[6:], is_inner_value=True, added_vocab=added_vocab)
-
- if len(output):
- return [output] if is_inner_value else output
- else:
- return [] if is_inner_value else {"text_sequence": tokens}
-
- @property
- def feature_extractor_class(self):
- warnings.warn(
- "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
- FutureWarning,
- )
- return self.image_processor_class
-
- @property
- def feature_extractor(self):
- warnings.warn(
- "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
- FutureWarning,
- )
- return self.image_processor
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/preprocess_hubert_f0.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/preprocess_hubert_f0.py
deleted file mode 100644
index 3871d8d78ff2ca1b847c6b6fa40e0d3d68545007..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/preprocess_hubert_f0.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import math
-import multiprocessing
-import os
-import argparse
-from random import shuffle
-import random
-
-import torch
-from glob import glob
-from tqdm import tqdm
-from modules.mel_processing import spectrogram_torch
-import json
-
-import utils
-import logging
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-import diffusion.logger.utils as du
-from diffusion.vocoder import Vocoder
-
-import librosa
-import numpy as np
-
-hps = utils.get_hparams_from_file("configs/config.json")
-dconfig = du.load_config("configs/diffusion.yaml")
-sampling_rate = hps.data.sampling_rate
-hop_length = hps.data.hop_length
-speech_encoder = hps["model"]["speech_encoder"]
-
-
-def process_one(filename, hmodel,f0p,diff=False,mel_extractor=None):
- # print(filename)
- wav, sr = librosa.load(filename, sr=sampling_rate)
- audio_norm = torch.FloatTensor(wav)
- audio_norm = audio_norm.unsqueeze(0)
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
- soft_path = filename + ".soft.pt"
- if not os.path.exists(soft_path):
- wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
- wav16k = torch.from_numpy(wav16k).to(device)
- c = hmodel.encoder(wav16k)
- torch.save(c.cpu(), soft_path)
-
- f0_path = filename + ".f0.npy"
- if not os.path.exists(f0_path):
- f0_predictor = utils.get_f0_predictor(f0p,sampling_rate=sampling_rate, hop_length=hop_length,device=None,threshold=0.05)
- f0,uv = f0_predictor.compute_f0_uv(
- wav
- )
- np.save(f0_path, np.asanyarray((f0,uv),dtype=object))
-
-
- spec_path = filename.replace(".wav", ".spec.pt")
- if not os.path.exists(spec_path):
- # Process spectrogram
- # The following code can't be replaced by torch.FloatTensor(wav)
- # because load_wav_to_torch return a tensor that need to be normalized
-
- if sr != hps.data.sampling_rate:
- raise ValueError(
- "{} SR doesn't match target {} SR".format(
- sr, hps.data.sampling_rate
- )
- )
-
- #audio_norm = audio / hps.data.max_wav_value
-
- spec = spectrogram_torch(
- audio_norm,
- hps.data.filter_length,
- hps.data.sampling_rate,
- hps.data.hop_length,
- hps.data.win_length,
- center=False,
- )
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_path)
-
- if diff or hps.model.vol_embedding:
- volume_path = filename + ".vol.npy"
- volume_extractor = utils.Volume_Extractor(hop_length)
- if not os.path.exists(volume_path):
- volume = volume_extractor.extract(audio_norm)
- np.save(volume_path, volume.to('cpu').numpy())
-
- if diff:
- mel_path = filename + ".mel.npy"
- if not os.path.exists(mel_path) and mel_extractor is not None:
- mel_t = mel_extractor.extract(audio_norm.to(device), sampling_rate)
- mel = mel_t.squeeze().to('cpu').numpy()
- np.save(mel_path, mel)
- aug_mel_path = filename + ".aug_mel.npy"
- aug_vol_path = filename + ".aug_vol.npy"
- max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5
- max_shift = min(1, np.log10(1/max_amp))
- log10_vol_shift = random.uniform(-1, max_shift)
- keyshift = random.uniform(-5, 5)
- if mel_extractor is not None:
- aug_mel_t = mel_extractor.extract(audio_norm * (10 ** log10_vol_shift), sampling_rate, keyshift = keyshift)
- aug_mel = aug_mel_t.squeeze().to('cpu').numpy()
- aug_vol = volume_extractor.extract(audio_norm * (10 ** log10_vol_shift))
- if not os.path.exists(aug_mel_path):
- np.save(aug_mel_path,np.asanyarray((aug_mel,keyshift),dtype=object))
- if not os.path.exists(aug_vol_path):
- np.save(aug_vol_path,aug_vol.to('cpu').numpy())
-
-
-def process_batch(filenames,f0p,diff=False,mel_extractor=None):
- print("Loading speech encoder for content...")
- device = "cuda" if torch.cuda.is_available() else "cpu"
- hmodel = utils.get_speech_encoder(speech_encoder,device=device)
- print("Loaded speech encoder.")
- for filename in tqdm(filenames):
- process_one(filename, hmodel,f0p,diff,mel_extractor)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--in_dir", type=str, default="dataset/44k", help="path to input dir"
- )
- parser.add_argument(
- '--use_diff',action='store_true', help='Whether to use the diffusion model'
- )
- parser.add_argument(
- '--f0_predictor', type=str, default="dio", help='Select F0 predictor, can select crepe,pm,dio,harvest, default pm(note: crepe is original F0 using mean filter)'
- )
- parser.add_argument(
- '--num_processes', type=int, default=1, help='You are advised to set the number of processes to the same as the number of CPU cores'
- )
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- args = parser.parse_args()
- f0p = args.f0_predictor
- print(speech_encoder)
- print(f0p)
- if args.use_diff:
- print("use_diff")
- print("Loading Mel Extractor...")
- mel_extractor = Vocoder(dconfig.vocoder.type, dconfig.vocoder.ckpt, device = device)
- print("Loaded Mel Extractor.")
- else:
- mel_extractor = None
- filenames = glob(f"{args.in_dir}/*/*.wav", recursive=True) # [:10]
- shuffle(filenames)
- multiprocessing.set_start_method("spawn", force=True)
-
- num_processes = args.num_processes
- chunk_size = int(math.ceil(len(filenames) / num_processes))
- chunks = [
- filenames[i : i + chunk_size] for i in range(0, len(filenames), chunk_size)
- ]
- print([len(c) for c in chunks])
- processes = [
- multiprocessing.Process(target=process_batch, args=(chunk,f0p,args.use_diff,mel_extractor)) for chunk in chunks
- ]
- for p in processes:
- p.start()
diff --git a/spaces/yoinked/audio-diffusion/scripts/train_unet.py b/spaces/yoinked/audio-diffusion/scripts/train_unet.py
deleted file mode 100644
index a29a7b23a39bb83a362d8376a16084c933c0ccd2..0000000000000000000000000000000000000000
--- a/spaces/yoinked/audio-diffusion/scripts/train_unet.py
+++ /dev/null
@@ -1,453 +0,0 @@
-# based on https://github.com/huggingface/diffusers/blob/main/examples/train_unconditional.py
-
-import argparse
-import os
-import pickle
-import random
-from pathlib import Path
-from typing import Optional
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from datasets import load_dataset, load_from_disk
-from diffusers import (AutoencoderKL, DDIMScheduler, DDPMScheduler,
- UNet2DConditionModel, UNet2DModel)
-from diffusers.optimization import get_scheduler
-from diffusers.pipelines.audio_diffusion import Mel
-from diffusers.training_utils import EMAModel
-from huggingface_hub import HfFolder, Repository, whoami
-from librosa.util import normalize
-from torchvision.transforms import Compose, Normalize, ToTensor
-from tqdm.auto import tqdm
-
-from audiodiffusion.pipeline_audio_diffusion import AudioDiffusionPipeline
-
-logger = get_logger(__name__)
-
-
-def get_full_repo_name(model_id: str,
- organization: Optional[str] = None,
- token: Optional[str] = None):
- if token is None:
- token = HfFolder.get_token()
- if organization is None:
- username = whoami(token)["name"]
- return f"{username}/{model_id}"
- else:
- return f"{organization}/{model_id}"
-
-
-def main(args):
- output_dir = os.environ.get("SM_MODEL_DIR", None) or args.output_dir
- logging_dir = os.path.join(output_dir, args.logging_dir)
- accelerator = Accelerator(
- gradient_accumulation_steps=args.gradient_accumulation_steps,
- mixed_precision=args.mixed_precision,
- log_with="tensorboard",
- logging_dir=logging_dir,
- )
-
- if args.dataset_name is not None:
- if os.path.exists(args.dataset_name):
- dataset = load_from_disk(
- args.dataset_name,
- storage_options=args.dataset_config_name)["train"]
- else:
- dataset = load_dataset(
- args.dataset_name,
- args.dataset_config_name,
