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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Dispensary Management Software Free Download [PORTABLE].md b/spaces/1gistliPinn/ChatGPT4/Examples/Dispensary Management Software Free Download [PORTABLE].md deleted file mode 100644 index b53ba7a16b32417f822f8ac5df7ebbf999239aad..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Dispensary Management Software Free Download [PORTABLE].md +++ /dev/null @@ -1,6 +0,0 @@ -
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- diff --git a/spaces/1phancelerku/anime-remove-background/Download Real Car Parking 3D and Become a Parking Master.md b/spaces/1phancelerku/anime-remove-background/Download Real Car Parking 3D and Become a Parking Master.md deleted file mode 100644 index b49a38efc77e7045fbe2627e6722dfc4d1c30045..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Real Car Parking 3D and Become a Parking Master.md +++ /dev/null @@ -1,124 +0,0 @@ - -
Real Car Parking and Driving Simulator 3D Download: A Review
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- Realistic cars and physics: You can choose from a variety of cars, from sports cars to SUVs, each with its own characteristics and performance. The game also uses realistic physics to simulate the car's movement, weight, speed, and braking. -
- Challenging parking courses and levels: You can test your parking skills in different environments, such as city streets, parking lots, airports, docks, factories, and more. Each level has its own objectives, obstacles, and time limits. You can earn stars based on your performance and unlock new levels. -
- Amazing graphics and sound effects: The game has stunning 3D graphics that create a lifelike atmosphere. You can also enjoy the realistic sound effects of the engine, horn, brakes, tires, and collisions. -
- Customizable controls and camera angles: You can adjust the controls according to your preference, whether you want to use buttons, steering wheel, or tilt. You can also switch between different camera views, such as top-down, rear-view, or cockpit. -
- Offline and online modes: You can play the game offline without an internet connection, or you can play online with other players from around the world. You can compete in multiplayer races, join clans, chat with friends, and rank on the leaderboard. -
How to Download Real Car Parking and Driving Simulator 3D
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-You can download the game from the Google Play Store by following these steps:
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- Open the Google Play Store app on your device. -
- Search for "real car parking 3d" or click [here](^1^). -
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- Tap on "Install" and wait for the download to finish. -
- Tap on "Open" to launch the game. -
For iOS devices
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- Search for "real car parking 3d" or click [here](^2^). -
- Select the game from the list of results. -
- Tap on "Get" and enter your Apple ID password if prompted. -
- Wait for the download to finish. -
- Tap on the game icon to launch the game. -
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- Open the Microsoft Store app on your device. -
- Search for "real car parking 3d" or click [here]. -
- Select the game from the list of results. -
- Click on "Get" and sign in with your Microsoft account if prompted. -
- Wait for the download to finish. -
- Click on "Play" to launch the game. -
Tips and Tricks for Playing Real Car Parking and Driving Simulator 3D
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-Practice your parking skills in free mode
-The game has a free mode where you can drive around without any time limit or objectives. This is a great way to get familiar with the controls, the car's behavior, and the environment. You can also practice parking in different spots and angles, and learn from your mistakes.
-Use the brake and steering buttons wisely
-The game has two buttons for braking and steering, which are located on the bottom left and right corners of the screen. You can use them to control the speed and direction of your car. However, you should not overuse them or press them too hard, as this might cause your car to skid, spin, or crash. You should also release them when you are not using them, as this will save your fuel and prevent overheating.
-Follow the arrows and avoid obstacles
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Collect coins and gems to unlock new cars
-The game has coins and gems that you can collect by driving around or completing levels. You can use them to buy new cars or upgrade your existing ones. Each car has its own stats, such as speed, acceleration, handling, and braking. You should try different cars and find the one that suits your style and preference.
-Try different camera views to find the best angle
-The game has four camera views that you can switch between by tapping on the camera icon on the top left corner of the screen. They are: top-down, rear-view, cockpit, and side-view. Each view has its own advantages and disadvantages, depending on the situation and your preference. You should experiment with different views and find the one that gives you the best visibility and comfort.
-Conclusion
-Real car parking and driving simulator 3d is a game that will challenge your driving and parking skills in a realistic and fun way. It has many features that make it stand out from other car simulation games, such as realistic cars and physics, challenging parking courses and levels, amazing graphics and sound effects, customizable controls and camera angles, offline and online modes, and more. You can download it for free on various platforms, such as Android, iOS, and Windows devices. If you are looking for a game that will keep you entertained for hours, then you should definitely give real car parking and driving simulator 3d a try!
-If you liked this article, please share it with your friends and leave a comment below. We would love to hear your feedback and suggestions. Also, if you have any questions about the game or need more tips and tricks, feel free to ask us. We will be happy to help you!
-Frequently Asked Questions
--
-
- Q: How do I change the language of the game? -
- A: You can change the language of the game by tapping on the settings icon on the main menu screen. Then, select "Language" from the list of options. You can choose from English, Spanish, French, German, Italian, Portuguese, Russian, Turkish, Arabic, Chinese, Japanese, Korean, Hindi, Indonesian, or Vietnamese. -
- Q: How do I save my progress in the game? -
- A: The game automatically saves your progress every time you complete a level or exit the game. You can also manually save your progress by tapping on the settings icon on the main menu screen. Then, select "Save Game" from the list of options. You can also load your saved game by selecting "Load Game" from the same menu. -
- Q: How do I reset my progress in the game? -
- A: You can reset your progress in the game by tapping on the settings icon on the main menu screen. Then, select "Reset Game" from the list of options. This will erase all your data and start the game from scratch. Be careful, as this action cannot be undone. -
- Q: How do I contact the developers of the game? -
- A: You can contact the developers of the game by tapping on the settings icon on the main menu screen. Then, select "Contact Us" from the list of options. You can send them an email with your feedback, suggestions, or issues. You can also follow them on their social media accounts, such as Facebook, Twitter, Instagram, or YouTube. -
- Q: How do I rate and review the game? -
- A: You can rate and review the game by tapping on the settings icon on the main menu screen. Then, select "Rate Us" from the list of options. This will redirect you to the store page of the game, where you can give it a star rating and write a comment. Your feedback is very important for us and helps us improve the game. -
-
-
\ No newline at end of file diff --git a/spaces/1toTree/lora_test/ppdiffusers/commands/env.py b/spaces/1toTree/lora_test/ppdiffusers/commands/env.py deleted file mode 100644 index 4cb2bcfe9032bb62692dbbdc17316c962dbc5787..0000000000000000000000000000000000000000 --- a/spaces/1toTree/lora_test/ppdiffusers/commands/env.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. -# Copyright 2022 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 platform -from argparse import ArgumentParser - -from .. import __version__ as version -from ..utils import is_paddle_available, is_paddlenlp_available -from . import BasePPDiffusersCLICommand - - -def info_command_factory(_): - return EnvironmentCommand() - - -class EnvironmentCommand(BasePPDiffusersCLICommand): - @staticmethod - def register_subcommand(parser: ArgumentParser): - download_parser = parser.add_parser("env") - download_parser.set_defaults(func=info_command_factory) - - def run(self): - - pd_version = "not installed" - pd_cuda_available = "NA" - if is_paddle_available(): - import paddle - - pd_version = paddle.__version__ - pd_cuda_available = paddle.device.is_compiled_with_cuda() - - paddlenlp_version = "not installed" - if is_paddlenlp_available: - import paddlenlp - - paddlenlp_version = paddlenlp.__version__ - - info = { - "`ppdiffusers` version": version, - "Platform": platform.platform(), - "Python version": platform.python_version(), - "Paddle version (GPU?)": f"{pd_version} ({pd_cuda_available})", - "PaddleNLP version": paddlenlp_version, - "Using GPU in script?": "
{{ title|e }}
') -
-
-{% endfor %}
-
-
-"""
-
-template = Template(jinja2_template_source)
-
-# Note: ideally this would be a dataclass, but we're supporting Python 3.5+ so we can't do this yet
-NamedDiagram = NamedTuple(
- "NamedDiagram",
- [("name", str), ("diagram", typing.Optional[railroad.DiagramItem]), ("index", int)],
-)
-"""
-A simple structure for associating a name with a railroad diagram
-"""
-
-T = TypeVar("T")
-
-
-class EachItem(railroad.Group):
- """
- Custom railroad item to compose a:
- - Group containing a
- - OneOrMore containing a
- - Choice of the elements in the Each
- with the group label indicating that all must be matched
- """
-
- all_label = "[ALL]"
-
- def __init__(self, *items):
- choice_item = railroad.Choice(len(items) - 1, *items)
- one_or_more_item = railroad.OneOrMore(item=choice_item)
- super().__init__(one_or_more_item, label=self.all_label)
-
-
-class AnnotatedItem(railroad.Group):
- """
- Simple subclass of Group that creates an annotation label
- """
-
- def __init__(self, label: str, item):
- super().__init__(item=item, label="[{}]".format(label) if label else label)
-
-
-class EditablePartial(Generic[T]):
- """
- Acts like a functools.partial, but can be edited. In other words, it represents a type that hasn't yet been
- constructed.
- """
-
- # We need this here because the railroad constructors actually transform the data, so can't be called until the
- # entire tree is assembled
-
- def __init__(self, func: Callable[..., T], args: list, kwargs: dict):
- self.func = func
- self.args = args
- self.kwargs = kwargs
-
- @classmethod
- def from_call(cls, func: Callable[..., T], *args, **kwargs) -> "EditablePartial[T]":
- """
- If you call this function in the same way that you would call the constructor, it will store the arguments
- as you expect. For example EditablePartial.from_call(Fraction, 1, 3)() == Fraction(1, 3)
- """
- return EditablePartial(func=func, args=list(args), kwargs=kwargs)
-
- @property
- def name(self):
- return self.kwargs["name"]
-
- def __call__(self) -> T:
- """
- Evaluate the partial and return the result
- """
- args = self.args.copy()
- kwargs = self.kwargs.copy()
-
- # This is a helpful hack to allow you to specify varargs parameters (e.g. *args) as keyword args (e.g.
- # args=['list', 'of', 'things'])
- arg_spec = inspect.getfullargspec(self.func)
- if arg_spec.varargs in self.kwargs:
- args += kwargs.pop(arg_spec.varargs)
-
- return self.func(*args, **kwargs)
-
-
-def railroad_to_html(diagrams: List[NamedDiagram], **kwargs) -> str:
- """
- Given a list of NamedDiagram, produce a single HTML string that visualises those diagrams
- :params kwargs: kwargs to be passed in to the template
- """
- data = []
- for diagram in diagrams:
- if diagram.diagram is None:
- continue
- io = StringIO()
- diagram.diagram.writeSvg(io.write)
- title = diagram.name
- if diagram.index == 0:
- title += " (root)"
- data.append({"title": title, "text": "", "svg": io.getvalue()})
-
- return template.render(diagrams=data, **kwargs)
-
-
-def resolve_partial(partial: "EditablePartial[T]") -> T:
- """
- Recursively resolves a collection of Partials into whatever type they are
- """
- if isinstance(partial, EditablePartial):
- partial.args = resolve_partial(partial.args)
- partial.kwargs = resolve_partial(partial.kwargs)
- return partial()
- elif isinstance(partial, list):
- return [resolve_partial(x) for x in partial]
- elif isinstance(partial, dict):
- return {key: resolve_partial(x) for key, x in partial.items()}
- else:
- return partial
-
-
-def to_railroad(
- element: pyparsing.ParserElement,
- diagram_kwargs: typing.Optional[dict] = None,
- vertical: int = 3,
- show_results_names: bool = False,
- show_groups: bool = False,
-) -> List[NamedDiagram]:
- """
- Convert a pyparsing element tree into a list of diagrams. This is the recommended entrypoint to diagram
- creation if you want to access the Railroad tree before it is converted to HTML
- :param element: base element of the parser being diagrammed
- :param diagram_kwargs: kwargs to pass to the Diagram() constructor
- :param vertical: (optional) - int - limit at which number of alternatives should be
- shown vertically instead of horizontally
- :param show_results_names - bool to indicate whether results name annotations should be
- included in the diagram
- :param show_groups - bool to indicate whether groups should be highlighted with an unlabeled
- surrounding box
- """
- # Convert the whole tree underneath the root
- lookup = ConverterState(diagram_kwargs=diagram_kwargs or {})
- _to_diagram_element(
- element,
- lookup=lookup,
- parent=None,
- vertical=vertical,
- show_results_names=show_results_names,
- show_groups=show_groups,
- )
-
- root_id = id(element)
- # Convert the root if it hasn't been already
- if root_id in lookup:
- if not element.customName:
- lookup[root_id].name = ""
- lookup[root_id].mark_for_extraction(root_id, lookup, force=True)
-
- # Now that we're finished, we can convert from intermediate structures into Railroad elements
- diags = list(lookup.diagrams.values())
- if len(diags) > 1:
- # collapse out duplicate diags with the same name
- seen = set()
- deduped_diags = []
- for d in diags:
- # don't extract SkipTo elements, they are uninformative as subdiagrams
- if d.name == "...":
- continue
- if d.name is not None and d.name not in seen:
- seen.add(d.name)
- deduped_diags.append(d)
- resolved = [resolve_partial(partial) for partial in deduped_diags]
- else:
- # special case - if just one diagram, always display it, even if
- # it has no name
- resolved = [resolve_partial(partial) for partial in diags]
- return sorted(resolved, key=lambda diag: diag.index)
-
-
-def _should_vertical(
- specification: int, exprs: Iterable[pyparsing.ParserElement]
-) -> bool:
- """
- Returns true if we should return a vertical list of elements
- """
- if specification is None:
- return False
- else:
- return len(_visible_exprs(exprs)) >= specification
-
-
-class ElementState:
- """
- State recorded for an individual pyparsing Element
- """
-
- # Note: this should be a dataclass, but we have to support Python 3.5
- def __init__(
- self,
- element: pyparsing.ParserElement,
- converted: EditablePartial,
- parent: EditablePartial,
- number: int,
- name: str = None,
- parent_index: typing.Optional[int] = None,
- ):
- #: The pyparsing element that this represents
- self.element: pyparsing.ParserElement = element
- #: The name of the element
- self.name: typing.Optional[str] = name
- #: The output Railroad element in an unconverted state
- self.converted: EditablePartial = converted
- #: The parent Railroad element, which we store so that we can extract this if it's duplicated
- self.parent: EditablePartial = parent
- #: The order in which we found this element, used for sorting diagrams if this is extracted into a diagram
- self.number: int = number
- #: The index of this inside its parent
- self.parent_index: typing.Optional[int] = parent_index
- #: If true, we should extract this out into a subdiagram
- self.extract: bool = False
- #: If true, all of this element's children have been filled out
- self.complete: bool = False
-
- def mark_for_extraction(
- self, el_id: int, state: "ConverterState", name: str = None, force: bool = False
- ):
- """
- Called when this instance has been seen twice, and thus should eventually be extracted into a sub-diagram
- :param el_id: id of the element
- :param state: element/diagram state tracker
- :param name: name to use for this element's text
- :param force: If true, force extraction now, regardless of the state of this. Only useful for extracting the
- root element when we know we're finished
- """
- self.extract = True
-
- # Set the name
- if not self.name:
- if name:
- # Allow forcing a custom name
- self.name = name
- elif self.element.customName:
- self.name = self.element.customName
- else:
- self.name = ""
-
- # Just because this is marked for extraction doesn't mean we can do it yet. We may have to wait for children
- # to be added
- # Also, if this is just a string literal etc, don't bother extracting it
- if force or (self.complete and _worth_extracting(self.element)):
- state.extract_into_diagram(el_id)
-
-
-class ConverterState:
- """
- Stores some state that persists between recursions into the element tree
- """
-
- def __init__(self, diagram_kwargs: typing.Optional[dict] = None):
- #: A dictionary mapping ParserElements to state relating to them
- self._element_diagram_states: Dict[int, ElementState] = {}
- #: A dictionary mapping ParserElement IDs to subdiagrams generated from them
- self.diagrams: Dict[int, EditablePartial[NamedDiagram]] = {}
- #: The index of the next unnamed element
- self.unnamed_index: int = 1
- #: The index of the next element. This is used for sorting
- self.index: int = 0
- #: Shared kwargs that are used to customize the construction of diagrams
- self.diagram_kwargs: dict = diagram_kwargs or {}
- self.extracted_diagram_names: Set[str] = set()
-
- def __setitem__(self, key: int, value: ElementState):
- self._element_diagram_states[key] = value
-
- def __getitem__(self, key: int) -> ElementState:
- return self._element_diagram_states[key]
-
- def __delitem__(self, key: int):
- del self._element_diagram_states[key]
-
- def __contains__(self, key: int):
- return key in self._element_diagram_states
-
- def generate_unnamed(self) -> int:
- """
- Generate a number used in the name of an otherwise unnamed diagram
- """
- self.unnamed_index += 1
- return self.unnamed_index
-
- def generate_index(self) -> int:
- """
- Generate a number used to index a diagram
- """
- self.index += 1
- return self.index
-
- def extract_into_diagram(self, el_id: int):
- """
- Used when we encounter the same token twice in the same tree. When this
- happens, we replace all instances of that token with a terminal, and
- create a new subdiagram for the token
- """
- position = self[el_id]
-
- # Replace the original definition of this element with a regular block
- if position.parent:
- ret = EditablePartial.from_call(railroad.NonTerminal, text=position.name)
- if "item" in position.parent.kwargs:
- position.parent.kwargs["item"] = ret
- elif "items" in position.parent.kwargs:
- position.parent.kwargs["items"][position.parent_index] = ret
-
- # If the element we're extracting is a group, skip to its content but keep the title
- if position.converted.func == railroad.Group:
- content = position.converted.kwargs["item"]
- else:
- content = position.converted
-
- self.diagrams[el_id] = EditablePartial.from_call(
- NamedDiagram,
- name=position.name,
- diagram=EditablePartial.from_call(
- railroad.Diagram, content, **self.diagram_kwargs
- ),
- index=position.number,
- )
-
- del self[el_id]
-
-
-def _worth_extracting(element: pyparsing.ParserElement) -> bool:
- """
- Returns true if this element is worth having its own sub-diagram. Simply, if any of its children
- themselves have children, then its complex enough to extract
- """
- children = element.recurse()
- return any(child.recurse() for child in children)
-
-
-def _apply_diagram_item_enhancements(fn):
- """
- decorator to ensure enhancements to a diagram item (such as results name annotations)
- get applied on return from _to_diagram_element (we do this since there are several
- returns in _to_diagram_element)
- """
-
- def _inner(
- element: pyparsing.ParserElement,
- parent: typing.Optional[EditablePartial],
- lookup: ConverterState = None,
- vertical: int = None,
- index: int = 0,
- name_hint: str = None,
- show_results_names: bool = False,
- show_groups: bool = False,
- ) -> typing.Optional[EditablePartial]:
-
- ret = fn(
- element,
- parent,
- lookup,
- vertical,
- index,
- name_hint,
- show_results_names,
- show_groups,
- )
-
- # apply annotation for results name, if present
- if show_results_names and ret is not None:
- element_results_name = element.resultsName
- if element_results_name:
- # add "*" to indicate if this is a "list all results" name
- element_results_name += "" if element.modalResults else "*"
- ret = EditablePartial.from_call(
- railroad.Group, item=ret, label=element_results_name
- )
-
- return ret
-
- return _inner
-
-
-def _visible_exprs(exprs: Iterable[pyparsing.ParserElement]):
- non_diagramming_exprs = (
- pyparsing.ParseElementEnhance,
- pyparsing.PositionToken,
- pyparsing.And._ErrorStop,
- )
- return [
- e
- for e in exprs
- if not (e.customName or e.resultsName or isinstance(e, non_diagramming_exprs))
- ]
-
-
-@_apply_diagram_item_enhancements
-def _to_diagram_element(
- element: pyparsing.ParserElement,
- parent: typing.Optional[EditablePartial],
- lookup: ConverterState = None,
- vertical: int = None,
- index: int = 0,
- name_hint: str = None,
- show_results_names: bool = False,
- show_groups: bool = False,
-) -> typing.Optional[EditablePartial]:
- """
- Recursively converts a PyParsing Element to a railroad Element
- :param lookup: The shared converter state that keeps track of useful things
- :param index: The index of this element within the parent
- :param parent: The parent of this element in the output tree
- :param vertical: Controls at what point we make a list of elements vertical. If this is an integer (the default),
- it sets the threshold of the number of items before we go vertical. If True, always go vertical, if False, never
- do so
- :param name_hint: If provided, this will override the generated name
- :param show_results_names: bool flag indicating whether to add annotations for results names
- :returns: The converted version of the input element, but as a Partial that hasn't yet been constructed
- :param show_groups: bool flag indicating whether to show groups using bounding box
- """
- exprs = element.recurse()
- name = name_hint or element.customName or element.__class__.__name__
-
- # Python's id() is used to provide a unique identifier for elements
- el_id = id(element)
-
- element_results_name = element.resultsName
-
- # Here we basically bypass processing certain wrapper elements if they contribute nothing to the diagram
- if not element.customName:
- if isinstance(
- element,
- (
- # pyparsing.TokenConverter,
- # pyparsing.Forward,
- pyparsing.Located,
- ),
- ):
- # However, if this element has a useful custom name, and its child does not, we can pass it on to the child
- if exprs:
- if not exprs[0].customName:
- propagated_name = name
- else:
- propagated_name = None
-
- return _to_diagram_element(
- element.expr,
- parent=parent,
- lookup=lookup,
- vertical=vertical,
- index=index,
- name_hint=propagated_name,
- show_results_names=show_results_names,
- show_groups=show_groups,
- )
-
- # If the element isn't worth extracting, we always treat it as the first time we say it
- if _worth_extracting(element):
- if el_id in lookup:
- # If we've seen this element exactly once before, we are only just now finding out that it's a duplicate,
- # so we have to extract it into a new diagram.
- looked_up = lookup[el_id]
- looked_up.mark_for_extraction(el_id, lookup, name=name_hint)
- ret = EditablePartial.from_call(railroad.NonTerminal, text=looked_up.name)
- return ret
-
- elif el_id in lookup.diagrams:
- # If we have seen the element at least twice before, and have already extracted it into a subdiagram, we
- # just put in a marker element that refers to the sub-diagram
- ret = EditablePartial.from_call(
- railroad.NonTerminal, text=lookup.diagrams[el_id].kwargs["name"]
- )
- return ret
-
- # Recursively convert child elements
- # Here we find the most relevant Railroad element for matching pyparsing Element
- # We use ``items=[]`` here to hold the place for where the child elements will go once created
- if isinstance(element, pyparsing.And):
- # detect And's created with ``expr*N`` notation - for these use a OneOrMore with a repeat
- # (all will have the same name, and resultsName)
- if not exprs:
- return None
- if len(set((e.name, e.resultsName) for e in exprs)) == 1:
- ret = EditablePartial.from_call(
- railroad.OneOrMore, item="", repeat=str(len(exprs))
- )
- elif _should_vertical(vertical, exprs):
- ret = EditablePartial.from_call(railroad.Stack, items=[])
- else:
- ret = EditablePartial.from_call(railroad.Sequence, items=[])
- elif isinstance(element, (pyparsing.Or, pyparsing.MatchFirst)):
- if not exprs:
- return None
- if _should_vertical(vertical, exprs):
- ret = EditablePartial.from_call(railroad.Choice, 0, items=[])
- else:
- ret = EditablePartial.from_call(railroad.HorizontalChoice, items=[])
- elif isinstance(element, pyparsing.Each):
- if not exprs:
- return None
- ret = EditablePartial.from_call(EachItem, items=[])
- elif isinstance(element, pyparsing.NotAny):
- ret = EditablePartial.from_call(AnnotatedItem, label="NOT", item="")
- elif isinstance(element, pyparsing.FollowedBy):
- ret = EditablePartial.from_call(AnnotatedItem, label="LOOKAHEAD", item="")
- elif isinstance(element, pyparsing.PrecededBy):
- ret = EditablePartial.from_call(AnnotatedItem, label="LOOKBEHIND", item="")
- elif isinstance(element, pyparsing.Group):
- if show_groups:
- ret = EditablePartial.from_call(AnnotatedItem, label="", item="")
- else:
- ret = EditablePartial.from_call(railroad.Group, label="", item="")
- elif isinstance(element, pyparsing.TokenConverter):
- ret = EditablePartial.from_call(
- AnnotatedItem, label=type(element).__name__.lower(), item=""
- )
- elif isinstance(element, pyparsing.Opt):
- ret = EditablePartial.from_call(railroad.Optional, item="")
- elif isinstance(element, pyparsing.OneOrMore):
- ret = EditablePartial.from_call(railroad.OneOrMore, item="")
- elif isinstance(element, pyparsing.ZeroOrMore):
- ret = EditablePartial.from_call(railroad.ZeroOrMore, item="")
- elif isinstance(element, pyparsing.Group):
- ret = EditablePartial.from_call(
- railroad.Group, item=None, label=element_results_name
- )
- elif isinstance(element, pyparsing.Empty) and not element.customName:
- # Skip unnamed "Empty" elements
- ret = None
- elif len(exprs) > 1:
- ret = EditablePartial.from_call(railroad.Sequence, items=[])
- elif len(exprs) > 0 and not element_results_name:
- ret = EditablePartial.from_call(railroad.Group, item="", label=name)
- else:
- terminal = EditablePartial.from_call(railroad.Terminal, element.defaultName)
- ret = terminal
-
- if ret is None:
- return
-
- # Indicate this element's position in the tree so we can extract it if necessary
- lookup[el_id] = ElementState(
- element=element,
- converted=ret,
- parent=parent,
- parent_index=index,
- number=lookup.generate_index(),
- )
- if element.customName:
- lookup[el_id].mark_for_extraction(el_id, lookup, element.customName)
-
- i = 0
- for expr in exprs:
- # Add a placeholder index in case we have to extract the child before we even add it to the parent
- if "items" in ret.kwargs:
- ret.kwargs["items"].insert(i, None)
-
- item = _to_diagram_element(
- expr,
- parent=ret,
- lookup=lookup,
- vertical=vertical,
- index=i,
- show_results_names=show_results_names,
- show_groups=show_groups,
- )
-
- # Some elements don't need to be shown in the diagram
- if item is not None:
- if "item" in ret.kwargs:
- ret.kwargs["item"] = item
- elif "items" in ret.kwargs:
- # If we've already extracted the child, don't touch this index, since it's occupied by a nonterminal
- ret.kwargs["items"][i] = item
- i += 1
- elif "items" in ret.kwargs:
- # If we're supposed to skip this element, remove it from the parent
- del ret.kwargs["items"][i]
-
- # If all this items children are none, skip this item
- if ret and (
- ("items" in ret.kwargs and len(ret.kwargs["items"]) == 0)
- or ("item" in ret.kwargs and ret.kwargs["item"] is None)
- ):
- ret = EditablePartial.from_call(railroad.Terminal, name)
-
- # Mark this element as "complete", ie it has all of its children
- if el_id in lookup:
- lookup[el_id].complete = True
-
- if el_id in lookup and lookup[el_id].extract and lookup[el_id].complete:
- lookup.extract_into_diagram(el_id)
- if ret is not None:
- ret = EditablePartial.from_call(
- railroad.NonTerminal, text=lookup.diagrams[el_id].kwargs["name"]
- )
-
- return ret
diff --git a/spaces/Blessin/yes-and-improv-game/app.py b/spaces/Blessin/yes-and-improv-game/app.py
deleted file mode 100644
index 9bdf81daece9439d99cd8a83bfaf1787c7eb96aa..0000000000000000000000000000000000000000
--- a/spaces/Blessin/yes-and-improv-game/app.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import gradio as gr
-import openai
-
-# Function to extract the last statement from the input
-def extract_last_statement(input_text):
- lines = input_text.strip().split('\n')
- last_line = lines[-1]
- last_statement = last_line.split(':')[-1].strip() if ':' in last_line else last_line
- return last_statement
-
-def yes_and_game(api_key, user_input):
- # Initialize OpenAI API client
- openai.api_key = api_key
-
- # Extract the last statement from the user input
- last_statement = extract_last_statement(user_input)
-
- # Create the prompt for GPT
- gpt_prompt = (f"Play the Yes, And improv game. "
- f"You will start your response with 'Yes, and'. "
- f"Keep your responses short. Not more than one statement. Responses can be funny or absurd. "
- f"The input statement can be a single line or a multi line statement.\n"
- f"Yes, And {last_statement}\n"
- f"Yes, And ")
-
- # Generate GPT response
- gpt_response = openai.Completion.create(
- engine="text-davinci-002",
- prompt=gpt_prompt,
- max_tokens=20,
- temperature=0.9 # Increased temperature for more randomness
- )['choices'][0]['text'].strip()
-
- # Format and return the result
- result = f"{last_statement}\nYes, And {gpt_response}"
- return result
-
-iface = gr.Interface(
- fn=yes_and_game,
- inputs=[
- gr.Textbox(label="OpenAI API Key", type="password"),
- gr.Textbox(lines=5, label="Statement"),
- ],
- outputs=gr.Textbox(label="Game Transcript", live=True, flagging=True), # Setting live=True for real-time updates, flagging=True to allow copying
- title="The Yes, And Game" # Adding title here
-)
-
-
-# This will create a link to host your model on Hugging Face Spaces when executed
-iface.launch(share=True)
diff --git a/spaces/CM-15/NLP-demo/README.md b/spaces/CM-15/NLP-demo/README.md
deleted file mode 100644
index 47a4db86d17ca2f7f73282888fcb4bebbcfb1efa..0000000000000000000000000000000000000000
--- a/spaces/CM-15/NLP-demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: NLP Demo
-emoji: 😻
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CVPR/CVPR2022_papers/style.css b/spaces/CVPR/CVPR2022_papers/style.css
deleted file mode 100644
index e2b871457d13980ddfbbc35bf5da02a75ece292e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/CVPR2022_papers/style.css
+++ /dev/null
@@ -1,22 +0,0 @@
-h1 {
- text-align: center;
-}
-table a {
- background-color: transparent;
- color: #58a6ff;
- text-decoration: none;
-}
-a:active,
-a:hover {
- outline-width: 0;
-}
-a:hover {
- text-decoration: underline;
-}
-table, th, td {
- border: 1px solid;
-}
-img#visitor-badge {
- display: block;
- margin: auto;
-}
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mmnasnet/adapter.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mmnasnet/adapter.py
deleted file mode 100644
index 307f85b7236767009b378d3c677c1fd17b1b3e2c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mmnasnet/adapter.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Zhenwei Shao https://github.com/ParadoxZW
-# --------------------------------------------------------
-
-import torch.nn as nn
-import torch
-from openvqa.core.base_dataset import BaseAdapter
-from openvqa.utils.make_mask import make_mask
-
-
-class Adapter(BaseAdapter):
- def __init__(self, __C):
- super(Adapter, self).__init__(__C)
- self.__C = __C
-
-
- def relation_embedding(self, f_g):
- x_min, y_min, x_max, y_max = torch.chunk(f_g, 4, dim=2) # [bs, n_obj, 1]
-
- cx = (x_min + x_max) * 0.5 # [bs, n_obj, 1]
- cy = (y_min + y_max) * 0.5 # [bs, n_obj, 1]
- w = (x_max - x_min) + 1. # [bs, n_obj, 1]
- h = (y_max - y_min) + 1. # [bs, n_obj, 1]
-
- delta_x = cx - cx.transpose(-1, -2)
- delta_x = torch.clamp(torch.abs(delta_x / w), min=1e-3)
- delta_x = torch.log(delta_x) # [bs, n_obj, n_obj]
-
- delta_y = cy - cy.transpose(-1, -2)
- delta_y = torch.clamp(torch.abs(delta_y / h), min=1e-3)
- delta_y = torch.log(delta_y) # [bs, n_obj, n_obj]
-
- delta_w = torch.log(w / w.transpose(-1, -2)) # [bs, n_obj, n_obj]
- delta_h = torch.log(h / h.transpose(-1, -2)) # [bs, n_obj, n_obj]
- size = delta_h.size()
-
- delta_x = delta_x.view(size[0], size[1], size[2], 1)
- delta_y = delta_y.view(size[0], size[1], size[2], 1)
- delta_w = delta_w.view(size[0], size[1], size[2], 1)
- delta_h = delta_h.view(size[0], size[1], size[2], 1) # [bs, n_obj, n_obj, 1]
- position_mat = torch.cat(
- (delta_x, delta_y, delta_w, delta_h), -1) # [bs, n_obj, n_obj, 4]
-
- return position_mat
-
- def vqa_init(self, __C):
- imgfeat_linear_size = __C.FEAT_SIZE['vqa']['FRCN_FEAT_SIZE'][1]
- if __C.USE_BBOX_FEAT:
- self.bbox_linear = nn.Linear(5, __C.BBOXFEAT_EMB_SIZE)
- imgfeat_linear_size += __C.BBOXFEAT_EMB_SIZE
- self.frcn_linear = nn.Linear(imgfeat_linear_size, __C.HIDDEN_SIZE)
-
-
- def gqa_init(self, __C):
- imgfeat_linear_size = __C.FEAT_SIZE['gqa']['FRCN_FEAT_SIZE'][1]
- if __C.USE_BBOX_FEAT:
- self.bbox_linear = nn.Linear(5, __C.BBOXFEAT_EMB_SIZE)
- imgfeat_linear_size += __C.BBOXFEAT_EMB_SIZE
- self.frcn_linear = nn.Linear(imgfeat_linear_size, __C.HIDDEN_SIZE)
-
- if __C.USE_AUX_FEAT:
- self.grid_linear = nn.Linear(__C.FEAT_SIZE['gqa']['GRID_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-
-
- def clevr_init(self, __C):
- self.grid_linear = nn.Linear(__C.FEAT_SIZE['clevr']['GRID_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-
-
- def vqa_forward(self, feat_dict):
- frcn_feat = feat_dict['FRCN_FEAT']
- bbox_feat = feat_dict['BBOX_FEAT']
-
- img_feat_mask = make_mask(frcn_feat)
-
- if self.__C.USE_BBOX_FEAT:
- bbox_feat = self.bbox_proc(bbox_feat)
- bbox_feat = self.bbox_linear(bbox_feat)
- frcn_feat = torch.cat((frcn_feat, bbox_feat), dim=-1)
- img_feat = self.frcn_linear(frcn_feat)
- rel_embed = self.relation_embedding(bbox_feat)
-
- return img_feat, rel_embed, img_feat_mask
-
-
- def gqa_forward(self, feat_dict):
- frcn_feat = feat_dict['FRCN_FEAT']
- bbox_feat = feat_dict['BBOX_FEAT']
- grid_feat = feat_dict['GRID_FEAT']
-
- img_feat_mask = make_mask(frcn_feat)
-
- if self.__C.USE_BBOX_FEAT:
- bbox_feat = self.bbox_linear(bbox_feat)
- frcn_feat = torch.cat((frcn_feat, bbox_feat), dim=-1)
- img_feat = self.frcn_linear(frcn_feat)
-
- if self.__C.USE_AUX_FEAT:
- grid_feat_mask = make_mask(grid_feat)
- img_feat_mask = torch.cat((img_feat_mask, grid_feat_mask), dim=-1)
- grid_feat = self.grid_linear(grid_feat)
- img_feat = torch.cat((img_feat, grid_feat), dim=1)
-
- rel_embed = self.relation_embedding(bbox_feat)
-
- return img_feat, rel_embed, img_feat_mask
-
-
- def clevr_forward(self, feat_dict):
- grid_feat = feat_dict['GRID_FEAT']
-
- img_feat_mask = make_mask(grid_feat)
- img_feat = self.grid_linear(grid_feat)
-
- rel_embed = self.relation_embedding(bbox_feat)
-
- return img_feat, rel_embed, img_feat_mask
-
-
-
diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_modules.py b/spaces/CVPR/LIVE/pybind11/tests/test_modules.py
deleted file mode 100644
index 7e2100524506b13a5d3189a3fabb9dead628c2a5..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tests/test_modules.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# -*- coding: utf-8 -*-
-from pybind11_tests import modules as m
-from pybind11_tests.modules import subsubmodule as ms
-from pybind11_tests import ConstructorStats
-
-
-def test_nested_modules():
- import pybind11_tests
- assert pybind11_tests.__name__ == "pybind11_tests"
- assert pybind11_tests.modules.__name__ == "pybind11_tests.modules"
- assert pybind11_tests.modules.subsubmodule.__name__ == "pybind11_tests.modules.subsubmodule"
- assert m.__name__ == "pybind11_tests.modules"
- assert ms.__name__ == "pybind11_tests.modules.subsubmodule"
-
- assert ms.submodule_func() == "submodule_func()"
-
-
-def test_reference_internal():
- b = ms.B()
- assert str(b.get_a1()) == "A[1]"
- assert str(b.a1) == "A[1]"
- assert str(b.get_a2()) == "A[2]"
- assert str(b.a2) == "A[2]"
-
- b.a1 = ms.A(42)
- b.a2 = ms.A(43)
- assert str(b.get_a1()) == "A[42]"
- assert str(b.a1) == "A[42]"
- assert str(b.get_a2()) == "A[43]"
- assert str(b.a2) == "A[43]"
-
- astats, bstats = ConstructorStats.get(ms.A), ConstructorStats.get(ms.B)
- assert astats.alive() == 2
- assert bstats.alive() == 1
- del b
- assert astats.alive() == 0
- assert bstats.alive() == 0
- assert astats.values() == ['1', '2', '42', '43']
- assert bstats.values() == []
- assert astats.default_constructions == 0
- assert bstats.default_constructions == 1
- assert astats.copy_constructions == 0
- assert bstats.copy_constructions == 0
- # assert astats.move_constructions >= 0 # Don't invoke any
- # assert bstats.move_constructions >= 0 # Don't invoke any
- assert astats.copy_assignments == 2
- assert bstats.copy_assignments == 0
- assert astats.move_assignments == 0
- assert bstats.move_assignments == 0
-
-
-def test_importing():
- from pybind11_tests.modules import OD
- from collections import OrderedDict
-
- assert OD is OrderedDict
- assert str(OD([(1, 'a'), (2, 'b')])) == "OrderedDict([(1, 'a'), (2, 'b')])"
-
-
-def test_pydoc():
- """Pydoc needs to be able to provide help() for everything inside a pybind11 module"""
- import pybind11_tests
- import pydoc
-
- assert pybind11_tests.__name__ == "pybind11_tests"
- assert pybind11_tests.__doc__ == "pybind11 test module"
- assert pydoc.text.docmodule(pybind11_tests)
-
-
-def test_duplicate_registration():
- """Registering two things with the same name"""
-
- assert m.duplicate_registration() == []
diff --git a/spaces/CVPR/WALT/mmdet/datasets/samplers/distributed_sampler.py b/spaces/CVPR/WALT/mmdet/datasets/samplers/distributed_sampler.py
deleted file mode 100644
index cc61019484655ee2829f7908dc442caa20cf1d54..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/datasets/samplers/distributed_sampler.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import math
-
-import torch
-from torch.utils.data import DistributedSampler as _DistributedSampler
-
-
-class DistributedSampler(_DistributedSampler):
-
- def __init__(self,
- dataset,
- num_replicas=None,
- rank=None,
- shuffle=True,
- seed=0):
- super().__init__(
- dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- # for the compatibility from PyTorch 1.3+
- self.seed = seed if seed is not None else 0
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- if self.shuffle:
- g = torch.Generator()
- g.manual_seed(self.epoch + self.seed)
- indices = torch.randperm(len(self.dataset), generator=g).tolist()
- else:
- indices = torch.arange(len(self.dataset)).tolist()
-
- # add extra samples to make it evenly divisible
- # in case that indices is shorter than half of total_size
- indices = (indices *
- math.ceil(self.total_size / len(indices)))[:self.total_size]
- assert len(indices) == self.total_size
-
- # subsample
- indices = indices[self.rank:self.total_size:self.num_replicas]
- assert len(indices) == self.num_samples
-
- return iter(indices)
diff --git a/spaces/Candyraider/Proxy4/README.md b/spaces/Candyraider/Proxy4/README.md
deleted file mode 100644
index 0881d5470838143571654518654052ae2eff9dc4..0000000000000000000000000000000000000000
--- a/spaces/Candyraider/Proxy4/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Proxy4
-emoji: 🏢
-colorFrom: purple
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Chris4K/llms_compare/Antares Mic Mod Efx Mac ~UPD~ Crack Torrent.md b/spaces/Chris4K/llms_compare/Antares Mic Mod Efx Mac ~UPD~ Crack Torrent.md
deleted file mode 100644
index ee71c05a939b76b62b1fcd1736b96bbb9eeb8593..0000000000000000000000000000000000000000
--- a/spaces/Chris4K/llms_compare/Antares Mic Mod Efx Mac ~UPD~ Crack Torrent.md
+++ /dev/null
@@ -1,84 +0,0 @@
-## Antares Mic Mod Efx Mac Crack Torrent
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-So what is the best way to get Antares Mic Mod Efx Mac for free? The answer is simple: use a trial version. Antares offers a free 14-day trial of Mic Mod Efx on their website. You can download and install the plugin on your Mac and use it for two weeks without any limitations or restrictions. You can try out all the features and functions of the plugin and see how it works for you. You can also compare the sound of different microphones and find the ones that suit your style and preference.
-
-
-
-After the trial period is over, you can decide whether you want to buy the full version of Antares Mic Mod Efx Mac or not. The full version costs $129 and comes with lifetime updates and support. You can also get it as part of the Antares AVOX bundle, which includes other vocal processing plugins such as Auto-Tune, Harmony Engine, Articulator, and more.
-
-
-
-If you are serious about your music production and want to get the best sound possible, then investing in Antares Mic Mod Efx Mac is worth it. You will get access to a huge collection of microphone models that will enhance your recordings and give you more creative options. You will also get a legal and safe software that will work smoothly and reliably on your Mac.
-
-
-
-So don't waste your time and risk your security by downloading Antares Mic Mod Efx Mac crack torrent from shady sites. Instead, go to the official Antares website and download the free trial version of Mic Mod Efx today. You will be amazed by what this plugin can do for your sound.
-
-
-
-## What Users Say About Antares Mic Mod Efx Mac
-
-
-
-If you are still not convinced by the benefits of Antares Mic Mod Efx Mac, you might want to hear what other users have to say about it. Many users have shared their positive experiences and reviews of this plugin on various platforms and websites. Here are some of the testimonials from real users who have tried Antares Mic Mod Efx Mac:
-
-
-
-- "I was just recording on the Sony C800g not too long ago and when I use this plugin at home (with my ml 770) and hear myself it sounds like I'm on the Sony. Blown away by how good this plugin is." - Michael from Newport Beach, CA[^1^]
-
-- "This tool is just that... A tool. I used it alongside my 1977 U87 and my U87ai. I was unable to tell the difference between my Ai and my Vintage U87 when I used this plugin to turn one into the other. Like a few others have stated... I'm shocked this tool doesn't get more exposure." - CC from Colorado[^1^]
-
-- "I'm using this plug-in with a Manley ref cad, I have no clie what the actual version of most of these mics are really suppose to sound like. All I know is they sound great!!" - Rony from Philadelphia[^1^]
-
-- "I'm astounded at the lack of credit MIc Mod has gotten. This software is really easy to use and also sounds extremely convincing to my ear. By no means does it sound like my own mic being EQ'ed. What I hear is dynamic frequency response change and saturation as well." - Anthony Lowery from Manteca, CA[^1^]
-
-- "This is clearly not something you could do in the real world, but if it creates a sound that works then it's more than justified. The mic models themselves are stored as separate files which, in the case of Mac users, are located within the Preferences folder in the System folder." - Paul White from Sound On Sound[^3^]
-
-
-
-As you can see, Antares Mic Mod Efx Mac has received rave reviews from users who have tried it and loved it. They have praised its ease of use, its realism, its versatility, and its quality. They have also compared it favorably to some of the most expensive and sought-after microphones in the world.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/ChrisPreston/diff-svc_minato_aqua/modules/commons/ssim.py b/spaces/ChrisPreston/diff-svc_minato_aqua/modules/commons/ssim.py
deleted file mode 100644
index 3f77c95803206138dd05095a037fed5acb1c4112..0000000000000000000000000000000000000000
--- a/spaces/ChrisPreston/diff-svc_minato_aqua/modules/commons/ssim.py
+++ /dev/null
@@ -1,84 +0,0 @@
-"""
-Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim
-"""
-
-from math import exp
-
-import torch
-import torch.nn.functional as F
-from torch.autograd import Variable
-
-
-def gaussian(window_size, sigma):
- gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
- return gauss / gauss.sum()
-
-
-def create_window(window_size, channel):
- _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
- _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
- window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
- return window
-
-
-def _ssim(img1, img2, window, window_size, channel, size_average=True):
- mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
- mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
-
- mu1_sq = mu1.pow(2)
- mu2_sq = mu2.pow(2)
- mu1_mu2 = mu1 * mu2
-
- sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
- sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
- sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
-
- C1 = 0.01 ** 2
- C2 = 0.03 ** 2
-
- ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-
- if size_average:
- return ssim_map.mean()
- else:
- return ssim_map.mean(1)
-
-
-class SSIM(torch.nn.Module):
- def __init__(self, window_size=11, size_average=True):
- super(SSIM, self).__init__()
- self.window_size = window_size
- self.size_average = size_average
- self.channel = 1
- self.window = create_window(window_size, self.channel)
-
- def forward(self, img1, img2):
- (_, channel, _, _) = img1.size()
-
- if channel == self.channel and self.window.data.type() == img1.data.type():
- window = self.window
- else:
- window = create_window(self.window_size, channel)
-
- if img1.is_cuda:
- window = window.cuda(img1.get_device())
- window = window.type_as(img1)
-
- self.window = window
- self.channel = channel
-
- return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
-
-
-window = None
-
-
-def ssim(img1, img2, window_size=11, size_average=True):
- (_, channel, _, _) = img1.size()
- global window
- if window is None:
- window = create_window(window_size, channel)
- if img1.is_cuda:
- window = window.cuda(img1.get_device())
- window = window.type_as(img1)
- return _ssim(img1, img2, window, window_size, channel, size_average)
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/segmentation_mask.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/segmentation_mask.py
deleted file mode 100644
index 5e1ba07767df487c9b4cccca4a87540a4bce3b99..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/segmentation_mask.py
+++ /dev/null
@@ -1,535 +0,0 @@
-import cv2
-import copy
-import torch
-import numpy as np
-from maskrcnn_benchmark.layers.misc import interpolate
-
-import pycocotools.mask as mask_utils
-
-# transpose
-FLIP_LEFT_RIGHT = 0
-FLIP_TOP_BOTTOM = 1
-
-
-""" ABSTRACT
-Segmentations come in either:
-1) Binary masks
-2) Polygons
-
-Binary masks can be represented in a contiguous array
-and operations can be carried out more efficiently,
-therefore BinaryMaskList handles them together.
-
-Polygons are handled separately for each instance,
-by PolygonInstance and instances are handled by
-PolygonList.
-
-SegmentationList is supposed to represent both,
-therefore it wraps the functions of BinaryMaskList
-and PolygonList to make it transparent.
-"""
-
-
-class BinaryMaskList(object):
- """
- This class handles binary masks for all objects in the image
- """
-
- def __init__(self, masks, size):
- """
- Arguments:
- masks: Either torch.tensor of [num_instances, H, W]
- or list of torch.tensors of [H, W] with num_instances elems,
- or RLE (Run Length Encoding) - interpreted as list of dicts,
- or BinaryMaskList.
- size: absolute image size, width first
-
- After initialization, a hard copy will be made, to leave the
- initializing source data intact.
- """
-
- if isinstance(masks, torch.Tensor):
- # The raw data representation is passed as argument
- masks = masks.clone()
- elif isinstance(masks, (list, tuple)):
- if isinstance(masks[0], torch.Tensor):
- masks = torch.stack(masks, dim=2).clone()
- elif isinstance(masks[0], dict) and "count" in masks[0]:
- # RLE interpretation
-
- masks = mask_utils
- else:
- RuntimeError(
- "Type of `masks[0]` could not be interpreted: %s" % type(masks)
- )
- elif isinstance(masks, BinaryMaskList):
- # just hard copy the BinaryMaskList instance's underlying data
- masks = masks.masks.clone()
- else:
- RuntimeError(
- "Type of `masks` argument could not be interpreted:%s" % type(masks)
- )
-
- if len(masks.shape) == 2:
- # if only a single instance mask is passed
- masks = masks[None]
-
- assert len(masks.shape) == 3
- assert masks.shape[1] == size[1], "%s != %s" % (masks.shape[1], size[1])
- assert masks.shape[2] == size[0], "%s != %s" % (masks.shape[2], size[0])
-
- self.masks = masks
- self.size = tuple(size)
-
- def transpose(self, method):
- dim = 1 if method == FLIP_TOP_BOTTOM else 2
- flipped_masks = self.masks.flip(dim)
- return BinaryMaskList(flipped_masks, self.size)
-
- def crop(self, box):
- assert isinstance(box, (list, tuple, torch.Tensor)), str(type(box))
- # box is assumed to be xyxy
- current_width, current_height = self.size
- xmin, ymin, xmax, ymax = [round(float(b)) for b in box]
-
- assert xmin <= xmax and ymin <= ymax, str(box)
- xmin = min(max(xmin, 0), current_width - 1)
- ymin = min(max(ymin, 0), current_height - 1)
-
- xmax = min(max(xmax, 0), current_width)
- ymax = min(max(ymax, 0), current_height)
-
- xmax = max(xmax, xmin + 1)
- ymax = max(ymax, ymin + 1)
-
- width, height = xmax - xmin, ymax - ymin
- cropped_masks = self.masks[:, ymin:ymax, xmin:xmax]
- cropped_size = width, height
- return BinaryMaskList(cropped_masks, cropped_size)
-
- def resize(self, size):
- try:
- iter(size)
- except TypeError:
- assert isinstance(size, (int, float))
- size = size, size
- width, height = map(int, size)
-
- assert width > 0
- assert height > 0
-
- # Height comes first here!
- resized_masks = torch.nn.functional.interpolate(
- input=self.masks[None].float(),
- size=(height, width),
- mode="bilinear",
- align_corners=False,
- )[0].type_as(self.masks)
- resized_size = width, height
- return BinaryMaskList(resized_masks, resized_size)
-
- def convert_to_polygon(self):
- contours = self._findContours()
- return PolygonList(contours, self.size)
-
- def to(self, *args, **kwargs):
- return self
-
- def _findContours(self):
- contours = []
- masks = self.masks.detach().numpy()
- for mask in masks:
- mask = cv2.UMat(mask)
- contour, hierarchy = cv2.findContours(
- mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
- )
-
- reshaped_contour = []
- for entity in contour:
- assert len(entity.shape) == 3
- assert entity.shape[1] == 1, "Hierarchical contours are not allowed"
- reshaped_contour.append(entity.reshape(-1).tolist())
- contours.append(reshaped_contour)
- return contours
-
- def __len__(self):
- return len(self.masks)
-
- def __getitem__(self, index):
- # Probably it can cause some overhead
- # but preserves consistency
- masks = self.masks[index].clone()
- return BinaryMaskList(masks, self.size)
-
- def __iter__(self):
- return iter(self.masks)
-
- def __repr__(self):
- s = self.__class__.__name__ + "("
- s += "num_instances={}, ".format(len(self.masks))
- s += "image_width={}, ".format(self.size[0])
- s += "image_height={})".format(self.size[1])
- return s
-
-
-class PolygonInstance(object):
- """
- This class holds a set of polygons that represents a single instance
- of an object mask. The object can be represented as a set of
- polygons
- """
-
- def __init__(self, polygons, size):
- """
- Arguments:
- a list of lists of numbers.
- The first level refers to all the polygons that compose the
- object, and the second level to the polygon coordinates.
- """
- if isinstance(polygons, (list, tuple)):
- valid_polygons = []
- for p in polygons:
- p = torch.as_tensor(p, dtype=torch.float32)
- if len(p) >= 6: # 3 * 2 coordinates
- valid_polygons.append(p)
- polygons = valid_polygons
-
- elif isinstance(polygons, PolygonInstance):
- polygons = copy.copy(polygons.polygons)
- else:
- RuntimeError(
- "Type of argument `polygons` is not allowed:%s" % (type(polygons))
- )
-
- """ This crashes the training way too many times...
- for p in polygons:
- assert p[::2].min() >= 0
- assert p[::2].max() < size[0]
- assert p[1::2].min() >= 0
- assert p[1::2].max() , size[1]
- """
-
- self.polygons = polygons
- self.size = tuple(size)
-
- def transpose(self, method):
- if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
- raise NotImplementedError(
- "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
- )
-
- flipped_polygons = []
- width, height = self.size
- if method == FLIP_LEFT_RIGHT:
- dim = width
- idx = 0
- elif method == FLIP_TOP_BOTTOM:
- dim = height
- idx = 1
-
- for poly in self.polygons:
- p = poly.clone()
- TO_REMOVE = 1
- p[idx::2] = dim - poly[idx::2] - TO_REMOVE
- flipped_polygons.append(p)
-
- return PolygonInstance(flipped_polygons, size=self.size)
-
- def crop(self, box):
- assert isinstance(box, (list, tuple, torch.Tensor)), str(type(box))
-
- # box is assumed to be xyxy
- current_width, current_height = self.size
- xmin, ymin, xmax, ymax = map(float, box)
-
- assert xmin <= xmax and ymin <= ymax, str(box)
- xmin = min(max(xmin, 0), current_width - 1)
- ymin = min(max(ymin, 0), current_height - 1)
-
- xmax = min(max(xmax, 0), current_width)
- ymax = min(max(ymax, 0), current_height)
-
- xmax = max(xmax, xmin + 1)
- ymax = max(ymax, ymin + 1)
-
- w, h = xmax - xmin, ymax - ymin
-
- cropped_polygons = []
- for poly in self.polygons:
- p = poly.clone()
- p[0::2] = p[0::2] - xmin # .clamp(min=0, max=w)
- p[1::2] = p[1::2] - ymin # .clamp(min=0, max=h)
- cropped_polygons.append(p)
-
- return PolygonInstance(cropped_polygons, size=(w, h))
-
- def resize(self, size):
- try:
- iter(size)
- except TypeError:
- assert isinstance(size, (int, float))
- size = size, size
-
- ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
-
- if ratios[0] == ratios[1]:
- ratio = ratios[0]
- scaled_polys = [p * ratio for p in self.polygons]
- return PolygonInstance(scaled_polys, size)
-
- ratio_w, ratio_h = ratios
- scaled_polygons = []
- for poly in self.polygons:
- p = poly.clone()
- p[0::2] *= ratio_w
- p[1::2] *= ratio_h
- scaled_polygons.append(p)
-
- return PolygonInstance(scaled_polygons, size=size)
-
- def convert_to_binarymask(self):
- width, height = self.size
- # formatting for COCO PythonAPI
- polygons = [p.numpy() for p in self.polygons]
- rles = mask_utils.frPyObjects(polygons, height, width)
- rle = mask_utils.merge(rles)
- mask = mask_utils.decode(rle)
- mask = torch.from_numpy(mask)
- return mask
-
- def __len__(self):
- return len(self.polygons)
-
- def __repr__(self):
- s = self.__class__.__name__ + "("
- s += "num_groups={}, ".format(len(self.polygons))
- s += "image_width={}, ".format(self.size[0])
- s += "image_height={}, ".format(self.size[1])
- return s
-
-
-class PolygonList(object):
- """
- This class handles PolygonInstances for all objects in the image
- """
-
- def __init__(self, polygons, size):
- """
- Arguments:
- polygons:
- a list of list of lists of numbers. The first
- level of the list correspond to individual instances,
- the second level to all the polygons that compose the
- object, and the third level to the polygon coordinates.
-
- OR
-
- a list of PolygonInstances.
-
- OR
-
- a PolygonList
-
- size: absolute image size
-
- """
- if isinstance(polygons, (list, tuple)):
- if len(polygons) == 0:
- polygons = [[[]]]
- if isinstance(polygons[0], (list, tuple)):
- assert isinstance(polygons[0][0], (list, tuple)), str(
- type(polygons[0][0])
- )
- else:
- assert isinstance(polygons[0], PolygonInstance), str(type(polygons[0]))
-
- elif isinstance(polygons, PolygonList):
- size = polygons.size
- polygons = polygons.polygons
-
- else:
- RuntimeError(
- "Type of argument `polygons` is not allowed:%s" % (type(polygons))
- )
-
- assert isinstance(size, (list, tuple)), str(type(size))
-
- self.polygons = []
- for p in polygons:
- p = PolygonInstance(p, size)
- if len(p) > 0:
- self.polygons.append(p)
-
- self.size = tuple(size)
-
- def transpose(self, method):
- if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
- raise NotImplementedError(
- "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
- )
-
- flipped_polygons = []
- for polygon in self.polygons:
- flipped_polygons.append(polygon.transpose(method))
-
- return PolygonList(flipped_polygons, size=self.size)
-
- def crop(self, box):
- w, h = box[2] - box[0], box[3] - box[1]
- cropped_polygons = []
- for polygon in self.polygons:
- cropped_polygons.append(polygon.crop(box))
-
- cropped_size = w, h
- return PolygonList(cropped_polygons, cropped_size)
-
- def resize(self, size):
- resized_polygons = []
- for polygon in self.polygons:
- resized_polygons.append(polygon.resize(size))
-
- resized_size = size
- return PolygonList(resized_polygons, resized_size)
-
- def to(self, *args, **kwargs):
- return self
-
- def convert_to_binarymask(self):
- if len(self) > 0:
- masks = torch.stack([p.convert_to_binarymask() for p in self.polygons])
- else:
- size = self.size
- masks = torch.empty([0, size[1], size[0]], dtype=torch.uint8)
-
- return BinaryMaskList(masks, size=self.size)
-
- def __len__(self):
- return len(self.polygons)
-
- def __getitem__(self, item):
- if isinstance(item, int):
- selected_polygons = [self.polygons[item]]
- elif isinstance(item, slice):
- selected_polygons = self.polygons[item]
- else:
- # advanced indexing on a single dimension
- selected_polygons = []
- if isinstance(item, torch.Tensor) and item.dtype == torch.uint8:
- item = item.nonzero()
- item = item.squeeze(1) if item.numel() > 0 else item
- item = item.tolist()
- for i in item:
- selected_polygons.append(self.polygons[i])
- return PolygonList(selected_polygons, size=self.size)
-
- def __iter__(self):
- return iter(self.polygons)
-
- def __repr__(self):
- s = self.__class__.__name__ + "("
- s += "num_instances={}, ".format(len(self.polygons))
- s += "image_width={}, ".format(self.size[0])
- s += "image_height={})".format(self.size[1])
- return s
-
-
-class SegmentationMask(object):
-
- """
- This class stores the segmentations for all objects in the image.
- It wraps BinaryMaskList and PolygonList conveniently.
- """
-
- def __init__(self, instances, size, mode="poly"):
- """
- Arguments:
- instances: two types
- (1) polygon
- (2) binary mask
- size: (width, height)
- mode: 'poly', 'mask'. if mode is 'mask', convert mask of any format to binary mask
- """
-
- assert isinstance(size, (list, tuple))
- assert len(size) == 2
- if isinstance(size[0], torch.Tensor):
- assert isinstance(size[1], torch.Tensor)
- size = size[0].item(), size[1].item()
-
- assert isinstance(size[0], (int, float))
- assert isinstance(size[1], (int, float))
-
- if mode == "poly":
- self.instances = PolygonList(instances, size)
- elif mode == "mask":
- self.instances = BinaryMaskList(instances, size)
- else:
- raise NotImplementedError("Unknown mode: %s" % str(mode))
-
- self.mode = mode
- self.size = tuple(size)
-
- def transpose(self, method):
- flipped_instances = self.instances.transpose(method)
- return SegmentationMask(flipped_instances, self.size, self.mode)
-
- def crop(self, box):
- cropped_instances = self.instances.crop(box)
- cropped_size = cropped_instances.size
- return SegmentationMask(cropped_instances, cropped_size, self.mode)
-
- def resize(self, size, *args, **kwargs):
- resized_instances = self.instances.resize(size)
- resized_size = size
- return SegmentationMask(resized_instances, resized_size, self.mode)
-
- def to(self, *args, **kwargs):
- return self
-
- def convert(self, mode):
- if mode == self.mode:
- return self
-
- if mode == "poly":
- converted_instances = self.instances.convert_to_polygon()
- elif mode == "mask":
- converted_instances = self.instances.convert_to_binarymask()
- else:
- raise NotImplementedError("Unknown mode: %s" % str(mode))
-
- return SegmentationMask(converted_instances, self.size, mode)
-
- def get_mask_tensor(self):
- instances = self.instances
- if self.mode == "poly":
- instances = instances.convert_to_binarymask()
- # If there is only 1 instance
- return instances.masks.squeeze(0)
-
- def __len__(self):
- return len(self.instances)
-
- def __getitem__(self, item):
- selected_instances = self.instances.__getitem__(item)
- return SegmentationMask(selected_instances, self.size, self.mode)
-
- def __iter__(self):
- self.iter_idx = 0
- return self
-
- def __next__(self):
- if self.iter_idx < self.__len__():
- next_segmentation = self.__getitem__(self.iter_idx)
- self.iter_idx += 1
- return next_segmentation
- raise StopIteration()
-
- next = __next__ # Python 2 compatibility
-
- def __repr__(self):
- s = self.__class__.__name__ + "("
- s += "num_instances={}, ".format(len(self.instances))
- s += "image_width={}, ".format(self.size[0])
- s += "image_height={}, ".format(self.size[1])
- s += "mode={})".format(self.mode)
- return s
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/background.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/background.py
deleted file mode 100644
index dd3bbe249130348881331aea569ce3ec3f295128..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/background.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.background import BackgroundTasks as BackgroundTasks # noqa
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_C_.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_C_.py
deleted file mode 100644
index 573b3f9c3970766ea817994509f4939ef4f70f0c..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_C_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_T_S_I_C_(BaseTTXConverter):
- pass
diff --git a/spaces/DaleChen/AutoGPT/autogpt/__main__.py b/spaces/DaleChen/AutoGPT/autogpt/__main__.py
deleted file mode 100644
index 128f9eea4900429e88276abdde3419b806001ac7..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/__main__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""Auto-GPT: A GPT powered AI Assistant"""
-import autogpt.cli
-
-if __name__ == "__main__":
- autogpt.cli.main()
diff --git a/spaces/DanteOz/Minimal-Endpoint/app.py b/spaces/DanteOz/Minimal-Endpoint/app.py
deleted file mode 100644
index 06d3b5bcbd4eadf5eece2457c5cf4d11556fc628..0000000000000000000000000000000000000000
--- a/spaces/DanteOz/Minimal-Endpoint/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from flask import Flask
-
-app = Flask(__name__)
-
-@app.route("/")
-def index():
- return "{{ diagram.title }}
-{{ diagram.text }}
-
- {{ diagram.svg }}
-
- Hello, World!
" - -@app.route("/predict") -def predict(): - return {"output": "prediction"} - -if __name__ == "__main__": - app.run(host="0.0.0.0", port=7860) \ No newline at end of file diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/loss/boundary_loss.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/loss/boundary_loss.py deleted file mode 100644 index 86049218de0f273b3d053641a13c92458c577759..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/loss/boundary_loss.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -@Date: 2021/08/12 -@description: For HorizonNet, using latitudes to calculate loss. -""" -import torch -import torch.nn as nn -from utils.conversion import depth2xyz, xyz2lonlat - - -class BoundaryLoss(nn.Module): - def __init__(self): - super().__init__() - self.loss = nn.L1Loss() - - def forward(self, gt, dt): - gt_floor_xyz = depth2xyz(gt['depth']) - gt_ceil_xyz = gt_floor_xyz.clone() - gt_ceil_xyz[..., 1] = -gt['ratio'] - - gt_floor_boundary = xyz2lonlat(gt_floor_xyz)[..., -1:] - gt_ceil_boundary = xyz2lonlat(gt_ceil_xyz)[..., -1:] - - gt_boundary = torch.cat([gt_floor_boundary, gt_ceil_boundary], dim=-1).permute(0, 2, 1) - dt_boundary = dt['boundary'] - - loss = self.loss(gt_boundary, dt_boundary) - return loss - - -if __name__ == '__main__': - import numpy as np - from dataset.mp3d_dataset import MP3DDataset - - mp3d_dataset = MP3DDataset(root_dir='../src/dataset/mp3d', mode='train') - gt = mp3d_dataset.__getitem__(0) - - gt['depth'] = torch.from_numpy(gt['depth'][np.newaxis]) # batch size is 1 - gt['ratio'] = torch.from_numpy(gt['ratio'][np.newaxis]) # batch size is 1 - - dummy_dt = { - 'depth': gt['depth'].clone(), - 'boundary': torch.cat([ - xyz2lonlat(depth2xyz(gt['depth']))[..., -1:], - xyz2lonlat(depth2xyz(gt['depth'], plan_y=-gt['ratio']))[..., -1:] - ], dim=-1).permute(0, 2, 1) - } - # dummy_dt['boundary'][:, :, :20] /= 1.2 # some different - - boundary_loss = BoundaryLoss() - loss = boundary_loss(gt, dummy_dt) - print(loss) diff --git a/spaces/DemoLou/moe-tts/text/shanghainese.py b/spaces/DemoLou/moe-tts/text/shanghainese.py deleted file mode 100644 index 1c28c17d0dc0d920fd222c909a53d703c95e043b..0000000000000000000000000000000000000000 --- a/spaces/DemoLou/moe-tts/text/shanghainese.py +++ /dev/null @@ -1,64 +0,0 @@ -import re -import cn2an -import opencc - - -converter = opencc.OpenCC('chinese_dialect_lexicons/zaonhe') - -# List of (Latin alphabet, ipa) pairs: -_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('A', 'ᴇ'), - ('B', 'bi'), - ('C', 'si'), - ('D', 'di'), - ('E', 'i'), - ('F', 'ᴇf'), - ('G', 'dʑi'), - ('H', 'ᴇtɕʰ'), - ('I', 'ᴀi'), - ('J', 'dʑᴇ'), - ('K', 'kʰᴇ'), - ('L', 'ᴇl'), - ('M', 'ᴇm'), - ('N', 'ᴇn'), - ('O', 'o'), - ('P', 'pʰi'), - ('Q', 'kʰiu'), - ('R', 'ᴀl'), - ('S', 'ᴇs'), - ('T', 'tʰi'), - ('U', 'ɦiu'), - ('V', 'vi'), - ('W', 'dᴀbɤliu'), - ('X', 'ᴇks'), - ('Y', 'uᴀi'), - ('Z', 'zᴇ') -]] - - -def _number_to_shanghainese(num): - num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两') - return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num) - - -def number_to_shanghainese(text): - return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text) - - -def latin_to_ipa(text): - for regex, replacement in _latin_to_ipa: - text = re.sub(regex, replacement, text) - return text - - -def shanghainese_to_ipa(text): - text = number_to_shanghainese(text.upper()) - text = converter.convert(text).replace('-','').replace('$',' ') - text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text) - text = re.sub(r'[、；：]', '，', text) - text = re.sub(r'\s*，\s*', ', ', text) - text = re.sub(r'\s*。\s*', '. ', text) - text = re.sub(r'\s*？\s*', '? ', text) - text = re.sub(r'\s*！\s*', '! ', text) - text = re.sub(r'\s*$', '', text) - return text diff --git a/spaces/DiamondYin/AnewGame/index.html b/spaces/DiamondYin/AnewGame/index.html deleted file mode 100644 index 1622704f5da4b3eee451b0cea52165044bb263e0..0000000000000000000000000000000000000000 --- a/spaces/DiamondYin/AnewGame/index.html +++ /dev/null @@ -1,122 +0,0 @@ - - - - - -
-
-
-
-
-
-
-
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/training_scripts/sg3/training/networks_stylegan2.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/training_scripts/sg3/training/networks_stylegan2.py
deleted file mode 100644
index 832c7faf0baa0ddf6a1d39ad867a0b3d03bb47d2..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/training_scripts/sg3/training/networks_stylegan2.py
+++ /dev/null
@@ -1,1007 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# 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.
-
-"""Network architectures from the paper
-"Analyzing and Improving the Image Quality of StyleGAN".
-Matches the original implementation of configs E-F by Karras et al. at
-https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py"""
-
-import numpy as np
-import torch
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_resample
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import bias_act
-from torch_utils.ops import fma
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def normalize_2nd_moment(x, dim=1, eps=1e-8):
- return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def modulated_conv2d(
- # Input tensor of shape [batch_size, in_channels, in_height, in_width].
- x,
- # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
- weight,
- # Modulation coefficients of shape [batch_size, in_channels].
- styles,
- noise=None, # Optional noise tensor to add to the output activations.
- up=1, # Integer upsampling factor.
- down=1, # Integer downsampling factor.
- padding=0, # Padding with respect to the upsampled image.
- # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
- resample_filter=None,
- demodulate=True, # Apply weight demodulation?
- # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
- flip_weight=True,
- # Perform modulation, convolution, and demodulation as a single fused operation?
- fused_modconv=True,
-):
- batch_size = x.shape[0]
- out_channels, in_channels, kh, kw = weight.shape
- misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk]
- misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
- misc.assert_shape(styles, [batch_size, in_channels]) # [NI]
-
- # Pre-normalize inputs to avoid FP16 overflow.
- if x.dtype == torch.float16 and demodulate:
- weight = weight * (1 / np.sqrt(in_channels * kh * kw) /
- weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True)) # max_Ikk
- styles = styles / \
- styles.norm(float('inf'), dim=1, keepdim=True) # max_I
-
- # Calculate per-sample weights and demodulation coefficients.
- w = None
- dcoefs = None
- if demodulate or fused_modconv:
- w = weight.unsqueeze(0) # [NOIkk]
- w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
- if demodulate:
- dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO]
- if demodulate and fused_modconv:
- w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
-
- # Execute by scaling the activations before and after the convolution.
- if not fused_modconv:
- x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
- x = conv2d_resample.conv2d_resample(x=x, w=weight.to(
- x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
- if demodulate and noise is not None:
- x = fma.fma(x, dcoefs.to(x.dtype).reshape(
- batch_size, -1, 1, 1), noise.to(x.dtype))
- elif demodulate:
- x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
- elif noise is not None:
- x = x.add_(noise.to(x.dtype))
- return x
-
- # Execute as one fused op using grouped convolution.
- with misc.suppress_tracer_warnings(): # this value will be treated as a constant
- batch_size = int(batch_size)
- misc.assert_shape(x, [batch_size, in_channels, None, None])
- x = x.reshape(1, -1, *x.shape[2:])
- w = w.reshape(-1, in_channels, kh, kw)
- x = conv2d_resample.conv2d_resample(x=x, w=w.to(
- x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
- x = x.reshape(batch_size, -1, *x.shape[2:])
- if noise is not None:
- x = x.add_(noise)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
- def __init__(self,
- in_features, # Number of input features.
- out_features, # Number of output features.
- bias=True, # Apply additive bias before the activation function?
- # Activation function: 'relu', 'lrelu', etc.
- activation='linear',
- lr_multiplier=1, # Learning rate multiplier.
- bias_init=0, # Initial value for the additive bias.
- ):
- super().__init__()
- self.in_features = in_features
- self.out_features = out_features
- self.activation = activation
- self.weight = torch.nn.Parameter(torch.randn(
- [out_features, in_features]) / lr_multiplier)
- self.bias = torch.nn.Parameter(torch.full(
- [out_features], np.float32(bias_init))) if bias else None
- self.weight_gain = lr_multiplier / np.sqrt(in_features)
- self.bias_gain = lr_multiplier
-
- def forward(self, x):
- w = self.weight.to(x.dtype) * self.weight_gain
- b = self.bias
- if b is not None:
- b = b.to(x.dtype)
- if self.bias_gain != 1:
- b = b * self.bias_gain
-
- if self.activation == 'linear' and b is not None:
- x = torch.addmm(b.unsqueeze(0), x, w.t())
- else:
- x = x.matmul(w.t())
- x = bias_act.bias_act(x, b, act=self.activation)
- return x
-
- def extra_repr(self):
- return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Conv2dLayer(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- out_channels, # Number of output channels.
- # Width and height of the convolution kernel.
- kernel_size,
- bias=True, # Apply additive bias before the activation function?
- # Activation function: 'relu', 'lrelu', etc.
- activation='linear',
- up=1, # Integer upsampling factor.
- down=1, # Integer downsampling factor.
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output to +-X, None = disable clamping.
- conv_clamp=None,
- channels_last=False, # Expect the input to have memory_format=channels_last?
- trainable=True, # Update the weights of this layer during training?
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.activation = activation
- self.up = up
- self.down = down
- self.conv_clamp = conv_clamp
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.padding = kernel_size // 2
- self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
- self.act_gain = bias_act.activation_funcs[activation].def_gain
-
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
- memory_format=memory_format)
- bias = torch.zeros([out_channels]) if bias else None
- if trainable:
- self.weight = torch.nn.Parameter(weight)
- self.bias = torch.nn.Parameter(bias) if bias is not None else None
- else:
- self.register_buffer('weight', weight)
- if bias is not None:
- self.register_buffer('bias', bias)
- else:
- self.bias = None
-
- def forward(self, x, gain=1):
- w = self.weight * self.weight_gain
- b = self.bias.to(x.dtype) if self.bias is not None else None
- flip_weight = (self.up == 1) # slightly faster
- x = conv2d_resample.conv2d_resample(x=x, w=w.to(
- x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
-
- act_gain = self.act_gain * gain
- act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
- x = bias_act.bias_act(x, b, act=self.activation,
- gain=act_gain, clamp=act_clamp)
- return x
-
- def extra_repr(self):
- return ' '.join([
- f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},',
- f'up={self.up}, down={self.down}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
- def __init__(self,
- # Input latent (Z) dimensionality, 0 = no latent.
- z_dim,
- # Conditioning label (C) dimensionality, 0 = no label.
- c_dim,
- # Intermediate latent (W) dimensionality.
- w_dim,
- # Number of intermediate latents to output, None = do not broadcast.