- cache_dir=args.cache_dir,
- use_auth_token=True if args.use_auth_token else None,
- split="train",
- )
- else:
- dataset = load_dataset(
- "imagefolder",
- data_dir=args.train_data_dir,
- cache_dir=args.cache_dir,
- split="train",
- )
- # Determine image resolution
- resolution = dataset[0]["image"].height, dataset[0]["image"].width
-
- augmentations = Compose([
- ToTensor(),
- Normalize([0.5], [0.5]),
- ])
-
- def transforms(examples):
- if args.vae is not None and vqvae.config["in_channels"] == 3:
- images = [
- augmentations(image.convert("RGB"))
- for image in examples["image"]
- ]
- else:
- images = [augmentations(image) for image in examples["image"]]
- if args.encodings is not None:
- encoding = [encodings[file] for file in examples["audio_file"]]
- return {"input": images, "encoding": encoding}
- return {"input": images}
-
- dataset.set_transform(transforms)
- train_dataloader = torch.utils.data.DataLoader(
- dataset, batch_size=args.train_batch_size, shuffle=True)
-
- if args.encodings is not None:
- encodings = pickle.load(open(args.encodings, "rb"))
-
- vqvae = None
- if args.vae is not None:
- try:
- vqvae = AutoencoderKL.from_pretrained(args.vae)
- except EnvironmentError:
- vqvae = AudioDiffusionPipeline.from_pretrained(args.vae).vqvae
- # Determine latent resolution
- with torch.no_grad():
- latent_resolution = vqvae.encode(
- torch.zeros((1, 1) +
- resolution)).latent_dist.sample().shape[2:]
-
- if args.from_pretrained is not None:
- pipeline = AudioDiffusionPipeline.from_pretrained(args.from_pretrained)
- mel = pipeline.mel
- model = pipeline.unet
- if hasattr(pipeline, "vqvae"):
- vqvae = pipeline.vqvae
-
- else:
- if args.encodings is None:
- model = UNet2DModel(
- sample_size=resolution if vqvae is None else latent_resolution,
- in_channels=1
- if vqvae is None else vqvae.config["latent_channels"],
- out_channels=1
- if vqvae is None else vqvae.config["latent_channels"],
- layers_per_block=2,
- block_out_channels=(128, 128, 256, 256, 512, 512),
- down_block_types=(
- "DownBlock2D",
- "DownBlock2D",
- "DownBlock2D",
- "DownBlock2D",
- "AttnDownBlock2D",
- "DownBlock2D",
- ),
- up_block_types=(
- "UpBlock2D",
- "AttnUpBlock2D",
- "UpBlock2D",
- "UpBlock2D",
- "UpBlock2D",
- "UpBlock2D",
- ),
- )
-
- else:
- model = UNet2DConditionModel(
- sample_size=resolution if vqvae is None else latent_resolution,
- in_channels=1
- if vqvae is None else vqvae.config["latent_channels"],
- out_channels=1
- if vqvae is None else vqvae.config["latent_channels"],
- layers_per_block=2,
- block_out_channels=(128, 256, 512, 512),
- down_block_types=(
- "CrossAttnDownBlock2D",
- "CrossAttnDownBlock2D",
- "CrossAttnDownBlock2D",
- "DownBlock2D",
- ),
- up_block_types=(
- "UpBlock2D",
- "CrossAttnUpBlock2D",
- "CrossAttnUpBlock2D",
- "CrossAttnUpBlock2D",
- ),
- cross_attention_dim=list(encodings.values())[0].shape[-1],
- )
-
- if args.scheduler == "ddpm":
- noise_scheduler = DDPMScheduler(
- num_train_timesteps=args.num_train_steps)
- else:
- noise_scheduler = DDIMScheduler(
- num_train_timesteps=args.num_train_steps)
-
- optimizer = torch.optim.AdamW(
- model.parameters(),
- lr=args.learning_rate,
- betas=(args.adam_beta1, args.adam_beta2),
- weight_decay=args.adam_weight_decay,
- eps=args.adam_epsilon,
- )
-
- lr_scheduler = get_scheduler(
- args.lr_scheduler,
- optimizer=optimizer,
- num_warmup_steps=args.lr_warmup_steps,
- num_training_steps=(len(train_dataloader) * args.num_epochs) //
- args.gradient_accumulation_steps,
- )
-
- model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
- model, optimizer, train_dataloader, lr_scheduler)
-
- ema_model = EMAModel(
- getattr(model, "module", model),
- inv_gamma=args.ema_inv_gamma,
- power=args.ema_power,
- max_value=args.ema_max_decay,
- )
-
- if args.push_to_hub:
- if args.hub_model_id is None:
- repo_name = get_full_repo_name(Path(output_dir).name,
- token=args.hub_token)
- else:
- repo_name = args.hub_model_id
- repo = Repository(output_dir, clone_from=repo_name)
-
- if accelerator.is_main_process:
- run = os.path.split(__file__)[-1].split(".")[0]
- accelerator.init_trackers(run)
-
- mel = Mel(
- x_res=resolution[1],
- y_res=resolution[0],
- hop_length=args.hop_length,
- sample_rate=args.sample_rate,
- n_fft=args.n_fft,
- )
-
- global_step = 0
- for epoch in range(args.num_epochs):
- progress_bar = tqdm(total=len(train_dataloader),
- disable=not accelerator.is_local_main_process)
- progress_bar.set_description(f"Epoch {epoch}")
-
- if epoch < args.start_epoch:
- for step in range(len(train_dataloader)):
- optimizer.step()
- lr_scheduler.step()
- progress_bar.update(1)
- global_step += 1
- if epoch == args.start_epoch - 1 and args.use_ema:
- ema_model.optimization_step = global_step
- continue
-
- model.train()
- for step, batch in enumerate(train_dataloader):
- clean_images = batch["input"]
-
- if vqvae is not None:
- vqvae.to(clean_images.device)
- with torch.no_grad():
- clean_images = vqvae.encode(
- clean_images).latent_dist.sample()
- # Scale latent images to ensure approximately unit variance
- clean_images = clean_images * 0.18215
-
- # Sample noise that we'll add to the images
- noise = torch.randn(clean_images.shape).to(clean_images.device)
- bsz = clean_images.shape[0]
- # Sample a random timestep for each image
- timesteps = torch.randint(
- 0,
- noise_scheduler.config.num_train_timesteps,
- (bsz, ),
- device=clean_images.device,
- ).long()
-
- # Add noise to the clean images according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_images = noise_scheduler.add_noise(clean_images, noise,
- timesteps)
-
- with accelerator.accumulate(model):
- # Predict the noise residual
- if args.encodings is not None:
- noise_pred = model(noisy_images, timesteps,
- batch["encoding"])["sample"]
- else:
- noise_pred = model(noisy_images, timesteps)["sample"]
- loss = F.mse_loss(noise_pred, noise)
- accelerator.backward(loss)
-
- if accelerator.sync_gradients:
- accelerator.clip_grad_norm_(model.parameters(), 1.0)
- optimizer.step()
- lr_scheduler.step()
- if args.use_ema:
- ema_model.step(model)
- optimizer.zero_grad()
-
- progress_bar.update(1)
- global_step += 1
-
- logs = {
- "loss": loss.detach().item(),
- "lr": lr_scheduler.get_last_lr()[0],
- "step": global_step,
- }
- if args.use_ema:
- logs["ema_decay"] = ema_model.decay
- progress_bar.set_postfix(**logs)
- accelerator.log(logs, step=global_step)
- progress_bar.close()
-
- accelerator.wait_for_everyone()
-
- # Generate sample images for visual inspection
- if accelerator.is_main_process:
- if ((epoch + 1) % args.save_model_epochs == 0
- or (epoch + 1) % args.save_images_epochs == 0
- or epoch == args.num_epochs - 1):
- unet = accelerator.unwrap_model(model)
- if args.use_ema:
- ema_model.copy_to(unet.parameters())
- pipeline = AudioDiffusionPipeline(
- vqvae=vqvae,
- unet=unet,
- mel=mel,
- scheduler=noise_scheduler,
- )
-
- if (
- epoch + 1
- ) % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
- pipeline.save_pretrained(output_dir)
-
- # save the model
- if args.push_to_hub:
- repo.push_to_hub(
- commit_message=f"Epoch {epoch}",
- blocking=False,
- auto_lfs_prune=True,
- )
-
- if (epoch + 1) % args.save_images_epochs == 0:
- generator = torch.Generator(
- device=clean_images.device).manual_seed(42)
-
- if args.encodings is not None:
- random.seed(42)
- encoding = torch.stack(
- random.sample(list(encodings.values()),
- args.eval_batch_size)).to(
- clean_images.device)
- else:
- encoding = None
-
- # run pipeline in inference (sample random noise and denoise)
- images, (sample_rate, audios) = pipeline(
- generator=generator,
- batch_size=args.eval_batch_size,
- return_dict=False,
- encoding=encoding,
- )
-
- # denormalize the images and save to tensorboard
- images = np.array([
- np.frombuffer(image.tobytes(), dtype="uint8").reshape(
- (len(image.getbands()), image.height, image.width))
- for image in images
- ])
- accelerator.trackers[0].writer.add_images(
- "test_samples", images, epoch)
- for _, audio in enumerate(audios):
- accelerator.trackers[0].writer.add_audio(
- f"test_audio_{_}",
- normalize(audio),
- epoch,
- sample_rate=sample_rate,
- )
- accelerator.wait_for_everyone()
-
- accelerator.end_training()
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(
- description="Simple example of a training script.")