- num_ws,
- num_layers=8, # Number of mapping layers.
- # Label embedding dimensionality, None = same as w_dim.
- embed_features=None,
- # Number of intermediate features in the mapping layers, None = same as w_dim.
- layer_features=None,
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Learning rate multiplier for the mapping layers.
- lr_multiplier=0.01,
- # Decay for tracking the moving average of W during training, None = do not track.
- w_avg_beta=0.998,
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.num_ws = num_ws
- self.num_layers = num_layers
- self.w_avg_beta = w_avg_beta
-
- if embed_features is None:
- embed_features = w_dim
- if c_dim == 0:
- embed_features = 0
- if layer_features is None:
- layer_features = w_dim
- features_list = [z_dim + embed_features] + \
- [layer_features] * (num_layers - 1) + [w_dim]
-
- if c_dim > 0:
- self.embed = FullyConnectedLayer(c_dim, embed_features)
- for idx in range(num_layers):
- in_features = features_list[idx]
- out_features = features_list[idx + 1]
- layer = FullyConnectedLayer(
- in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
- setattr(self, f'fc{idx}', layer)
-
- if num_ws is not None and w_avg_beta is not None:
- self.register_buffer('w_avg', torch.zeros([w_dim]))
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
- # Embed, normalize, and concat inputs.
- x = None
- with torch.autograd.profiler.record_function('input'):
- if self.z_dim > 0:
- misc.assert_shape(z, [None, self.z_dim])
- x = normalize_2nd_moment(z.to(torch.float32))
- if self.c_dim > 0:
- misc.assert_shape(c, [None, self.c_dim])
- y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
- x = torch.cat([x, y], dim=1) if x is not None else y
-
- # Main layers.
- for idx in range(self.num_layers):
- layer = getattr(self, f'fc{idx}')
- x = layer(x)
-
- # Update moving average of W.
- if update_emas and self.w_avg_beta is not None:
- with torch.autograd.profiler.record_function('update_w_avg'):
- self.w_avg.copy_(x.detach().mean(
- dim=0).lerp(self.w_avg, self.w_avg_beta))
-
- # Broadcast.
- if self.num_ws is not None:
- with torch.autograd.profiler.record_function('broadcast'):
- x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
-
- # Apply truncation.
- if truncation_psi != 1:
- with torch.autograd.profiler.record_function('truncate'):
- assert self.w_avg_beta is not None
- if self.num_ws is None or truncation_cutoff is None:
- x = self.w_avg.lerp(x, truncation_psi)
- else:
- x[:, :truncation_cutoff] = self.w_avg.lerp(
- x[:, :truncation_cutoff], truncation_psi)
- return x
-
- def extra_repr(self):
- return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- out_channels, # Number of output channels.
- # Intermediate latent (W) dimensionality.
- w_dim,
- resolution, # Resolution of this layer.
- kernel_size=3, # Convolution kernel size.
- up=1, # Integer upsampling factor.
- use_noise=True, # Enable noise input?
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- channels_last=False, # Use channels_last format for the weights?
- square=False, # default if for rectangle images
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.w_dim = w_dim
- self.resolution = resolution
- self.up = up
- self.use_noise = use_noise
- self.activation = activation
- self.conv_clamp = conv_clamp
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.padding = kernel_size // 2
- self.act_gain = bias_act.activation_funcs[activation].def_gain
- self.square = square
-
- self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- self.weight = torch.nn.Parameter(torch.randn(
- [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
- if use_noise:
- if self.square:
- self.register_buffer(
- 'noise_const', torch.randn([resolution, resolution]))
- else:
- self.register_buffer('noise_const', torch.randn(
- [resolution, resolution // 2]))
- self.noise_strength = torch.nn.Parameter(torch.zeros([]))
- self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-
- def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
- assert noise_mode in ['random', 'const', 'none']
- in_resolution = self.resolution // self.up
- if self.square:
- misc.assert_shape(
- x, [None, self.weight.shape[1], in_resolution, in_resolution])
- else:
- misc.assert_shape(
- x, [None, self.weight.shape[1], in_resolution, in_resolution // 2])
- styles = self.affine(w)
-
- noise = None
- if self.use_noise and noise_mode == 'random':
- if self.square:
- noise = torch.randn(
- [x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
- else:
- noise = torch.randn(
- [x.shape[0], 1, self.resolution, self.resolution // 2], device=x.device) * self.noise_strength
- if self.use_noise and noise_mode == 'const':
- noise = self.noise_const * self.noise_strength
-
- flip_weight = (self.up == 1) # slightly faster
- x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
- padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
-
- act_gain = self.act_gain * gain
- act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
- x = bias_act.bias_act(x, self.bias.to(
- x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
- return x
-
- def extra_repr(self):
- return ' '.join([
- f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},',
- f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class ToRGBLayer(torch.nn.Module):
- def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.w_dim = w_dim
- self.conv_clamp = conv_clamp
- self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- self.weight = torch.nn.Parameter(torch.randn(
- [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
- self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
- self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-
- def forward(self, x, w, fused_modconv=True):
- styles = self.affine(w) * self.weight_gain
- x = modulated_conv2d(x=x, weight=self.weight, styles=styles,
- demodulate=False, fused_modconv=fused_modconv)
- x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
- return x
-
- def extra_repr(self):
- return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisBlock(torch.nn.Module):
- def __init__(self,
- # Number of input channels, 0 = first block.
- in_channels,
- # Number of output channels.
- out_channels,
- # Intermediate latent (W) dimensionality.
- w_dim,
- # Resolution of this block.
- resolution,
- # Number of output color channels.
- img_channels,
- is_last, # Is this the last block?
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='skip',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=256,
- use_fp16=False, # Use FP16 for this block?
- fp16_channels_last=False, # Use channels-last memory format with FP16?
- square=False, # default is for rectangle images
- # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
- fused_modconv_default=True,
- # Arguments for SynthesisLayer.
- **layer_kwargs,
- ):
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.w_dim = w_dim
- self.resolution = resolution
- self.img_channels = img_channels
- self.is_last = is_last
- self.architecture = architecture
- self.use_fp16 = use_fp16
- self.channels_last = (use_fp16 and fp16_channels_last)
- self.fused_modconv_default = fused_modconv_default
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.num_conv = 0
- self.num_torgb = 0
- self.square = square
-
- if in_channels == 0:
- if self.square:
- self.const = torch.nn.Parameter(torch.randn(
- [out_channels, resolution, resolution]))
- else: # rectangle
- self.const = torch.nn.Parameter(torch.randn(
- [out_channels, resolution, resolution // 2]))
-
- if in_channels != 0:
- self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
- resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs)
- self.num_conv += 1
-
- self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
- conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs)
- self.num_conv += 1
-
- if is_last or architecture == 'skip':
- self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
- conv_clamp=conv_clamp, channels_last=self.channels_last)
- self.num_torgb += 1
-
- if in_channels != 0 and architecture == 'resnet':
- self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
- resample_filter=resample_filter, channels_last=self.channels_last)
-
- def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs):
- _ = update_emas # unused
- misc.assert_shape(
- ws, [None, self.num_conv + self.num_torgb, self.w_dim])
- w_iter = iter(ws.unbind(dim=1))
- if ws.device.type != 'cuda':
- force_fp32 = True
- dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
- memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
- if fused_modconv is None:
- fused_modconv = self.fused_modconv_default
- if fused_modconv == 'inference_only':
- fused_modconv = (not self.training)
-
- # Input.
- if self.in_channels == 0:
- x = self.const.to(dtype=dtype, memory_format=memory_format)
- x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
- else:
- if self.square:
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
- else: # rectangle
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution // 2, self.resolution // 4])
- x = x.to(dtype=dtype, memory_format=memory_format)
-
- # Main layers.
- if self.in_channels == 0:
- x = self.conv1(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- elif self.architecture == 'resnet':
- y = self.skip(x, gain=np.sqrt(0.5))
- x = self.conv0(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv,
- gain=np.sqrt(0.5), **layer_kwargs)
- x = y.add_(x)
- else:
- x = self.conv0(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- x = self.conv1(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
-
- # ToRGB.
- if img is not None:
- if self.square:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
- else:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution // 2, self.resolution // 4])
- img = upfirdn2d.upsample2d(img, self.resample_filter)
- if self.is_last or self.architecture == 'skip':
- y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
- y = y.to(dtype=torch.float32,
- memory_format=torch.contiguous_format)
- img = img.add_(y) if img is not None else y
-
- assert x.dtype == dtype
- assert img is None or img.dtype == torch.float32
- return x, img
-
- def extra_repr(self):
- return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
- def __init__(self,
- # Intermediate latent (W) dimensionality.
- w_dim,
- img_resolution, # Output image resolution.
- img_channels, # Number of color channels.
- square,
- # Overall multiplier for the number of channels.
- channel_base=32768,
- # Maximum number of channels in any layer.
- channel_max=512,
- # Use FP16 for the N highest resolutions.
- num_fp16_res=4,
- **block_kwargs, # Arguments for SynthesisBlock.
- ):
- assert img_resolution >= 4 and img_resolution & (
- img_resolution - 1) == 0
- super().__init__()
- self.w_dim = w_dim
- self.img_resolution = img_resolution
- self.img_resolution_log2 = int(np.log2(img_resolution))
- self.img_channels = img_channels
- self.square = square
- self.num_fp16_res = num_fp16_res
- self.block_resolutions = [
- 2 ** i for i in range(2, self.img_resolution_log2 + 1)]
- channels_dict = {res: min(channel_base // res, channel_max)
- for res in self.block_resolutions}
- fp16_resolution = max(
- 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
- self.num_ws = 0
- for res in self.block_resolutions:
- in_channels = channels_dict[res // 2] if res > 4 else 0
- out_channels = channels_dict[res]
- use_fp16 = (res >= fp16_resolution)
- is_last = (res == self.img_resolution)
- block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
- img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, square=square, **block_kwargs)
- self.num_ws += block.num_conv
- if is_last:
- self.num_ws += block.num_torgb
- setattr(self, f'b{res}', block)
-
- def forward(self, ws, **block_kwargs):
- block_ws = []
- with torch.autograd.profiler.record_function('split_ws'):
- misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
- ws = ws.to(torch.float32)
- w_idx = 0
- for res in self.block_resolutions:
- block = getattr(self, f'b{res}')
- block_ws.append(
- ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
- w_idx += block.num_conv
-
- x = img = None
- for res, cur_ws in zip(self.block_resolutions, block_ws):
- block = getattr(self, f'b{res}')
- x, img = block(x, img, cur_ws, **block_kwargs)
- return img
-
- def extra_repr(self):
- return ' '.join([
- f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
- f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
- f'num_fp16_res={self.num_fp16_res:d}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
- def __init__(self,
- z_dim, # Input latent (Z) dimensionality.
- # Conditioning label (C) dimensionality.
- c_dim,
- # Intermediate latent (W) dimensionality.
- w_dim,
- square,
- img_resolution, # Output resolution.
- img_channels, # Number of output color channels.
- mapping_kwargs={}, # Arguments for MappingNetwork.
- **synthesis_kwargs, # Arguments for SynthesisNetwork.
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.square = square
- self.img_resolution = img_resolution
- self.img_channels = img_channels
- self.synthesis = SynthesisNetwork(
- w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, square=square, **synthesis_kwargs)
- self.num_ws = self.synthesis.num_ws
- self.mapping = MappingNetwork(
- z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
- ws = self.mapping(z, c, truncation_psi=truncation_psi,
- truncation_cutoff=truncation_cutoff, update_emas=update_emas)
- img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
- return img
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorBlock(torch.nn.Module):
- def __init__(self,
- # Number of input channels, 0 = first block.
- in_channels,
- # Number of intermediate channels.
- tmp_channels,
- # Number of output channels.
- out_channels,
- # Resolution of this block.
- resolution,
- # Number of input color channels.
- img_channels,
- # Index of the first layer.
- first_layer_idx,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- use_fp16=False, # Use FP16 for this block?
- fp16_channels_last=False, # Use channels-last memory format with FP16?
- # Freeze-D: Number of layers to freeze.
- freeze_layers=0,
- square=False,
- ):
- assert in_channels in [0, tmp_channels]
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.resolution = resolution
- self.img_channels = img_channels
- self.first_layer_idx = first_layer_idx
- self.architecture = architecture
- self.use_fp16 = use_fp16
- self.channels_last = (use_fp16 and fp16_channels_last)
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.square = square
-
- self.num_layers = 0
-
- def trainable_gen():
- while True:
- layer_idx = self.first_layer_idx + self.num_layers
- trainable = (layer_idx >= freeze_layers)
- self.num_layers += 1
- yield trainable
- trainable_iter = trainable_gen()
-
- if in_channels == 0 or architecture == 'skip':
- self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
- trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
- trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
- trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- if architecture == 'resnet':
- self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
- trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
-
- def forward(self, x, img, force_fp32=False):
- if (x if x is not None else img).device.type != 'cuda':
- force_fp32 = True
- dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
- memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-
- # Input.
- if x is not None:
- if self.square:
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution, self.resolution])
- else:
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution, self.resolution // 2])
- x = x.to(dtype=dtype, memory_format=memory_format)
-
- # FromRGB.
- if self.in_channels == 0 or self.architecture == 'skip':
- if self.square:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution])
- else:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution // 2])
- img = img.to(dtype=dtype, memory_format=memory_format)
- y = self.fromrgb(img)
- x = x + y if x is not None else y
- img = upfirdn2d.downsample2d(
- img, self.resample_filter) if self.architecture == 'skip' else None
-
- # Main layers.
- if self.architecture == 'resnet':
- y = self.skip(x, gain=np.sqrt(0.5))
- x = self.conv0(x)
- x = self.conv1(x, gain=np.sqrt(0.5))
- x = y.add_(x)
- else:
- x = self.conv0(x)
- x = self.conv1(x)
-
- assert x.dtype == dtype
- return x, img
-
- def extra_repr(self):
- return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MinibatchStdLayer(torch.nn.Module):
- def __init__(self, group_size, num_channels=1):
- super().__init__()
- self.group_size = group_size
- self.num_channels = num_channels
-
- def forward(self, x):
- N, C, H, W = x.shape
- with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants
- G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(
- N)) if self.group_size is not None else N
- F = self.num_channels
- c = C // F
-
- # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
- y = x.reshape(G, -1, F, c, H, W)
- # [GnFcHW] Subtract mean over group.
- y = y - y.mean(dim=0)
- # [nFcHW] Calc variance over group.
- y = y.square().mean(dim=0)
- y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group.
- # [nF] Take average over channels and pixels.
- y = y.mean(dim=[2, 3, 4])
- y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions.
- # [NFHW] Replicate over group and pixels.
- y = y.repeat(G, 1, H, W)
- # [NCHW] Append to input as new channels.
- x = torch.cat([x, y], dim=1)
- return x
-
- def extra_repr(self):
- return f'group_size={self.group_size}, num_channels={self.num_channels:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorEpilogue(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- # Dimensionality of mapped conditioning label, 0 = no label.
- cmap_dim,
- resolution, # Resolution of this block.
- # Number of input color channels.
- img_channels,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Group size for the minibatch standard deviation layer, None = entire minibatch.
- mbstd_group_size=4,
- # Number of features for the minibatch standard deviation layer, 0 = disable.
- mbstd_num_channels=1,
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- square=False,
- ):
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.cmap_dim = cmap_dim
- self.resolution = resolution
- self.img_channels = img_channels
- self.architecture = architecture
- self.square = square
-
- if architecture == 'skip':
- self.fromrgb = Conv2dLayer(
- img_channels, in_channels, kernel_size=1, activation=activation)
- self.mbstd = MinibatchStdLayer(
- group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
- self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels,
- kernel_size=3, activation=activation, conv_clamp=conv_clamp)
-
- if self.square:
- self.fc = FullyConnectedLayer(
- in_channels * (resolution ** 2), in_channels, activation=activation)
- else:
- self.fc = FullyConnectedLayer(
- in_channels * (resolution ** 2 // 2), in_channels, activation=activation)
-
- self.out = FullyConnectedLayer(
- in_channels, 1 if cmap_dim == 0 else cmap_dim)
-
- def forward(self, x, img, cmap, force_fp32=False):
- if self.square:
- misc.assert_shape(x, [None, self.in_channels,
- self.resolution, self.resolution])
- else:
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution, self.resolution // 2]) # [NCHW]
-
- _ = force_fp32 # unused
- dtype = torch.float32
- memory_format = torch.contiguous_format
-
- # FromRGB.
- x = x.to(dtype=dtype, memory_format=memory_format)
- if self.architecture == 'skip':
- if self.square:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution])
- else:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution // 2])
-
- img = img.to(dtype=dtype, memory_format=memory_format)
- x = x + self.fromrgb(img)
-
- # Main layers.
- if self.mbstd is not None:
- x = self.mbstd(x)
- x = self.conv(x)
- x = self.fc(x.flatten(1))
- x = self.out(x)
-
- # Conditioning.
- if self.cmap_dim > 0:
- misc.assert_shape(cmap, [None, self.cmap_dim])
- x = (x * cmap).sum(dim=1, keepdim=True) * \
- (1 / np.sqrt(self.cmap_dim))
-
- assert x.dtype == dtype
- return x
-
- def extra_repr(self):
- return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Discriminator(torch.nn.Module):
- def __init__(self,
- # Conditioning label (C) dimensionality.
- c_dim,
- img_resolution, # Input resolution.
- # Number of input color channels.
- img_channels,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Overall multiplier for the number of channels.
- channel_base=32768,
- # Maximum number of channels in any layer.
- channel_max=512,
- # Use FP16 for the N highest resolutions.
- num_fp16_res=4,
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=256,
- # Dimensionality of mapped conditioning label, None = default.
- cmap_dim=None,
- square=False, # default for rectangle images
- block_kwargs={}, # Arguments for DiscriminatorBlock.
- mapping_kwargs={}, # Arguments for MappingNetwork.
- # Arguments for DiscriminatorEpilogue.
- epilogue_kwargs={},
- ):
- super().__init__()
- self.c_dim = c_dim
- self.img_resolution = img_resolution
- self.img_resolution_log2 = int(np.log2(img_resolution))
- self.img_channels = img_channels
- self.square = square
- self.block_resolutions = [
- 2 ** i for i in range(self.img_resolution_log2, 2, -1)]
- channels_dict = {res: min(channel_base // res, channel_max)
- for res in self.block_resolutions + [4]}
- fp16_resolution = max(
- 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
- if cmap_dim is None:
- cmap_dim = channels_dict[4]
- if c_dim == 0:
- cmap_dim = 0
-
- common_kwargs = dict(img_channels=img_channels,
- architecture=architecture, conv_clamp=conv_clamp)
- cur_layer_idx = 0
- for res in self.block_resolutions:
- in_channels = channels_dict[res] if res < img_resolution else 0
- tmp_channels = channels_dict[res]
- out_channels = channels_dict[res // 2]
- use_fp16 = (res >= fp16_resolution)
- block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
- first_layer_idx=cur_layer_idx, use_fp16=use_fp16, square=square, **block_kwargs, **common_kwargs)
- setattr(self, f'b{res}', block)
- cur_layer_idx += block.num_layers
- if c_dim > 0:
- self.mapping = MappingNetwork(
- z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
- self.b4 = DiscriminatorEpilogue(
- channels_dict[4], cmap_dim=cmap_dim, resolution=4, square=square, **epilogue_kwargs, **common_kwargs)
-
- def forward(self, img, c, update_emas=False, **block_kwargs):
- _ = update_emas # unused
- x = None
- for res in self.block_resolutions:
- block = getattr(self, f'b{res}')
- x, img = block(x, img, **block_kwargs)
-
- cmap = None
- if self.c_dim > 0:
- cmap = self.mapping(None, c)
- x = self.b4(x, img, cmap)
- return x
-
- def extra_repr(self):
- return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/DragGan/DragGan/stylegan_human/torch_utils/op_edit/fused_act.py b/spaces/DragGan/DragGan/stylegan_human/torch_utils/op_edit/fused_act.py
deleted file mode 100644
index 138f090bc67b94b363c346cbf405990f1bbdff68..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/torch_utils/op_edit/fused_act.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-
-module_path = os.path.dirname(__file__)
-fused = load(
- "fused",
- sources=[
- os.path.join(module_path, "fused_bias_act.cpp"),
- os.path.join(module_path, "fused_bias_act_kernel.cu"),
- ],
-)
-
-
-class FusedLeakyReLUFunctionBackward(Function):
- @staticmethod
- def forward(ctx, grad_output, out, negative_slope, scale):
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- empty = grad_output.new_empty(0)
-
- grad_input = fused.fused_bias_act(
- grad_output, empty, out, 3, 1, negative_slope, scale
- )
-
- dim = [0]
-
- if grad_input.ndim > 2:
- dim += list(range(2, grad_input.ndim))
-
- grad_bias = grad_input.sum(dim).detach()
-
- return grad_input, grad_bias
-
- @staticmethod
- def backward(ctx, gradgrad_input, gradgrad_bias):
- (out,) = ctx.saved_tensors
- gradgrad_out = fused.fused_bias_act(
- gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
- )
-
- return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
- @staticmethod
- def forward(ctx, input, bias, negative_slope, scale):
- empty = input.new_empty(0)
- out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- (out,) = ctx.saved_tensors
-
- grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
- grad_output, out, ctx.negative_slope, ctx.scale
- )
-
- return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
- def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
- super().__init__()
-
- self.bias = nn.Parameter(torch.zeros(channel))
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
- if input.device.type == "cpu":
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
- )
- * scale
- )
-
- else:
- return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/spaces/DreamSunny/stable-diffusion-webui-cpu/app.py b/spaces/DreamSunny/stable-diffusion-webui-cpu/app.py
deleted file mode 100644
index 86d44c530a07a58d5c32663b9c07ecd6310b742c..0000000000000000000000000000000000000000
--- a/spaces/DreamSunny/stable-diffusion-webui-cpu/app.py
+++ /dev/null
@@ -1,165 +0,0 @@
-"""
-Stable Diffusion Webui Version 1.6
-https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.6.0
-
-"""
-commit_id=r"5ef669de080814067961f28357256e8fe27544f4" #Version 1.3.0
-import os
-from sys import executable
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:pathlib.Path ) -> int :
- if pathlib.Path.exists(ClonePath):
- return 0
- for z in range(10):
- i=subprocess.run([r"git",r"clone",str(URI),str(ClonePath)])
- if(i.returncode == 0 ):
- del i
- return 0
- else :
- del i
- raise Exception(str.format("clone \'{0}\' failed",URI))
-
-
-def DownLoad(URI:str,DownloadPath:pathlib.Path,DownLoadFileName:str ) -> int:
- if (DownloadPath / DownLoadFileName).is_file(): return 0
- for z in range(10):
- i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",str(DownloadPath),r"-o",DownLoadFileName,URI]);
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
- raise Exception(str.format("download \'{0}\' failed",URI))
-
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",user_home / r"stable-diffusion-webui")
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard "+commit_id)
-#install extensions
-print("installing extensions")
-Gitclone(r"https://huggingface.co/embed/negative",user_home / r"stable-diffusion-webui" / r"embeddings" / r"negative")
-Gitclone(r"https://huggingface.co/embed/lora",user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive")
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN" ,r"4x-UltraSharp.pth")
-while (True):
- i=subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")])
- if(i.returncode == 0 ):
- del i
- gc.collect()
- break
- else :
- del i
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" )
-Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser")
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface")
-Gitclone(r"https://github.com/camenduru/sd-civitai-browser",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-civitai-browser")
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks")
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet")
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor")
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib")
-Gitclone(r"https://github.com/hnmr293/posex",user_home / r"stable-diffusion-webui" / r"extensions" / r"posex")
-Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor")
-#中文本地化的请解除下一行的注释
-#Gitclone(r"https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN.git",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-localization-zh_CN")
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete")
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels")
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui")
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg")
-Gitclone(r"https://github.com/ashen-sensored/stable-diffusion-webui-two-shot",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-two-shot")
-Gitclone(r"https://github.com/camenduru/sd_webui_stealth_pnginfo",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_webui_stealth_pnginfo")
-os.chdir(user_home / r"stable-diffusion-webui")
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-for i in range(0,len(dList)): DownLoad(dList[i],user_home / r"stable-diffusion-webui" / r"extensions" / "sd-webui-controlnet" / r"models",pathlib.Path(dList[i]).name)
-del dList
-#download model
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-#Stable Diffusion Checkpoint Model
-#anything version4.5
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"anything-v4.5-pruned.ckpt")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"anything-v4.0.vae.pt")
-#Counterfeit-V3.0
-DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"Counterfeit-V3.0_fp16.safetensors")
-#AbyssOrangeMix2 sfw
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_sfw.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"AbyssOrangeMix2_sfw.safetensors")
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"orangemix.vae.pt")
-#MeinaPastelV5
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"MeinaPastelV5_BakedVAE.safetensors")
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Without%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"MeinaPastelV5_WithoutVAE.safetensors")
-
-#Lora Model
-#Better Light
-DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"Better_light.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" / r"models"/ r"lora",r"Better_light.safetensors")
-#LAS
-DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"LAS.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" / r"models"/ r"lora",r"LAS.safetensors")
-#Backlighting
-DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"backlighting.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" / r"models"/ r"lora",r"backlighting.safetensors")
-#GFPGAN Model
-#detection Resnet50
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"detection_Resnet50_Final.pth")
-#parsing_parsenet
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"parsing_parsenet.pth")
-#GFPGANv1.4
-DownLoad(r"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"GFPGANv1.4.pth")
-#strt Stable Diffusion Webui
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-gc.collect()
-while True:
- ret=subprocess.run([executable ,user_home / r"stable-diffusion-webui" / r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
- if(ret.returncode == 0 ):
- del ret
- gc.collect()
- else :
- del ret
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/Egrt/MaskGAN/models/resnest/ablation.py b/spaces/Egrt/MaskGAN/models/resnest/ablation.py
deleted file mode 100644
index 00743ccdcf8c909b262c37476488c92ba947fde5..0000000000000000000000000000000000000000
--- a/spaces/Egrt/MaskGAN/models/resnest/ablation.py
+++ /dev/null
@@ -1,106 +0,0 @@
-##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-## Created by: Hang Zhang
-## Email: zhanghang0704@gmail.com
-## Copyright (c) 2020
-##
-## LICENSE file in the root directory of this source tree
-##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-"""ResNeSt ablation study models"""
-
-import torch
-from .resnet import ResNet, Bottleneck
-
-__all__ = ['resnest50_fast_1s1x64d', 'resnest50_fast_2s1x64d', 'resnest50_fast_4s1x64d',
- 'resnest50_fast_1s2x40d', 'resnest50_fast_2s2x40d', 'resnest50_fast_4s2x40d',
- 'resnest50_fast_1s4x24d']
-
-_url_format = 'https://s3.us-west-1.wasabisys.com/resnest/torch/{}-{}.pth'
-
-_model_sha256 = {name: checksum for checksum, name in [
- ('d8fbf808', 'resnest50_fast_1s1x64d'),
- ('44938639', 'resnest50_fast_2s1x64d'),
- ('f74f3fc3', 'resnest50_fast_4s1x64d'),
- ('32830b84', 'resnest50_fast_1s2x40d'),
- ('9d126481', 'resnest50_fast_2s2x40d'),
- ('41d14ed0', 'resnest50_fast_4s2x40d'),
- ('d4a4f76f', 'resnest50_fast_1s4x24d'),
- ]}
-
-def short_hash(name):
- if name not in _model_sha256:
- raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
- return _model_sha256[name][:8]
-
-resnest_model_urls = {name: _url_format.format(name, short_hash(name)) for
- name in _model_sha256.keys()
-}
-
-def resnest50_fast_1s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
- model = ResNet(Bottleneck, [3, 4, 6, 3],
- radix=1, groups=1, bottleneck_width=64,
- deep_stem=True, stem_width=32, avg_down=True,
- avd=True, avd_first=True, **kwargs)
- if pretrained:
- model.load_state_dict(torch.hub.load_state_dict_from_url(
- resnest_model_urls['resnest50_fast_1s1x64d'], progress=True, check_hash=True))
- return model
-
-def resnest50_fast_2s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
- model = ResNet(Bottleneck, [3, 4, 6, 3],
- radix=2, groups=1, bottleneck_width=64,
- deep_stem=True, stem_width=32, avg_down=True,
- avd=True, avd_first=True, **kwargs)
- if pretrained:
- model.load_state_dict(torch.hub.load_state_dict_from_url(
- resnest_model_urls['resnest50_fast_2s1x64d'], progress=True, check_hash=True))
- return model
-
-def resnest50_fast_4s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
- model = ResNet(Bottleneck, [3, 4, 6, 3],
- radix=4, groups=1, bottleneck_width=64,
- deep_stem=True, stem_width=32, avg_down=True,
- avd=True, avd_first=True, **kwargs)
- if pretrained:
- model.load_state_dict(torch.hub.load_state_dict_from_url(
- resnest_model_urls['resnest50_fast_4s1x64d'], progress=True, check_hash=True))
- return model
-
-def resnest50_fast_1s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
- model = ResNet(Bottleneck, [3, 4, 6, 3],
- radix=1, groups=2, bottleneck_width=40,
- deep_stem=True, stem_width=32, avg_down=True,
- avd=True, avd_first=True, **kwargs)
- if pretrained:
- model.load_state_dict(torch.hub.load_state_dict_from_url(
- resnest_model_urls['resnest50_fast_1s2x40d'], progress=True, check_hash=True))
- return model
-
-def resnest50_fast_2s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
- model = ResNet(Bottleneck, [3, 4, 6, 3],
- radix=2, groups=2, bottleneck_width=40,
- deep_stem=True, stem_width=32, avg_down=True,
- avd=True, avd_first=True, **kwargs)
- if pretrained:
- model.load_state_dict(torch.hub.load_state_dict_from_url(
- resnest_model_urls['resnest50_fast_2s2x40d'], progress=True, check_hash=True))
- return model
-
-def resnest50_fast_4s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
- model = ResNet(Bottleneck, [3, 4, 6, 3],
- radix=4, groups=2, bottleneck_width=40,
- deep_stem=True, stem_width=32, avg_down=True,
- avd=True, avd_first=True, **kwargs)
- if pretrained:
- model.load_state_dict(torch.hub.load_state_dict_from_url(
- resnest_model_urls['resnest50_fast_4s2x40d'], progress=True, check_hash=True))
- return model
-
-def resnest50_fast_1s4x24d(pretrained=False, root='~/.encoding/models', **kwargs):
- model = ResNet(Bottleneck, [3, 4, 6, 3],
- radix=1, groups=4, bottleneck_width=24,
- deep_stem=True, stem_width=32, avg_down=True,
- avd=True, avd_first=True, **kwargs)
- if pretrained:
- model.load_state_dict(torch.hub.load_state_dict_from_url(
- resnest_model_urls['resnest50_fast_1s4x24d'], progress=True, check_hash=True))
- return model
diff --git a/spaces/Epoching/GLIDE_Inpaint/glide_text2im/tokenizer/simple_tokenizer.py b/spaces/Epoching/GLIDE_Inpaint/glide_text2im/tokenizer/simple_tokenizer.py
deleted file mode 100644
index c84cc8fb3adff99225d3e3a75b2a3d81564adcef..0000000000000000000000000000000000000000
--- a/spaces/Epoching/GLIDE_Inpaint/glide_text2im/tokenizer/simple_tokenizer.py
+++ /dev/null
@@ -1,163 +0,0 @@
-"""
-Copied from: https://github.com/openai/CLIP/blob/573315e83f07b53a61ff5098757e8fc885f1703e/clip/simple_tokenizer.py
-"""
-
-import gzip
-import html
-import os
-from functools import lru_cache
-from typing import List, Tuple
-
-import ftfy
-import regex as re
-
-
-@lru_cache()
-def default_bpe():
- return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
- """
- Returns list of utf-8 byte and a corresponding list of unicode strings.
- The reversible bpe codes work on unicode strings.
- This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
- When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
- This is a signficant percentage of your normal, say, 32K bpe vocab.
- To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
- And avoids mapping to whitespace/control characters the bpe code barfs on.
- """
- bs = (
- list(range(ord("!"), ord("~") + 1))
- + list(range(ord("¡"), ord("¬") + 1))
- + list(range(ord("®"), ord("ÿ") + 1))
- )
- cs = bs[:]
- n = 0
- for b in range(2 ** 8):
- if b not in bs:
- bs.append(b)
- cs.append(2 ** 8 + n)
- n += 1
- cs = [chr(n) for n in cs]
- return dict(zip(bs, cs))
-
-
-def get_pairs(word):
- """Return set of symbol pairs in a word.
- Word is represented as tuple of symbols (symbols being variable-length strings).
- """
- pairs = set()
- prev_char = word[0]
- for char in word[1:]:
- pairs.add((prev_char, char))
- prev_char = char
- return pairs
-
-
-def basic_clean(text):
- text = ftfy.fix_text(text)
- text = html.unescape(html.unescape(text))
- return text.strip()
-
-
-def whitespace_clean(text):
- text = re.sub(r"\s+", " ", text)
- text = text.strip()
- return text
-
-
-class SimpleTokenizer(object):
- def __init__(self, bpe_path: str = default_bpe()):
- self.byte_encoder = bytes_to_unicode()
- self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
- merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
- merges = merges[1 : 49152 - 256 - 2 + 1]
- merges = [tuple(merge.split()) for merge in merges]
- vocab = list(bytes_to_unicode().values())
- vocab = vocab + [v + "" for v in vocab]
- for merge in merges:
- vocab.append("".join(merge))
- vocab.extend(["<|startoftext|>", "<|endoftext|>"])
- self.encoder = dict(zip(vocab, range(len(vocab))))
- self.decoder = {v: k for k, v in self.encoder.items()}
- self.bpe_ranks = dict(zip(merges, range(len(merges))))
- self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"}
- self.pat = re.compile(
- r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
- re.IGNORECASE,
- )
-
- @property
- def start_token(self):
- return self.encoder["<|startoftext|>"]
-
- @property
- def end_token(self):
- return self.encoder["<|endoftext|>"]
-
- def padded_tokens_and_len(self, tokens: List[int], text_ctx: int) -> Tuple[List[int], int]:
- tokens = [self.start_token] + tokens[: text_ctx - 2] + [self.end_token]
- text_len = len(tokens)
- padding = text_ctx - len(tokens)
- padded_tokens = tokens + [0] * padding
- return padded_tokens, text_len
-
- def bpe(self, token):
- if token in self.cache:
- return self.cache[token]
- word = tuple(token[:-1]) + (token[-1] + "",)
- pairs = get_pairs(word)
-
- if not pairs:
- return token + ""
-
- while True:
- bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
- if bigram not in self.bpe_ranks:
- break
- first, second = bigram
- new_word = []
- i = 0
- while i < len(word):
- try:
- j = word.index(first, i)
- new_word.extend(word[i:j])
- i = j
- except: # pylint: disable=bare-except
- new_word.extend(word[i:])
- break
-
- if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
- new_word.append(first + second)
- i += 2
- else:
- new_word.append(word[i])
- i += 1
- new_word = tuple(new_word)
- word = new_word
- if len(word) == 1:
- break
- else:
- pairs = get_pairs(word)
- word = " ".join(word)
- self.cache[token] = word
- return word
-
- def encode(self, text):
- bpe_tokens = []
- text = whitespace_clean(basic_clean(text)).lower()
- for token in re.findall(self.pat, text):
- token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
- bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
- return bpe_tokens
-
- def decode(self, tokens):
- text = "".join([self.decoder[token] for token in tokens])
- text = (
- bytearray([self.byte_decoder[c] for c in text])
- .decode("utf-8", errors="replace")
- .replace("", " ")
- )
- return text
diff --git a/spaces/EsoCode/text-generation-webui/extensions/ngrok/README.md b/spaces/EsoCode/text-generation-webui/extensions/ngrok/README.md
deleted file mode 100644
index 0324bf9852408d9d2b86cc0165c2d548996f9c94..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/extensions/ngrok/README.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Adding an ingress URL through the ngrok Agent SDK for Python
-
-[ngrok](https://ngrok.com) is a globally distributed reverse proxy commonly used for quickly getting a public URL to a
-service running inside a private network, such as on your local laptop. The ngrok agent is usually
-deployed inside a private network and is used to communicate with the ngrok cloud service.
-
-By default the authtoken in the NGROK_AUTHTOKEN environment variable will be used. Alternatively one may be specified in
-the `settings.json` file, see the Examples below. Retrieve your authtoken on the [Auth Token page of your ngrok dashboard](https://dashboard.ngrok.com/get-started/your-authtoken), signing up is free.
-
-# Documentation
-
-For a list of all available options, see [the configuration documentation](https://ngrok.com/docs/ngrok-agent/config/) or [the connect example](https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py).
-
-The ngrok Python SDK is [on github here](https://github.com/ngrok/ngrok-py). A quickstart guide and a full API reference are included in the [ngrok-py Python API documentation](https://ngrok.github.io/ngrok-py/).
-
-# Running
-
-To enable ngrok install the requirements and then add `--extension ngrok` to the command line options, for instance:
-
-```bash
-pip install -r extensions/ngrok/requirements.txt
-python server.py --extension ngrok
-```
-
-In the output you should then see something like this:
-
-```bash
-INFO:Loading the extension "ngrok"...
-INFO:Session created
-INFO:Created tunnel "9d9d0944dc75ff9d3aae653e5eb29fe9" with url "https://d83706cf7be7.ngrok.app"
-INFO:Tunnel "9d9d0944dc75ff9d3aae653e5eb29fe9" TCP forwarding to "localhost:7860"
-INFO:Ingress established at https://d83706cf7be7.ngrok.app
-```
-
-You can now access the webui via the url shown, in this case `https://d83706cf7be7.ngrok.app`. It is recommended to add some authentication to the ingress, see below.
-
-# Example Settings
-
-In `settings.json` add a `ngrok` key with a dictionary of options, for instance:
-
-To enable basic authentication:
-```json
-{
- "ngrok": {
- "basic_auth": "user:password"
- }
-}
-```
-
-To enable OAUTH authentication:
-```json
-{
- "ngrok": {
- "oauth_provider": "google",
- "oauth_allow_domains": "asdf.com",
- "oauth_allow_emails": "asdf@asdf.com"
- }
-}
-```
-
-To add an authtoken instead of using the NGROK_AUTHTOKEN environment variable:
-```json
-{
- "ngrok": {
- "authtoken": "
-
-
-
-
-
-
-
-"""
-
-article = """
-
-
-
-
- - Pix2Pix Video -
-- Apply Instruct Pix2Pix Diffusion to a video -
-
-
-
-"""
-
-with gr.Blocks(css='style.css') as demo:
- with gr.Column(elem_id="col-container"):
- gr.HTML(title)
- with gr.Row():
- with gr.Column():
- video_inp = gr.Video(label="Video source", source="upload", type="filepath", elem_id="input-vid")
- prompt = gr.Textbox(label="Prompt", placeholder="enter prompt", show_label=False, elem_id="prompt-in")
- with gr.Row():
- seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=123456)
- trim_in = gr.Slider(label="Cut video at (s)", minimun=1, maximum=3, step=1, value=1)
- with gr.Column():
- video_out = gr.Video(label="Pix2pix video result", elem_id="video-output")
- gr.HTML("""
- You may also like:
-
-
-
-
-
-
- 
- work with longer videos / skip the queue:
- """, elem_id="duplicate-container")
- submit_btn = gr.Button("Generate Pix2Pix video")
-
- with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
- community_icon = gr.HTML(community_icon_html)
- loading_icon = gr.HTML(loading_icon_html)
- share_button = gr.Button("Share to community", elem_id="share-btn")
-
- inputs = [prompt,video_inp,seed_inp, trim_in]
- outputs = [video_out, share_group]
-
- ex = gr.Examples(
- [
- ["Make it a marble sculpture", "./examples/pexels-jill-burrow-7665249_512x512.mp4", 422112651, 4],
- ["Make it molten lava", "./examples/Ocean_Pexels_ 8953474_512x512.mp4", 43571876, 4]
- ],
- inputs=inputs,
- outputs=outputs,
- fn=infer,
- cache_examples=True,
- )
-
- gr.HTML(article)
-
- submit_btn.click(infer, inputs, outputs)
- share_button.click(None, [], [], _js=share_js)
-
-
-
-demo.launch().queue(max_size=12)
diff --git a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/data_objects/random_cycler.py b/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/data_objects/random_cycler.py
deleted file mode 100644
index c405db6b27f46d874d8feb37e3f9c1e12c251109..0000000000000000000000000000000000000000
--- a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/data_objects/random_cycler.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import random
-
-class RandomCycler:
- """
- Creates an internal copy of a sequence and allows access to its items in a constrained random
- order. For a source sequence of n items and one or several consecutive queries of a total
- of m items, the following guarantees hold (one implies the other):
- - Each item will be returned between m // n and ((m - 1) // n) + 1 times.
- - Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
- """
-
- def __init__(self, source):
- if len(source) == 0:
- raise Exception("Can't create RandomCycler from an empty collection")
- self.all_items = list(source)
- self.next_items = []
-
- def sample(self, count: int):
- shuffle = lambda l: random.sample(l, len(l))
-
- out = []
- while count > 0:
- if count >= len(self.all_items):
- out.extend(shuffle(list(self.all_items)))
- count -= len(self.all_items)
- continue
- n = min(count, len(self.next_items))
- out.extend(self.next_items[:n])
- count -= n
- self.next_items = self.next_items[n:]
- if len(self.next_items) == 0:
- self.next_items = shuffle(list(self.all_items))
- return out
-
- def __next__(self):
- return self.sample(1)[0]
-
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
deleted file mode 100644
index 81f61c6ee136628940e8bcc146d785840ac83c38..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
+++ /dev/null
@@ -1,44 +0,0 @@
-_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
-model = dict(
- pretrained='open-mmlab://detectron/resnet101_caffe',
- backbone=dict(depth=101))
-img_norm_cfg = dict(
- mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gfl/gfl_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gfl/gfl_r50_fpn_1x_coco.py
deleted file mode 100644
index 29fb077369977688174a4c5e2a0cda548e8e3931..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gfl/gfl_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,57 +0,0 @@
-_base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
- type='GFL',
- pretrained='torchvision://resnet50',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch'),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- start_level=1,
- add_extra_convs='on_output',
- num_outs=5),
- bbox_head=dict(
- type='GFLHead',
- num_classes=80,
- in_channels=256,
- stacked_convs=4,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- strides=[8, 16, 32, 64, 128]),
- loss_cls=dict(
- type='QualityFocalLoss',
- use_sigmoid=True,
- beta=2.0,
- loss_weight=1.0),
- loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
- reg_max=16,
- loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(type='ATSSAssigner', topk=9),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- test_cfg=dict(
- nms_pre=1000,
- min_bbox_size=0,
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.6),
- max_per_img=100))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
deleted file mode 100644
index 5b72ac830be29b865ed52adaf41f2fe800f252cc..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,12 +0,0 @@
-_base_ = '../pspnet/pspnet_r101-d8_512x512_160k_ade20k.py'
-model = dict(
- pretrained='mmcls://mobilenet_v2',
- backbone=dict(
- _delete_=True,
- type='MobileNetV2',
- widen_factor=1.,
- strides=(1, 2, 2, 1, 1, 1, 1),
- dilations=(1, 1, 1, 2, 2, 4, 4),
- out_indices=(1, 2, 4, 6)),
- decode_head=dict(in_channels=320),
- auxiliary_head=dict(in_channels=96))
diff --git a/spaces/GuXiaoBei/wechat-chatbot/docker/build.alpine.sh b/spaces/GuXiaoBei/wechat-chatbot/docker/build.alpine.sh
deleted file mode 100644
index 6fda600d2d6cac087c5798a53788e8d3da8e17d8..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/docker/build.alpine.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-
-CHATGPT_ON_WECHAT_TAG=1.0.2
-
-docker build -f Dockerfile.alpine \
- --build-arg CHATGPT_ON_WECHAT_VER=$CHATGPT_ON_WECHAT_TAG \
- -t zhayujie/chatgpt-on-wechat .
-
-docker tag zhayujie/chatgpt-on-wechat zhayujie/chatgpt-on-wechat:$CHATGPT_ON_WECHAT_TAG-alpine
-
\ No newline at end of file
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_ocnli.sh b/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_ocnli.sh
deleted file mode 100644
index 5598ee8027a9bc41c4c196d71d98341557e0f4eb..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_ocnli.sh
+++ /dev/null
@@ -1,93 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=zen2_large_ocnli # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=1 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:1 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc.
-#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id)
-
-
-export CUDA_VISIBLE_DEVICES='6'
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
-
-MODEL_NAME=zen2_large
-
-TASK=ocnli
-
-ZERO_STAGE=1
-STRATEGY=deepspeed_stage_${ZERO_STAGE}
-
-ROOT_DIR=/cognitive_comp/ganruyi/experiments/classification_finetune/${MODEL_NAME}_${TASK}
-if [ ! -d ${ROOT_DIR} ];then
- mkdir -p ${ROOT_DIR}
- echo ${ROOT_DIR} created!!!!!!!!!!!!!!
-else
- echo ${ROOT_DIR} exist!!!!!!!!!!!!!!!
-fi
-
-DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/${TASK}_public/
-PRETRAINED_MODEL_PATH=/cognitive_comp/ganruyi/hf_models/zen/zh_zen_large_2.0
-
-CHECKPOINT_PATH=${ROOT_DIR}/ckpt/
-OUTPUT_PATH=${ROOT_DIR}/predict.json
-
-DATA_ARGS="\
- --data_dir $DATA_DIR \
- --train_data train.json \
- --valid_data dev.json \
- --test_data test.json \
- --train_batchsize 32 \
- --valid_batchsize 16 \
- --max_seq_length 128 \
- --texta_name sentence \
- --label_name label \
- --id_name id \
- --task_name ocnli \
- "
-
-MODEL_ARGS="\
- --learning_rate 2e-5 \
- --weight_decay 0.1 \
- --warmup_ratio 0.01 \
- --num_labels 3 \
- "
-
-MODEL_CHECKPOINT_ARGS="\
- --monitor val_acc \
- --save_top_k 3 \
- --mode max \
- --every_n_train_steps 100 \
- --save_weights_only True \
- --dirpath $CHECKPOINT_PATH \
- --filename model-{epoch:02d}-{val_acc:.4f} \
- "
-
-TRAINER_ARGS="\
- --max_epochs 10 \
- --gpus 1 \
- --check_val_every_n_epoch 1 \
- --val_check_interval 100 \
- --default_root_dir $ROOT_DIR \
- "
-
-
-options=" \
- --pretrained_model_path $PRETRAINED_MODEL_PATH \
- --vocab_file $PRETRAINED_MODEL_PATH/vocab.txt \
- --do_lower_case \
- --output_save_path $OUTPUT_PATH \
- $DATA_ARGS \
- $MODEL_ARGS \
- $MODEL_CHECKPOINT_ARGS \
- $TRAINER_ARGS \
-"
-SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/zen2_finetune/fengshen_sequence_level_ft_task.py
-/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
-# SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif
-# python3 $SCRIPT_PATH $options
-# source activate base
-# singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-# /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Harshveer/Finetuned_Diffusion_Max/style.css b/spaces/Harshveer/Finetuned_Diffusion_Max/style.css
deleted file mode 100644
index 9bfa78cc983f84693cf7cbab1e3bfd0e0d36c944..0000000000000000000000000000000000000000
--- a/spaces/Harshveer/Finetuned_Diffusion_Max/style.css
+++ /dev/null
@@ -1,24 +0,0 @@
-.finetuned-diffusion-div div{
- display:inline-flex;
- align-items:center;
- gap:.8rem;
- font-size:1.75rem
-}
-.finetuned-diffusion-div div h1{
- font-weight:900;
- margin-bottom:7px
-}
-.finetuned-diffusion-div p{
- margin-bottom:10px;
- font-size:94%
-}
-a{
- text-decoration:underline
-}
-.tabs{
- margin-top:0;
- margin-bottom:0
-}
-#gallery{
- min-height:20rem
-}
diff --git a/spaces/HighCWu/GPEN/retinaface/data/wider_face.py b/spaces/HighCWu/GPEN/retinaface/data/wider_face.py
deleted file mode 100644
index e1862d5bc432566a57c10b90412929b881bb9447..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/GPEN/retinaface/data/wider_face.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import os
-import os.path
-import sys
-import torch
-import torch.utils.data as data
-import cv2
-import numpy as np
-
-class WiderFaceDetection(data.Dataset):
- def __init__(self, txt_path, preproc=None):
- self.preproc = preproc
- self.imgs_path = []
- self.words = []
- f = open(txt_path,'r')
- lines = f.readlines()
- isFirst = True
- labels = []
- for line in lines:
- line = line.rstrip()
- if line.startswith('#'):
- if isFirst==True:
- isFirst = False
- else:
- labels_copy = labels.copy()
- self.words.append(labels_copy)
- labels.clear()
- path = line[2:]
- path = txt_path.replace('label.txt','images/') + path
- self.imgs_path.append(path)
- else:
- line = line.split(' ')
- label = [float(x) for x in line]
- labels.append(label)
-
- self.words.append(labels)
-
- def __len__(self):
- return len(self.imgs_path)
-
- def __getitem__(self, index):
- img = cv2.imread(self.imgs_path[index])
- height, width, _ = img.shape
-
- labels = self.words[index]
- annotations = np.zeros((0, 15))
- if len(labels) == 0:
- return annotations
- for idx, label in enumerate(labels):
- annotation = np.zeros((1, 15))
- # bbox
- annotation[0, 0] = label[0] # x1
- annotation[0, 1] = label[1] # y1
- annotation[0, 2] = label[0] + label[2] # x2
- annotation[0, 3] = label[1] + label[3] # y2
-
- # landmarks
- annotation[0, 4] = label[4] # l0_x
- annotation[0, 5] = label[5] # l0_y
- annotation[0, 6] = label[7] # l1_x
- annotation[0, 7] = label[8] # l1_y
- annotation[0, 8] = label[10] # l2_x
- annotation[0, 9] = label[11] # l2_y
- annotation[0, 10] = label[13] # l3_x
- annotation[0, 11] = label[14] # l3_y
- annotation[0, 12] = label[16] # l4_x
- annotation[0, 13] = label[17] # l4_y
- if (annotation[0, 4]<0):
- annotation[0, 14] = -1
- else:
- annotation[0, 14] = 1
-
- annotations = np.append(annotations, annotation, axis=0)
- target = np.array(annotations)
- if self.preproc is not None:
- img, target = self.preproc(img, target)
-
- return torch.from_numpy(img), target
-
-def detection_collate(batch):
- """Custom collate fn for dealing with batches of images that have a different
- number of associated object annotations (bounding boxes).
-
- Arguments:
- batch: (tuple) A tuple of tensor images and lists of annotations
-
- Return:
- A tuple containing:
- 1) (tensor) batch of images stacked on their 0 dim
- 2) (list of tensors) annotations for a given image are stacked on 0 dim
- """
- targets = []
- imgs = []
- for _, sample in enumerate(batch):
- for _, tup in enumerate(sample):
- if torch.is_tensor(tup):
- imgs.append(tup)
- elif isinstance(tup, type(np.empty(0))):
- annos = torch.from_numpy(tup).float()
- targets.append(annos)
-
- return (torch.stack(imgs, 0), targets)
diff --git a/spaces/Hoodady/3DFuse/ldm/modules/midas/midas/__init__.py b/spaces/Hoodady/3DFuse/ldm/modules/midas/midas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HuggingFaceH4/falcon-chat/README.md b/spaces/HuggingFaceH4/falcon-chat/README.md
deleted file mode 100644
index 0b9fc060fc23144275a9c8130e9d8a019c9b5a3b..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceH4/falcon-chat/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Falcon-Chat
-emoji: 💬
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: true
-license: apache-2.0
----
diff --git a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/cache_dir/HuggingFaceM4/OBELICS_default_train_texts/text_duplicates/text_duplicates.html b/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/cache_dir/HuggingFaceM4/OBELICS_default_train_texts/text_duplicates/text_duplicates.html
deleted file mode 100644
index 72a73cb3f6bd75f13e687ee364303cfc8a971362..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/cache_dir/HuggingFaceM4/OBELICS_default_train_texts/text_duplicates/text_duplicates.html
+++ /dev/null
@@ -1,110 +0,0 @@
-| duplicate_fraction | 0.0011676271846117192 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| duplicates_dict |
|
- 
- 
-
- 
- 
-
-
-
-
-
-
-
- """
- )
-
- with gr.Row():
-
- with gr.Column(scale=55):
- with gr.Group():
- model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
- generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-
- image_out = gr.Image(height=512)
- # gallery = gr.Gallery(
- # label="Generated images", show_label=False, elem_id="gallery"
- # ).style(grid=[1], height="auto")
-
- with gr.Column(scale=45):
- with gr.Tab("Options"):
- with gr.Group():
- neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-
- # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
-
- with gr.Row():
- guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
- steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=100, step=1)
-
- with gr.Row():
- width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
- height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
- seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
- with gr.Tab("Image to image"):
- with gr.Group():
- image = gr.Image(label="Image", height=256, tool="editor", type="pil")
- strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
- # model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=custom_model_group)
-
- inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
- prompt.submit(inference, inputs=inputs, outputs=image_out)
-
- generate.click(inference, inputs=inputs, outputs=image_out)
-
-
- gr.Markdown('''
- Stable-diffusion Models by [CompVis](https://huggingface.co/CompVis) and [stabilityai](https://huggingface.co/stabilityai), Waifu-diffusion models by [@hakurei](https://huggingface.co/hakurei). Most of the code of this demo are copied from [@anzorq's fintuned-diffusion](https://huggingface.co/spaces/anzorq/finetuned_diffusion/tree/main) ❤️
-
- Stable-Diffusion with DPM-Solver (fastest sampler for diffusion models)
--
- ❤️ Acknowledgement: Hardware resources of this demo are supported by HuggingFace 🤗 . Many thanks for the help! -
--
- This is a demo of sampling by DPM-Solver with two variants of Stable Diffusion models, including Stable-Diffusion-v1.4 and Waifu. -
--
- DPM-Solver (Neurips 2022 Oral) is a fast high-order solver customized for diffusion ODEs, which can generate high-quality samples by diffusion models within only 10-25 steps. DPM-Solver has an analytical formulation and is very easy to use for all types of Gaussian diffusion models, and includes DDIM as a first-order special case. -
-- We use Diffusers 🧨 to implement this demo, which currently supports the multistep DPM-Solver scheduler. For more details of DPM-Solver with Diffusers, check this pull request. -
--
- Currently, the default sampler of stable-diffusion is PNDM, which needs 50 steps to generate high-quality samples. However, DPM-Solver can generate high-quality samples within only 20-25 steps, and for some samples even within 10-15 steps. -
--
- Running on {device} -
-- Space by [Cheng Lu](https://github.com/LuChengTHU). [](https://twitter.com/ChengLu05671218) - -  - ''') - -demo.queue(concurrency_count=1) -demo.launch(debug=False, share=False) diff --git a/spaces/Matthijs/image2reverb/image2reverb/util.py b/spaces/Matthijs/image2reverb/image2reverb/util.py deleted file mode 100644 index b37bd91a2f8d73e368234285d69c521f907e24a1..0000000000000000000000000000000000000000 --- a/spaces/Matthijs/image2reverb/image2reverb/util.py +++ /dev/null @@ -1,167 +0,0 @@ -import os -import math -import numpy -import torch -import torch.fft -from PIL import Image - - -def compare_t60(a, b, sr=86): - try: - a = a.detach().clone().abs() - b = b.detach().clone().abs() - a = (a - a.min())/(a.max() - a.min()) - b = (b - b.min())/(b.max() - b.min()) - t_a = estimate_t60(a, sr) - t_b = estimate_t60(b, sr) - return abs((t_b - t_a)/t_a) * 100 - except Exception as error: - return 100 - - -def estimate_t60(audio, sr): - fs = float(sr) - audio = audio.detach().clone() - - decay_db = 20 - - # The power of the impulse response in dB - power = audio ** 2 - energy = torch.flip(torch.cumsum(torch.flip(power, [0]), 0), [0]) # Integration according to Schroeder - - # remove the possibly all zero tail - i_nz = torch.max(torch.where(energy > 0)[0]) - n = energy[:i_nz] - db = 10 * torch.log10(n) - db = db - db[0] - - # -5 dB headroom - i_5db = torch.min(torch.where(-5 - db > 0)[0]) - e_5db = db[i_5db] - t_5db = i_5db / fs - - # after decay - i_decay = torch.min(torch.where(-5 - decay_db - db > 0)[0]) - t_decay = i_decay / fs - - # compute the decay time - decay_time = t_decay - t_5db - est_rt60 = (60 / decay_db) * decay_time - - return est_rt60 - -def hilbert(x): #hilbert transform - N = x.shape[1] - Xf = torch.fft.fft(x, n=None, dim=-1) - h = torch.zeros(N) - if N % 2 == 0: - h[0] = h[N//2] = 1 - h[1:N//2] = 2 - else: - h[0] = 1 - h[1:(N + 1)//2] = 2 - x = torch.fft.ifft(Xf * h) - return x - - -def spectral_centroid(x): #calculate the spectral centroid "brightness" of an audio input - Xf = torch.abs(torch.fft.fft(x,n=None,dim=-1)) #take fft and abs of x - norm_Xf = Xf / sum(sum(Xf)) # like probability mass function - norm_freqs = torch.linspace(0, 1, Xf.shape[1]) - spectral_centroid = sum(sum(norm_freqs * norm_Xf)) - return spectral_centroid - - -# Converts a Tensor into a Numpy array -# |imtype|: the desired type of the converted numpy array -def tensor2im(image_tensor, imtype=numpy.uint8, normalize=True): - if isinstance(image_tensor, list): - image_numpy = [] - for i in range(len(image_tensor)): - image_numpy.append(tensor2im(image_tensor[i], imtype, normalize)) - return image_numpy - image_numpy = image_tensor.cpu().float().numpy() - if normalize: - image_numpy = (numpy.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 - else: - image_numpy = numpy.transpose(image_numpy, (1, 2, 0)) * 255.0 - image_numpy = numpy.clip(image_numpy, 0, 255) - if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3: - image_numpy = image_numpy[:,:,0] - return image_numpy.astype(imtype) - -# Converts a one-hot tensor into a colorful label map -def tensor2label(label_tensor, n_label, imtype=numpy.uint8): - if n_label == 0: - return tensor2im(label_tensor, imtype) - label_tensor = label_tensor.cpu().float() - if label_tensor.size()[0] > 1: - label_tensor = label_tensor.max(0, keepdim=True)[1] - label_tensor = Colorize(n_label)(label_tensor) - label_numpy = numpy.transpose(label_tensor.numpy(), (1, 2, 0)) - return label_numpy.astype(imtype) - -def save_image(image_numpy, image_path): - image_pil = Image.fromarray(image_numpy) - image_pil.save(image_path) - -def mkdirs(paths): - if isinstance(paths, list) and not isinstance(paths, str): - for path in paths: - mkdir(path) - else: - mkdir(paths) - -def mkdir(path): - if not os.path.exists(path): - os.makedirs(path) - -############################################################################### -# Code from -# https://github.com/ycszen/pytorch-seg/blob/master/transform.py -# Modified so it complies with the Citscape label map colors -############################################################################### -def uint82bin(n, count=8): - """returns the binary of integer n, count refers to amount of bits""" - return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)]) - -def labelcolormap(N): - if N == 35: # cityscape - cmap = numpy.array([( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), (111, 74, 0), ( 81, 0, 81), - (128, 64,128), (244, 35,232), (250,170,160), (230,150,140), ( 70, 70, 70), (102,102,156), (190,153,153), - (180,165,180), (150,100,100), (150,120, 90), (153,153,153), (153,153,153), (250,170, 30), (220,220, 0), - (107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255, 0, 0), ( 0, 0,142), ( 0, 0, 70), - ( 0, 60,100), ( 0, 0, 90), ( 0, 0,110), ( 0, 80,100), ( 0, 0,230), (119, 11, 32), ( 0, 0,142)], - dtype=numpy.uint8) - else: - cmap = numpy.zeros((N, 3), dtype=numpy.uint8) - for i in range(N): - r, g, b = 0, 0, 0 - id = i - for j in range(7): - str_id = uint82bin(id) - r = r ^ (numpy.uint8(str_id[-1]) << (7-j)) - g = g ^ (numpy.uint8(str_id[-2]) << (7-j)) - b = b ^ (numpy.uint8(str_id[-3]) << (7-j)) - id = id >> 3 - cmap[i, 0] = r - cmap[i, 1] = g - cmap[i, 2] = b - return cmap - -class Colorize(object): - def __init__(self, n=35): - self.cmap = labelcolormap(n) - self.cmap = torch.from_numpy(self.cmap[:n]) - - def __call__(self, gray_image): - size = gray_image.size() - color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0) - - for label in range(0, len(self.cmap)): - mask = (label == gray_image[0]).cpu() - color_image[0][mask] = self.cmap[label][0] - color_image[1][mask] = self.cmap[label][1] - color_image[2][mask] = self.cmap[label][2] - - return color_image diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/base_runner.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/base_runner.py deleted file mode 100644 index 4928db0a73b56fe0218a4bf66ec4ffa082d31ccc..0000000000000000000000000000000000000000 --- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/base_runner.py +++ /dev/null @@ -1,542 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy -import logging -import os.path as osp -import warnings -from abc import ABCMeta, abstractmethod - -import torch -from torch.optim import Optimizer - -import annotator.uniformer.mmcv as mmcv -from ..parallel import is_module_wrapper -from .checkpoint import load_checkpoint -from .dist_utils import get_dist_info -from .hooks import HOOKS, Hook -from .log_buffer import LogBuffer -from .priority import Priority, get_priority -from .utils import get_time_str - - -class BaseRunner(metaclass=ABCMeta): - """The base class of Runner, a training helper for PyTorch. - - All subclasses should implement the following APIs: - - - ``run()`` - - ``train()`` - - ``val()`` - - ``save_checkpoint()`` - - Args: - model (:obj:`torch.nn.Module`): The model to be run. - batch_processor (callable): A callable method that process a data - batch. The interface of this method should be - `batch_processor(model, data, train_mode) -> dict` - optimizer (dict or :obj:`torch.optim.Optimizer`): It can be either an - optimizer (in most cases) or a dict of optimizers (in models that - requires more than one optimizer, e.g., GAN). - work_dir (str, optional): The working directory to save checkpoints - and logs. Defaults to None. - logger (:obj:`logging.Logger`): Logger used during training. - Defaults to None. (The default value is just for backward - compatibility) - meta (dict | None): A dict records some import information such as - environment info and seed, which will be logged in logger hook. - Defaults to None. - max_epochs (int, optional): Total training epochs. - max_iters (int, optional): Total training iterations. - """ - - def __init__(self, - model, - batch_processor=None, - optimizer=None, - work_dir=None, - logger=None, - meta=None, - max_iters=None, - max_epochs=None): - if batch_processor is not None: - if not callable(batch_processor): - raise TypeError('batch_processor must be callable, ' - f'but got {type(batch_processor)}') - warnings.warn('batch_processor is deprecated, please implement ' - 'train_step() and val_step() in the model instead.') - # raise an error is `batch_processor` is not None and - # `model.train_step()` exists. - if is_module_wrapper(model): - _model = model.module - else: - _model = model - if hasattr(_model, 'train_step') or hasattr(_model, 'val_step'): - raise RuntimeError( - 'batch_processor and model.train_step()/model.val_step() ' - 'cannot be both available.') - else: - assert hasattr(model, 'train_step') - - # check the type of `optimizer` - if isinstance(optimizer, dict): - for name, optim in optimizer.items(): - if not isinstance(optim, Optimizer): - raise TypeError( - f'optimizer must be a dict of torch.optim.Optimizers, ' - f'but optimizer["{name}"] is a {type(optim)}') - elif not isinstance(optimizer, Optimizer) and optimizer is not None: - raise TypeError( - f'optimizer must be a torch.optim.Optimizer object ' - f'or dict or None, but got {type(optimizer)}') - - # check the type of `logger` - if not isinstance(logger, logging.Logger): - raise TypeError(f'logger must be a logging.Logger object, ' - f'but got {type(logger)}') - - # check the type of `meta` - if meta is not None and not isinstance(meta, dict): - raise TypeError( - f'meta must be a dict or None, but got {type(meta)}') - - self.model = model - self.batch_processor = batch_processor - self.optimizer = optimizer - self.logger = logger - self.meta = meta - # create work_dir - if mmcv.is_str(work_dir): - self.work_dir = osp.abspath(work_dir) - mmcv.mkdir_or_exist(self.work_dir) - elif work_dir is None: - self.work_dir = None - else: - raise TypeError('"work_dir" must be a str or None') - - # get model name from the model class - if hasattr(self.model, 'module'): - self._model_name = self.model.module.__class__.__name__ - else: - self._model_name = self.model.__class__.__name__ - - self._rank, self._world_size = get_dist_info() - self.timestamp = get_time_str() - self.mode = None - self._hooks = [] - self._epoch = 0 - self._iter = 0 - self._inner_iter = 0 - - if max_epochs is not None and max_iters is not None: - raise ValueError( - 'Only one of `max_epochs` or `max_iters` can be set.') - - self._max_epochs = max_epochs - self._max_iters = max_iters - # TODO: Redesign LogBuffer, it is not flexible and elegant enough - self.log_buffer = LogBuffer() - - @property - def model_name(self): - """str: Name of the model, usually the module class name.""" - return self._model_name - - @property - def rank(self): - """int: Rank of current process. (distributed training)""" - return self._rank - - @property - def world_size(self): - """int: Number of processes participating in the job. - (distributed training)""" - return self._world_size - - @property - def hooks(self): - """list[:obj:`Hook`]: A list of registered hooks.""" - return self._hooks - - @property - def epoch(self): - """int: Current epoch.""" - return self._epoch - - @property - def iter(self): - """int: Current iteration.""" - return self._iter - - @property - def inner_iter(self): - """int: Iteration in an epoch.""" - return self._inner_iter - - @property - def max_epochs(self): - """int: Maximum training epochs.""" - return self._max_epochs - - @property - def max_iters(self): - """int: Maximum training iterations.""" - return self._max_iters - - @abstractmethod - def train(self): - pass - - @abstractmethod - def val(self): - pass - - @abstractmethod - def run(self, data_loaders, workflow, **kwargs): - pass - - @abstractmethod - def save_checkpoint(self, - out_dir, - filename_tmpl, - save_optimizer=True, - meta=None, - create_symlink=True): - pass - - def current_lr(self): - """Get current learning rates. - - Returns: - list[float] | dict[str, list[float]]: Current learning rates of all - param groups. If the runner has a dict of optimizers, this - method will return a dict. - """ - if isinstance(self.optimizer, torch.optim.Optimizer): - lr = [group['lr'] for group in self.optimizer.param_groups] - elif isinstance(self.optimizer, dict): - lr = dict() - for name, optim in self.optimizer.items(): - lr[name] = [group['lr'] for group in optim.param_groups] - else: - raise RuntimeError( - 'lr is not applicable because optimizer does not exist.') - return lr - - def current_momentum(self): - """Get current momentums. - - Returns: - list[float] | dict[str, list[float]]: Current momentums of all - param groups. If the runner has a dict of optimizers, this - method will return a dict. - """ - - def _get_momentum(optimizer): - momentums = [] - for group in optimizer.param_groups: - if 'momentum' in group.keys(): - momentums.append(group['momentum']) - elif 'betas' in group.keys(): - momentums.append(group['betas'][0]) - else: - momentums.append(0) - return momentums - - if self.optimizer is None: - raise RuntimeError( - 'momentum is not applicable because optimizer does not exist.') - elif isinstance(self.optimizer, torch.optim.Optimizer): - momentums = _get_momentum(self.optimizer) - elif isinstance(self.optimizer, dict): - momentums = dict() - for name, optim in self.optimizer.items(): - momentums[name] = _get_momentum(optim) - return momentums - - def register_hook(self, hook, priority='NORMAL'): - """Register a hook into the hook list. - - The hook will be inserted into a priority queue, with the specified - priority (See :class:`Priority` for details of priorities). - For hooks with the same priority, they will be triggered in the same - order as they are registered. - - Args: - hook (:obj:`Hook`): The hook to be registered. - priority (int or str or :obj:`Priority`): Hook priority. - Lower value means higher priority. - """ - assert isinstance(hook, Hook) - if hasattr(hook, 'priority'): - raise ValueError('"priority" is a reserved attribute for hooks') - priority = get_priority(priority) - hook.priority = priority - # insert the hook to a sorted list - inserted = False - for i in range(len(self._hooks) - 1, -1, -1): - if priority >= self._hooks[i].priority: - self._hooks.insert(i + 1, hook) - inserted = True - break - if not inserted: - self._hooks.insert(0, hook) - - def register_hook_from_cfg(self, hook_cfg): - """Register a hook from its cfg. - - Args: - hook_cfg (dict): Hook config. It should have at least keys 'type' - and 'priority' indicating its type and priority. - - Notes: - The specific hook class to register should not use 'type' and - 'priority' arguments during initialization. - """ - hook_cfg = hook_cfg.copy() - priority = hook_cfg.pop('priority', 'NORMAL') - hook = mmcv.build_from_cfg(hook_cfg, HOOKS) - self.register_hook(hook, priority=priority) - - def call_hook(self, fn_name): - """Call all hooks. - - Args: - fn_name (str): The function name in each hook to be called, such as - "before_train_epoch". - """ - for hook in self._hooks: - getattr(hook, fn_name)(self) - - def get_hook_info(self): - # Get hooks info in each stage - stage_hook_map = {stage: [] for stage in Hook.stages} - for hook in self.hooks: - try: - priority = Priority(hook.priority).name - except ValueError: - priority = hook.priority - classname = hook.__class__.__name__ - hook_info = f'({priority:<12}) {classname:<35}' - for trigger_stage in hook.get_triggered_stages(): - stage_hook_map[trigger_stage].append(hook_info) - - stage_hook_infos = [] - for stage in Hook.stages: - hook_infos = stage_hook_map[stage] - if len(hook_infos) > 0: - info = f'{stage}:\n' - info += '\n'.join(hook_infos) - info += '\n -------------------- ' - stage_hook_infos.append(info) - return '\n'.join(stage_hook_infos) - - def load_checkpoint(self, - filename, - map_location='cpu', - strict=False, - revise_keys=[(r'^module.', '')]): - return load_checkpoint( - self.model, - filename, - map_location, - strict, - self.logger, - revise_keys=revise_keys) - - def resume(self, - checkpoint, - resume_optimizer=True, - map_location='default'): - if map_location == 'default': - if torch.cuda.is_available(): - device_id = torch.cuda.current_device() - checkpoint = self.load_checkpoint( - checkpoint, - map_location=lambda storage, loc: storage.cuda(device_id)) - else: - checkpoint = self.load_checkpoint(checkpoint) - else: - checkpoint = self.load_checkpoint( - checkpoint, map_location=map_location) - - self._epoch = checkpoint['meta']['epoch'] - self._iter = checkpoint['meta']['iter'] - if self.meta is None: - self.meta = {} - self.meta.setdefault('hook_msgs', {}) - # load `last_ckpt`, `best_score`, `best_ckpt`, etc. for hook messages - self.meta['hook_msgs'].update(checkpoint['meta'].get('hook_msgs', {})) - - # Re-calculate the number of iterations when resuming - # models with different number of GPUs - if 'config' in checkpoint['meta']: - config = mmcv.Config.fromstring( - checkpoint['meta']['config'], file_format='.py') - previous_gpu_ids = config.get('gpu_ids', None) - if previous_gpu_ids and len(previous_gpu_ids) > 0 and len( - previous_gpu_ids) != self.world_size: - self._iter = int(self._iter * len(previous_gpu_ids) / - self.world_size) - self.logger.info('the iteration number is changed due to ' - 'change of GPU number') - - # resume meta information meta - self.meta = checkpoint['meta'] - - if 'optimizer' in checkpoint and resume_optimizer: - if isinstance(self.optimizer, Optimizer): - self.optimizer.load_state_dict(checkpoint['optimizer']) - elif isinstance(self.optimizer, dict): - for k in self.optimizer.keys(): - self.optimizer[k].load_state_dict( - checkpoint['optimizer'][k]) - else: - raise TypeError( - 'Optimizer should be dict or torch.optim.Optimizer ' - f'but got {type(self.optimizer)}') - - self.logger.info('resumed epoch %d, iter %d', self.epoch, self.iter) - - def register_lr_hook(self, lr_config): - if lr_config is None: - return - elif isinstance(lr_config, dict): - assert 'policy' in lr_config - policy_type = lr_config.pop('policy') - # If the type of policy is all in lower case, e.g., 'cyclic', - # then its first letter will be capitalized, e.g., to be 'Cyclic'. - # This is for the convenient usage of Lr updater. - # Since this is not applicable for ` - # CosineAnnealingLrUpdater`, - # the string will not be changed if it contains capital letters. - if policy_type == policy_type.lower(): - policy_type = policy_type.title() - hook_type = policy_type + 'LrUpdaterHook' - lr_config['type'] = hook_type - hook = mmcv.build_from_cfg(lr_config, HOOKS) - else: - hook = lr_config - self.register_hook(hook, priority='VERY_HIGH') - - def register_momentum_hook(self, momentum_config): - if momentum_config is None: - return - if isinstance(momentum_config, dict): - assert 'policy' in momentum_config - policy_type = momentum_config.pop('policy') - # If the type of policy is all in lower case, e.g., 'cyclic', - # then its first letter will be capitalized, e.g., to be 'Cyclic'. - # This is for the convenient usage of momentum updater. - # Since this is not applicable for - # `CosineAnnealingMomentumUpdater`, - # the string will not be changed if it contains capital letters. - if policy_type == policy_type.lower(): - policy_type = policy_type.title() - hook_type = policy_type + 'MomentumUpdaterHook' - momentum_config['type'] = hook_type - hook = mmcv.build_from_cfg(momentum_config, HOOKS) - else: - hook = momentum_config - self.register_hook(hook, priority='HIGH') - - def register_optimizer_hook(self, optimizer_config): - if optimizer_config is None: - return - if isinstance(optimizer_config, dict): - optimizer_config.setdefault('type', 'OptimizerHook') - hook = mmcv.build_from_cfg(optimizer_config, HOOKS) - else: - hook = optimizer_config - self.register_hook(hook, priority='ABOVE_NORMAL') - - def register_checkpoint_hook(self, checkpoint_config): - if checkpoint_config is None: - return - if isinstance(checkpoint_config, dict): - checkpoint_config.setdefault('type', 'CheckpointHook') - hook = mmcv.build_from_cfg(checkpoint_config, HOOKS) - else: - hook = checkpoint_config - self.register_hook(hook, priority='NORMAL') - - def register_logger_hooks(self, log_config): - if log_config is None: - return - log_interval = log_config['interval'] - for info in log_config['hooks']: - logger_hook = mmcv.build_from_cfg( - info, HOOKS, default_args=dict(interval=log_interval)) - self.register_hook(logger_hook, priority='VERY_LOW') - - def register_timer_hook(self, timer_config): - if timer_config is None: - return - if isinstance(timer_config, dict): - timer_config_ = copy.deepcopy(timer_config) - hook = mmcv.build_from_cfg(timer_config_, HOOKS) - else: - hook = timer_config - self.register_hook(hook, priority='LOW') - - def register_custom_hooks(self, custom_config): - if custom_config is None: - return - - if not isinstance(custom_config, list): - custom_config = [custom_config] - - for item in custom_config: - if isinstance(item, dict): - self.register_hook_from_cfg(item) - else: - self.register_hook(item, priority='NORMAL') - - def register_profiler_hook(self, profiler_config): - if profiler_config is None: - return - if isinstance(profiler_config, dict): - profiler_config.setdefault('type', 'ProfilerHook') - hook = mmcv.build_from_cfg(profiler_config, HOOKS) - else: - hook = profiler_config - self.register_hook(hook) - - def register_training_hooks(self, - lr_config, - optimizer_config=None, - checkpoint_config=None, - log_config=None, - momentum_config=None, - timer_config=dict(type='IterTimerHook'), - custom_hooks_config=None): - """Register default and custom hooks for training. - - Default and custom hooks include: - - +----------------------+-------------------------+ - | Hooks | Priority | - +======================+=========================+ - | LrUpdaterHook | VERY_HIGH (10) | - +----------------------+-------------------------+ - | MomentumUpdaterHook | HIGH (30) | - +----------------------+-------------------------+ - | OptimizerStepperHook | ABOVE_NORMAL (40) | - +----------------------+-------------------------+ - | CheckpointSaverHook | NORMAL (50) | - +----------------------+-------------------------+ - | IterTimerHook | LOW (70) | - +----------------------+-------------------------+ - | LoggerHook(s) | VERY_LOW (90) | - +----------------------+-------------------------+ - | CustomHook(s) | defaults to NORMAL (50) | - +----------------------+-------------------------+ - - If custom hooks have same priority with default hooks, custom hooks - will be triggered after default hooks. - """ - self.register_lr_hook(lr_config) - self.register_momentum_hook(momentum_config) - self.register_optimizer_hook(optimizer_config) - self.register_checkpoint_hook(checkpoint_config) - self.register_timer_hook(timer_config) - self.register_logger_hooks(log_config) - self.register_custom_hooks(custom_hooks_config) diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/midas/midas/blocks.py b/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/midas/midas/blocks.py deleted file mode 100644 index 2145d18fa98060a618536d9a64fe6589e9be4f78..0000000000000000000000000000000000000000 --- a/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/midas/midas/blocks.py +++ /dev/null @@ -1,342 +0,0 @@ -import torch -import torch.nn as nn - -from .vit import ( - _make_pretrained_vitb_rn50_384, - _make_pretrained_vitl16_384, - _make_pretrained_vitb16_384, - forward_vit, -) - -def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",): - if backbone == "vitl16_384": - pretrained = _make_pretrained_vitl16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [256, 512, 1024, 1024], features, groups=groups, expand=expand - ) # ViT-L/16 - 85.0% Top1 (backbone) - elif backbone == "vitb_rn50_384": - pretrained = _make_pretrained_vitb_rn50_384( - use_pretrained, - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) - scratch = _make_scratch( - [256, 512, 768, 768], features, groups=groups, expand=expand - ) # ViT-H/16 - 85.0% Top1 (backbone) - elif backbone == "vitb16_384": - pretrained = _make_pretrained_vitb16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [96, 192, 384, 768], features, groups=groups, expand=expand - ) # ViT-B/16 - 84.6% Top1 (backbone) - elif backbone == "resnext101_wsl": - pretrained = _make_pretrained_resnext101_wsl(use_pretrained) - scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3 - elif backbone == "efficientnet_lite3": - pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) - scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3 - else: - print(f"Backbone '{backbone}' not implemented") - assert False - - return pretrained, scratch - - -def _make_scratch(in_shape, out_shape, groups=1, expand=False): - scratch = nn.Module() - - out_shape1 = out_shape - out_shape2 = out_shape - out_shape3 = out_shape - out_shape4 = out_shape - if expand==True: - out_shape1 = out_shape - out_shape2 = out_shape*2 - out_shape3 = out_shape*4 - out_shape4 = out_shape*8 - - scratch.layer1_rn = nn.Conv2d( - in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer2_rn = nn.Conv2d( - in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer3_rn = nn.Conv2d( - in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer4_rn = nn.Conv2d( - in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - - return scratch - - -def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): - efficientnet = torch.hub.load( - "rwightman/gen-efficientnet-pytorch", - "tf_efficientnet_lite3", - pretrained=use_pretrained, - exportable=exportable - ) - return _make_efficientnet_backbone(efficientnet) - - -def _make_efficientnet_backbone(effnet): - pretrained = nn.Module() - - pretrained.layer1 = nn.Sequential( - effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] - ) - pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) - pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) - pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) - - return pretrained - - -def _make_resnet_backbone(resnet): - pretrained = nn.Module() - pretrained.layer1 = nn.Sequential( - resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 - ) - - pretrained.layer2 = resnet.layer2 - pretrained.layer3 = resnet.layer3 - pretrained.layer4 = resnet.layer4 - - return pretrained - - -def _make_pretrained_resnext101_wsl(use_pretrained): - resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") - return _make_resnet_backbone(resnet) - - - -class Interpolate(nn.Module): - """Interpolation module. - """ - - def __init__(self, scale_factor, mode, align_corners=False): - """Init. - - Args: - scale_factor (float): scaling - mode (str): interpolation mode - """ - super(Interpolate, self).