- parser.add_argument("--local_rank", type=int, default=-1)
- parser.add_argument("--dataset_name", type=str, default=None)
- parser.add_argument("--dataset_config_name", type=str, default=None)
- parser.add_argument(
- "--train_data_dir",
- type=str,
- default=None,
- help="A folder containing the training data.",
- )
- parser.add_argument("--output_dir", type=str, default="ddpm-model-64")
- parser.add_argument("--overwrite_output_dir", type=bool, default=False)
- parser.add_argument("--cache_dir", type=str, default=None)
- parser.add_argument("--train_batch_size", type=int, default=16)
- parser.add_argument("--eval_batch_size", type=int, default=16)
- parser.add_argument("--num_epochs", type=int, default=100)
- parser.add_argument("--save_images_epochs", type=int, default=10)
- parser.add_argument("--save_model_epochs", type=int, default=10)
- parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
- parser.add_argument("--learning_rate", type=float, default=1e-4)
- parser.add_argument("--lr_scheduler", type=str, default="cosine")
- parser.add_argument("--lr_warmup_steps", type=int, default=500)
- parser.add_argument("--adam_beta1", type=float, default=0.95)
- parser.add_argument("--adam_beta2", type=float, default=0.999)
- parser.add_argument("--adam_weight_decay", type=float, default=1e-6)
- parser.add_argument("--adam_epsilon", type=float, default=1e-08)
- parser.add_argument("--use_ema", type=bool, default=True)
- parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
- parser.add_argument("--ema_power", type=float, default=3 / 4)
- parser.add_argument("--ema_max_decay", type=float, default=0.9999)
- parser.add_argument("--push_to_hub", type=bool, default=False)
- parser.add_argument("--use_auth_token", type=bool, default=False)
- parser.add_argument("--hub_token", type=str, default=None)
- parser.add_argument("--hub_model_id", type=str, default=None)
- parser.add_argument("--hub_private_repo", type=bool, default=False)
- parser.add_argument("--logging_dir", type=str, default="logs")
- parser.add_argument(
- "--mixed_precision",
- type=str,
- default="no",
- choices=["no", "fp16", "bf16"],
- help=(
- "Whether to use mixed precision. Choose"
- "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
- "and an Nvidia Ampere GPU."),
- )
- parser.add_argument("--hop_length", type=int, default=512)
- parser.add_argument("--sample_rate", type=int, default=22050)
- parser.add_argument("--n_fft", type=int, default=2048)
- parser.add_argument("--from_pretrained", type=str, default=None)
- parser.add_argument("--start_epoch", type=int, default=0)
- parser.add_argument("--num_train_steps", type=int, default=1000)
- parser.add_argument("--scheduler",
- type=str,
- default="ddpm",
- help="ddpm or ddim")
- parser.add_argument(
- "--vae",
- type=str,
- default=None,
- help="pretrained VAE model for latent diffusion",
- )
- parser.add_argument(
- "--encodings",
- type=str,
- default=None,
- help="picked dictionary mapping audio_file to encoding",
- )
-
- args = parser.parse_args()
- env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
- if env_local_rank != -1 and env_local_rank != args.local_rank:
- args.local_rank = env_local_rank
-
- if args.dataset_name is None and args.train_data_dir is None:
- raise ValueError(
- "You must specify either a dataset name from the hub or a train data directory."
- )
-
- main(args)
diff --git a/spaces/yunfei0710/gpt-academic/docs/README.md.Italian.md b/spaces/yunfei0710/gpt-academic/docs/README.md.Italian.md
deleted file mode 100644
index 301ce697dca4a150eb522b8522aa9bc3352ff4dd..0000000000000000000000000000000000000000
--- a/spaces/yunfei0710/gpt-academic/docs/README.md.Italian.md
+++ /dev/null
@@ -1,316 +0,0 @@
-> **Nota**
->
-> Durante l'installazione delle dipendenze, selezionare rigorosamente le **versioni specificate** nel file requirements.txt.
->
-> ` pip install -r requirements.txt`
-
-# GPT Ottimizzazione Accademica (GPT Academic)
-
-**Se ti piace questo progetto, ti preghiamo di dargli una stella. Se hai sviluppato scorciatoie accademiche o plugin funzionali più utili, non esitare ad aprire una issue o pull request. Abbiamo anche una README in [Inglese|](README_EN.md)[Giapponese|](README_JP.md)[Coreano|](https://github.com/mldljyh/ko_gpt_academic)[Russo|](README_RS.md)[Francese](README_FR.md) tradotta da questo stesso progetto.
-Per tradurre questo progetto in qualsiasi lingua con GPT, leggere e eseguire [`multi_language.py`](multi_language.py) (sperimentale).
-
-> **Nota**
->
-> 1. Si prega di notare che solo i plugin (pulsanti) contrassegnati in **rosso** supportano la lettura di file, alcuni plugin sono posizionati nel **menu a discesa** nella zona dei plugin. Accettiamo e gestiamo PR per qualsiasi nuovo plugin con **massima priorità**!
->
-> 2. Le funzionalità di ogni file di questo progetto sono descritte dettagliatamente nella propria analisi di autotraduzione [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A). Con l'iterazione delle versioni, è possibile fare clic sui plugin funzionali correlati in qualsiasi momento per richiamare GPT e generare nuovamente il rapporto di analisi automatica del progetto. Le domande frequenti sono riassunte nella [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Metodo di installazione] (#installazione).
->
-> 3. Questo progetto è compatibile e incoraggia l'utilizzo di grandi modelli di linguaggio di produzione nazionale come chatglm, RWKV, Pangu ecc. Supporta la coesistenza di più api-key e può essere compilato nel file di configurazione come `API_KEY="openai-key1,openai-key2,api2d-key3"`. Per sostituire temporaneamente `API_KEY`, inserire `API_KEY` temporaneo nell'area di input e premere Invio per renderlo effettivo.
-
-
-
-Funzione | Descrizione
---- | ---
-Correzione immediata | Supporta correzione immediata e ricerca degli errori di grammatica del documento con un solo clic
-Traduzione cinese-inglese immediata | Traduzione cinese-inglese immediata con un solo clic
-Spiegazione del codice immediata | Visualizzazione del codice, spiegazione del codice, generazione del codice, annotazione del codice con un solo clic
-[Scorciatoie personalizzate](https://www.bilibili.com/video/BV14s4y1E7jN) | Supporta scorciatoie personalizzate
-Design modularizzato | Supporta potenti [plugin di funzioni](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions) personalizzati, i plugin supportano l'[aggiornamento in tempo reale](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Auto-profiling del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] [Comprensione immediata](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) del codice sorgente di questo progetto
-[Analisi del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] Un clic può analizzare l'albero di altri progetti Python/C/C++/Java/Lua/...
-Lettura del documento, [traduzione](https://www.bilibili.com/video/BV1KT411x7Wn) del documento | [Plugin di funzioni] La lettura immediata dell'intero documento latex/pdf di un documento e la generazione di un riassunto
-Traduzione completa di un documento Latex, [correzione immediata](https://www.bilibili.com/video/BV1FT411H7c5/) | [Plugin di funzioni] Una traduzione o correzione immediata di un documento Latex
-Generazione di annotazioni in batch | [Plugin di funzioni] Generazione automatica delle annotazioni di funzione con un solo clic
-[Traduzione cinese-inglese di Markdown](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin di funzioni] Hai letto il [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) delle cinque lingue sopra?
-Generazione di report di analisi di chat | [Plugin di funzioni] Generazione automatica di un rapporto di sintesi dopo l'esecuzione
-[Funzione di traduzione di tutto il documento PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin di funzioni] Estrarre il titolo e il sommario dell'articolo PDF + tradurre l'intero testo (multithreading)
-[Assistente di Arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugin di funzioni] Inserire l'URL dell'articolo di Arxiv e tradurre il sommario con un clic + scaricare il PDF
-[Assistente integrato di Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Plugin di funzioni] Con qualsiasi URL di pagina di ricerca di Google Scholar, lascia che GPT ti aiuti a scrivere il tuo [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
-Aggregazione delle informazioni su Internet + GPT | [Plugin di funzioni] Fai in modo che GPT rilevi le informazioni su Internet prima di rispondere alle domande, senza mai diventare obsolete
-Visualizzazione di formule/img/tabelle | È possibile visualizzare un'equazione in forma [tex e render](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) contemporaneamente, supporta equazioni e evidenziazione del codice
-Supporto per plugin di funzioni multithreading | Supporto per chiamata multithreaded di chatgpt, elaborazione con un clic di grandi quantità di testo o di un programma
-Avvia il tema di gradio [scuro](https://github.com/binary-husky/chatgpt_academic/issues/173) | Aggiungere ```/?__theme=dark``` dopo l'URL del browser per passare a un tema scuro
-Supporto per maggiori modelli LLM, supporto API2D | Sentirsi serviti simultaneamente da GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) deve essere una grande sensazione, giusto?
-Ulteriori modelli LLM supportat,i supporto per l'implementazione di Huggingface | Aggiunta di un'interfaccia Newbing (Nuovo Bing), introdotta la compatibilità con Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) e [PanGu-α](https://openi.org.cn/pangu/)
-Ulteriori dimostrazioni di nuove funzionalità (generazione di immagini, ecc.)... | Vedere la fine di questo documento...
-
-
-
-- Nuova interfaccia (modificare l'opzione LAYOUT in `config.py` per passare dal layout a sinistra e a destra al layout superiore e inferiore)
-
-
-
Sei un traduttore professionista di paper accademici.
-
-- Tutti i pulsanti vengono generati dinamicamente leggendo il file functional.py, e aggiungerci nuove funzionalità è facile, liberando la clipboard.
-
-
-
-
-- Revisione/Correzione
-
-
-
-
-- Se l'output contiene una formula, viene visualizzata sia come testo che come formula renderizzata, per facilitare la copia e la visualizzazione.
-
-
-
-
-- Non hai tempo di leggere il codice del progetto? Passa direttamente a chatgpt e chiedi informazioni.
-
-
-
-
-- Chiamata mista di vari modelli di lingua di grandi dimensioni (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-
-
-
-
----
-# Installazione
-## Installazione - Metodo 1: Esecuzione diretta (Windows, Linux o MacOS)
-
-1. Scarica il progetto
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Configura API_KEY
-
-In `config.py`, configura la tua API KEY e altre impostazioni, [configs for special network environments](https://github.com/binary-husky/gpt_academic/issues/1).
-
-(N.B. Quando il programma viene eseguito, verifica prima se esiste un file di configurazione privato chiamato `config_private.py` e sovrascrive le stesse configurazioni in `config.py`. Pertanto, se capisci come funziona la nostra logica di lettura della configurazione, ti consigliamo vivamente di creare un nuovo file di configurazione chiamato `config_private.py` accanto a `config.py`, e spostare (copiare) le configurazioni di `config.py` in `config_private.py`. 'config_private.py' non è sotto la gestione di git e può proteggere ulteriormente le tue informazioni personali. NB Il progetto supporta anche la configurazione della maggior parte delle opzioni tramite "variabili d'ambiente". La sintassi della variabile d'ambiente è descritta nel file `docker-compose`. Priorità di lettura: "variabili d'ambiente" > "config_private.py" > "config.py")
-
-
-3. Installa le dipendenze
-```sh
-# (Scelta I: se sei familiare con python) (python 3.9 o superiore, più nuovo è meglio), N.B.: utilizza il repository ufficiale pip o l'aliyun pip repository, metodo temporaneo per cambiare il repository: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-python -m pip install -r requirements.txt
-
-# (Scelta II: se non conosci Python) utilizza anaconda, il processo è simile (https://www.bilibili.com/video/BV1rc411W7Dr):
-conda create -n gptac_venv python=3.11 # crea l'ambiente anaconda
-conda activate gptac_venv # attiva l'ambiente anaconda
-python -m pip install -r requirements.txt # questo passaggio funziona allo stesso modo dell'installazione con pip
-```
-
-Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, fare clic qui per espandere
-
-
-【Passaggio facoltativo】 Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, è necessario installare ulteriori dipendenze (prerequisiti: conoscenza di Python, esperienza con Pytorch e computer sufficientemente potente):
-```sh
-# 【Passaggio facoltativo I】 Supporto a ChatGLM di Tsinghua. Note su ChatGLM di Tsinghua: in caso di errore "Call ChatGLM fail 不能正常加载ChatGLM的参数" , fare quanto segue: 1. Per impostazione predefinita, viene installata la versione di torch + cpu; per usare CUDA, è necessario disinstallare torch e installare nuovamente torch + cuda; 2. Se non è possibile caricare il modello a causa di una configurazione insufficiente del computer, è possibile modificare la precisione del modello in request_llm/bridge_chatglm.py, cambiando AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) in AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt
-
-# 【Passaggio facoltativo II】 Supporto a MOSS di Fudan
-python -m pip install -r request_llm/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Si prega di notare che quando si esegue questa riga di codice, si deve essere nella directory radice del progetto
-
-# 【Passaggio facoltativo III】 Assicurati che il file di configurazione config.py includa tutti i modelli desiderati, al momento tutti i modelli supportati sono i seguenti (i modelli della serie jittorllms attualmente supportano solo la soluzione docker):
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
-```
-
-
-
-
-
-
-4. Esegui
-```sh
-python main.py
-```5. Plugin di test delle funzioni
-```
-- Funzione plugin di test (richiede una risposta gpt su cosa è successo oggi in passato), puoi utilizzare questa funzione come template per implementare funzionalità più complesse
- Clicca su "[Demo del plugin di funzione] Oggi nella storia"
-```
-
-## Installazione - Metodo 2: Utilizzo di Docker
-
-1. Solo ChatGPT (consigliato per la maggior parte delle persone)
-
-``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git # scarica il progetto
-cd chatgpt_academic # entra nel percorso
-nano config.py # con un qualsiasi editor di testo, modifica config.py configurando "Proxy", "API_KEY" e "WEB_PORT" (ad esempio 50923)
-docker build -t gpt-academic . # installa
-
-#(ultimo passaggio - selezione 1) In un ambiente Linux, utilizzare '--net=host' è più conveniente e veloce
-docker run --rm -it --net=host gpt-academic
-#(ultimo passaggio - selezione 2) In un ambiente MacOS/Windows, l'opzione -p può essere utilizzata per esporre la porta del contenitore (ad es. 50923) alla porta della macchina
-docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
-```
-
-2. ChatGPT + ChatGLM + MOSS (richiede familiarità con Docker)
-
-``` sh
-# Modifica docker-compose.yml, elimina i piani 1 e 3, mantieni il piano 2. Modifica la configurazione del piano 2 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni
-docker-compose up
-```
-
-3. ChatGPT + LLAMA + Pangu + RWKV (richiede familiarità con Docker)
-
-``` sh
-# Modifica docker-compose.yml, elimina i piani 1 e 2, mantieni il piano 3. Modifica la configurazione del piano 3 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni
-docker-compose up
-```
-
-
-## Installazione - Metodo 3: Altre modalità di distribuzione
-
-1. Come utilizzare un URL di reindirizzamento / AzureAPI Cloud Microsoft
-Configura API_URL_REDIRECT seguendo le istruzioni nel file `config.py`.
-
-2. Distribuzione su un server cloud remoto (richiede conoscenze ed esperienza di server cloud)
-Si prega di visitare [wiki di distribuzione-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-3. Utilizzo di WSL2 (Windows Subsystem for Linux)
-Si prega di visitare [wiki di distribuzione-2] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-4. Come far funzionare ChatGPT all'interno di un sottodominio (ad es. `http://localhost/subpath`)
-Si prega di visitare [Istruzioni per l'esecuzione con FastAPI] (docs/WithFastapi.md)
-
-5. Utilizzo di docker-compose per l'esecuzione
-Si prega di leggere il file docker-compose.yml e seguire le istruzioni fornite.
-
----
-# Uso avanzato
-## Personalizzazione dei pulsanti / Plugin di funzione personalizzati
-
-1. Personalizzazione dei pulsanti (scorciatoie accademiche)
-Apri `core_functional.py` con qualsiasi editor di testo e aggiungi la voce seguente, quindi riavvia il programma (se il pulsante è già stato aggiunto con successo e visibile, il prefisso e il suffisso supportano la modifica in tempo reale, senza bisogno di riavviare il programma).