__init__() - - self.interp = nn.functional.interpolate - self.scale_factor = scale_factor - self.mode = mode - self.align_corners = align_corners - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: interpolated data - """ - - x = self.interp( - x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners - ) - - return x - - -class ResidualConvUnit(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - out = self.relu(x) - out = self.conv1(out) - out = self.relu(out) - out = self.conv2(out) - - return out + x - - -class FeatureFusionBlock(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock, self).__init__() - - self.resConfUnit1 = ResidualConvUnit(features) - self.resConfUnit2 = ResidualConvUnit(features) - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - output += self.resConfUnit1(xs[1]) - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=True - ) - - return output - - - - -class ResidualConvUnit_custom(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features, activation, bn): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.bn = bn - - self.groups=1 - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - if self.bn==True: - self.bn1 = nn.BatchNorm2d(features) - self.bn2 = nn.BatchNorm2d(features) - - self.activation = activation - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - - out = self.activation(x) - out = self.conv1(out) - if self.bn==True: - out = self.bn1(out) - - out = self.activation(out) - out = self.conv2(out) - if self.bn==True: - out = self.bn2(out) - - if self.groups > 1: - out = self.conv_merge(out) - - return self.skip_add.add(out, x) - - # return out + x - - -class FeatureFusionBlock_custom(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock_custom, self).__init__() - - self.deconv = deconv - self.align_corners = align_corners - - self.groups=1 - - self.expand = expand - out_features = features - if self.expand==True: - out_features = features//2 - - self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) - - self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) - self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - res = self.resConfUnit1(xs[1]) - output = self.skip_add.add(output, res) - # output += res - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=self.align_corners - ) - - output = self.out_conv(output) - - return output - diff --git a/spaces/MirageML/sjc/sd1/ldm/modules/diffusionmodules/__init__.py b/spaces/MirageML/sjc/sd1/ldm/modules/diffusionmodules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Monster/Llama-2-13B-chat/Dockerfile b/spaces/Monster/Llama-2-13B-chat/Dockerfile deleted file mode 100644 index 2febaaadf56245aec758f041ef8519da43b17db7..0000000000000000000000000000000000000000 --- a/spaces/Monster/Llama-2-13B-chat/Dockerfile +++ /dev/null @@ -1,5 +0,0 @@ -# Monster/Llama-2-13B-chat -FROM ghcr.io/ggerganov/llama.cpp:full -RUN apt update && apt upgrade -y && apt install wget -y -RUN wget "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q4_K_M.gguf" -O llama-2-13b-chat.Q4_K_M.gguf -CMD ["--server", "-m", "llama-2-13b-chat.Q4_K_M.gguf", "--port", "7860", "--host", "0.0.0.0", "-t", "2"] \ No newline at end of file diff --git a/spaces/NeuralInternet/InfiniteGPT/README.md b/spaces/NeuralInternet/InfiniteGPT/README.md deleted file mode 100644 index c9095ec1cdff08a20ae5164548655114afab1897..0000000000000000000000000000000000000000 --- a/spaces/NeuralInternet/InfiniteGPT/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: InfiniteGPT -emoji: 📚 -colorFrom: indigo -colorTo: yellow -sdk: streamlit -sdk_version: 1.19.0 -app_file: app.py -pinned: false -duplicated_from: asifhugs/InfiniteGPT ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/NimaBoscarino/aot-gan-inpainting/app.py b/spaces/NimaBoscarino/aot-gan-inpainting/app.py deleted file mode 100644 index ad375cea000649e1fd732e4cc9433124144f7020..0000000000000000000000000000000000000000 --- a/spaces/NimaBoscarino/aot-gan-inpainting/app.py +++ /dev/null @@ -1,74 +0,0 @@ -from PIL import Image -import streamlit as st -from streamlit_drawable_canvas import st_canvas -from torchvision.transforms import ToTensor -import torch -import numpy as np -import cv2 -import aotgan.model.aotgan as net - -@st.cache -def load_model(model_name): - model = net.InpaintGenerator.from_pretrained(model_name) - return model - -def postprocess(image): - image = torch.clamp(image, -1., 1.) - image = (image + 1) / 2.0 * 255.0 - image = image.permute(1, 2, 0) - image = image.cpu().numpy().astype(np.uint8) - return image - -def infer(img, mask): - with torch.no_grad(): - img_cv = cv2.resize(np.array(img), (512, 512)) # Fixing everything to 512 x 512 for this demo. - img_tensor = (ToTensor()(img_cv) * 2.0 - 1.0).unsqueeze(0) - mask_tensor = (ToTensor()(mask.astype(np.uint8))).unsqueeze(0) - masked_tensor = (img_tensor * (1 - mask_tensor).float()) + mask_tensor - pred_tensor = model(masked_tensor, mask_tensor) - comp_tensor = (pred_tensor * mask_tensor + img_tensor * (1 - mask_tensor)) - comp_np = postprocess(comp_tensor[0]) - - return comp_np - -stroke_width = 8 -stroke_color = "#FFF" -bg_color = "#000" -bg_image = st.sidebar.file_uploader("Image:", type=["png", "jpg", "jpeg"]) -sample_bg_image = st.sidebar.radio('Sample Images', [ - "man.png", - "pexels-ike-louie-natividad-2709388.jpg", - "pexels-christina-morillo-1181686.jpg", - "pexels-italo-melo-2379005.jpg", - "rainbow.jpeg", - "kitty.jpg", - "kitty_on_chair.jpeg", -]) -drawing_mode = st.sidebar.selectbox( - "Drawing tool:", ("freedraw", "rect", "circle") -) - -model_name = st.sidebar.selectbox( - "Select model:", ("NimaBoscarino/aot-gan-celebahq", "NimaBoscarino/aot-gan-places2") -) -model = load_model(model_name) - -bg_image = Image.open(bg_image) if bg_image else Image.open(f"./pictures/{sample_bg_image}") - -st.subheader("Draw on the image to erase features. The inpainted result will be generated and displayed below.") -canvas_result = st_canvas( - fill_color="rgb(255, 255, 255)", - stroke_width=stroke_width, - stroke_color=stroke_color, - background_color=bg_color, - background_image=bg_image, - update_streamlit=True, - height=512, - width=512, - drawing_mode=drawing_mode, - key="canvas", -) - -if canvas_result.image_data is not None and bg_image and len(canvas_result.json_data["objects"]) > 0: - result = infer(bg_image, canvas_result.image_data[:, :, 3]) - st.image(result) diff --git a/spaces/OAOA/DifFace/basicsr/models/srgan_model.py b/spaces/OAOA/DifFace/basicsr/models/srgan_model.py deleted file mode 100644 index 45387ca7908e3f38f59a605adb8242ad12fcf1a1..0000000000000000000000000000000000000000 --- a/spaces/OAOA/DifFace/basicsr/models/srgan_model.py +++ /dev/null @@ -1,149 +0,0 @@ -import torch -from collections import OrderedDict - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.utils import get_root_logger -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class SRGANModel(SRModel): - """SRGAN model for single image super-resolution.""" - - def init_training_settings(self): - train_opt = self.opt['train'] - - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - logger = get_root_logger() - logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') - # define network net_g with Exponential Moving Average (EMA) - # net_g_ema is used only for testing on one GPU and saving - # There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - self.net_g_ema.eval() - - # define network net_d - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # load pretrained models - load_path = self.opt['path'].get('pretrain_network_d', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_d', 'params') - self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) - - self.net_g.train() - self.net_d.train() - - # define losses - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('ldl_opt'): - self.cri_ldl = build_loss(train_opt['ldl_opt']).to(self.device) - else: - self.cri_ldl = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if train_opt.get('gan_opt'): - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - - self.net_d_iters = train_opt.get('net_d_iters', 1) - self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - # optimizer g - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - # optimizer d - optim_type = train_opt['optim_d'].pop('type') - self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) - self.optimizers.append(self.optimizer_d) - - def optimize_parameters(self, current_iter): - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss - fake_g_pred = self.net_d(self.output) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # real - real_d_pred = self.net_d(self.gt) - l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - l_d_real.backward() - # fake - fake_d_pred = self.net_d(self.output.detach()) - l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - l_d_fake.backward() - self.optimizer_d.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - def save(self, epoch, current_iter): - if hasattr(self, 'net_g_ema'): - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - else: - self.save_network(self.net_g, 'net_g', current_iter) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/simultaneous_translation/models/convtransformer_simul_trans.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/simultaneous_translation/models/convtransformer_simul_trans.py deleted file mode 100644 index 4a26422f650cf13ee7d4e8d2228b50ec49876fb8..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/simultaneous_translation/models/convtransformer_simul_trans.py +++ /dev/null @@ -1,204 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -from fairseq import checkpoint_utils -from fairseq.models import ( - register_model, - register_model_architecture, -) -from fairseq.models.speech_to_text import ( - ConvTransformerModel, - convtransformer_espnet, - ConvTransformerEncoder, -) -from fairseq.models.speech_to_text.modules.augmented_memory_attention import ( - augmented_memory, - SequenceEncoder, - AugmentedMemoryConvTransformerEncoder, -) - -from torch import nn, Tensor -from typing import Dict, List -from fairseq.models.speech_to_text.modules.emformer import NoSegAugmentedMemoryTransformerEncoderLayer - -@register_model("convtransformer_simul_trans") -class SimulConvTransformerModel(ConvTransformerModel): - """ - Implementation of the paper: - - SimulMT to SimulST: Adapting Simultaneous Text Translation to - End-to-End Simultaneous Speech Translation - - https://www.aclweb.org/anthology/2020.aacl-main.58.pdf - """ - - @staticmethod - def add_args(parser): - super(SimulConvTransformerModel, SimulConvTransformerModel).add_args(parser) - parser.add_argument( - "--train-monotonic-only", - action="store_true", - default=False, - help="Only train monotonic attention", - ) - - @classmethod - def build_decoder(cls, args, task, embed_tokens): - tgt_dict = task.tgt_dict - - from examples.simultaneous_translation.models.transformer_monotonic_attention import ( - TransformerMonotonicDecoder, - ) - - decoder = TransformerMonotonicDecoder(args, tgt_dict, embed_tokens) - - if getattr(args, "load_pretrained_decoder_from", None): - decoder = checkpoint_utils.load_pretrained_component_from_model( - component=decoder, checkpoint=args.load_pretrained_decoder_from - ) - return decoder - - -@register_model_architecture( - "convtransformer_simul_trans", "convtransformer_simul_trans_espnet" -) -def convtransformer_simul_trans_espnet(args): - convtransformer_espnet(args) - - -@register_model("convtransformer_augmented_memory") -@augmented_memory -class AugmentedMemoryConvTransformerModel(SimulConvTransformerModel): - @classmethod - def build_encoder(cls, args): - encoder = SequenceEncoder(args, AugmentedMemoryConvTransformerEncoder(args)) - - if getattr(args, "load_pretrained_encoder_from", None) is not None: - encoder = checkpoint_utils.load_pretrained_component_from_model( - component=encoder, checkpoint=args.load_pretrained_encoder_from - ) - - return encoder - - -@register_model_architecture( - "convtransformer_augmented_memory", "convtransformer_augmented_memory" -) -def augmented_memory_convtransformer_espnet(args): - convtransformer_espnet(args) - - -# ============================================================================ # -# Convtransformer -# with monotonic attention decoder -# with emformer encoder -# ============================================================================ # - - -class ConvTransformerEmformerEncoder(ConvTransformerEncoder): - def __init__(self, args): - super().__init__(args) - stride = self.conv_layer_stride(args) - trf_left_context = args.segment_left_context // stride - trf_right_context = args.segment_right_context // stride - context_config = [trf_left_context, trf_right_context] - self.transformer_layers = nn.ModuleList( - [ - NoSegAugmentedMemoryTransformerEncoderLayer( - input_dim=args.encoder_embed_dim, - num_heads=args.encoder_attention_heads, - ffn_dim=args.encoder_ffn_embed_dim, - num_layers=args.encoder_layers, - dropout_in_attn=args.dropout, - dropout_on_attn=args.dropout, - dropout_on_fc1=args.dropout, - dropout_on_fc2=args.dropout, - activation_fn=args.activation_fn, - context_config=context_config, - segment_size=args.segment_length, - max_memory_size=args.max_memory_size, - scaled_init=True, # TODO: use constant for now. - tanh_on_mem=args.amtrf_tanh_on_mem, - ) - ] - ) - self.conv_transformer_encoder = ConvTransformerEncoder(args) - - def forward(self, src_tokens, src_lengths): - encoder_out: Dict[str, List[Tensor]] = self.conv_transformer_encoder(src_tokens, src_lengths.to(src_tokens.device)) - output = encoder_out["encoder_out"][0] - encoder_padding_masks = encoder_out["encoder_padding_mask"] - - return { - "encoder_out": [output], - # This is because that in the original implementation - # the output didn't consider the last segment as right context. - "encoder_padding_mask": [encoder_padding_masks[0][:, : output.size(0)]] if len(encoder_padding_masks) > 0 - else [], - "encoder_embedding": [], - "encoder_states": [], - "src_tokens": [], - "src_lengths": [], - } - - @staticmethod - def conv_layer_stride(args): - # TODO: make it configurable from the args - return 4 - - -@register_model("convtransformer_emformer") -class ConvtransformerEmformer(SimulConvTransformerModel): - @staticmethod - def add_args(parser): - super(ConvtransformerEmformer, ConvtransformerEmformer).add_args(parser) - - parser.add_argument( - "--segment-length", - type=int, - metavar="N", - help="length of each segment (not including left context / right context)", - ) - parser.add_argument( - "--segment-left-context", - type=int, - help="length of left context in a segment", - ) - parser.add_argument( - "--segment-right-context", - type=int, - help="length of right context in a segment", - ) - parser.add_argument( - "--max-memory-size", - type=int, - default=-1, - help="Right context for the segment.", - ) - parser.add_argument( - "--amtrf-tanh-on-mem", - default=False, - action="store_true", - help="whether to use tanh on memory vector", - ) - - @classmethod - def build_encoder(cls, args): - encoder = ConvTransformerEmformerEncoder(args) - if getattr(args, "load_pretrained_encoder_from", None): - encoder = checkpoint_utils.load_pretrained_component_from_model( - component=encoder, checkpoint=args.load_pretrained_encoder_from - ) - return encoder - - -@register_model_architecture( - "convtransformer_emformer", - "convtransformer_emformer", -) -def convtransformer_emformer_base(args): - convtransformer_espnet(args) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/config/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/config/__init__.py deleted file mode 100644 index 6264236915a7269a4d920ee8213004374dd86a9a..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/config/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_amp_optimizer.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_amp_optimizer.py deleted file mode 100644 index 3a785e1830e91b7e090e841d428fe4ea61f3a65c..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_amp_optimizer.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import copy -import unittest - -import torch -from torch.cuda.amp import autocast, GradScaler -from fairseq.optim import build_optimizer - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestGradientScalingAMP(unittest.TestCase): - def setUp(self): - self.x = torch.tensor([2.0]).cuda().half() - weight = 3.0 - bias = 5.0 - self.error = 1.0 - self.target = torch.tensor([self.x * weight + bias + self.error]).cuda() - self.loss_fn = torch.nn.L1Loss() - - self.model = torch.nn.Linear(1, 1) - self.model.weight.data = torch.tensor([[weight]]) - self.model.bias.data = torch.tensor([bias]) - self.model.cuda() - self.params = list(self.model.parameters()) - - self.namespace_dls = argparse.Namespace( - optimizer="adam", - lr=[0.1], - adam_betas="(0.9, 0.999)", - adam_eps=1e-8, - weight_decay=0.0, - threshold_loss_scale=1, - min_loss_scale=1e-4, - ) - self.scaler = GradScaler( - init_scale=1, - growth_interval=1, - ) - - def run_iter(self, model, params, optimizer): - optimizer.zero_grad() - with autocast(): - y = model(self.x) - loss = self.loss_fn(y, self.target) - self.scaler.scale(loss).backward() - self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) - - self.scaler.unscale_(optimizer) - grad_norm = optimizer.clip_grad_norm(0) - self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) - - self.scaler.step(optimizer) - self.scaler.update() - self.assertEqual( - model.weight, - torch.tensor( - [[3.1]], device="cuda:0", requires_grad=True - ), - ) - self.assertEqual( - model.bias, - torch.tensor( - [5.1], device="cuda:0", requires_grad=True - ), - ) - self.assertEqual(self.scaler.get_scale(), 2.0) - - def test_automatic_mixed_precision(self): - model = copy.deepcopy(self.model) - params = list(model.parameters()) - optimizer = build_optimizer(self.namespace_dls, params) - - self.run_iter(model, params, optimizer) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_ema.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_ema.py deleted file mode 100644 index 88ea65a434e49775d40f2b08ce6df0f8d9929c18..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_ema.py +++ /dev/null @@ -1,199 +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 unittest -from copy import deepcopy -from dataclasses import dataclass -from typing import Optional - -import torch -from fairseq.models.ema import EMA - - -class DummyModule(torch.nn.Module): - def __init__(self) -> None: - """LightningModule for testing purposes - - Args: - epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum - validation loss for testing purposes (zero based). If None this is ignored. Defaults to None. - """ - super().__init__() - self.layer = torch.nn.Linear(in_features=32, out_features=2) - self.another_layer = torch.nn.Linear(in_features=2, out_features=2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.layer(x) - return self.another_layer(x) - - -@dataclass -class EMAConfig(object): - ema_decay: float = 0.99 - ema_start_update: int = 0 - ema_fp32: bool = False - ema_seed_model: Optional[str] = None - - -class TestEMAGPU(unittest.TestCase): - def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None): - diff = x.float() - y.float() - diff_norm = torch.norm(diff) - other_norm = torch.norm(y.float()) - - if msg is None: - msg = "|input - other| > {} + {} * |other|".format( - atol, rtol - ) - - self.assertLessEqual( - diff_norm, - atol + rtol * other_norm, - msg=msg, - ) - - def test_ema(self): - model = DummyModule() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig() - ema = EMA(model, config) - - # set decay - ema._set_decay(config.ema_decay) - self.assertEqual(ema.get_decay(), config.ema_decay) - - # get model - self.assertEqual(ema.get_model(), ema.model) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - # EMA step - x = torch.randn(32) - y = model(x) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - ema_state_dict = ema.get_model().state_dict() - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema_state_dict[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - self.assertTorchAllClose( - ema_param, - config.ema_decay * prev_param + (1 - config.ema_decay) * param, - ) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - # Load EMA into model - model2 = DummyModule() - ema.reverse(model2) - - for key, param in model2.state_dict().items(): - ema_param = ema_state_dict[key] - self.assertTrue( - torch.allclose(ema_param, param) - ) - - def test_ema_fp32(self): - model = DummyModule().half() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig(ema_fp32=True) - ema = EMA(model, config) - - x = torch.randn(32) - y = model(x.half()) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema.get_model().state_dict()[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - self.assertIn(key, ema.fp32_params) - - # EMA update is done in fp32, and hence the EMA param must be - # closer to the EMA update done in fp32 than in fp16. - self.assertLessEqual( - torch.norm( - ema_param.float() - - (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half().float() - ), - torch.norm( - ema_param.float() - - (config.ema_decay * prev_param + (1 - config.ema_decay) * param).float() - ), - ) - self.assertTorchAllClose( - ema_param, - (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half(), - ) - - def test_ema_fp16(self): - model = DummyModule().half() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig(ema_fp32=False) - ema = EMA(model, config) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - x = torch.randn(32) - y = model(x.half()) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema.get_model().state_dict()[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - - # EMA update is done in fp16, and hence the EMA param must be - # closer to the EMA update done in fp16 than in fp32. - self.assertLessEqual( - torch.norm( - ema_param.float() - - (config.ema_decay * prev_param + (1 - config.ema_decay) * param).float() - ), - torch.norm( - ema_param.float() - - (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half().float() - ), - ) - self.assertTorchAllClose( - ema_param, - config.ema_decay * prev_param + (1 - config.ema_decay) * param, - ) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/OFA-Sys/OFA-Image_Caption/models/sequence_generator.py b/spaces/OFA-Sys/OFA-Image_Caption/models/sequence_generator.py deleted file mode 100644 index 7afe0757e38603740f7c2186d5410f9346e6b568..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/models/sequence_generator.py +++ /dev/null @@ -1,1053 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Dict, List, Optional -import sys - -import torch -import torch.nn as nn -from fairseq import search, utils -from fairseq.models import FairseqIncrementalDecoder -from torch import Tensor -from fairseq.ngram_repeat_block import NGramRepeatBlock - -from data import data_utils - -class SequenceGenerator(nn.Module): - def __init__( - self, - models, - tgt_dict, - beam_size=1, - max_len_a=0, - max_len_b=200, - max_len=0, - min_len=1, - normalize_scores=True, - len_penalty=1.0, - unk_penalty=0.0, - temperature=1.0, - match_source_len=False, - no_repeat_ngram_size=0, - search_strategy=None, - eos=None, - symbols_to_strip_from_output=None, - lm_model=None, - lm_weight=1.0, - constraint_trie=None, - constraint_range=None, - gen_code=False, - gen_box=False, - ignore_eos=False, - zero_shot=False - ): - """Generates translations of a given source sentence. - - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models, - currently support fairseq.models.TransformerModel for scripting - beam_size (int, optional): beam width (default: 1) - max_len_a/b (int, optional): generate sequences of maximum length - ax + b, where x is the source length - max_len (int, optional): the maximum length of the generated output - (not including end-of-sentence) - min_len (int, optional): the minimum length of the generated output - (not including end-of-sentence) - normalize_scores (bool, optional): normalize scores by the length - of the output (default: True) - len_penalty (float, optional): length penalty, where <1.0 favors - shorter, >1.0 favors longer sentences (default: 1.0) - unk_penalty (float, optional): unknown word penalty, where <0 - produces more unks, >0 produces fewer (default: 0.0) - temperature (float, optional): temperature, where values - >1.0 produce more uniform samples and values <1.0 produce - sharper samples (default: 1.0) - match_source_len (bool, optional): outputs should match the source - length (default: False) - """ - super().__init__() - if isinstance(models, EnsembleModel): - self.model = models - else: - self.model = EnsembleModel(models) - self.gen_code = gen_code - self.gen_box = gen_box - self.ignore_eos = ignore_eos - self.tgt_dict = tgt_dict - self.pad = tgt_dict.pad() - self.unk = tgt_dict.unk() - self.bos = tgt_dict.bos() - self.eos = tgt_dict.eos() if eos is None else eos - self.symbols_to_strip_from_output = ( - symbols_to_strip_from_output.union({self.eos}) - if symbols_to_strip_from_output is not None - else {self.bos, self.eos} - ) - self.vocab_size = len(tgt_dict) - self.beam_size = beam_size - # the max beam size is the dictionary size - 1, since we never select pad - self.beam_size = min(beam_size, self.vocab_size - 1) - self.max_len_a = max_len_a - self.max_len_b = max_len_b - self.min_len = min_len - self.max_len = max_len or self.model.max_decoder_positions() - - self.normalize_scores = normalize_scores - self.len_penalty = len_penalty - self.unk_penalty = unk_penalty - self.temperature = temperature - self.match_source_len = match_source_len - self.zero_shot = zero_shot - - if no_repeat_ngram_size > 0: - self.repeat_ngram_blocker = NGramRepeatBlock(no_repeat_ngram_size) - else: - self.repeat_ngram_blocker = None - - assert temperature > 0, "--temperature must be greater than 0" - - self.search = ( - search.BeamSearch(tgt_dict) if search_strategy is None else search_strategy - ) - # We only need to set src_lengths in LengthConstrainedBeamSearch. - # As a module attribute, setting it would break in multithread - # settings when the model is shared. - self.should_set_src_lengths = ( - hasattr(self.search, "needs_src_lengths") and self.search.needs_src_lengths - ) - - self.model.eval() - - self.lm_model = lm_model - self.lm_weight = lm_weight - if self.lm_model is not None: - self.lm_model.eval() - - self.constraint_trie = constraint_trie - - self.constraint_start = None - self.constraint_end = None - if constraint_range is not None: - constraint_start, constraint_end = constraint_range.split(',') - self.constraint_start = int(constraint_start) - self.constraint_end = int(constraint_end) - - def cuda(self): - self.model.cuda() - return self - - @torch.no_grad() - def forward( - self, - sample: Dict[str, Dict[str, Tensor]], - prefix_tokens: Optional[Tensor] = None, - bos_token: Optional[int] = None, - ): - """Generate a batch of translations. - - Args: - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - bos_token (int, optional): beginning of sentence token - (default: self.eos) - """ - return self._generate(sample, prefix_tokens, bos_token=bos_token) - - # TODO(myleott): unused, deprecate after pytorch-translate migration - def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None): - """Iterate over a batched dataset and yield individual translations. - Args: - cuda (bool, optional): use GPU for generation - timer (StopwatchMeter, optional): time generations - """ - for sample in data_itr: - s = utils.move_to_cuda(sample) if cuda else sample - if "net_input" not in s: - continue - input = s["net_input"] - # model.forward normally channels prev_output_tokens into the decoder - # separately, but SequenceGenerator directly calls model.encoder - encoder_input = { - k: v for k, v in input.items() if k != "prev_output_tokens" - } - if timer is not None: - timer.start() - with torch.no_grad(): - hypos = self.generate(encoder_input) - if timer is not None: - timer.stop(sum(len(h[0]["tokens"]) for h in hypos)) - for i, id in enumerate(s["id"].data): - # remove padding - src = utils.strip_pad(input["src_tokens"].data[i, :], self.pad) - ref = ( - utils.strip_pad(s["target"].data[i, :], self.pad) - if s["target"] is not None - else None - ) - yield id, src, ref, hypos[i] - - @torch.no_grad() - def generate(self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs) -> List[List[Dict[str, Tensor]]]: - """Generate translations. Match the api of other fairseq generators. - - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - constraints (torch.LongTensor, optional): force decoder to include - the list of constraints - bos_token (int, optional): beginning of sentence token - (default: self.eos) - """ - return self._generate(models, sample, **kwargs) - - def _generate( - self, - models, - sample: Dict[str, Dict[str, Tensor]], - prefix_tokens: Optional[Tensor] = None, - constraints: Optional[Tensor] = None, - bos_token: Optional[int] = None, - ): - model = EnsembleModel(models) - incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(model.models_size) - ], - ) - net_input = sample["net_input"] - - if "src_tokens" in net_input: - src_tokens = net_input["src_tokens"] - # length of the source text being the character length except EndOfSentence and pad - src_lengths = ( - (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) - ) - elif "source" in net_input: - src_tokens = net_input["source"] - src_lengths = ( - net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) - if net_input["padding_mask"] is not None - else torch.tensor(src_tokens.size(-1)).to(src_tokens) - ) - elif "features" in net_input: - src_tokens = net_input["features"] - src_lengths = ( - net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) - if net_input["padding_mask"] is not None - else torch.tensor(src_tokens.size(-1)).to(src_tokens) - ) - else: - raise Exception("expected src_tokens or source in net input. input keys: " + str(net_input.keys())) - - # bsz: total number of sentences in beam - # Note that src_tokens may have more than 2 dimensions (i.e. audio features) - bsz, src_len = src_tokens.size()[:2] - beam_size = self.beam_size - - if constraints is not None and not self.search.supports_constraints: - raise NotImplementedError( - "Target-side constraints were provided, but search method doesn't support them" - ) - - # Initialize constraints, when active - self.search.init_constraints(constraints, beam_size) - - max_len: int = -1 - if self.match_source_len: - max_len = src_lengths.max().item() - else: - max_len = int(self.max_len_a * src_len + self.max_len_b) - assert ( - self.min_len <= max_len - ), "min_len cannot be larger than max_len, please adjust these!" - # compute the encoder output for each beam - with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"): - encoder_outs = model.forward_encoder(net_input) - - # placeholder of indices for bsz * beam_size to hold tokens and accumulative scores - new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) - new_order = new_order.to(src_tokens.device).long() - encoder_outs = model.reorder_encoder_out(encoder_outs, new_order) - # ensure encoder_outs is a List. - assert encoder_outs is not None - - # initialize buffers - scores = ( - torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() - ) # +1 for eos; pad is never chosen for scoring - tokens = ( - torch.zeros(bsz * beam_size, max_len + 2) - .to(src_tokens) - .long() - .fill_(self.pad) - ) # +2 for eos and pad - # tokens[:, 0] = self.eos if bos_token is None else bos_token - tokens[:, 0] = self.bos - attn: Optional[Tensor] = None - - # A list that indicates candidates that should be ignored. - # For example, suppose we're sampling and have already finalized 2/5 - # samples. Then cands_to_ignore would mark 2 positions as being ignored, - # so that we only finalize the remaining 3 samples. - cands_to_ignore = ( - torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) - ) # forward and backward-compatible False mask - - # list of completed sentences - finalized = torch.jit.annotate( - List[List[Dict[str, Tensor]]], - [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], - ) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step - - # a boolean array indicating if the sentence at the index is finished or not - finished = [False for i in range(bsz)] - num_remaining_sent = bsz # number of sentences remaining - - # number of candidate hypos per step - cand_size = 2 * beam_size # 2 x beam size in case half are EOS - - # offset arrays for converting between different indexing schemes - bbsz_offsets = ( - (torch.arange(0, bsz) * beam_size) - .unsqueeze(1) - .type_as(tokens) - .to(src_tokens.device) - ) - cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device) - - reorder_state: Optional[Tensor] = None - batch_idxs: Optional[Tensor] = None - - original_batch_idxs: Optional[Tensor] = None - if "id" in sample and isinstance(sample["id"], Tensor): - original_batch_idxs = sample["id"] - else: - original_batch_idxs = torch.arange(0, bsz).type_as(tokens) - - for step in range(max_len + 1): # one extra step for EOS marker - # reorder decoder internal states based on the prev choice of beams - if reorder_state is not None: - if batch_idxs is not None: - # update beam indices to take into account removed sentences - corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( - batch_idxs - ) - reorder_state.view(-1, beam_size).add_( - corr.unsqueeze(-1) * beam_size - ) - original_batch_idxs = original_batch_idxs[batch_idxs] - model.reorder_incremental_state(incremental_states, reorder_state) - encoder_outs = model.reorder_encoder_out( - encoder_outs, reorder_state - ) - with torch.autograd.profiler.record_function("EnsembleModel: forward_decoder"): - lprobs, avg_attn_scores = model.forward_decoder( - tokens[:, : step + 1], - encoder_outs, - incremental_states, - self.temperature, - constraint_trie=self.constraint_trie, - constraint_start=self.constraint_start, - constraint_end=self.constraint_end, - gen_code=self.gen_code, - zero_shot=self.zero_shot, - prefix_tokens=prefix_tokens - ) - - if self.lm_model is not None: - lm_out = self.lm_model(tokens[:, : step + 1]) - probs = self.lm_model.get_normalized_probs( - lm_out, log_probs=True, sample=None - ) - probs = probs[:, -1, :] * self.lm_weight - lprobs += probs - # handle prefix tokens (possibly with different lengths) - if ( - prefix_tokens is not None - and step < prefix_tokens.size(1) - and step < max_len - ): - lprobs, tokens, scores = self._prefix_tokens( - step, lprobs, scores, tokens, prefix_tokens, beam_size - ) - elif step < self.min_len: - # minimum length constraint (does not apply if using prefix_tokens) - lprobs[:, self.eos] = -math.inf - - lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) - - lprobs[:, self.pad] = -math.inf # never select pad - lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty - - if (self.gen_code or self.gen_box) and step < max_len: - lprobs[:, :4] = -math.inf - if self.gen_box: - lprobs[:, -1] = -math.inf - if (step + 1) % 5 == 0: - lprobs[:, self.constraint_start:59457] = -math.inf - else: - lprobs[:, 59457:] = -math.inf - - # handle max length constraint - if step >= max_len: - lprobs[:, : self.eos] = -math.inf - lprobs[:, self.eos + 1 :] = -math.inf - if self.ignore_eos: - lprobs[:, self.eos] = 1 - - # Record attention scores, only support avg_attn_scores is a Tensor - if avg_attn_scores is not None: - if attn is None: - attn = torch.empty( - bsz * beam_size, avg_attn_scores.size(1), max_len + 2 - ).to(scores) - attn[:, :, step + 1].copy_(avg_attn_scores) - - scores = scores.type_as(lprobs) - eos_bbsz_idx = torch.empty(0).to( - tokens - ) # indices of hypothesis ending with eos (finished sentences) - eos_scores = torch.empty(0).to( - scores - ) # scores of hypothesis ending with eos (finished sentences) - - if self.should_set_src_lengths: - self.search.set_src_lengths(src_lengths) - - if self.repeat_ngram_blocker is not None: - lprobs = self.repeat_ngram_blocker(tokens, lprobs, bsz, beam_size, step) - - # Shape: (batch, cand_size) - cand_scores, cand_indices, cand_beams = self.search.step( - step, - lprobs.view(bsz, -1, self.vocab_size), - scores.view(bsz, beam_size, -1)[:, :, :step], - tokens[:, : step + 1], - original_batch_idxs, - ) - - # cand_bbsz_idx contains beam indices for the top candidate - # hypotheses, with a range of values: [0, bsz*beam_size), - # and dimensions: [bsz, cand_size] - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - - # finalize hypotheses that end in eos - # Shape of eos_mask: (batch size, beam size) - eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) - eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask) - - # only consider eos when it's among the top beam_size indices - # Now we know what beam item(s) to finish - # Shape: 1d list of absolute-numbered - eos_bbsz_idx = torch.masked_select( - cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents: List[int] = [] - if eos_bbsz_idx.numel() > 0: - eos_scores = torch.masked_select( - cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents = self.finalize_hypos( - step, - eos_bbsz_idx, - eos_scores, - tokens, - scores, - finalized, - finished, - beam_size, - attn, - src_lengths, - max_len, - ) - num_remaining_sent -= len(finalized_sents) - - assert num_remaining_sent >= 0 - if num_remaining_sent == 0: - break - if self.search.stop_on_max_len and step >= max_len: - break - assert step < max_len, f"{step} < {max_len}" - - # Remove finalized sentences (ones for which {beam_size} - # finished hypotheses have been generated) from the batch. - if len(finalized_sents) > 0: - new_bsz = bsz - len(finalized_sents) - - # construct batch_idxs which holds indices of batches to keep for the next pass - batch_mask = torch.ones( - bsz, dtype=torch.bool, device=cand_indices.device - ) - batch_mask[finalized_sents] = False - # TODO replace `nonzero(as_tuple=False)` after TorchScript supports it - batch_idxs = torch.arange( - bsz, device=cand_indices.device - ).masked_select(batch_mask) - - # Choose the subset of the hypothesized constraints that will continue - self.search.prune_sentences(batch_idxs) - - eos_mask = eos_mask[batch_idxs] - cand_beams = cand_beams[batch_idxs] - bbsz_offsets.resize_(new_bsz, 1) - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - cand_scores = cand_scores[batch_idxs] - cand_indices = cand_indices[batch_idxs] - - if prefix_tokens is not None: - prefix_tokens = prefix_tokens[batch_idxs] - src_lengths = src_lengths[batch_idxs] - cands_to_ignore = cands_to_ignore[batch_idxs] - - scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - if attn is not None: - attn = attn.view(bsz, -1)[batch_idxs].view( - new_bsz * beam_size, attn.size(1), -1 - ) - bsz = new_bsz - else: - batch_idxs = None - - # Set active_mask so that values > cand_size indicate eos hypos - # and values < cand_size indicate candidate active hypos. - # After, the min values per row are the top candidate active hypos - - # Rewrite the operator since the element wise or is not supported in torchscript. - - eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size])) - active_mask = torch.add( - eos_mask.type_as(cand_offsets) * cand_size, - cand_offsets[: eos_mask.size(1)], - ) - - # get the top beam_size active hypotheses, which are just - # the hypos with the smallest values in active_mask. - # {active_hypos} indicates which {beam_size} hypotheses - # from the list of {2 * beam_size} candidates were - # selected. Shapes: (batch size, beam size) - new_cands_to_ignore, active_hypos = torch.topk( - active_mask, k=beam_size, dim=1, largest=False - ) - - # update cands_to_ignore to ignore any finalized hypos. - cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] - # Make sure there is at least one active item for each sentence in the batch. - assert (~cands_to_ignore).any(dim=1).all() - - # update cands_to_ignore to ignore any finalized hypos - - # {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam - # can be selected more than once). - active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) - active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) - - active_bbsz_idx = active_bbsz_idx.view(-1) - active_scores = active_scores.view(-1) - - # copy tokens and scores for active hypotheses - - # Set the tokens for each beam (can select the same row more than once) - tokens[:, : step + 1] = torch.index_select( - tokens[:, : step + 1], dim=0, index=active_bbsz_idx - ) - # Select the next token for each of them - tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( - cand_indices, dim=1, index=active_hypos - ) - if step > 0: - scores[:, :step] = torch.index_select( - scores[:, :step], dim=0, index=active_bbsz_idx - ) - scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather( - cand_scores, dim=1, index=active_hypos - ) - - # Update constraints based on which candidates were selected for the next beam - self.search.update_constraints(active_hypos) - - # copy attention for active hypotheses - if attn is not None: - attn[:, :, : step + 2] = torch.index_select( - attn[:, :, : step + 2], dim=0, index=active_bbsz_idx - ) - - # reorder incremental state in decoder - reorder_state = active_bbsz_idx - - # sort by score descending - for sent in range(len(finalized)): - scores = torch.tensor( - [float(elem["score"].item()) for elem in finalized[sent]] - ) - _, sorted_scores_indices = torch.sort(scores, descending=True) - finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices] - finalized[sent] = torch.jit.annotate( - List[Dict[str, Tensor]], finalized[sent] - ) - return finalized - - def _prefix_tokens( - self, step: int, lprobs, scores, tokens, prefix_tokens, beam_size: int - ): - """Handle prefix tokens""" - prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) - prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - prefix_mask = prefix_toks.ne(self.pad) - if self.constraint_trie is None: - lprobs[prefix_mask] = torch.min(prefix_lprobs) - 1 - else: - lprobs[prefix_mask] = -math.inf - lprobs[prefix_mask] = lprobs[prefix_mask].scatter( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask] - ) - # if prefix includes eos, then we should make sure tokens and - # scores are the same across all beams - eos_mask = prefix_toks.eq(self.eos) - if eos_mask.any(): - # validate that the first beam matches the prefix - first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[ - :, 0, 1 : step + 1 - ] - eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] - target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] - assert (first_beam == target_prefix).all() - - # copy tokens, scores and lprobs from the first beam to all beams - tokens = self.replicate_first_beam(tokens, eos_mask_batch_dim, beam_size) - scores = self.replicate_first_beam(scores, eos_mask_batch_dim, beam_size) - lprobs = self.replicate_first_beam(lprobs, eos_mask_batch_dim, beam_size) - return lprobs, tokens, scores - - def replicate_first_beam(self, tensor, mask, beam_size: int): - tensor = tensor.view(-1, beam_size, tensor.size(-1)) - tensor[mask] = tensor[mask][:, :1, :] - return tensor.view(-1, tensor.size(-1)) - - def finalize_hypos( - self, - step: int, - bbsz_idx, - eos_scores, - tokens, - scores, - finalized: List[List[Dict[str, Tensor]]], - finished: List[bool], - beam_size: int, - attn: Optional[Tensor], - src_lengths, - max_len: int, - ): - """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. - A sentence is finalized when {beam_size} finished items have been collected for it. - - Returns number of sentences (not beam items) being finalized. - These will be removed from the batch and not processed further. - Args: - bbsz_idx (Tensor): - """ - assert bbsz_idx.numel() == eos_scores.numel() - - # clone relevant token and attention tensors. - # tokens is (batch * beam, max_len). So the index_select - # gets the newly EOS rows, then selects cols 1..