-
-ad esempio
-```
-"超级英译中": {
- # Prefisso, verrà aggiunto prima del tuo input. Ad esempio, descrivi la tua richiesta, come tradurre, spiegare il codice, correggere errori, ecc.
- "Prefix": "Per favore traduci questo testo in Cinese, e poi spiega tutti i termini tecnici nel testo con una tabella markdown:\n\n",
-
- # Suffisso, verrà aggiunto dopo il tuo input. Ad esempio, con il prefisso puoi circondare il tuo input con le virgolette.
- "Suffix": "",
-},
-```
-
-
-
-
-2. Plugin di funzione personalizzati
-
-Scrivi plugin di funzione personalizzati e esegui tutte le attività che desideri o non hai mai pensato di fare.
-La difficoltà di scrittura e debug dei plugin del nostro progetto è molto bassa. Se si dispone di una certa conoscenza di base di Python, è possibile realizzare la propria funzione del plugin seguendo il nostro modello. Per maggiori dettagli, consultare la [guida al plugin per funzioni](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-
----
-# Ultimo aggiornamento
-## Nuove funzionalità dinamiche
-
-1. Funzionalità di salvataggio della conversazione. Nell'area dei plugin della funzione, fare clic su "Salva la conversazione corrente" per salvare la conversazione corrente come file html leggibile e ripristinabile, inoltre, nell'area dei plugin della funzione (menu a discesa), fare clic su "Carica la cronologia della conversazione archiviata" per ripristinare la conversazione precedente. Suggerimento: fare clic su "Carica la cronologia della conversazione archiviata" senza specificare il file consente di visualizzare la cache degli archivi html di cronologia, fare clic su "Elimina tutti i record di cronologia delle conversazioni locali" per eliminare tutte le cache degli archivi html.
-
-
-
-
-2. Generazione di rapporti. La maggior parte dei plugin genera un rapporto di lavoro dopo l'esecuzione.
-
-
-
-
-
-
-3. Progettazione modulare delle funzioni, semplici interfacce ma in grado di supportare potenti funzionalità.
-
-
-
-
-
-4. Questo è un progetto open source che può "tradursi da solo".
-
-
-
-
-5. Tradurre altri progetti open source è semplice.
-
-
-
-
-
-
-
-
-6. Piccola funzione decorativa per [live2d](https://github.com/fghrsh/live2d_demo) (disattivata per impostazione predefinita, è necessario modificare `config.py`).
-
-
-
-
-7. Supporto del grande modello linguistico MOSS
-
-
-
-
-8. Generazione di immagini OpenAI
-
-
-
-
-9. Analisi e sintesi audio OpenAI
-
-
-
-
-10. Verifica completa dei testi in LaTeX
-
-
-
-
-
-## Versione:
-- versione 3.5(Todo): utilizzo del linguaggio naturale per chiamare tutti i plugin di funzioni del progetto (alta priorità)
-- versione 3.4(Todo): supporto multi-threading per il grande modello linguistico locale Chatglm
-- versione 3.3: +funzionalità di sintesi delle informazioni su Internet
-- versione 3.2: i plugin di funzioni supportano più interfacce dei parametri (funzionalità di salvataggio della conversazione, lettura del codice in qualsiasi lingua + richiesta simultanea di qualsiasi combinazione di LLM)
-- versione 3.1: supporto per interrogare contemporaneamente più modelli gpt! Supporto api2d, bilanciamento del carico per più apikey
-- versione 3.0: supporto per Chatglm e altri piccoli LLM
-- versione 2.6: ristrutturazione della struttura del plugin, miglioramento dell'interattività, aggiunta di più plugin
-- versione 2.5: auto-aggiornamento, risoluzione del problema di testo troppo lungo e overflow del token durante la sintesi di grandi progetti di ingegneria
-- versione 2.4: (1) funzionalità di traduzione dell'intero documento in formato PDF aggiunta; (2) funzionalità di scambio dell'area di input aggiunta; (3) opzione di layout verticale aggiunta; (4) ottimizzazione della funzione di plugin multi-threading.
-- versione 2.3: miglioramento dell'interattività multi-threading
-- versione 2.2: i plugin di funzioni supportano l'hot-reload
-- versione 2.1: layout ripiegabile
-- versione 2.0: introduzione di plugin di funzioni modulari
-- versione 1.0: funzione di basegpt_academic sviluppatori gruppo QQ-2: 610599535
-
-- Problemi noti
- - Alcuni plugin di traduzione del browser interferiscono con l'esecuzione del frontend di questo software
- - La versione di gradio troppo alta o troppo bassa può causare diversi malfunzionamenti
-
-## Riferimenti e apprendimento
-
-```
-Il codice fa riferimento a molte altre eccellenti progettazioni di progetti, principalmente:
-
-# Progetto 1: ChatGLM-6B di Tsinghua:
-https://github.com/THUDM/ChatGLM-6B
-
-# Progetto 2: JittorLLMs di Tsinghua:
-https://github.com/Jittor/JittorLLMs
-
-# Progetto 3: Edge-GPT:
-https://github.com/acheong08/EdgeGPT
-
-# Progetto 4: ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Progetto 5: ChatPaper:
-https://github.com/kaixindelele/ChatPaper
-
-# Altro:
-https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
-```
diff --git a/spaces/zhoucr/ai-koni/models.py b/spaces/zhoucr/ai-koni/models.py
deleted file mode 100644
index d151bf1571504b66b7fef70d52bdec9e98b03868..0000000000000000000000000000000000000000
--- a/spaces/zhoucr/ai-koni/models.py
+++ /dev/null
@@ -1,535 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
- super().__init__()
- filter_channels = in_channels # it needs to be removed from future version.
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.log_flow = modules.Log()
- self.flows = nn.ModuleList()
- self.flows.append(modules.ElementwiseAffine(2))
- for i in range(n_flows):
- self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.flows.append(modules.Flip())
-
- self.post_pre = nn.Conv1d(1, filter_channels, 1)
- self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- self.post_flows = nn.ModuleList()
- self.post_flows.append(modules.ElementwiseAffine(2))
- for i in range(4):
- self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.post_flows.append(modules.Flip())
-
- self.pre = nn.Conv1d(in_channels, filter_channels, 1)
- self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
- def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
- x = torch.detach(x)
- x = self.pre(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.convs(x, x_mask)
- x = self.proj(x) * x_mask
-
- if not reverse:
- flows = self.flows
- assert w is not None
-
- logdet_tot_q = 0
- h_w = self.post_pre(w)
- h_w = self.post_convs(h_w, x_mask)
- h_w = self.post_proj(h_w) * x_mask
- e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
- z_q = e_q
- for flow in self.post_flows:
- z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
- logdet_tot_q += logdet_q
- z_u, z1 = torch.split(z_q, [1, 1], 1)
- u = torch.sigmoid(z_u) * x_mask
- z0 = (w - u) * x_mask
- logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
- logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
- logdet_tot = 0
- z0, logdet = self.log_flow(z0, x_mask)
- logdet_tot += logdet
- z = torch.cat([z0, z1], 1)
- for flow in flows:
- z, logdet = flow(z, x_mask, g=x, reverse=reverse)
- logdet_tot = logdet_tot + logdet
- nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
- return nll + logq # [b]
- else:
- flows = list(reversed(self.flows))
- flows = flows[:-2] + [flows[-1]] # remove a useless vflow
- z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
- for flow in flows:
- z = flow(z, x_mask, g=x, reverse=reverse)
- z0, z1 = torch.split(z, [1, 1], 1)
- logw = z0
- return logw
-
-
-class DurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
- super().__init__()
-
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
-
- self.drop = nn.Dropout(p_dropout)
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_1 = modules.LayerNorm(filter_channels)
- self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_2 = modules.LayerNorm(filter_channels)
- self.proj = nn.Conv1d(filter_channels, 1, 1)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
- def forward(self, x, x_mask, g=None):
- x = torch.detach(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
- x = self.proj(x * x_mask)
- return x * x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- n_vocab,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout):
- super().__init__()
- self.n_vocab = n_vocab
- 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 = nn.Embedding(n_vocab, hidden_channels)
- nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
- 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, x, x_lengths):
- x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
- # print(x.shape)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, 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 i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-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
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
- k, u, padding=(k-u)//2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel//(2**(i+1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i*self.num_kernels+j](x)
- else:
- xs += self.resblocks[i*self.num_kernels+j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-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 MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- 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 SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- n_vocab,
- 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,
- n_speakers=0,
- gin_channels=0,
- use_sdp=True,
- **kwargs):
-
- super().__init__()
- self.n_vocab = n_vocab
- 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.n_speakers = n_speakers
- self.gin_channels = gin_channels
-
- self.use_sdp = use_sdp
-
- self.enc_p = TextEncoder(n_vocab,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- 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, 4, gin_channels=gin_channels)
-
- if use_sdp:
- self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
- else:
- self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
- if n_speakers > 1:
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
- def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = None
-
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
-
- with torch.no_grad():
- # negative cross-entropy
- s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
- neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
- neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
- neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
- w = attn.sum(2)
- if self.use_sdp:
- l_length = self.dp(x, x_mask, w, g=g)
- l_length = l_length / torch.sum(x_mask)
- else:
- logw_ = torch.log(w + 1e-6) * x_mask
- logw = self.dp(x, x_mask, g=g)
- l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
-
- # expand prior
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
- return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = None
-
- if self.use_sdp:
- logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
- else:
- logw = self.dp(x, x_mask, g=g)
- w = torch.exp(logw) * x_mask * length_scale
- w_ceil = torch.ceil(w)
- y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
- y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = commons.generate_path(w_ceil, attn_mask)
-
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
- z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
- z = self.flow(z_p, y_mask, g=g, reverse=True)
- o = self.dec((z * y_mask)[:,:,:max_len], g=g)
- return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
- def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
- assert self.n_speakers > 0, "n_speakers have to be larger than 0."
- g_src = self.emb_g(sid_src).unsqueeze(-1)
- g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
- z_p = self.flow(z, y_mask, g=g_src)
- z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
- o_hat = self.dec(z_hat * y_mask, g=g_tgt)
- return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/zlc99/M4Singer/vocoders/vocoder_utils.py b/spaces/zlc99/M4Singer/vocoders/vocoder_utils.py
deleted file mode 100644
index 98792f79b1ccdf9a136e8b9526b8e2ea65a4c004..0000000000000000000000000000000000000000
--- a/spaces/zlc99/M4Singer/vocoders/vocoder_utils.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import librosa
-
-from utils.hparams import hparams
-import numpy as np
-
-
-def denoise(wav, v=0):
- spec = librosa.stft(y=wav, n_fft=hparams['fft_size'], hop_length=hparams['hop_size'],
- win_length=hparams['win_size'], pad_mode='constant')
- spec_m = np.abs(spec)
- spec_m = np.clip(spec_m - v, a_min=0, a_max=None)
- spec_a = np.angle(spec)
-
- return librosa.istft(spec_m * np.exp(1j * spec_a), hop_length=hparams['hop_size'],
- win_length=hparams['win_size'])
diff --git a/spaces/zomehwh/sovits-models/cluster/train_cluster.py b/spaces/zomehwh/sovits-models/cluster/train_cluster.py
deleted file mode 100644
index 4ac025d400414226e66849407f477ae786c3d5d3..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-models/cluster/train_cluster.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import os
-from glob import glob
-from pathlib import Path
-import torch
-import logging
-import argparse
-import torch
-import numpy as np
-from sklearn.cluster import KMeans, MiniBatchKMeans
-import tqdm
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-import time
-import random
-
-def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False):
-
- logger.info(f"Loading features from {in_dir}")
- features = []
- nums = 0
- for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
- features.append(torch.load(path).squeeze(0).numpy().T)
- # print(features[-1].shape)
- features = np.concatenate(features, axis=0)
- print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
- features = features.astype(np.float32)
- logger.info(f"Clustering features of shape: {features.shape}")
- t = time.time()
- if use_minibatch:
- kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
- else:
- kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
- print(time.time()-t, "s")
-
- x = {
- "n_features_in_": kmeans.n_features_in_,
- "_n_threads": kmeans._n_threads,
- "cluster_centers_": kmeans.cluster_centers_,
- }
- print("end")
-
- return x
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument('--dataset', type=Path, default="./dataset/44k",
- help='path of training data directory')
- parser.add_argument('--output', type=Path, default="logs/44k",
- help='path of model output directory')
-
- args = parser.parse_args()
-
- checkpoint_dir = args.output
- dataset = args.dataset
- n_clusters = 10000
-
- ckpt = {}
- for spk in os.listdir(dataset):
- if os.path.isdir(dataset/spk):
- print(f"train kmeans for {spk}...")
- in_dir = dataset/spk
- x = train_cluster(in_dir, n_clusters, verbose=False)
- ckpt[spk] = x
-
- checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
- checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
- torch.save(
- ckpt,
- checkpoint_path,
- )
-
-
- # import cluster
- # for spk in tqdm.tqdm(os.listdir("dataset")):
- # if os.path.isdir(f"dataset/{spk}"):
- # print(f"start kmeans inference for {spk}...")
- # for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)):
- # mel_path = feature_path.replace(".discrete.npy",".mel.npy")
- # mel_spectrogram = np.load(mel_path)
- # feature_len = mel_spectrogram.shape[-1]
- # c = np.load(feature_path)
- # c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy()
- # feature = c.T
- # feature_class = cluster.get_cluster_result(feature, spk)
- # np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class)
-
-
-
-" - html = html + html2 - html += "
GPT Ottimizzazione Accademica (GPT Academic)
-
-**Se ti piace questo progetto, ti preghiamo di dargli una stella. Se hai sviluppato scorciatoie accademiche o plugin funzionali più utili, non esitare ad aprire una issue o pull request. Abbiamo anche una README in [Inglese|](README_EN.md)[Giapponese|](README_JP.md)[Coreano|](https://github.com/mldljyh/ko_gpt_academic)[Russo|](README_RS.md)[Francese](README_FR.md) tradotta da questo stesso progetto.
-Per tradurre questo progetto in qualsiasi lingua con GPT, leggere e eseguire [`multi_language.py`](multi_language.py) (sperimentale).
-
-> **Nota**
->
-> 1. Si prega di notare che solo i plugin (pulsanti) contrassegnati in **rosso** supportano la lettura di file, alcuni plugin sono posizionati nel **menu a discesa** nella zona dei plugin. Accettiamo e gestiamo PR per qualsiasi nuovo plugin con **massima priorità**!
->
-> 2. Le funzionalità di ogni file di questo progetto sono descritte dettagliatamente nella propria analisi di autotraduzione [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A). Con l'iterazione delle versioni, è possibile fare clic sui plugin funzionali correlati in qualsiasi momento per richiamare GPT e generare nuovamente il rapporto di analisi automatica del progetto. Le domande frequenti sono riassunte nella [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Metodo di installazione] (#installazione).
->
-> 3. Questo progetto è compatibile e incoraggia l'utilizzo di grandi modelli di linguaggio di produzione nazionale come chatglm, RWKV, Pangu ecc. Supporta la coesistenza di più api-key e può essere compilato nel file di configurazione come `API_KEY="openai-key1,openai-key2,api2d-key3"`. Per sostituire temporaneamente `API_KEY`, inserire `API_KEY` temporaneo nell'area di input e premere Invio per renderlo effettivo.