{step + 2} - tokens_clone = tokens.index_select(0, bbsz_idx)[ - :, 1 : step + 2 - ] # skip the first index, which is EOS - - tokens_clone[:, step] = self.eos - attn_clone = ( - attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2] - if attn is not None - else None - ) - - # compute scores per token position - pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1] - pos_scores[:, step] = eos_scores - # convert from cumulative to per-position scores - pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] - - # normalize sentence-level scores - if self.normalize_scores: - eos_scores /= (step + 1) ** self.len_penalty - - # cum_unfin records which sentences in the batch are finished. - # It helps match indexing between (a) the original sentences - # in the batch and (b) the current, possibly-reduced set of - # sentences. - cum_unfin: List[int] = [] - prev = 0 - for f in finished: - if f: - prev += 1 - else: - cum_unfin.append(prev) - cum_fin_tensor = torch.tensor(cum_unfin, dtype=torch.int).to(bbsz_idx) - - unfin_idx = bbsz_idx // beam_size - sent = unfin_idx + torch.index_select(cum_fin_tensor, 0, unfin_idx) - - # Create a set of "{sent}{unfin_idx}", where - # "unfin_idx" is the index in the current (possibly reduced) - # list of sentences, and "sent" is the index in the original, - # unreduced batch - # For every finished beam item - # sentence index in the current (possibly reduced) batch - seen = (sent << 32) + unfin_idx - unique_seen: List[int] = torch.unique(seen).tolist() - - if self.match_source_len: - condition = step > torch.index_select(src_lengths, 0, unfin_idx) - eos_scores = torch.where(condition, torch.tensor(-math.inf), eos_scores) - sent_list: List[int] = sent.tolist() - for i in range(bbsz_idx.size()[0]): - # An input sentence (among those in a batch) is finished when - # beam_size hypotheses have been collected for it - if len(finalized[sent_list[i]]) < beam_size: - if attn_clone is not None: - # remove padding tokens from attn scores - hypo_attn = attn_clone[i] - else: - hypo_attn = torch.empty(0) - - finalized[sent_list[i]].append( - { - "tokens": tokens_clone[i], - "score": eos_scores[i], - "attention": hypo_attn, # src_len x tgt_len - "alignment": torch.empty(0), - "positional_scores": pos_scores[i], - } - ) - - newly_finished: List[int] = [] - for unique_s in unique_seen: - # check termination conditions for this sentence - unique_sent: int = unique_s >> 32 - unique_unfin_idx: int = unique_s - (unique_sent << 32) - - if not finished[unique_sent] and self.is_finished( - step, unique_unfin_idx, max_len, len(finalized[unique_sent]), beam_size - ): - finished[unique_sent] = True - newly_finished.append(unique_unfin_idx) - - return newly_finished - - def is_finished( - self, - step: int, - unfin_idx: int, - max_len: int, - finalized_sent_len: int, - beam_size: int, - ): - """ - Check whether decoding for a sentence is finished, which - occurs when the list of finalized sentences has reached the - beam size, or when we reach the maximum length. - """ - assert finalized_sent_len <= beam_size - if finalized_sent_len == beam_size or step == max_len: - return True - return False - - -class EnsembleModel(nn.Module): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__() - self.models_size = len(models) - # method '__len__' is not supported in ModuleList for torch script - self.single_model = models[0] - self.models = nn.ModuleList(models) - - self.has_incremental: bool = False - if all( - hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder) - for m in models - ): - self.has_incremental = True - - def forward(self): - pass - - def has_encoder(self): - return hasattr(self.single_model, "encoder") - - def has_incremental_states(self): - return self.has_incremental - - def max_decoder_positions(self): - return min([m.max_decoder_positions() for m in self.models if hasattr(m, "max_decoder_positions")] + [sys.maxsize]) - - @torch.jit.export - def forward_encoder(self, net_input: Dict[str, Tensor]): - if not self.has_encoder(): - return None - return [model.encoder.forward_torchscript(net_input) for model in self.models] - - @torch.jit.export - def forward_decoder( - self, - tokens, - encoder_outs: List[Dict[str, List[Tensor]]], - incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], - temperature: float = 1.0, - constraint_trie=None, - constraint_start=None, - constraint_end=None, - gen_code=False, - zero_shot=False, - prefix_tokens=None - ): - log_probs = [] - avg_attn: Optional[Tensor] = None - encoder_out: Optional[Dict[str, List[Tensor]]] = None - code_mask = (tokens.new_ones(tokens.size(0))*gen_code).bool() - for i, model in enumerate(self.models): - if self.has_encoder(): - encoder_out = encoder_outs[i] - # decode each model - if self.has_incremental_states(): - decoder_out = model.decoder.forward( - tokens, - code_masks=code_mask, - encoder_out=encoder_out, - incremental_state=incremental_states[i], - ) - else: - if hasattr(model, "decoder"): - decoder_out = model.decoder.forward(tokens, code_masks=code_mask, encoder_out=encoder_out) - else: - decoder_out = model.forward(tokens) - - attn: Optional[Tensor] = None - decoder_len = len(decoder_out) - if decoder_len > 1 and decoder_out[1] is not None: - if isinstance(decoder_out[1], Tensor): - attn = decoder_out[1] - else: - attn_holder = decoder_out[1]["attn"] - if isinstance(attn_holder, Tensor): - attn = attn_holder - elif attn_holder is not None: - attn = attn_holder[0] - if attn is not None: - attn = attn[:, -1, :] - - decoder_out_tuple = ( - decoder_out[0][:, -1:, :].div_(temperature), - None if decoder_len <= 1 else decoder_out[1], - ) - - beam_size = decoder_out_tuple[0].size(0) // prefix_tokens.size(0) if prefix_tokens is not None else 0 - if constraint_trie is not None and not zero_shot: - assert constraint_start is None and constraint_end is None - constraint_masks = decoder_out_tuple[0].new_zeros(decoder_out_tuple[0].size()).bool() - constraint_prefix_tokens = tokens.tolist() - for token_index, constraint_prefix_token in enumerate(constraint_prefix_tokens): - prefix_len = prefix_tokens[token_index // beam_size].ne(1).sum().item() if prefix_tokens is not None else 0 - if len(constraint_prefix_token) > prefix_len: - constraint_prefix_token = [0] + constraint_prefix_token[prefix_len+1:] - constraint_nodes = constraint_trie.get_next_layer(constraint_prefix_token) - constraint_masks[token_index][:, constraint_nodes] = True - else: - constraint_masks[token_index] = True - decoder_out_tuple[0].masked_fill_(~constraint_masks, -math.inf) - if constraint_start is not None and constraint_end is not None and not zero_shot: - assert constraint_trie is None - decoder_out_tuple[0][:, :, 4:constraint_start] = -math.inf - decoder_out_tuple[0][:, :, constraint_end:] = -math.inf - - probs = model.get_normalized_probs( - decoder_out_tuple, log_probs=True, sample=None - ) - if constraint_trie is not None and zero_shot: - assert constraint_start is None and constraint_end is None - constraint_masks = decoder_out_tuple[0].new_zeros(decoder_out_tuple[0].size()).bool() - constraint_prefix_tokens = tokens.tolist() - for token_index, constraint_prefix_token in enumerate(constraint_prefix_tokens): - constraint_nodes = constraint_trie.get_next_layer(constraint_prefix_token) - constraint_masks[token_index][:, constraint_nodes] = True - probs.masked_fill_(~constraint_masks, -math.inf) - if constraint_start is not None and constraint_end is not None and zero_shot: - assert constraint_trie is None - probs[:, :, 4:constraint_start] = -math.inf - probs[:, :, constraint_end:] = -math.inf - probs = probs[:, -1, :] - if self.models_size == 1: - return probs, attn - - log_probs.append(probs) - if attn is not None: - if avg_attn is None: - avg_attn = attn - else: - avg_attn.add_(attn) - - avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( - self.models_size - ) - - if avg_attn is not None: - avg_attn.div_(self.models_size) - return avg_probs, avg_attn - - @torch.jit.export - def reorder_encoder_out( - self, encoder_outs: Optional[List[Dict[str, List[Tensor]]]], new_order - ): - """ - Reorder encoder output according to *new_order*. - - Args: - encoder_out: output from the ``forward()`` method - new_order (LongTensor): desired order - - Returns: - *encoder_out* rearranged according to *new_order* - """ - new_outs: List[Dict[str, List[Tensor]]] = [] - if not self.has_encoder(): - return new_outs - for i, model in enumerate(self.models): - assert encoder_outs is not None - new_outs.append( - model.encoder.reorder_encoder_out(encoder_outs[i], new_order) - ) - return new_outs - - @torch.jit.export - def reorder_incremental_state( - self, - incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], - new_order, - ): - if not self.has_incremental_states(): - return - for i, model in enumerate(self.models): - model.decoder.reorder_incremental_state_scripting( - incremental_states[i], new_order - ) - - -class SequenceGeneratorWithAlignment(SequenceGenerator): - def __init__( - self, models, tgt_dict, left_pad_target=False, print_alignment="hard", **kwargs - ): - """Generates translations of a given source sentence. - - Produces alignments following "Jointly Learning to Align and - Translate with Transformer Models" (Garg et al., EMNLP 2019). - - Args: - left_pad_target (bool, optional): Whether or not the - hypothesis should be left padded or not when they are - teacher forced for generating alignments. - """ - super().__init__(EnsembleModelWithAlignment(models), tgt_dict, **kwargs) - self.left_pad_target = left_pad_target - - if print_alignment == "hard": - self.extract_alignment = utils.extract_hard_alignment - elif print_alignment == "soft": - self.extract_alignment = utils.extract_soft_alignment - - @torch.no_grad() - def generate(self, models, sample, **kwargs): - finalized = super()._generate(sample, **kwargs) - - src_tokens = sample["net_input"]["src_tokens"] - bsz = src_tokens.shape[0] - beam_size = self.beam_size - ( - src_tokens, - src_lengths, - prev_output_tokens, - tgt_tokens, - ) = self._prepare_batch_for_alignment(sample, finalized) - if any(getattr(m, "full_context_alignment", False) for m in self.model.models): - attn = self.model.forward_align(src_tokens, src_lengths, prev_output_tokens) - else: - attn = [ - finalized[i // beam_size][i % beam_size]["attention"].transpose(1, 0) - for i in range(bsz * beam_size) - ] - - if src_tokens.device != "cpu": - src_tokens = src_tokens.to("cpu") - tgt_tokens = tgt_tokens.to("cpu") - attn = [i.to("cpu") for i in attn] - - # Process the attn matrix to extract hard alignments. - for i in range(bsz * beam_size): - alignment = self.extract_alignment( - attn[i], src_tokens[i], tgt_tokens[i], self.pad, self.eos - ) - finalized[i // beam_size][i % beam_size]["alignment"] = alignment - return finalized - - def _prepare_batch_for_alignment(self, sample, hypothesis): - src_tokens = sample["net_input"]["src_tokens"] - bsz = src_tokens.shape[0] - src_tokens = ( - src_tokens[:, None, :] - .expand(-1, self.beam_size, -1) - .contiguous() - .view(bsz * self.beam_size, -1) - ) - src_lengths = sample["net_input"]["src_lengths"] - src_lengths = ( - src_lengths[:, None] - .expand(-1, self.beam_size) - .contiguous() - .view(bsz * self.beam_size) - ) - prev_output_tokens = data_utils.collate_tokens( - [beam["tokens"] for example in hypothesis for beam in example], - self.pad, - self.eos, - self.left_pad_target, - move_eos_to_beginning=True, - ) - tgt_tokens = data_utils.collate_tokens( - [beam["tokens"] for example in hypothesis for beam in example], - self.pad, - self.eos, - self.left_pad_target, - move_eos_to_beginning=False, - ) - return src_tokens, src_lengths, prev_output_tokens, tgt_tokens - - -class EnsembleModelWithAlignment(EnsembleModel): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__(models) - - def forward_align(self, src_tokens, src_lengths, prev_output_tokens): - avg_attn = None - for model in self.models: - decoder_out = model(src_tokens, src_lengths, prev_output_tokens) - attn = decoder_out[1]["attn"][0] - if avg_attn is None: - avg_attn = attn - else: - avg_attn.add_(attn) - if len(self.models) > 1: - avg_attn.div_(len(self.models)) - return avg_attn diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py deleted file mode 100644 index b41bfbe38789ba14e6a5ea938c75d761424c00ab..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py +++ /dev/null @@ -1,92 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -import argparse -import glob - -import numpy as np - - -DIM = 1024 - - -def compute_dist(source_embs, target_embs, k=5, return_sim_mat=False): - target_ids = [tid for tid in target_embs] - source_mat = np.stack(source_embs.values(), axis=0) - normalized_source_mat = source_mat / np.linalg.norm( - source_mat, axis=1, keepdims=True - ) - target_mat = np.stack(target_embs.values(), axis=0) - normalized_target_mat = target_mat / np.linalg.norm( - target_mat, axis=1, keepdims=True - ) - sim_mat = normalized_source_mat.dot(normalized_target_mat.T) - if return_sim_mat: - return sim_mat - neighbors_map = {} - for i, sentence_id in enumerate(source_embs): - idx = np.argsort(sim_mat[i, :])[::-1][:k] - neighbors_map[sentence_id] = [target_ids[tid] for tid in idx] - return neighbors_map - - -def load_embeddings(directory, LANGS): - sentence_embeddings = {} - sentence_texts = {} - for lang in LANGS: - sentence_embeddings[lang] = {} - sentence_texts[lang] = {} - lang_dir = f"{directory}/{lang}" - embedding_files = glob.glob(f"{lang_dir}/all_avg_pool.{lang}.*") - for embed_file in embedding_files: - shard_id = embed_file.split(".")[-1] - embeddings = np.fromfile(embed_file, dtype=np.float32) - num_rows = embeddings.shape[0] // DIM - embeddings = embeddings.reshape((num_rows, DIM)) - - with open(f"{lang_dir}/sentences.{lang}.{shard_id}") as sentence_file: - for idx, line in enumerate(sentence_file): - sentence_id, sentence = line.strip().split("\t") - sentence_texts[lang][sentence_id] = sentence - sentence_embeddings[lang][sentence_id] = embeddings[idx, :] - - return sentence_embeddings, sentence_texts - - -def compute_accuracy(directory, LANGS): - sentence_embeddings, sentence_texts = load_embeddings(directory, LANGS) - - top_1_accuracy = {} - - top1_str = " ".join(LANGS) + "\n" - for source_lang in LANGS: - top_1_accuracy[source_lang] = {} - top1_str += f"{source_lang} " - for target_lang in LANGS: - top1 = 0 - top5 = 0 - neighbors_map = compute_dist( - sentence_embeddings[source_lang], sentence_embeddings[target_lang] - ) - for sentence_id, neighbors in neighbors_map.items(): - if sentence_id == neighbors[0]: - top1 += 1 - if sentence_id in neighbors[:5]: - top5 += 1 - n = len(sentence_embeddings[target_lang]) - top1_str += f"{top1/n} " - top1_str += "\n" - - print(top1_str) - print(top1_str, file=open(f"{directory}/accuracy", "w")) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Analyze encoder outputs") - parser.add_argument("directory", help="Source language corpus") - parser.add_argument("--langs", help="List of langs") - args = parser.parse_args() - langs = args.langs.split(",") - compute_accuracy(args.directory, langs) diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/modules/transformer_layer.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/modules/transformer_layer.py deleted file mode 100644 index 7ab53c6e5f12f15562717effb86ab8cb8d6b4fa3..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/modules/transformer_layer.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq.model_parallel.modules import ModelParallelMultiheadAttention -from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer - - -try: - from fairseq.model_parallel.megatron.mpu import ( - ColumnParallelLinear, - RowParallelLinear, - ) - - has_megatron_submodule = True -except (ImportError, ModuleNotFoundError): - has_megatron_submodule = False - - -class ModelParallelTransformerEncoderLayer(TransformerEncoderLayer): - """Encoder layer block over multiple gpus. - - See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. - """ - - def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): - if q_noise > 0: - raise NotImplementedError - return ColumnParallelLinear(input_dim, output_dim, gather_output=False) - - def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): - if q_noise > 0: - raise NotImplementedError - return RowParallelLinear(input_dim, output_dim, input_is_parallel=True) - - def build_self_attention(self, embed_dim, args, **unused_kwargs): - return ModelParallelMultiheadAttention( - embed_dim, - args.encoder_attention_heads, - dropout=args.attention_dropout, - self_attention=True, - ) - - -class ModelParallelTransformerDecoderLayer(TransformerDecoderLayer): - """Decoder layer block. - - See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. - """ - - def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): - if q_noise > 0: - raise NotImplementedError - return ColumnParallelLinear(input_dim, output_dim, gather_output=False) - - def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): - if q_noise > 0: - raise NotImplementedError - return RowParallelLinear(input_dim, output_dim, input_is_parallel=True) - - def build_self_attention(self, embed_dim, args, **unused_kwargs): - return ModelParallelMultiheadAttention( - embed_dim=embed_dim, - num_heads=args.decoder_attention_heads, - dropout=args.attention_dropout, - self_attention=not getattr(args, "cross_self_attention", False), - ) - - def build_encoder_attention(self, embed_dim, args, **unused_kwargs): - return ModelParallelMultiheadAttention( - embed_dim=embed_dim, - num_heads=args.decoder_attention_heads, - kdim=getattr(args, "encoder_embed_dim", None), - vdim=getattr(args, "encoder_embed_dim", None), - dropout=args.attention_dropout, - encoder_decoder_attention=True, - ) diff --git a/spaces/OlaWod/FreeVC/speaker_encoder/params_data.py b/spaces/OlaWod/FreeVC/speaker_encoder/params_data.py deleted file mode 100644 index bdb1716ed45617f2b127a7fb8885afe6cc74fb71..0000000000000000000000000000000000000000 --- a/spaces/OlaWod/FreeVC/speaker_encoder/params_data.py +++ /dev/null @@ -1,29 +0,0 @@ - -## Mel-filterbank -mel_window_length = 25 # In milliseconds -mel_window_step = 10 # In milliseconds -mel_n_channels = 40 - - -## Audio -sampling_rate = 16000 -# Number of spectrogram frames in a partial utterance -partials_n_frames = 160 # 1600 ms -# Number of spectrogram frames at inference -inference_n_frames = 80 # 800 ms - - -## Voice Activation Detection -# Window size of the VAD. Must be either 10, 20 or 30 milliseconds. -# This sets the granularity of the VAD. Should not need to be changed. -vad_window_length = 30 # In milliseconds -# Number of frames to average together when performing the moving average smoothing. -# The larger this value, the larger the VAD variations must be to not get smoothed out. -vad_moving_average_width = 8 -# Maximum number of consecutive silent frames a segment can have. -vad_max_silence_length = 6 - - -## Audio volume normalization -audio_norm_target_dBFS = -30 - diff --git a/spaces/Omnibus/2-button-Story-Board/README.md b/spaces/Omnibus/2-button-Story-Board/README.md deleted file mode 100644 index 84bd079a6c839b3fe58cfc4e45efb054e8b6dca1..0000000000000000000000000000000000000000 --- a/spaces/Omnibus/2-button-Story-Board/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: 2 Button Story Book -emoji: 🌖 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.16.1 -app_file: app.py -duplicated_from: Omnibus/2-button-Story-Book ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/testing.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/testing.py deleted file mode 100644 index 161fa6b80845ecabb6f71f28aa3333c3178c8756..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/testing.py +++ /dev/null @@ -1,137 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import io -import numpy as np -import torch - -from detectron2 import model_zoo -from detectron2.data import DatasetCatalog -from detectron2.data.detection_utils import read_image -from detectron2.modeling import build_model -from detectron2.structures import Boxes, Instances, ROIMasks -from detectron2.utils.file_io import PathManager - - -""" -Internal utilities for tests. Don't use except for writing tests. -""" - - -def get_model_no_weights(config_path): - """ - Like model_zoo.get, but do not load any weights (even pretrained) - """ - cfg = model_zoo.get_config(config_path) - if not torch.cuda.is_available(): - cfg.MODEL.DEVICE = "cpu" - return build_model(cfg) - - -def random_boxes(num_boxes, max_coord=100, device="cpu"): - """ - Create a random Nx4 boxes tensor, with coordinates < max_coord. - """ - boxes = torch.rand(num_boxes, 4, device=device) * (max_coord * 0.5) - boxes.clamp_(min=1.0) # tiny boxes cause numerical instability in box regression - # Note: the implementation of this function in torchvision is: - # boxes[:, 2:] += torch.rand(N, 2) * 100 - # but it does not guarantee non-negative widths/heights constraints: - # boxes[:, 2] >= boxes[:, 0] and boxes[:, 3] >= boxes[:, 1]: - boxes[:, 2:] += boxes[:, :2] - return boxes - - -def get_sample_coco_image(tensor=True): - """ - Args: - tensor (bool): if True, returns 3xHxW tensor. - else, returns a HxWx3 numpy array. - - Returns: - an image, in BGR color. - """ - try: - file_name = DatasetCatalog.get("coco_2017_val_100")[0]["file_name"] - if not PathManager.exists(file_name): - raise FileNotFoundError() - except IOError: - # for public CI to run - file_name = PathManager.get_local_path( - "http://images.cocodataset.org/train2017/000000000009.jpg" - ) - ret = read_image(file_name, format="BGR") - if tensor: - ret = torch.from_numpy(np.ascontiguousarray(ret.transpose(2, 0, 1))) - return ret - - -def convert_scripted_instances(instances): - """ - Convert a scripted Instances object to a regular :class:`Instances` object - """ - assert hasattr( - instances, "image_size" - ), f"Expect an Instances object, but got {type(instances)}!" - ret = Instances(instances.image_size) - for name in instances._field_names: - val = getattr(instances, "_" + name, None) - if val is not None: - ret.set(name, val) - return ret - - -def assert_instances_allclose(input, other, *, rtol=1e-5, msg="", size_as_tensor=False): - """ - Args: - input, other (Instances): - size_as_tensor: compare image_size of the Instances as tensors (instead of tuples). - Useful for comparing outputs of tracing. - """ - if not isinstance(input, Instances): - input = convert_scripted_instances(input) - if not isinstance(other, Instances): - other = convert_scripted_instances(other) - - if not msg: - msg = "Two Instances are different! " - else: - msg = msg.rstrip() + " " - - size_error_msg = msg + f"image_size is {input.image_size} vs. {other.image_size}!" - if size_as_tensor: - assert torch.equal( - torch.tensor(input.image_size), torch.tensor(other.image_size) - ), size_error_msg - else: - assert input.image_size == other.image_size, size_error_msg - fields = sorted(input.get_fields().keys()) - fields_other = sorted(other.get_fields().keys()) - assert fields == fields_other, msg + f"Fields are {fields} vs {fields_other}!" - - for f in fields: - val1, val2 = input.get(f), other.get(f) - if isinstance(val1, (Boxes, ROIMasks)): - # boxes in the range of O(100) and can have a larger tolerance - assert torch.allclose(val1.tensor, val2.tensor, atol=100 * rtol), ( - msg + f"Field {f} differs too much!" - ) - elif isinstance(val1, torch.Tensor): - if val1.dtype.is_floating_point: - mag = torch.abs(val1).max().cpu().item() - assert torch.allclose(val1, val2, atol=mag * rtol), ( - msg + f"Field {f} differs too much!" - ) - else: - assert torch.equal(val1, val2), msg + f"Field {f} is different!" - else: - raise ValueError(f"Don't know how to compare type {type(val1)}") - - -def reload_script_model(module): - """ - Save a jit module and load it back. - Similar to the `getExportImportCopy` function in torch/testing/ - """ - buffer = io.BytesIO() - torch.jit.save(module, buffer) - buffer.seek(0) - return torch.jit.load(buffer) diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/point_sample.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/point_sample.py deleted file mode 100644 index 267f4b3c56630acd85f9bdc630b7be09abab0aba..0000000000000000000000000000000000000000 --- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/point_sample.py +++ /dev/null @@ -1,336 +0,0 @@ -# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa - -from os import path as osp - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.modules.utils import _pair -from torch.onnx.operators import shape_as_tensor - - -def bilinear_grid_sample(im, grid, align_corners=False): - """Given an input and a flow-field grid, computes the output using input - values and pixel locations from grid. Supported only bilinear interpolation - method to sample the input pixels. - - Args: - im (torch.Tensor): Input feature map, shape (N, C, H, W) - grid (torch.Tensor): Point coordinates, shape (N, Hg, Wg, 2) - align_corners {bool}: If set to True, the extrema (-1 and 1) are - considered as referring to the center points of the input’s - corner pixels. If set to False, they are instead considered as - referring to the corner points of the input’s corner pixels, - making the sampling more resolution agnostic. - Returns: - torch.Tensor: A tensor with sampled points, shape (N, C, Hg, Wg) - """ - n, c, h, w = im.shape - gn, gh, gw, _ = grid.shape - assert n == gn - - x = grid[:, :, :, 0] - y = grid[:, :, :, 1] - - if align_corners: - x = ((x + 1) / 2) * (w - 1) - y = ((y + 1) / 2) * (h - 1) - else: - x = ((x + 1) * w - 1) / 2 - y = ((y + 1) * h - 1) / 2 - - x = x.view(n, -1) - y = y.view(n, -1) - - x0 = torch.floor(x).long() - y0 = torch.floor(y).long() - x1 = x0 + 1 - y1 = y0 + 1 - - wa = ((x1 - x) * (y1 - y)).unsqueeze(1) - wb = ((x1 - x) * (y - y0)).unsqueeze(1) - wc = ((x - x0) * (y1 - y)).unsqueeze(1) - wd = ((x - x0) * (y - y0)).unsqueeze(1) - - # Apply default for grid_sample function zero padding - im_padded = F.pad(im, pad=[1, 1, 1, 1], mode='constant', value=0) - padded_h = h + 2 - padded_w = w + 2 - # save points positions after padding - x0, x1, y0, y1 = x0 + 1, x1 + 1, y0 + 1, y1 + 1 - - # Clip coordinates to padded image size - x0 = torch.where(x0 < 0, torch.tensor(0), x0) - x0 = torch.where(x0 > padded_w - 1, torch.tensor(padded_w - 1), x0) - x1 = torch.where(x1 < 0, torch.tensor(0), x1) - x1 = torch.where(x1 > padded_w - 1, torch.tensor(padded_w - 1), x1) - y0 = torch.where(y0 < 0, torch.tensor(0), y0) - y0 = torch.where(y0 > padded_h - 1, torch.tensor(padded_h - 1), y0) - y1 = torch.where(y1 < 0, torch.tensor(0), y1) - y1 = torch.where(y1 > padded_h - 1, torch.tensor(padded_h - 1), y1) - - im_padded = im_padded.view(n, c, -1) - - x0_y0 = (x0 + y0 * padded_w).unsqueeze(1).expand(-1, c, -1) - x0_y1 = (x0 + y1 * padded_w).unsqueeze(1).expand(-1, c, -1) - x1_y0 = (x1 + y0 * padded_w).unsqueeze(1).expand(-1, c, -1) - x1_y1 = (x1 + y1 * padded_w).unsqueeze(1).expand(-1, c, -1) - - Ia = torch.gather(im_padded, 2, x0_y0) - Ib = torch.gather(im_padded, 2, x0_y1) - Ic = torch.gather(im_padded, 2, x1_y0) - Id = torch.gather(im_padded, 2, x1_y1) - - return (Ia * wa + Ib * wb + Ic * wc + Id * wd).reshape(n, c, gh, gw) - - -def is_in_onnx_export_without_custom_ops(): - from annotator.uniformer.mmcv.ops import get_onnxruntime_op_path - ort_custom_op_path = get_onnxruntime_op_path() - return torch.onnx.is_in_onnx_export( - ) and not osp.exists(ort_custom_op_path) - - -def normalize(grid): - """Normalize input grid from [-1, 1] to [0, 1] - Args: - grid (Tensor): The grid to be normalize, range [-1, 1]. - Returns: - Tensor: Normalized grid, range [0, 1]. - """ - - return (grid + 1.0) / 2.0 - - -def denormalize(grid): - """Denormalize input grid from range [0, 1] to [-1, 1] - Args: - grid (Tensor): The grid to be denormalize, range [0, 1]. - Returns: - Tensor: Denormalized grid, range [-1, 1]. - """ - - return grid * 2.0 - 1.0 - - -def generate_grid(num_grid, size, device): - """Generate regular square grid of points in [0, 1] x [0, 1] coordinate - space. - - Args: - num_grid (int): The number of grids to sample, one for each region. - size (tuple(int, int)): The side size of the regular grid. - device (torch.device): Desired device of returned tensor. - - Returns: - (torch.Tensor): A tensor of shape (num_grid, size[0]*size[1], 2) that - contains coordinates for the regular grids. - """ - - affine_trans = torch.tensor([[[1., 0., 0.], [0., 1., 0.]]], device=device) - grid = F.affine_grid( - affine_trans, torch.Size((1, 1, *size)), align_corners=False) - grid = normalize(grid) - return grid.view(1, -1, 2).expand(num_grid, -1, -1) - - -def rel_roi_point_to_abs_img_point(rois, rel_roi_points): - """Convert roi based relative point coordinates to image based absolute - point coordinates. - - Args: - rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5) - rel_roi_points (Tensor): Point coordinates inside RoI, relative to - RoI, location, range (0, 1), shape (N, P, 2) - Returns: - Tensor: Image based absolute point coordinates, shape (N, P, 2) - """ - - with torch.no_grad(): - assert rel_roi_points.size(0) == rois.size(0) - assert rois.dim() == 2 - assert rel_roi_points.dim() == 3 - assert rel_roi_points.size(2) == 2 - # remove batch idx - if rois.size(1) == 5: - rois = rois[:, 1:] - abs_img_points = rel_roi_points.clone() - # To avoid an error during exporting to onnx use independent - # variables instead inplace computation - xs = abs_img_points[:, :, 0] * (rois[:, None, 2] - rois[:, None, 0]) - ys = abs_img_points[:, :, 1] * (rois[:, None, 3] - rois[:, None, 1]) - xs += rois[:, None, 0] - ys += rois[:, None, 1] - abs_img_points = torch.stack([xs, ys], dim=2) - return abs_img_points - - -def get_shape_from_feature_map(x): - """Get spatial resolution of input feature map considering exporting to - onnx mode. - - Args: - x (torch.Tensor): Input tensor, shape (N, C, H, W) - Returns: - torch.Tensor: Spatial resolution (width, height), shape (1, 1, 2) - """ - if torch.onnx.is_in_onnx_export(): - img_shape = shape_as_tensor(x)[2:].flip(0).view(1, 1, 2).to( - x.device).float() - else: - img_shape = torch.tensor(x.shape[2:]).flip(0).view(1, 1, 2).to( - x.device).float() - return img_shape - - -def abs_img_point_to_rel_img_point(abs_img_points, img, spatial_scale=1.): - """Convert image based absolute point coordinates to image based relative - coordinates for sampling. - - Args: - abs_img_points (Tensor): Image based absolute point coordinates, - shape (N, P, 2) - img (tuple/Tensor): (height, width) of image or feature map. - spatial_scale (float): Scale points by this factor. Default: 1. - - Returns: - Tensor: Image based relative point coordinates for sampling, - shape (N, P, 2) - """ - - assert (isinstance(img, tuple) and len(img) == 2) or \ - (isinstance(img, torch.Tensor) and len(img.shape) == 4) - - if isinstance(img, tuple): - h, w = img - scale = torch.tensor([w, h], - dtype=torch.float, - device=abs_img_points.device) - scale = scale.view(1, 1, 2) - else: - scale = get_shape_from_feature_map(img) - - return abs_img_points / scale * spatial_scale - - -def rel_roi_point_to_rel_img_point(rois, - rel_roi_points, - img, - spatial_scale=1.): - """Convert roi based relative point coordinates to image based absolute - point coordinates. - - Args: - rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5) - rel_roi_points (Tensor): Point coordinates inside RoI, relative to - RoI, location, range (0, 1), shape (N, P, 2) - img (tuple/Tensor): (height, width) of image or feature map. - spatial_scale (float): Scale points by this factor. Default: 1. - - Returns: - Tensor: Image based relative point coordinates for sampling, - shape (N, P, 2) - """ - - abs_img_point = rel_roi_point_to_abs_img_point(rois, rel_roi_points) - rel_img_point = abs_img_point_to_rel_img_point(abs_img_point, img, - spatial_scale) - - return rel_img_point - - -def point_sample(input, points, align_corners=False, **kwargs): - """A wrapper around :func:`grid_sample` to support 3D point_coords tensors - Unlike :func:`torch.nn.functional.grid_sample` it assumes point_coords to - lie inside ``[0, 1] x [0, 1]`` square. - - Args: - input (Tensor): Feature map, shape (N, C, H, W). - points (Tensor): Image based absolute point coordinates (normalized), - range [0, 1] x [0, 1], shape (N, P, 2) or (N, Hgrid, Wgrid, 2). - align_corners (bool): Whether align_corners. Default: False - - Returns: - Tensor: Features of `point` on `input`, shape (N, C, P) or - (N, C, Hgrid, Wgrid). - """ - - add_dim = False - if points.dim() == 3: - add_dim = True - points = points.unsqueeze(2) - if is_in_onnx_export_without_custom_ops(): - # If custom ops for onnx runtime not compiled use python - # implementation of grid_sample function to make onnx graph - # with supported nodes - output = bilinear_grid_sample( - input, denormalize(points), align_corners=align_corners) - else: - output = F.grid_sample( - input, denormalize(points), align_corners=align_corners, **kwargs) - if add_dim: - output = output.squeeze(3) - return output - - -class SimpleRoIAlign(nn.Module): - - def __init__(self, output_size, spatial_scale, aligned=True): - """Simple RoI align in PointRend, faster than standard RoIAlign. - - Args: - output_size (tuple[int]): h, w - spatial_scale (float): scale the input boxes by this number - aligned (bool): if False, use the legacy implementation in - MMDetection, align_corners=True will be used in F.grid_sample. - If True, align the results more perfectly. - """ - - super(SimpleRoIAlign, self).__init__() - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - # to be consistent with other RoI ops - self.use_torchvision = False - self.aligned = aligned - - def forward(self, features, rois): - num_imgs = features.size(0) - num_rois = rois.size(0) - rel_roi_points = generate_grid( - num_rois, self.output_size, device=rois.device) - - if torch.onnx.is_in_onnx_export(): - rel_img_points = rel_roi_point_to_rel_img_point( - rois, rel_roi_points, features, self.spatial_scale) - rel_img_points = rel_img_points.reshape(num_imgs, -1, - *rel_img_points.shape[1:]) - point_feats = point_sample( - features, rel_img_points, align_corners=not self.aligned) - point_feats = point_feats.transpose(1, 2) - else: - point_feats = [] - for batch_ind in range(num_imgs): - # unravel batch dim - feat = features[batch_ind].unsqueeze(0) - inds = (rois[:, 0].long() == batch_ind) - if inds.any(): - rel_img_points = rel_roi_point_to_rel_img_point( - rois[inds], rel_roi_points[inds], feat, - self.spatial_scale).unsqueeze(0) - point_feat = point_sample( - feat, rel_img_points, align_corners=not self.aligned) - point_feat = point_feat.squeeze(0).transpose(0, 1) - point_feats.append(point_feat) - - point_feats = torch.cat(point_feats, dim=0) - - channels = features.size(1) - roi_feats = point_feats.reshape(num_rois, channels, *self.output_size) - - return roi_feats - - def __repr__(self): - format_str = self.__class__.__name__ - format_str += '(output_size={}, spatial_scale={}'.format( - self.output_size, self.spatial_scale) - return format_str diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/debug.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/debug.go deleted file mode 100644 index 144b588c51b385b9900c9041da4ba20d76c4d8e9..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/debug.go and /dev/null differ diff --git a/spaces/Pengyey/bingo-chuchu/src/app/page.tsx b/spaces/Pengyey/bingo-chuchu/src/app/page.tsx deleted file mode 100644 index 0dff3431b098ce4fe282cc83fc87a93a28a43090..0000000000000000000000000000000000000000 --- a/spaces/Pengyey/bingo-chuchu/src/app/page.tsx +++ /dev/null @@ -1,15 +0,0 @@ -import dynamic from 'next/dynamic' - -const DynamicComponentWithNoSSR = dynamic( - () => import('../components/chat'), - { ssr: false } -) - -export default function IndexPage() { - return ( - <> - -
-
"""
-
-article="""Echo Sense is an innovative image captioning application that utilizes cutting-edge technology, specifically the powerful Transformer Model Architecture. This state-of-the-art approach has revolutionized Natural Language Processing (NLP) tasks, including image captioning, making it highly accurate and efficient. By leveraging pretrained models from Hugging Face and fine-tuning them on the COCO dataset, Echo Sense achieves exceptional performance while significantly reducing the computational cost and training time. The result is a versatile and reliable solution that not only produces accurate image captions but also generalizes well across various tasks. Experience the power of Echo Sense and witness firsthand the remarkable capabilities of the Transformer Model Architecture."""