-
-- -Funzione | Descrizione ---- | --- -Correzione immediata | Supporta correzione immediata e ricerca degli errori di grammatica del documento con un solo clic -Traduzione cinese-inglese immediata | Traduzione cinese-inglese immediata con un solo clic -Spiegazione del codice immediata | Visualizzazione del codice, spiegazione del codice, generazione del codice, annotazione del codice con un solo clic -[Scorciatoie personalizzate](https://www.bilibili.com/video/BV14s4y1E7jN) | Supporta scorciatoie personalizzate -Design modularizzato | Supporta potenti [plugin di funzioni](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions) personalizzati, i plugin supportano l'[aggiornamento in tempo reale](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97) -[Auto-profiling del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] [Comprensione immediata](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) del codice sorgente di questo progetto -[Analisi del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] Un clic può analizzare l'albero di altri progetti Python/C/C++/Java/Lua/... -Lettura del documento, [traduzione](https://www.bilibili.com/video/BV1KT411x7Wn) del documento | [Plugin di funzioni] La lettura immediata dell'intero documento latex/pdf di un documento e la generazione di un riassunto -Traduzione completa di un documento Latex, [correzione immediata](https://www.bilibili.com/video/BV1FT411H7c5/) | [Plugin di funzioni] Una traduzione o correzione immediata di un documento Latex -Generazione di annotazioni in batch | [Plugin di funzioni] Generazione automatica delle annotazioni di funzione con un solo clic -[Traduzione cinese-inglese di Markdown](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin di funzioni] Hai letto il [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) delle cinque lingue sopra? -Generazione di report di analisi di chat | [Plugin di funzioni] Generazione automatica di un rapporto di sintesi dopo l'esecuzione -[Funzione di traduzione di tutto il documento PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin di funzioni] Estrarre il titolo e il sommario dell'articolo PDF + tradurre l'intero testo (multithreading) -[Assistente di Arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugin di funzioni] Inserire l'URL dell'articolo di Arxiv e tradurre il sommario con un clic + scaricare il PDF -[Assistente integrato di Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Plugin di funzioni] Con qualsiasi URL di pagina di ricerca di Google Scholar, lascia che GPT ti aiuti a scrivere il tuo [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/) -Aggregazione delle informazioni su Internet + GPT | [Plugin di funzioni] Fai in modo che GPT rilevi le informazioni su Internet prima di rispondere alle domande, senza mai diventare obsolete -Visualizzazione di formule/img/tabelle | È possibile visualizzare un'equazione in forma [tex e render](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) contemporaneamente, supporta equazioni e evidenziazione del codice -Supporto per plugin di funzioni multithreading | Supporto per chiamata multithreaded di chatgpt, elaborazione con un clic di grandi quantità di testo o di un programma -Avvia il tema di gradio [scuro](https://github.com/binary-husky/chatgpt_academic/issues/173) | Aggiungere ```/?__theme=dark``` dopo l'URL del browser per passare a un tema scuro -Supporto per maggiori modelli LLM, supporto API2D | Sentirsi serviti simultaneamente da GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) deve essere una grande sensazione, giusto? -Ulteriori modelli LLM supportat,i supporto per l'implementazione di Huggingface | Aggiunta di un'interfaccia Newbing (Nuovo Bing), introdotta la compatibilità con Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) e [PanGu-α](https://openi.org.cn/pangu/) -Ulteriori dimostrazioni di nuove funzionalità (generazione di immagini, ecc.)... | Vedere la fine di questo documento... -- - -- Nuova interfaccia (modificare l'opzione LAYOUT in `config.py` per passare dal layout a sinistra e a destra al layout superiore e inferiore) --Sei un traduttore professionista di paper accademici. - -- Tutti i pulsanti vengono generati dinamicamente leggendo il file functional.py, e aggiungerci nuove funzionalità è facile, liberando la clipboard. -
--- -- Revisione/Correzione -
--- -- Se l'output contiene una formula, viene visualizzata sia come testo che come formula renderizzata, per facilitare la copia e la visualizzazione. -
--- -- Non hai tempo di leggere il codice del progetto? Passa direttamente a chatgpt e chiedi informazioni. -
--- -- Chiamata mista di vari modelli di lingua di grandi dimensioni (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4) -
--- ---- -# Installazione -## Installazione - Metodo 1: Esecuzione diretta (Windows, Linux o MacOS) - -1. Scarica il progetto -```sh -git clone https://github.com/binary-husky/chatgpt_academic.git -cd chatgpt_academic -``` - -2. Configura API_KEY - -In `config.py`, configura la tua API KEY e altre impostazioni, [configs for special network environments](https://github.com/binary-husky/gpt_academic/issues/1). - -(N.B. Quando il programma viene eseguito, verifica prima se esiste un file di configurazione privato chiamato `config_private.py` e sovrascrive le stesse configurazioni in `config.py`. Pertanto, se capisci come funziona la nostra logica di lettura della configurazione, ti consigliamo vivamente di creare un nuovo file di configurazione chiamato `config_private.py` accanto a `config.py`, e spostare (copiare) le configurazioni di `config.py` in `config_private.py`. 'config_private.py' non è sotto la gestione di git e può proteggere ulteriormente le tue informazioni personali. NB Il progetto supporta anche la configurazione della maggior parte delle opzioni tramite "variabili d'ambiente". La sintassi della variabile d'ambiente è descritta nel file `docker-compose`. Priorità di lettura: "variabili d'ambiente" > "config_private.py" > "config.py") - - -3. Installa le dipendenze -```sh -# (Scelta I: se sei familiare con python) (python 3.9 o superiore, più nuovo è meglio), N.B.: utilizza il repository ufficiale pip o l'aliyun pip repository, metodo temporaneo per cambiare il repository: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ -python -m pip install -r requirements.txt - -# (Scelta II: se non conosci Python) utilizza anaconda, il processo è simile (https://www.bilibili.com/video/BV1rc411W7Dr): -conda create -n gptac_venv python=3.11 # crea l'ambiente anaconda -conda activate gptac_venv # attiva l'ambiente anaconda -python -m pip install -r requirements.txt # questo passaggio funziona allo stesso modo dell'installazione con pip -``` - -
-- - - -4. Esegui -```sh -python main.py -```5. Plugin di test delle funzioni -``` -- Funzione plugin di test (richiede una risposta gpt su cosa è successo oggi in passato), puoi utilizzare questa funzione come template per implementare funzionalità più complesse - Clicca su "[Demo del plugin di funzione] Oggi nella storia" -``` - -## Installazione - Metodo 2: Utilizzo di Docker - -1. Solo ChatGPT (consigliato per la maggior parte delle persone) - -``` sh -git clone https://github.com/binary-husky/chatgpt_academic.git # scarica il progetto -cd chatgpt_academic # entra nel percorso -nano config.py # con un qualsiasi editor di testo, modifica config.py configurando "Proxy", "API_KEY" e "WEB_PORT" (ad esempio 50923) -docker build -t gpt-academic . # installa - -#(ultimo passaggio - selezione 1) In un ambiente Linux, utilizzare '--net=host' è più conveniente e veloce -docker run --rm -it --net=host gpt-academic -#(ultimo passaggio - selezione 2) In un ambiente MacOS/Windows, l'opzione -p può essere utilizzata per esporre la porta del contenitore (ad es. 50923) alla porta della macchina -docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic -``` - -2. ChatGPT + ChatGLM + MOSS (richiede familiarità con Docker) - -``` sh -# Modifica docker-compose.yml, elimina i piani 1 e 3, mantieni il piano 2. Modifica la configurazione del piano 2 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni -docker-compose up -``` - -3. ChatGPT + LLAMA + Pangu + RWKV (richiede familiarità con Docker) - -``` sh -# Modifica docker-compose.yml, elimina i piani 1 e 2, mantieni il piano 3. Modifica la configurazione del piano 3 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni -docker-compose up -``` - - -## Installazione - Metodo 3: Altre modalità di distribuzione - -1. Come utilizzare un URL di reindirizzamento / AzureAPI Cloud Microsoft -Configura API_URL_REDIRECT seguendo le istruzioni nel file `config.py`. - -2. Distribuzione su un server cloud remoto (richiede conoscenze ed esperienza di server cloud) -Si prega di visitare [wiki di distribuzione-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97) - -3. Utilizzo di WSL2 (Windows Subsystem for Linux) -Si prega di visitare [wiki di distribuzione-2] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2) - -4. Come far funzionare ChatGPT all'interno di un sottodominio (ad es. `http://localhost/subpath`) -Si prega di visitare [Istruzioni per l'esecuzione con FastAPI] (docs/WithFastapi.md) - -5. Utilizzo di docker-compose per l'esecuzione -Si prega di leggere il file docker-compose.yml e seguire le istruzioni fornite. - ---- -# Uso avanzato -## Personalizzazione dei pulsanti / Plugin di funzione personalizzati - -1. Personalizzazione dei pulsanti (scorciatoie accademiche) -Apri `core_functional.py` con qualsiasi editor di testo e aggiungi la voce seguente, quindi riavvia il programma (se il pulsante è già stato aggiunto con successo e visibile, il prefisso e il suffisso supportano la modifica in tempo reale, senza bisogno di riavviare il programma). - -ad esempio -``` -"超级英译中": { - # Prefisso, verrà aggiunto prima del tuo input. Ad esempio, descrivi la tua richiesta, come tradurre, spiegare il codice, correggere errori, ecc. - "Prefix": "Per favore traduci questo testo in Cinese, e poi spiega tutti i termini tecnici nel testo con una tabella markdown:\n\n", - - # Suffisso, verrà aggiunto dopo il tuo input. Ad esempio, con il prefisso puoi circondare il tuo input con le virgolette. - "Suffix": "", -}, -``` -
--- -2. Generazione di rapporti. La maggior parte dei plugin genera un rapporto di lavoro dopo l'esecuzione. -
--- -3. Progettazione modulare delle funzioni, semplici interfacce ma in grado di supportare potenti funzionalità. -
-
-
--- -4. Questo è un progetto open source che può "tradursi da solo". -
-
--- -5. Tradurre altri progetti open source è semplice. -
--- -
--- -7. Supporto del grande modello linguistico MOSS -
--- -8. Generazione di immagini OpenAI -
--- -9. Analisi e sintesi audio OpenAI --
-- -10. Verifica completa dei testi in LaTeX --
-- - -## Versione: -- versione 3.5(Todo): utilizzo del linguaggio naturale per chiamare tutti i plugin di funzioni del progetto (alta priorità) -- versione 3.4(Todo): supporto multi-threading per il grande modello linguistico locale Chatglm -- versione 3.3: +funzionalità di sintesi delle informazioni su Internet -- versione 3.2: i plugin di funzioni supportano più interfacce dei parametri (funzionalità di salvataggio della conversazione, lettura del codice in qualsiasi lingua + richiesta simultanea di qualsiasi combinazione di LLM) -- versione 3.1: supporto per interrogare contemporaneamente più modelli gpt! Supporto api2d, bilanciamento del carico per più apikey -- versione 3.0: supporto per Chatglm e altri piccoli LLM -- versione 2.6: ristrutturazione della struttura del plugin, miglioramento dell'interattività, aggiunta di più plugin -- versione 2.5: auto-aggiornamento, risoluzione del problema di testo troppo lungo e overflow del token durante la sintesi di grandi progetti di ingegneria -- versione 2.4: (1) funzionalità di traduzione dell'intero documento in formato PDF aggiunta; (2) funzionalità di scambio dell'area di input aggiunta; (3) opzione di layout verticale aggiunta; (4) ottimizzazione della funzione di plugin multi-threading. -- versione 2.3: miglioramento dell'interattività multi-threading -- versione 2.2: i plugin di funzioni supportano l'hot-reload -- versione 2.1: layout ripiegabile -- versione 2.0: introduzione di plugin di funzioni modulari -- versione 1.0: funzione di basegpt_academic sviluppatori gruppo QQ-2: 610599535 - -- Problemi noti - - Alcuni plugin di traduzione del browser interferiscono con l'esecuzione del frontend di questo software - - La versione di gradio troppo alta o troppo bassa può causare diversi malfunzionamenti - -## Riferimenti e apprendimento - -``` -Il codice fa riferimento a molte altre eccellenti progettazioni di progetti, principalmente: - -# Progetto 1: ChatGLM-6B di Tsinghua: -https://github.com/THUDM/ChatGLM-6B - -# Progetto 2: JittorLLMs di Tsinghua: -https://github.com/Jittor/JittorLLMs - -# Progetto 3: Edge-GPT: -https://github.com/acheong08/EdgeGPT - -# Progetto 4: ChuanhuChatGPT: -https://github.com/GaiZhenbiao/ChuanhuChatGPT - -# Progetto 5: ChatPaper: -https://github.com/kaixindelele/ChatPaper - -# Altro: -https://github.com/gradio-app/gradio -https://github.com/fghrsh/live2d_demo -``` diff --git a/spaces/zhoucr/ai-koni/models.py b/spaces/zhoucr/ai-koni/models.py deleted file mode 100644 index d151bf1571504b66b7fef70d52bdec9e98b03868..0000000000000000000000000000000000000000 --- a/spaces/zhoucr/ai-koni/models.py +++ /dev/null @@ -1,535 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import modules -import attentions -import monotonic_align - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from commons import init_weights, get_padding - - -class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2]) - logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout): - super().__init__() - self.n_vocab = n_vocab - 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 = nn.Embedding(n_vocab, hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) - - 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, x, x_lengths): - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - # print(x.shape) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, 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 i in range(n_flows): - self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -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 - - -class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) - resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), - k, u, padding=(k-u)//2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel//(2**(i+1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x) - else: - xs += self.resblocks[i*self.num_kernels+j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -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 MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2,3,5,7,11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - 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 SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - 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, - n_speakers=0, - gin_channels=0, - use_sdp=True, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab - 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.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - 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, 4, gin_channels=gin_channels) - - if use_sdp: - self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - else: - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - - if n_speakers > 1: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - def forward(self, x, x_lengths, y, y_lengths, sid=None): - - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - - with torch.no_grad(): - # negative cross-entropy - s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] - neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] - neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] - neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 - - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() - - w = attn.sum(2) - if self.use_sdp: - l_length = self.dp(x, x_mask, w, g=g) - l_length = l_length / torch.sum(x_mask) - else: - logw_ = torch.log(w + 1e-6) * x_mask - logw = self.dp(x, x_mask, g=g) - l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging - - # expand prior - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) - - z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) - o = self.dec(z_slice, g=g) - return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - if self.use_sdp: - logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) - else: - logw = self.dp(x, x_mask, g=g) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - z = self.flow(z_p, y_mask, g=g, reverse=True) - o = self.dec((z * y_mask)[:,:,:max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p) - - def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): - assert self.n_speakers > 0, "n_speakers have to be larger than 0." - g_src = self.emb_g(sid_src).unsqueeze(-1) - g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) - z_p = self.flow(z, y_mask, g=g_src) - z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) - o_hat = self.dec(z_hat * y_mask, g=g_tgt) - return o_hat, y_mask, (z, z_p, z_hat) - diff --git a/spaces/zlc99/M4Singer/vocoders/vocoder_utils.py b/spaces/zlc99/M4Singer/vocoders/vocoder_utils.py deleted file mode 100644 index 98792f79b1ccdf9a136e8b9526b8e2ea65a4c004..0000000000000000000000000000000000000000 --- a/spaces/zlc99/M4Singer/vocoders/vocoder_utils.py +++ /dev/null @@ -1,15 +0,0 @@ -import librosa - -from utils.hparams import hparams -import numpy as np - - -def denoise(wav, v=0): - spec = librosa.stft(y=wav, n_fft=hparams['fft_size'], hop_length=hparams['hop_size'], - win_length=hparams['win_size'], pad_mode='constant') - spec_m = np.abs(spec) - spec_m = np.clip(spec_m - v, a_min=0, a_max=None) - spec_a = np.angle(spec) - - return librosa.istft(spec_m * np.exp(1j * spec_a), hop_length=hparams['hop_size'], - win_length=hparams['win_size']) diff --git a/spaces/zomehwh/sovits-models/cluster/train_cluster.py b/spaces/zomehwh/sovits-models/cluster/train_cluster.py deleted file mode 100644 index 4ac025d400414226e66849407f477ae786c3d5d3..0000000000000000000000000000000000000000 --- a/spaces/zomehwh/sovits-models/cluster/train_cluster.py +++ /dev/null @@ -1,89 +0,0 @@ -import os -from glob import glob -from pathlib import Path -import torch -import logging -import argparse -import torch -import numpy as np -from sklearn.cluster import KMeans, MiniBatchKMeans -import tqdm -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) -import time -import random - -def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False): - - logger.info(f"Loading features from {in_dir}") - features = [] - nums = 0 - for path in tqdm.tqdm(in_dir.glob("*.soft.pt")): - features.append(torch.load(path).squeeze(0).numpy().T) - # print(features[-1].shape) - features = np.concatenate(features, axis=0) - print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype) - features = features.astype(np.float32) - logger.info(f"Clustering features of shape: {features.shape}") - t = time.time() - if use_minibatch: - kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features) - else: - kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features) - print(time.time()-t, "s") - - x = { - "n_features_in_": kmeans.n_features_in_, - "_n_threads": kmeans._n_threads, - "cluster_centers_": kmeans.cluster_centers_, - } - print("end") - - return x - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument('--dataset', type=Path, default="./dataset/44k", - help='path of training data directory') - parser.add_argument('--output', type=Path, default="logs/44k", - help='path of model output directory') - - args = parser.parse_args() - - checkpoint_dir = args.output - dataset = args.dataset - n_clusters = 10000 - - ckpt = {} - for spk in os.listdir(dataset): - if os.path.isdir(dataset/spk): - print(f"train kmeans for {spk}...") - in_dir = dataset/spk - x = train_cluster(in_dir, n_clusters, verbose=False) - ckpt[spk] = x - - checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt" - checkpoint_path.parent.mkdir(exist_ok=True, parents=True) - torch.save( - ckpt, - checkpoint_path, - ) - - - # import cluster - # for spk in tqdm.tqdm(os.listdir("dataset")): - # if os.path.isdir(f"dataset/{spk}"): - # print(f"start kmeans inference for {spk}...") - # for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)): - # mel_path = feature_path.replace(".discrete.npy",".mel.npy") - # mel_spectrogram = np.load(mel_path) - # feature_len = mel_spectrogram.shape[-1] - # c = np.load(feature_path) - # c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy() - # feature = c.T - # feature_class = cluster.get_cluster_result(feature, spk) - # np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class) - --