-
-interface = gr.Interface(
- fn=predict,
- inputs=inputs,
- outputs=outputs,
- title="",
- description=description,
- article=article,
- theme="grass",
- font=[
- gr.themes.GoogleFont("Open Sans"),
- "ui-sans-serif",
- "system-ui",
- "sans-serif",
- ],
-)
-interface.launch()
\ No newline at end of file
diff --git a/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/assets/+page-376b236d.css b/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/assets/+page-376b236d.css
deleted file mode 100644
index 54f1eed0ee54d701018006d3764fc3323df69aa7..0000000000000000000000000000000000000000
--- a/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/assets/+page-376b236d.css
+++ /dev/null
@@ -1 +0,0 @@
-span[contenteditable].svelte-1wfa7x9:empty:before{content:var(--placeholder);color:#9ca3af}
diff --git a/spaces/SarthakSidhant/Go-Cattle/diseases/bovine besnoitiosis.md b/spaces/SarthakSidhant/Go-Cattle/diseases/bovine besnoitiosis.md
deleted file mode 100644
index acc020d23baf7857d45a91376bc5d59b1ff35e7f..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/diseases/bovine besnoitiosis.md
+++ /dev/null
@@ -1,38 +0,0 @@
-## Bovine besnoitiosis
-
-**Information** : Bovine besnoitiosis is a parasitic disease of cattle caused by a protozoan parasite called Besnoitia besnoiti. The parasite is spread through the bite of infected biting flies, such as the stable fly (Stomoxys calcitrans) and the horn fly (Haematobia irritans).
-
-**Symptoms**
-
-The symptoms of bovine besnoitiosis can vary depending on the severity of the infection and the animal's individual immune response. Some infected cattle may show no symptoms at all, while others may develop a range of symptoms, including:
-
-* Fever
-* Depression
-* Weight loss
-* Anemia
-* Enlarged lymph nodes
-* Lameness
-* Skin lesions
-* Abortion
-* Death
-
-**Remedies**
-
-There is no specific treatment for bovine besnoitiosis. Treatment is usually supportive and may include:
-
-* Administering fluids and electrolytes
-* Treating secondary bacterial infections
-
-**Causes**
-
-Bovine besnoitiosis is caused by a protozoan parasite called Besnoitia besnoiti. The parasite is spread through the bite of infected biting flies, such as the stable fly (Stomoxys calcitrans) and the horn fly (Haematobia irritans).
-
-**Prevention**
-
-There is no vaccine available for bovine besnoitiosis. However, there are some preventive measures that can be taken to reduce the risk of infection, such as:
-
-* Controlling biting flies
-* Vaccinating cattle against other diseases that can weaken the immune system, such as bovine viral diarrhea virus (BVDV) and rotavirus
-* Testing cattle for bovine besnoitiosis
-* Isolating infected animals from healthy animals
-* Treating contaminated feed and water
diff --git a/spaces/Saturdays/ClassificationPeripheralBloodCell/about_pj.py b/spaces/Saturdays/ClassificationPeripheralBloodCell/about_pj.py
deleted file mode 100644
index bb2006e2661450dcb05a23d3dd748306c547a44c..0000000000000000000000000000000000000000
--- a/spaces/Saturdays/ClassificationPeripheralBloodCell/about_pj.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Dec 27 16:16:06 2022
-
-@author: Usuario
-"""
-import streamlit as st
-import imagen_subida as ims
-
-
-#ABout the project!
-#def add_bg_from_url():
-# st.markdown(
-# f"""
-#
-# """,
-# unsafe_allow_html=True
-# )
-
-#add_bg_from_url()
-
-def textito(idioma):
-
- if idioma == 1:
- st.title('About the project')
- container = st.container()
- st.markdown('🔉 EchoSense Image to Audio Playground
-This spaces helps generate audio descriptions for input Images
-Please note:This space is for demonstration purposes only.
-Visit Shreyas Dixit's personal website for more information about the creator.
-This project of Peripheral Blood Cells Classification have been made by Silvia García, María Ortiz and Jorge González (more information in About us button), for Final Master''s Thesis of the 3th Edition master''s degree in Deep Learning from SaturdaysAI.
', unsafe_allow_html=True) - - st.markdown('In this project, attention has been focused on the automation of the classification of peripheral blood cells using the Transfer Learning methodology, which consists of using a pre-trained artificial intelligence model, in this case the vgg19 model, and training it with an image dataset composed of 8 different classes (basophils, eosinophils, erythroblasts, immature granulocytes, lymphocytes, monocytes, neutrophils, and platelets) of different cell types.
', unsafe_allow_html=True) - st.markdown('The vgg19 pre-trained network architecture; a variant of the vgg model, consisting of 19 layers (16 convolutional and 3 connected layers, 5 MaxPool layers and one Softmax layer). The following image represents the structure of this network:
', unsafe_allow_html=True) - - st.image('./images/vgg19.png', use_column_width= True) - st.markdown('The results obtained were quite promising with a percentage of accuracy in the classification higher than 99% in all classes.
', unsafe_allow_html=True) - - st.image('./images/confusion_matrix.png', use_column_width= True) - st.markdown('This confusion matrix indicates the accuracy of the model when classifying cell types. As can be seen, the vgg19 model predicts the different images with great accuracy.
', unsafe_allow_html=True) - - - st.markdown('Tensorflow Projector (https://projector.tensorflow.org/) is a visual tool that allows us to interact and analyze multidimensional data (embeddings) and project them into a two- or three-dimensional space. Each embedding is represented by a point that has a certain position in space and these will form certain clusters based on a similarity score. Thanks to this tool, we are able to observe how the model is capable of distinguishing the different classes (ig, leukocytes, etc), and where it has the greatest problems in distinguishing them through the appearance of certain points of different classes within a cluster of a different class.
', unsafe_allow_html=True) - st.markdown('Dimensionality reduction methods such as t-stochastic neighbor embedding (t-SNE) allow us to visualize our embeddings in a three-dimensional way, constructing a probability distribution over pairs of embeddings in space, such that the most similar ones are more likely to be included in each other. the same cluster, reducing the dimensionality of the sample.
', unsafe_allow_html=True) - - - st.image('./images/tensor.png', use_column_width= True) - st.markdown('As can be seen in this figure, there are various insertions of certain groups within clusters belonging to other classes. In this case, the model is more confused giving a correct classification when dealing with neutrophils and immature granulocytes. Other notable insertions are erythroblasts, which are confused with platelets, neutrophils with basophils, and immature granulocytes with monocytes. Even so, the precision of the model when classifying the different cell types is very high.
', unsafe_allow_html=True) - - - - else: - st.title('Acerca del proyecto') - container = st.container() - #text_ini = '**Este trabajo de clasificación de células sanguíneas periféricas es un proyecto realizado por Silvia García, María Ortiz y Jorge González (más información en el apartado *Sobre nosotros*), para el Trabajo de Fin de Máster de la tercera edición del máster en Deep Learning de SaturdaysAI.**' - st.markdown('Este trabajo de clasificación de células sanguíneas periféricas es un proyecto realizado por Silvia García, María Ortiz y Jorge González (más información en el apartado Sobre nosotros), para el Trabajo de Fin de Máster de la tercera edición del máster en Deep Learning de SaturdaysAI.
', unsafe_allow_html=True) - - st.markdown('En este proyecto, se ha centrado la atención a la automatización de la clasificación de células sanguíneas periféricas utilizando la metodología de Transfer Learning, la cual consiste en utilizar un modelo de inteligencia artificial pre-entrenado, en este caso el modelo vgg19, y entrenarlo con un dataset de imágenes compuesto por 8 clases diferentes (basófilos, eosinófilos, eritroblastos, granulocitos inmaduros, linfocitos, monocitos, neutrófilos y plaquetas) de diferentes tipos celulares.
', unsafe_allow_html=True) - st.markdown('La arquitectura de red pre-entrenada vgg19; una variante del modelo vgg, que consta de 19 capas (16 de convolución y 3 capas conectadas, 5 capas de MaxPool y una de Softmax). La siguiente imagen representa la estructura de esta red:
', unsafe_allow_html=True) - - #st.write(text_ini) - # text1 = 'En este proyecto, se ha centrado la atención a la automatización de la clasificación de células sanguíneas periféricas utilizando la metodología de *Transfer Learning*, la cual consiste en utilizar un modelo de inteligencia artificial pre-entrenado, en este caso el modelo *vgg19*, y entrenarlo con un dataset de imágenes compuesto por 8 clases diferentes (basófilos, eosinófilos, eritroblastos, granulocitos inmaduros, linfocitos, monocitos, neutrófilos y plaquetas) de diferentes tipos celulares.' - # = 'La arquitectura de red pre-entrenada *vgg19*; una variante del modelo *vgg*, que consta de 19 capas (16 de convolución y 3 capas conectadas, 5 capas de MaxPool y una de Softmax). La siguiente imagen representa la estructura de esta red:' - # st.write(text1) - #st.write(text2) - st.image('./images/vgg19.png', use_column_width= True) - st.markdown('Los resultados obtenidos, fueron bastante prometedores con un porcentaje de precisión en la clasificación superior al 99% en todas las clases.
', unsafe_allow_html=True) - - #text3 = 'Los resultados obtenidos, fueron bastante prometedores con un porcentaje de precisión en la clasificación superior al 99% en todas las clases.' - #st.write(text3) - st.image('./images/confusion_matrix.png', use_column_width= True) - st.markdown('Esta matriz de confusión nos indica la precisión del modelo a la hora de clasificar los tipos celulares. Como se puede observar, el modelo vgg19 predice con gran exactitud las diferentes imágenes.
', unsafe_allow_html=True) - - st.markdown('Tensorflow Projector (https://projector.tensorflow.org/) es una herramienta visual que nos permite interactuar y analizar datos multidimensionales (embeddings) y proyectarlos en un espacio bi o tridimensional. Cada embedding es representado por un punto que tiene una posición determinada en el espacio y estos formarán determinados clusters basándose en una puntuación de similitud. Gracias a esta herramienta, somos capaces de observar cómo el modelo es capaz de distinguir las diferentes clases (ig, leucocitos, etc), y dónde tiene los mayores problemas para distinguirlas mediante la aparición de ciertos puntos de diferentes clases dentro de un cluster de una clase diferente.
', unsafe_allow_html=True) - st.markdown('Métodos de reducción de dimensionalidad como t-stochastic neighbor embedding (t-SNE) nos permiten visualizar nuestros embeddings de manera tridimensional, construyendo una distribución de probabilidad sobre parejas de embeddings en el espacio, de forma que los más similares son más probables de incluirse en un mismo cluster, reduciendo la dimensionalidad de la muestra.
', unsafe_allow_html=True) - - #text4 = 'Esta matriz de confusión nos indica la precisión del modelo a la hora de clasificar los tipos celulares. Como se puede observar, el modelo *vgg19* predice con gran exactitud las diferentes imágenes.' - #st.write(text4) - #text5 = 'Tensorflow Projector (https://projector.tensorflow.org/) es una herramienta visual que nos permite interactuar y analizar datos multidimensionales (embeddings) y proyectarlos en un espacio bi o tridimensional. Cada embedding es representado por un punto que tiene una posición determinada en el espacio y estos formarán determinados clusters basándose en una puntuación de similitud. Gracias a esta herramienta, somos capaces de observar cómo el modelo es capaz de distinguir las diferentes clases (ig, leucocitos, etc), y dónde tiene los mayores problemas para distinguirlas mediante la aparición de ciertos puntos de diferentes clases dentro de un cluster de una clase diferente. ' - #st.write(text5) - #text6 = 'Métodos de reducción de dimensionalidad como t-stochastic neighbor embedding (t-SNE) nos permiten visualizar nuestros embeddings de manera tridimensional, construyendo una distribución de probabilidad sobre parejas de embeddings en el espacio, de forma que los más similares son más probables de incluirse en un mismo cluster, reduciendo la dimensionalidad de la muestra. ' - #st.write(text6) - st.image('./images/tensor.png', use_column_width= True) - st.markdown('Como se puede observar en esta figura, existen diversas inserciones de ciertos grupos dentro de clusters pertenecientes a otras clases. En este caso, el modelo se encuentra más confuso dando una clasificación correcta cuando se trata de neutrófilos y granulocitos inmaduros. Otras inserciones destacables son los eritroblastos, que son confundidos con plaquetas, los neutrófilos con basófilos, y los granulocitos inmaduros con monocitos. Aun así, la precisión del modelo a la hora de clasificar los diferentes tipos celulares es muy alta.
', unsafe_allow_html=True) - - #text7 = 'Como se puede observar en esta figura, existen diversas inserciones de ciertos grupos dentro de clusters pertenecientes a otras clases. En este caso, el modelo se encuentra más confuso dando una clasificación correcta cuando se trata de neutrófilos y granulocitos inmaduros. Otras inserciones destacables son los eritroblastos, que son confundidos con plaquetas, los neutrófilos con basófilos, y los granulocitos inmaduros con monocitos. Aun así, la precisión del modelo a la hora de clasificar los diferentes tipos celulares es muy alta.' - #st.write(text7) \ No newline at end of file diff --git a/spaces/SeViLA/SeViLA/lavis/models/blip2_models/modeling_t5.py b/spaces/SeViLA/SeViLA/lavis/models/blip2_models/modeling_t5.py deleted file mode 100644 index 10e4d56f2c21b0cbe639e0f568bd352a6cb76351..0000000000000000000000000000000000000000 --- a/spaces/SeViLA/SeViLA/lavis/models/blip2_models/modeling_t5.py +++ /dev/null @@ -1,2063 +0,0 @@ -# coding=utf-8 -# Copyright 2018 Mesh TensorFlow authors, T5 Authors and 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. -""" PyTorch T5 model.""" - - -import copy -import math -import os -import warnings -from typing import Optional, Tuple, Union - -import torch -from torch import nn -from torch.nn import CrossEntropyLoss -from torch.utils.checkpoint import checkpoint - -from transformers.activations import ACT2FN -from transformers.modeling_outputs import ( - BaseModelOutput, - BaseModelOutputWithPastAndCrossAttentions, - Seq2SeqLMOutput, - Seq2SeqModelOutput, -) -from transformers.modeling_utils import PreTrainedModel -from transformers.pytorch_utils import ( - ALL_LAYERNORM_LAYERS, - find_pruneable_heads_and_indices, - prune_linear_layer, -) -from transformers.utils import ( - DUMMY_INPUTS, - DUMMY_MASK, - add_start_docstrings, - add_start_docstrings_to_model_forward, - is_torch_fx_proxy, - logging, - replace_return_docstrings, -) -from transformers.utils.model_parallel_utils import assert_device_map, get_device_map -from transformers.models.t5.configuration_t5 import T5Config - - -logger = logging.get_logger(__name__) - -_CONFIG_FOR_DOC = "T5Config" -_TOKENIZER_FOR_DOC = "T5Tokenizer" -_CHECKPOINT_FOR_DOC = "t5-small" - -#################################################### -# This dict contains ids and associated url -# for the pretrained weights provided with the models -#################################################### -T5_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "t5-small", - "t5-base", - "t5-large", - "t5-3b", - "t5-11b", - # See all T5 models at https://huggingface.co/models?filter=t5 -] - - -#################################################### -# This is a conversion method from TF 1.0 to PyTorch -# More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28 -#################################################### -def load_tf_weights_in_t5(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(f"Converting TensorFlow checkpoint from {tf_path}") - # Load weights from TF model - init_vars = tf.train.list_variables(tf_path) - names = [] - tf_weights = {} - 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) - tf_weights[name] = array - - for txt_name in names: - name = txt_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)}") - tf_weights.pop(txt_name, None) - continue - if "_slot_" in name[-1]: - logger.info(f"Skipping {'/'.join(name)}") - tf_weights.pop(txt_name, None) - continue - pointer = model - array = tf_weights[txt_name] - - 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] in ["kernel", "scale", "embedding"]: - pointer = getattr(pointer, "weight") - elif scope_names[0] == "self_attention": - pointer = getattr(pointer, "layer") - pointer = pointer[0] - elif scope_names[0] == "enc_dec_attention": - pointer = getattr(pointer, "layer") - pointer = pointer[1] - elif scope_names[0] == "dense_relu_dense": - pointer = getattr(pointer, "layer") - pointer = pointer[2] - elif scope_names[0] == "rms_norm": - if hasattr(pointer, "layer_norm"): - pointer = getattr(pointer, "layer_norm") - elif hasattr(pointer, "final_layer_norm"): - pointer = getattr(pointer, "final_layer_norm") - elif scope_names[0] == "scale": - pointer = getattr(pointer, "weight") - elif scope_names[0] == "output_bias" or scope_names[0] == "beta": - pointer = getattr(pointer, "bias") - elif scope_names[0] == "squad": - pointer = getattr(pointer, "classifier") - elif scope_names[0] == "decoder" and name[1] == "logits": - continue - elif scope_names[0] == "logits": - pointer = getattr(pointer, "lm_head") - elif ( - scope_names[0] == "wi" - and len(scope_names) > 1 - and scope_names[1].isdigit() - ): - pointer = getattr(pointer, f"wi_{scope_names[1]}") - continue - 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 scope_names[0] not in ["kernel", "scale", "embedding"]: - pointer = getattr(pointer, "weight") - if scope_names[0] != "embedding": - logger.info(f"Transposing numpy weight of shape {array.shape} for {name}") - 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(f"Initialize PyTorch weight {name}") - pointer.data = torch.from_numpy(array.astype(np.float32)) - tf_weights.pop(txt_name, None) - - logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.") - return model - - -#################################################### -# PyTorch Models are constructed by sub-classing -# - torch.nn.Module for the layers and -# - PreTrainedModel for the models (it-self a sub-class of nn.Module) -#################################################### -PARALLELIZE_DOCSTRING = r""" - This is an experimental feature and is a subject to change at a moment's notice. - - Uses a device map to distribute attention modules of the model across several devices. If no device map is given, - it will evenly distribute blocks across all devices. - - Args: - device_map (`Dict[int, list]`, optional, defaults to None): - A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always - automatically mapped to the first device (for esoteric reasons). That means that the first device should - have fewer attention modules mapped to it than other devices. For reference, the t5 models have the - following number of attention modules: - - - t5-small: 6 - - t5-base: 12 - - t5-large: 24 - - t5-3b: 24 - - t5-11b: 24 - - Example: - - ```python - # Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules: - model = T5ForConditionalGeneration.from_pretrained("t5-3b") - device_map = { - 0: [0, 1, 2], - 1: [3, 4, 5, 6, 7, 8, 9], - 2: [10, 11, 12, 13, 14, 15, 16], - 3: [17, 18, 19, 20, 21, 22, 23], - } - model.parallelize(device_map) - ``` -""" -DEPARALLELIZE_DOCSTRING = r""" - Moves the model to cpu from a model parallel state. - - Example: - - ```python - # On a 4 GPU machine with t5-3b: - model = T5ForConditionalGeneration.from_pretrained("t5-3b") - device_map = { - 0: [0, 1, 2], - 1: [3, 4, 5, 6, 7, 8, 9], - 2: [10, 11, 12, 13, 14, 15, 16], - 3: [17, 18, 19, 20, 21, 22, 23], - } - model.parallelize(device_map) # Splits the model across several devices - model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache() - ``` -""" - - -class T5LayerNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-6): - """ - Construct a layernorm module in the T5 style. No bias and no subtraction of mean. - """ - super().__init__() - self.weight = nn.Parameter(torch.ones(hidden_size)) - self.variance_epsilon = eps - - def forward(self, hidden_states): - - # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean - # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated - # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for - # half-precision inputs is done in fp32 - - variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) - - # convert into half-precision if necessary - if self.weight.dtype in [torch.float16, torch.bfloat16]: - hidden_states = hidden_states.to(self.weight.dtype) - - return self.weight * hidden_states - - -try: - from apex.normalization import FusedRMSNorm - - T5LayerNorm = FusedRMSNorm # noqa - - logger.info( - "Discovered apex.normalization.FusedRMSNorm - will use it instead of T5LayerNorm" - ) -except ImportError: - # using the normal T5LayerNorm - pass -except Exception: - logger.warning("discovered apex but it failed to load, falling back to T5LayerNorm") - pass - -ALL_LAYERNORM_LAYERS.append(T5LayerNorm) - - -class T5DenseActDense(nn.Module): - def __init__(self, config: T5Config): - super().__init__() - self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) - self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) - self.dropout = nn.Dropout(config.dropout_rate) - self.act = ACT2FN[config.dense_act_fn] - - def forward(self, hidden_states): - hidden_states = self.wi(hidden_states) - hidden_states = self.act(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.wo(hidden_states) - return hidden_states - - -class T5DenseGatedActDense(nn.Module): - def __init__(self, config: T5Config): - super().__init__() - self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False) - self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False) - self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) - self.dropout = nn.Dropout(config.dropout_rate) - self.act = ACT2FN[config.dense_act_fn] - - def forward(self, hidden_states): - hidden_gelu = self.act(self.wi_0(hidden_states)) - hidden_linear = self.wi_1(hidden_states) - hidden_states = hidden_gelu * hidden_linear - hidden_states = self.dropout(hidden_states) - hidden_states = self.wo(hidden_states) - return hidden_states - - -class T5LayerFF(nn.Module): - def __init__(self, config: T5Config): - super().__init__() - if config.is_gated_act: - self.DenseReluDense = T5DenseGatedActDense(config) - else: - self.DenseReluDense = T5DenseActDense(config) - - self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) - self.dropout = nn.Dropout(config.dropout_rate) - - def forward(self, hidden_states): - forwarded_states = self.layer_norm(hidden_states) - forwarded_states = self.DenseReluDense(forwarded_states) - hidden_states = hidden_states + self.dropout(forwarded_states) - return hidden_states - - -class T5Attention(nn.Module): - def __init__(self, config: T5Config, has_relative_attention_bias=False): - super().__init__() - self.is_decoder = config.is_decoder - self.has_relative_attention_bias = has_relative_attention_bias - self.relative_attention_num_buckets = config.relative_attention_num_buckets - self.relative_attention_max_distance = config.relative_attention_max_distance - self.d_model = config.d_model - self.key_value_proj_dim = config.d_kv - self.n_heads = config.num_heads - self.dropout = config.dropout_rate - self.inner_dim = self.n_heads * self.key_value_proj_dim - - # Mesh TensorFlow initialization to avoid scaling before softmax - self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) - self.k = nn.Linear(self.d_model, self.inner_dim, bias=False) - self.v = nn.Linear(self.d_model, self.inner_dim, bias=False) - self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) - - if self.has_relative_attention_bias: - self.relative_attention_bias = nn.Embedding( - self.relative_attention_num_buckets, self.n_heads - ) - self.pruned_heads = set() - self.gradient_checkpointing = False - - def prune_heads(self, heads): - if len(heads) == 0: - return - heads, index = find_pruneable_heads_and_indices( - heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads - ) - # Prune linear layers - self.q = prune_linear_layer(self.q, index) - self.k = prune_linear_layer(self.k, index) - self.v = prune_linear_layer(self.v, index) - self.o = prune_linear_layer(self.o, index, dim=1) - # Update hyper params - self.n_heads = self.n_heads - len(heads) - self.inner_dim = self.key_value_proj_dim * self.n_heads - self.pruned_heads = self.pruned_heads.union(heads) - - @staticmethod - def _relative_position_bucket( - relative_position, bidirectional=True, num_buckets=32, max_distance=128 - ): - """ - Adapted from Mesh Tensorflow: - https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 - - Translate relative position to a bucket number for relative attention. The relative position is defined as - memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to - position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for - small absolute relative_position and larger buckets for larger absolute relative_positions. All relative - positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. - This should allow for more graceful generalization to longer sequences than the model has been trained on - - Args: - relative_position: an int32 Tensor - bidirectional: a boolean - whether the attention is bidirectional - num_buckets: an integer - max_distance: an integer - - Returns: - a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) - """ - relative_buckets = 0 - if bidirectional: - num_buckets //= 2 - relative_buckets += (relative_position > 0).to(torch.long) * num_buckets - relative_position = torch.abs(relative_position) - else: - relative_position = -torch.min( - relative_position, torch.zeros_like(relative_position) - ) - # now relative_position is in the range [0, inf) - - # half of the buckets are for exact increments in positions - max_exact = num_buckets // 2 - is_small = relative_position < max_exact - - # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance - relative_position_if_large = max_exact + ( - torch.log(relative_position.float() / max_exact) - / math.log(max_distance / max_exact) - * (num_buckets - max_exact) - ).to(torch.long) - relative_position_if_large = torch.min( - relative_position_if_large, - torch.full_like(relative_position_if_large, num_buckets - 1), - ) - - relative_buckets += torch.where( - is_small, relative_position, relative_position_if_large - ) - return relative_buckets - - def compute_bias(self, query_length, key_length, device=None): - """Compute binned relative position bias""" - if device is None: - device = self.relative_attention_bias.weight.device - context_position = torch.arange(query_length, dtype=torch.long, device=device)[ - :, None - ] - memory_position = torch.arange(key_length, dtype=torch.long, device=device)[ - None, : - ] - relative_position = ( - memory_position - context_position - ) # shape (query_length, key_length) - relative_position_bucket = self._relative_position_bucket( - relative_position, # shape (query_length, key_length) - bidirectional=(not self.is_decoder), - num_buckets=self.relative_attention_num_buckets, - max_distance=self.relative_attention_max_distance, - ) - values = self.relative_attention_bias( - relative_position_bucket - ) # shape (query_length, key_length, num_heads) - values = values.permute([2, 0, 1]).unsqueeze( - 0 - ) # shape (1, num_heads, query_length, key_length) - return values - - def forward( - self, - hidden_states, - mask=None, - key_value_states=None, - position_bias=None, - past_key_value=None, - layer_head_mask=None, - query_length=None, - use_cache=False, - output_attentions=False, - ): - """ - Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). - """ - # Input is (batch_size, seq_length, dim) - # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) - # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head) - batch_size, seq_length = hidden_states.shape[:2] - - real_seq_length = seq_length - - if past_key_value is not None: - assert ( - len(past_key_value) == 2 - ), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states" - real_seq_length += ( - past_key_value[0].shape[2] if query_length is None else query_length - ) - - key_length = ( - real_seq_length if key_value_states is None else key_value_states.shape[1] - ) - - def shape(states): - """projection""" - return states.view( - batch_size, -1, self.n_heads, self.key_value_proj_dim - ).transpose(1, 2) - - def unshape(states): - """reshape""" - return ( - states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim) - ) - - def project(hidden_states, proj_layer, key_value_states, past_key_value): - """projects hidden states correctly to key/query states""" - if key_value_states is None: - # self-attn - # (batch_size, n_heads, seq_length, dim_per_head) - hidden_states = shape(proj_layer(hidden_states)) - elif past_key_value is None: - # cross-attn - # (batch_size, n_heads, seq_length, dim_per_head) - hidden_states = shape(proj_layer(key_value_states)) - - if past_key_value is not None: - if key_value_states is None: - # self-attn - # (batch_size, n_heads, key_length, dim_per_head) - hidden_states = torch.cat([past_key_value, hidden_states], dim=2) - else: - # cross-attn - hidden_states = past_key_value - return hidden_states - - # get query states - query_states = shape( - self.q(hidden_states) - ) # (batch_size, n_heads, seq_length, dim_per_head) - - # get key/value states - key_states = project( - hidden_states, - self.k, - key_value_states, - past_key_value[0] if past_key_value is not None else None, - ) - value_states = project( - hidden_states, - self.v, - key_value_states, - past_key_value[1] if past_key_value is not None else None, - ) - - # compute scores - scores = torch.matmul( - query_states, key_states.transpose(3, 2) - ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 - - if position_bias is None: - if not self.has_relative_attention_bias: - position_bias = torch.zeros( - (1, self.n_heads, real_seq_length, key_length), - device=scores.device, - dtype=scores.dtype, - ) - if self.gradient_checkpointing and self.training: - position_bias.requires_grad = True - else: - position_bias = self.compute_bias( - real_seq_length, key_length, device=scores.device - ) - - # if key and values are already calculated - # we want only the last query position bias - if past_key_value is not None: - position_bias = position_bias[:, :, -hidden_states.size(1) :, :] - - if mask is not None: - position_bias = ( - position_bias + mask - ) # (batch_size, n_heads, seq_length, key_length) - - if self.pruned_heads: - mask = torch.ones(position_bias.shape[1]) - mask[list(self.pruned_heads)] = 0 - position_bias_masked = position_bias[:, mask.bool()] - else: - position_bias_masked = position_bias - - scores += position_bias_masked - attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as( - scores - ) # (batch_size, n_heads, seq_length, key_length) - attn_weights = nn.functional.dropout( - attn_weights, p=self.dropout, training=self.training - ) # (batch_size, n_heads, seq_length, key_length) - - # Mask heads if we want to - if layer_head_mask is not None: - attn_weights = attn_weights * layer_head_mask - - attn_output = unshape( - torch.matmul(attn_weights, value_states) - ) # (batch_size, seq_length, dim) - attn_output = self.o(attn_output) - - present_key_value_state = ( - (key_states, value_states) if (self.is_decoder and use_cache) else None - ) - outputs = (attn_output,) + (present_key_value_state,) + (position_bias,) - - if output_attentions: - outputs = outputs + (attn_weights,) - return outputs - - -class T5LayerSelfAttention(nn.Module): - def __init__(self, config, has_relative_attention_bias=False): - super().__init__() - self.SelfAttention = T5Attention( - config, has_relative_attention_bias=has_relative_attention_bias - ) - self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) - self.dropout = nn.Dropout(config.dropout_rate) - - def forward( - self, - hidden_states, - attention_mask=None, - position_bias=None, - layer_head_mask=None, - past_key_value=None, - use_cache=False, - output_attentions=False, - ): - normed_hidden_states = self.layer_norm(hidden_states) - attention_output = self.SelfAttention( - normed_hidden_states, - mask=attention_mask, - position_bias=position_bias, - layer_head_mask=layer_head_mask, - past_key_value=past_key_value, - use_cache=use_cache, - output_attentions=output_attentions, - ) - hidden_states = hidden_states + self.dropout(attention_output[0]) - outputs = (hidden_states,) + attention_output[ - 1: - ] # add attentions if we output them - return outputs - - -class T5LayerCrossAttention(nn.Module): - def __init__(self, config): - super().__init__() - self.EncDecAttention = T5Attention(config, has_relative_attention_bias=False) - self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) - self.dropout = nn.Dropout(config.dropout_rate) - - def forward( - self, - hidden_states, - key_value_states, - attention_mask=None, - position_bias=None, - layer_head_mask=None, - past_key_value=None, - use_cache=False, - query_length=None, - output_attentions=False, - ): - normed_hidden_states = self.layer_norm(hidden_states) - attention_output = self.EncDecAttention( - normed_hidden_states, - mask=attention_mask, - key_value_states=key_value_states, - position_bias=position_bias, - layer_head_mask=layer_head_mask, - past_key_value=past_key_value, - use_cache=use_cache, - query_length=query_length, - output_attentions=output_attentions, - ) - layer_output = hidden_states + self.dropout(attention_output[0]) - outputs = (layer_output,) + attention_output[ - 1: - ] # add attentions if we output them - return outputs - - -class T5Block(nn.Module): - def __init__(self, config, has_relative_attention_bias=False): - super().__init__() - self.is_decoder = config.is_decoder - self.layer = nn.ModuleList() - self.layer.append( - T5LayerSelfAttention( - config, has_relative_attention_bias=has_relative_attention_bias - ) - ) - if self.is_decoder: - self.layer.append(T5LayerCrossAttention(config)) - - self.layer.append(T5LayerFF(config)) - - def forward( - self, - hidden_states, - attention_mask=None, - position_bias=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - encoder_decoder_position_bias=None, - layer_head_mask=None, - cross_attn_layer_head_mask=None, - past_key_value=None, - use_cache=False, - output_attentions=False, - return_dict=True, - ): - - if past_key_value is not None: - if not self.is_decoder: - logger.warning( - "`past_key_values` is passed to the encoder. Please make sure this is intended." - ) - expected_num_past_key_values = 2 if encoder_hidden_states is None else 4 - - if len(past_key_value) != expected_num_past_key_values: - raise ValueError( - f"There should be {expected_num_past_key_values} past states. " - f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}" - f"Got {len(past_key_value)} past key / value states" - ) - - self_attn_past_key_value = past_key_value[:2] - cross_attn_past_key_value = past_key_value[2:] - else: - self_attn_past_key_value, cross_attn_past_key_value = None, None - - self_attention_outputs = self.layer[0]( - hidden_states, - attention_mask=attention_mask, - position_bias=position_bias, - layer_head_mask=layer_head_mask, - past_key_value=self_attn_past_key_value, - use_cache=use_cache, - output_attentions=output_attentions, - ) - hidden_states, present_key_value_state = self_attention_outputs[:2] - attention_outputs = self_attention_outputs[ - 2: - ] # Keep self-attention outputs and relative position weights - - # clamp inf values to enable fp16 training - if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): - clamp_value = torch.finfo(hidden_states.dtype).max - 1000 - hidden_states = torch.clamp( - hidden_states, min=-clamp_value, max=clamp_value - ) - - do_cross_attention = self.is_decoder and encoder_hidden_states is not None - if do_cross_attention: - # the actual query length is unknown for cross attention - # if using past key value states. Need to inject it here - if present_key_value_state is not None: - query_length = present_key_value_state[0].shape[2] - else: - query_length = None - - cross_attention_outputs = self.layer[1]( - hidden_states, - key_value_states=encoder_hidden_states, - attention_mask=encoder_attention_mask, - position_bias=encoder_decoder_position_bias, - layer_head_mask=cross_attn_layer_head_mask, - past_key_value=cross_attn_past_key_value, - query_length=query_length, - use_cache=use_cache, - output_attentions=output_attentions, - ) - hidden_states = cross_attention_outputs[0] - - # clamp inf values to enable fp16 training - if ( - hidden_states.dtype == torch.float16 - and torch.isinf(hidden_states).any() - ): - clamp_value = torch.finfo(hidden_states.dtype).max - 1000 - hidden_states = torch.clamp( - hidden_states, min=-clamp_value, max=clamp_value - ) - - # Combine self attn and cross attn key value states - if present_key_value_state is not None: - present_key_value_state = ( - present_key_value_state + cross_attention_outputs[1] - ) - - # Keep cross-attention outputs and relative position weights - attention_outputs = attention_outputs + cross_attention_outputs[2:] - - # Apply Feed Forward layer - hidden_states = self.layer[-1](hidden_states) - - # clamp inf values to enable fp16 training - if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): - clamp_value = torch.finfo(hidden_states.dtype).max - 1000 - hidden_states = torch.clamp( - hidden_states, min=-clamp_value, max=clamp_value - ) - - outputs = (hidden_states,) - - if use_cache: - outputs = outputs + (present_key_value_state,) + attention_outputs - else: - outputs = outputs + attention_outputs - - return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) - - -class T5PreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = T5Config - load_tf_weights = load_tf_weights_in_t5 - base_model_prefix = "transformer" - is_parallelizable = True - supports_gradient_checkpointing = True - _no_split_modules = ["T5Block"] - - @property - def dummy_inputs(self): - input_ids = torch.tensor(DUMMY_INPUTS) - input_mask = torch.tensor(DUMMY_MASK) - dummy_inputs = { - "decoder_input_ids": input_ids, - "input_ids": input_ids, - "decoder_attention_mask": input_mask, - } - return dummy_inputs - - def _init_weights(self, module): - """Initialize the weights""" - factor = ( - self.config.initializer_factor - ) # Used for testing weights initialization - if isinstance(module, T5LayerNorm): - module.weight.data.fill_(factor * 1.0) - elif isinstance(module, (T5Model, T5ForConditionalGeneration, T5EncoderModel)): - # Mesh TensorFlow embeddings initialization - # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 - module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0) - if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: - module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0) - elif isinstance(module, T5DenseActDense): - # Mesh TensorFlow FF initialization - # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 - # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 - module.wi.weight.data.normal_( - mean=0.0, std=factor * ((self.config.d_model) ** -0.5) - ) - if hasattr(module.wi, "bias") and module.wi.bias is not None: - module.wi.bias.data.zero_() - module.wo.weight.data.normal_( - mean=0.0, std=factor * ((self.config.d_ff) ** -0.5) - ) - if hasattr(module.wo, "bias") and module.wo.bias is not None: - module.wo.bias.data.zero_() - elif isinstance(module, T5DenseGatedActDense): - module.wi_0.weight.data.normal_( - mean=0.0, std=factor * ((self.config.d_model) ** -0.5) - ) - if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: - module.wi_0.bias.data.zero_() - module.wi_1.weight.data.normal_( - mean=0.0, std=factor * ((self.config.d_model) ** -0.5) - ) - if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: - module.wi_1.bias.data.zero_() - module.wo.weight.data.normal_( - mean=0.0, std=factor * ((self.config.d_ff) ** -0.5) - ) - if hasattr(module.wo, "bias") and module.wo.bias is not None: - module.wo.bias.data.zero_() - elif isinstance(module, T5Attention): - # Mesh TensorFlow attention initialization to avoid scaling before softmax - # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 - d_model = self.config.d_model - key_value_proj_dim = self.config.d_kv - n_heads = self.config.num_heads - module.q.weight.data.normal_( - mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5) - ) - module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) - module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) - module.o.weight.data.normal_( - mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5) - ) - if module.has_relative_attention_bias: - module.relative_attention_bias.weight.data.normal_( - mean=0.0, std=factor * ((d_model) ** -0.5) - ) - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, (T5Attention, T5Stack)): - module.gradient_checkpointing = value - - def _shift_right(self, input_ids): - decoder_start_token_id = self.config.decoder_start_token_id - pad_token_id = self.config.pad_token_id - - assert decoder_start_token_id is not None, ( - "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id." - " See T5 docs for more information" - ) - - # shift inputs to the right - if is_torch_fx_proxy(input_ids): - # Item assignment is not supported natively for proxies. - shifted_input_ids = torch.full( - input_ids.shape[:-1] + (1,), decoder_start_token_id - ) - shifted_input_ids = torch.cat( - [shifted_input_ids, input_ids[..., :-1]], dim=-1 - ) - else: - shifted_input_ids = input_ids.new_zeros(input_ids.shape) - shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() - shifted_input_ids[..., 0] = decoder_start_token_id - - assert ( - pad_token_id is not None - ), "self.model.config.pad_token_id has to be defined." - # replace possible -100 values in labels by `pad_token_id` - shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) - - return shifted_input_ids - - -class T5Stack(T5PreTrainedModel): - def __init__(self, config, embed_tokens=None): - super().__init__(config) - - self.embed_tokens = embed_tokens - self.is_decoder = config.is_decoder - - self.block = nn.ModuleList( - [ - T5Block(config, has_relative_attention_bias=bool(i == 0)) - for i in range(config.num_layers) - ] - ) - self.final_layer_norm = T5LayerNorm( - config.d_model, eps=config.layer_norm_epsilon - ) - self.dropout = nn.Dropout(config.dropout_rate) - - # Initialize weights and apply final processing - self.post_init() - # Model parallel - self.model_parallel = False - self.device_map = None - self.gradient_checkpointing = False - - @add_start_docstrings(PARALLELIZE_DOCSTRING) - def parallelize(self, device_map=None): - # Check validity of device_map - self.device_map = ( - get_device_map(len(self.block), range(torch.cuda.device_count())) - if device_map is None - else device_map - ) - assert_device_map(self.device_map, len(self.block)) - self.model_parallel = True - self.first_device = ( - "cpu" - if "cpu" in self.device_map.keys() - else "cuda:" + str(min(self.device_map.keys())) - ) - self.last_device = "cuda:" + str(max(self.device_map.keys())) - # Load onto devices - for k, v in self.device_map.items(): - for layer in v: - cuda_device = "cuda:" + str(k) - self.block[layer] = self.block[layer].to(cuda_device) - - # Set embed_tokens to first layer - self.embed_tokens = self.embed_tokens.to(self.first_device) - # Set final layer norm to last device - self.final_layer_norm = self.final_layer_norm.to(self.last_device) - - @add_start_docstrings(PARALLELIZE_DOCSTRING) - def deparallelize(self): - self.model_parallel = False - self.device_map = None - self.first_device = "cpu" - self.last_device = "cpu" - for i in range(len(self.block)): - self.block[i] = self.block[i].to("cpu") - self.embed_tokens = self.embed_tokens.to("cpu") - self.final_layer_norm = self.final_layer_norm.to("cpu") - torch.cuda.empty_cache() - - def get_input_embeddings(self): - return self.embed_tokens - - def set_input_embeddings(self, new_embeddings): - self.embed_tokens = new_embeddings - - def forward( - self, - input_ids=None, - attention_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - inputs_embeds=None, - head_mask=None, - cross_attn_head_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - ): - # Model parallel - if self.model_parallel: - torch.cuda.set_device(self.first_device) - self.embed_tokens = self.embed_tokens.to(self.first_device) - use_cache = use_cache if use_cache is not None else self.config.use_cache - 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 input_ids is not None and inputs_embeds is not None: - err_msg_prefix = "decoder_" if self.is_decoder else "" - raise ValueError( - f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" - ) - elif input_ids is not None: - input_shape = input_ids.size() - input_ids = input_ids.view(-1, input_shape[-1]) - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - err_msg_prefix = "decoder_" if self.is_decoder else "" - raise ValueError( - f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds" - ) - - if inputs_embeds is None: - assert ( - self.embed_tokens is not None - ), "You have to initialize the model with valid token embeddings" - inputs_embeds = self.embed_tokens(input_ids) - - batch_size, seq_length = input_shape - - # required mask seq length can be calculated via length of past - mask_seq_length = ( - past_key_values[0][0].shape[2] + seq_length - if past_key_values is not None - else seq_length - ) - - if use_cache is True: - assert ( - self.is_decoder - ), f"`use_cache` can only be set to `True` if {self} is used as a decoder" - - if attention_mask is None: - attention_mask = torch.ones( - batch_size, mask_seq_length, device=inputs_embeds.device - ) - if ( - self.is_decoder - and encoder_attention_mask is None - and encoder_hidden_states is not None - ): - encoder_seq_length = encoder_hidden_states.shape[1] - encoder_attention_mask = torch.ones( - batch_size, - encoder_seq_length, - device=inputs_embeds.device, - dtype=torch.long, - ) - - # initialize past_key_values with `None` if past does not exist - if past_key_values is None: - past_key_values = [None] * len(self.block) - - # 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 = self.get_extended_attention_mask( - attention_mask, input_shape - ) - - # 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.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=inputs_embeds.device - ) - encoder_extended_attention_mask = self.invert_attention_mask( - encoder_attention_mask - ) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - head_mask = self.get_head_mask(head_mask, self.config.num_layers) - cross_attn_head_mask = self.get_head_mask( - cross_attn_head_mask, self.config.num_layers - ) - present_key_value_states = () if use_cache else None - all_hidden_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - all_cross_attentions = () if (output_attentions and self.is_decoder) else None - position_bias = None - encoder_decoder_position_bias = None - - hidden_states = self.dropout(inputs_embeds) - - for i, (layer_module, past_key_value) in enumerate( - zip(self.block, past_key_values) - ): - layer_head_mask = head_mask[i] - cross_attn_layer_head_mask = cross_attn_head_mask[i] - # Model parallel - if self.model_parallel: - torch.cuda.set_device(hidden_states.device) - # Ensure that attention_mask is always on the same device as hidden_states - if attention_mask is not None: - attention_mask = attention_mask.to(hidden_states.device) - if position_bias is not None: - position_bias = position_bias.to(hidden_states.device) - if encoder_hidden_states is not None: - encoder_hidden_states = encoder_hidden_states.to( - hidden_states.device - ) - if encoder_extended_attention_mask is not None: - encoder_extended_attention_mask = ( - encoder_extended_attention_mask.to(hidden_states.device) - ) - if encoder_decoder_position_bias is not None: - encoder_decoder_position_bias = encoder_decoder_position_bias.to( - hidden_states.device - ) - if layer_head_mask is not None: - layer_head_mask = layer_head_mask.to(hidden_states.device) - if cross_attn_layer_head_mask is not None: - cross_attn_layer_head_mask = cross_attn_layer_head_mask.to( - hidden_states.device - ) - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - def create_custom_forward(module): - def custom_forward(*inputs): - return tuple(module(*inputs, use_cache, output_attentions)) - - return custom_forward - - layer_outputs = checkpoint( - create_custom_forward(layer_module), - hidden_states, - extended_attention_mask, - position_bias, - encoder_hidden_states, - encoder_extended_attention_mask, - encoder_decoder_position_bias, - layer_head_mask, - cross_attn_layer_head_mask, - None, # past_key_value is always None with gradient checkpointing - ) - else: - layer_outputs = layer_module( - hidden_states, - attention_mask=extended_attention_mask, - position_bias=position_bias, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - encoder_decoder_position_bias=encoder_decoder_position_bias, - layer_head_mask=layer_head_mask, - cross_attn_layer_head_mask=cross_attn_layer_head_mask, - past_key_value=past_key_value, - use_cache=use_cache, - output_attentions=output_attentions, - ) - - # layer_outputs is a tuple with: - # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) - if use_cache is False: - layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] - - hidden_states, present_key_value_state = layer_outputs[:2] - - # We share the position biases between the layers - the first layer store them - # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), - # (cross-attention position bias), (cross-attention weights) - position_bias = layer_outputs[2] - if self.is_decoder and encoder_hidden_states is not None: - encoder_decoder_position_bias = layer_outputs[ - 4 if output_attentions else 3 - ] - # append next layer key value states - if use_cache: - present_key_value_states = present_key_value_states + ( - present_key_value_state, - ) - - if output_attentions: - all_attentions = all_attentions + (layer_outputs[3],) - if self.is_decoder: - all_cross_attentions = all_cross_attentions + (layer_outputs[5],) - - # Model Parallel: If it's the last layer for that device, put things on the next device - if self.model_parallel: - for k, v in self.device_map.items(): - if i == v[-1] and "cuda:" + str(k) != self.last_device: - hidden_states = hidden_states.to("cuda:" + str(k + 1)) - - hidden_states = self.final_layer_norm(hidden_states) - hidden_states = self.dropout(hidden_states) - - # Add last layer - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple( - v - for v in [ - hidden_states, - present_key_value_states, - all_hidden_states, - all_attentions, - all_cross_attentions, - ] - if v is not None - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=present_key_value_states, - hidden_states=all_hidden_states, - attentions=all_attentions, - cross_attentions=all_cross_attentions, - ) - - -T5_START_DOCSTRING = r""" - - The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text - Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan - Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a - text-to-text denoising generative setting. - - This model inherits from [`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](https://pytorch.org/docs/stable/nn.html#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 ([`T5Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. -""" - -T5_INPUTS_DOCSTRING = r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you - should be able to pad the inputs on both the right and the left. - - Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for detail. - - [What are input IDs?](../glossary#input-ids) - - To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training). - attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *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#attention-mask) - decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): - Indices of decoder input sequence tokens in the vocabulary. - - Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are decoder input IDs?](../glossary#decoder-input-ids) - - T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` - is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). - - To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5 - Training](./t5#training). - decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): - Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also - be used by default. - head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): - Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0, - 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): - Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0, - 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): - Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in - `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): - Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*) - `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at - the output of the last layer of the encoder. Used in the cross-attention of the decoder. - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(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 `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This - is useful if you want more control over how to convert `input_ids` indices into associated vectors than the - model's internal embedding lookup matrix. - decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded - representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be - input (see `past_key_values`). This is useful if you want more control over how to convert - `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - - If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value - of `inputs_embeds`. - - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - - output_attentions (`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 (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. -""" - -T5_ENCODER_INPUTS_DOCSTRING = r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you - should be able to pad the inputs on both the right and the left. - - Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for detail. - - To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training). - attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *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#attention-mask) - head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(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 (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This - is useful if you want more control over how to convert `input_ids` indices into associated vectors than the - model's internal embedding lookup matrix. - output_attentions (`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 (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. -""" - -# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask -__HEAD_MASK_WARNING_MSG = """ -The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently, -`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions. -If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers, -num_heads)`. -""" - - -@add_start_docstrings( - "The bare T5 Model transformer outputting raw hidden-states without any specific head on top.", - T5_START_DOCSTRING, -) -class T5Model(T5PreTrainedModel): - _keys_to_ignore_on_load_missing = [ - r"encoder.embed_tokens.weight", - r"decoder.embed_tokens.weight", - ] - _keys_to_ignore_on_load_unexpected = [ - r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight", - ] - - def __init__(self, config: T5Config): - super().__init__(config) - self.shared = nn.Embedding(config.vocab_size, config.d_model) - - encoder_config = copy.deepcopy(config) - encoder_config.is_decoder = False - encoder_config.use_cache = False - encoder_config.is_encoder_decoder = False - self.encoder = T5Stack(encoder_config, self.shared) - - decoder_config = copy.deepcopy(config) - decoder_config.is_decoder = True - decoder_config.is_encoder_decoder = False - decoder_config.num_layers = config.num_decoder_layers - self.decoder = T5Stack(decoder_config, self.shared) - - # Initialize weights and apply final processing - self.post_init() - - # Model parallel - self.model_parallel = False - self.device_map = None - - @add_start_docstrings(PARALLELIZE_DOCSTRING) - def parallelize(self, device_map=None): - self.device_map = ( - get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) - if device_map is None - else device_map - ) - assert_device_map(self.device_map, len(self.encoder.block)) - self.encoder.parallelize(self.device_map) - self.decoder.parallelize(self.device_map) - self.model_parallel = True - - @add_start_docstrings(DEPARALLELIZE_DOCSTRING) - def deparallelize(self): - self.encoder.deparallelize() - self.decoder.deparallelize() - self.encoder = self.encoder.to("cpu") - self.decoder = self.decoder.to("cpu") - self.model_parallel = False - self.device_map = None - torch.cuda.empty_cache() - - def get_input_embeddings(self): - return self.shared - - def set_input_embeddings(self, new_embeddings): - self.shared = new_embeddings - self.encoder.set_input_embeddings(new_embeddings) - self.decoder.set_input_embeddings(new_embeddings) - - def get_encoder(self): - return self.encoder - - def get_decoder(self): - return self.decoder - - 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(T5_INPUTS_DOCSTRING) - @replace_return_docstrings( - output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - decoder_input_ids: Optional[torch.LongTensor] = None, - decoder_attention_mask: Optional[torch.BoolTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - decoder_head_mask: Optional[torch.FloatTensor] = None, - cross_attn_head_mask: Optional[torch.Tensor] = None, - encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, - past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, - inputs_embeds: Optional[torch.Tensor] = None, - decoder_inputs_embeds: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]: - r""" - Returns: - - Example: - - ```python - >>> from transformers import T5Tokenizer, T5Model - - >>> tokenizer = T5Tokenizer.from_pretrained("t5-small") - >>> model = T5Model.from_pretrained("t5-small") - - >>> input_ids = tokenizer( - ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" - ... ).input_ids # Batch size 1 - >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 - - >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model. - >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg. - >>> decoder_input_ids = model._shift_right(decoder_input_ids) - - >>> # forward pass - >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) - >>> last_hidden_states = outputs.last_hidden_state - ```""" - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - - # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask - if head_mask is not None and decoder_head_mask is None: - if self.config.num_layers == self.config.num_decoder_layers: - warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) - decoder_head_mask = head_mask - - # Encode if needed (training, first prediction pass) - if encoder_outputs is None: - encoder_outputs = self.encoder( - input_ids=input_ids, - attention_mask=attention_mask, - inputs_embeds=inputs_embeds, - head_mask=head_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): - encoder_outputs = BaseModelOutput( - last_hidden_state=encoder_outputs[0], - hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, - attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, - ) - - hidden_states = encoder_outputs[0] - - # Set device for model parallelism - if self.model_parallel: - torch.cuda.set_device(self.decoder.first_device) - hidden_states = hidden_states.to(self.decoder.first_device) - if decoder_input_ids is not None: - decoder_input_ids = decoder_input_ids.to(self.decoder.first_device) - if attention_mask is not None: - attention_mask = attention_mask.to(self.decoder.first_device) - if decoder_attention_mask is not None: - decoder_attention_mask = decoder_attention_mask.to( - self.decoder.first_device - ) - - # Decode - decoder_outputs = self.decoder( - input_ids=decoder_input_ids, - attention_mask=decoder_attention_mask, - inputs_embeds=decoder_inputs_embeds, - past_key_values=past_key_values, - encoder_hidden_states=hidden_states, - encoder_attention_mask=attention_mask, - head_mask=decoder_head_mask, - cross_attn_head_mask=cross_attn_head_mask, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - if not return_dict: - return decoder_outputs + encoder_outputs - - return Seq2SeqModelOutput( - last_hidden_state=decoder_outputs.last_hidden_state, - past_key_values=decoder_outputs.past_key_values, - decoder_hidden_states=decoder_outputs.hidden_states, - decoder_attentions=decoder_outputs.attentions, - cross_attentions=decoder_outputs.cross_attentions, - encoder_last_hidden_state=encoder_outputs.last_hidden_state, - encoder_hidden_states=encoder_outputs.hidden_states, - encoder_attentions=encoder_outputs.attentions, - ) - - -@add_start_docstrings( - """T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING -) -class T5ForConditionalGeneration(T5PreTrainedModel): - _keys_to_ignore_on_load_missing = [ - r"encoder.embed_tokens.weight", - r"decoder.embed_tokens.weight", - r"lm_head.weight", - ] - _keys_to_ignore_on_load_unexpected = [ - r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight", - ] - - def __init__(self, config: T5Config): - super().__init__(config) - self.model_dim = config.d_model - - self.shared = nn.Embedding(config.vocab_size, config.d_model) - - encoder_config = copy.deepcopy(config) - encoder_config.is_decoder = False - encoder_config.use_cache = False - encoder_config.is_encoder_decoder = False - self.encoder = T5Stack(encoder_config, self.shared) - - decoder_config = copy.deepcopy(config) - decoder_config.is_decoder = True - decoder_config.is_encoder_decoder = False - decoder_config.num_layers = config.num_decoder_layers - self.decoder = T5Stack(decoder_config, self.shared) - - self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - # Model parallel - self.model_parallel = False - self.device_map = None - - @add_start_docstrings(PARALLELIZE_DOCSTRING) - def parallelize(self, device_map=None): - self.device_map = ( - get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) - if device_map is None - else device_map - ) - assert_device_map(self.device_map, len(self.encoder.block)) - self.encoder.parallelize(self.device_map) - self.decoder.parallelize(self.device_map) - self.lm_head = self.lm_head.to(self.decoder.first_device) - self.model_parallel = True - - @add_start_docstrings(DEPARALLELIZE_DOCSTRING) - def deparallelize(self): - self.encoder.deparallelize() - self.decoder.deparallelize() - self.encoder = self.encoder.to("cpu") - self.decoder = self.decoder.to("cpu") - self.lm_head = self.lm_head.to("cpu") - self.model_parallel = False - self.device_map = None - torch.cuda.empty_cache() - - def get_input_embeddings(self): - return self.shared - - def set_input_embeddings(self, new_embeddings): - self.shared = new_embeddings - self.encoder.set_input_embeddings(new_embeddings) - self.decoder.set_input_embeddings(new_embeddings) - - def set_output_embeddings(self, new_embeddings): - self.lm_head = new_embeddings - - def get_output_embeddings(self): - return self.lm_head - - def get_encoder(self): - return self.encoder - - def get_decoder(self): - return self.decoder - - @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING) - @replace_return_docstrings( - output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - decoder_input_ids: Optional[torch.LongTensor] = None, - decoder_attention_mask: Optional[torch.BoolTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - decoder_head_mask: Optional[torch.FloatTensor] = None, - cross_attn_head_mask: Optional[torch.Tensor] = None, - encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - decoder_inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - reduction: Optional[str] = "mean", - ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., - config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for - labels in `[0, ..., config.vocab_size]` - - Returns: - - Examples: - - ```python - >>> from transformers import T5Tokenizer, T5ForConditionalGeneration - - >>> tokenizer = T5Tokenizer.from_pretrained("t5-small") - >>> model = T5ForConditionalGeneration.from_pretrained("t5-small") - - >>> # training - >>> input_ids = tokenizer("TheAttention is All You Need in Speech Separation | Github Repo
" -examples = [ - ['samples_audio_samples_test_mixture.wav'] -] -gr.Interface(speechbrain, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() \ No newline at end of file diff --git a/spaces/alexray/btc_predictor/tests/test_data_quality.py b/spaces/alexray/btc_predictor/tests/test_data_quality.py deleted file mode 100644 index f6a35ae4e04bad37d4ef98e7f6d7625e60c53391..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/tests/test_data_quality.py +++ /dev/null @@ -1,55 +0,0 @@ -import unittest -import pandas as pd - - -class TestDataQuality(unittest.TestCase): - def setUp(self): - # Load your data here - self.data = pd.read_csv("data/assets_data.csv") - - def test_completeness(self): - # Check for missing values - missing_values = self.data.isnull().sum() - self.assertEqual(missing_values.sum(), 0, - "There are missing values in the dataset.") - - def test_accuracy(self): - # Define acceptable ranges for numerical columns - acceptable_ranges = { - "open": (0, 100000), - "high": (0, 100000), - "low": (0, 100000), - "close": (0, 100000), - } - - # Check if values are within acceptable ranges - for column, (min_val, max_val) in acceptable_ranges.items(): - values = self.data[column] - self.assertTrue(all(min_val <= values) and all(values <= max_val), - f"Values in {column} column are outside the" - " acceptable range." - ) - - def test_consistency(self): - # Check for duplicate rows - duplicate_rows = self.data[self.data.duplicated()] - self.assertTrue(duplicate_rows.empty, "Duplicate rows found in the" - " dataset." - ) - - def test_validity(self): - # Check date format validity - date_format_valid = pd.to_datetime(self.data.index, errors='coerce') \ - .notna().all() - self.assertTrue(date_format_valid, "Date format is not valid.") - - # Check format validity for specific columns (e.g., symbols) - symbol_columns = ["000001.SS", "AAPL", "CL=F", "GC=F", "HG=F", - "NVDA", "^DJI", "^GSPC", "^N100", "^N225"] - valid_symbol_format = self.data[symbol_columns].notna() - self.assertTrue(valid_symbol_format.all().all(), "Invalid symbol" - " format found.") - - -if __name__ == '__main__': - unittest.main() diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/windows.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/windows.py deleted file mode 100644 index ef972bdf29ce91b5abe3714eb92587458cf3f03c..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/windows.py +++ /dev/null @@ -1,182 +0,0 @@ -from __future__ import annotations - -import ctypes -import os -from functools import lru_cache -from typing import Callable - -from .api import PlatformDirsABC - - -class Windows(PlatformDirsABC): - """`MSDN on where to store app data files -'
- else:
- lines[i] = f"
" + line - text = "".join(lines) - return text - -def predict(_chatbot, task_history): - chat_query = _chatbot[-1][0] - query = task_history[-1][0] - history_cp = copy.deepcopy(task_history) - full_response = "" - - history_filter = [] - pic_idx = 1 - pre = "" - for i, (q, a) in enumerate(history_cp): - if isinstance(q, (tuple, list)): - q = f'Picture {pic_idx}:
🔥AssemblyAI: Conformer-2 Demo🔥
""" - -subtitle = ( - """Automatic Speech Recognition using the AssemblyAI API
""" -) -link = """Click here to learn more about the Conformer-2 model
""" - - -def submit_to_AAI(api_key, radio, audio_file, mic_recording): - - if radio == "Audio File": - audio_data = audio_file - elif radio == "Record Audio": - audio_data = mic_recording - - if not api_key: - return "Error! Did you use a valid API key?" - - aai.settings.api_key = api_key - transcriber = aai.Transcriber() - - # Create temporary "file" and write data to it - sr, aud = audio_data - - bytes_wav = bytes() - temp_file = io.BytesIO(bytes_wav) - write(temp_file, sr, aud) - - # Workaround to upload a file-like object before transcribing - # This should be abstracted away in future SDK versions: - try: - upload_url = aai.api.upload_file(aai.Client.get_default().http_client, temp_file) - except aai.types.TranscriptError as e: - return str(e) - - # Now we can transcibe the url - transcript = transcriber.transcribe(upload_url) - - if transcript.error is not None: - return transcript.error - - paragraphs = transcript.get_paragraphs() - return "\n\n".join(p.text for p in paragraphs) - - -def change_audio_source(radio): - if radio == "Audio File": - return [gr.Audio.update(visible=True), gr.Audio.update(visible=False)] - elif radio == "Record Audio": - return [gr.Audio.update(visible=False), gr.Audio.update(visible=True)] - - -with gr.Blocks( - css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;} - #chatbot {height: 520px; overflow: auto;}""" -) as demo: - gr.HTML( - '
-
-
-
-
\ No newline at end of file
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/webui.sh b/spaces/awaawawawa/iurf7irfuyytruyyugb/webui.sh
deleted file mode 100644
index 980c0aaf33012afae0d1d1fda19ffb426cb35a00..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/webui.sh
+++ /dev/null
@@ -1,141 +0,0 @@
-#!/bin/bash
-#################################################
-# Please do not make any changes to this file, #
-# change the variables in webui-user.sh instead #
-#################################################
-# Read variables from webui-user.sh
-# shellcheck source=/dev/null
-if [[ -f webui-user.sh ]]
-then
- source ./webui-user.sh
-fi
-
-# Set defaults
-# Install directory without trailing slash
-if [[ -z "${install_dir}" ]]
-then
- install_dir="/home/$(whoami)"
-fi
-
-# Name of the subdirectory (defaults to stable-diffusion-webui)
-if [[ -z "${clone_dir}" ]]
-then
- clone_dir="stable-diffusion-webui"
-fi
-
-# python3 executable
-if [[ -z "${python_cmd}" ]]
-then
- python_cmd="python3"
-fi
-
-# git executable
-if [[ -z "${GIT}" ]]
-then
- export GIT="git"
-fi
-
-# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
-if [[ -z "${venv_dir}" ]]
-then
- venv_dir="venv"
-fi
-
-if [[ -z "${LAUNCH_SCRIPT}" ]]
-then
- LAUNCH_SCRIPT="launch.py"
-fi
-
-# Disable sentry logging
-export ERROR_REPORTING=FALSE
-
-# Do not reinstall existing pip packages on Debian/Ubuntu
-export PIP_IGNORE_INSTALLED=0
-
-# Pretty print
-delimiter="################################################################"
-
-printf "\n%s\n" "${delimiter}"
-printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n"
-printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m"
-printf "\n%s\n" "${delimiter}"
-
-# Do not run as root
-if [[ $(id -u) -eq 0 ]]
-then
- printf "\n%s\n" "${delimiter}"
- printf "\e[1m\e[31mERROR: This script must not be launched as root, aborting...\e[0m"
- printf "\n%s\n" "${delimiter}"
- exit 1
-else
- printf "\n%s\n" "${delimiter}"
- printf "Running on \e[1m\e[32m%s\e[0m user" "$(whoami)"
- printf "\n%s\n" "${delimiter}"
-fi
-
-if [[ -d .git ]]
-then
- printf "\n%s\n" "${delimiter}"
- printf "Repo already cloned, using it as install directory"
- printf "\n%s\n" "${delimiter}"
- install_dir="${PWD}/../"
- clone_dir="${PWD##*/}"
-fi
-
-# Check prerequisites
-for preq in "${GIT}" "${python_cmd}"
-do
- if ! hash "${preq}" &>/dev/null
- then
- printf "\n%s\n" "${delimiter}"
- printf "\e[1m\e[31mERROR: %s is not installed, aborting...\e[0m" "${preq}"
- printf "\n%s\n" "${delimiter}"
- exit 1
- fi
-done
-
-if ! "${python_cmd}" -c "import venv" &>/dev/null
-then
- printf "\n%s\n" "${delimiter}"
- printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m"
- printf "\n%s\n" "${delimiter}"
- exit 1
-fi
-
-printf "\n%s\n" "${delimiter}"
-printf "Clone or update stable-diffusion-webui"
-printf "\n%s\n" "${delimiter}"
-cd "${install_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/, aborting...\e[0m" "${install_dir}"; exit 1; }
-if [[ -d "${clone_dir}" ]]
-then
- cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
- "${GIT}" pull
-else
- "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
- cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-fi
-
-printf "\n%s\n" "${delimiter}"
-printf "Create and activate python venv"
-printf "\n%s\n" "${delimiter}"
-cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-if [[ ! -d "${venv_dir}" ]]
-then
- "${python_cmd}" -m venv "${venv_dir}"
- first_launch=1
-fi
-# shellcheck source=/dev/null
-if [[ -f "${venv_dir}"/bin/activate ]]
-then
- source "${venv_dir}"/bin/activate
-else
- printf "\n%s\n" "${delimiter}"
- printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
- printf "\n%s\n" "${delimiter}"
- exit 1
-fi
-
-printf "\n%s\n" "${delimiter}"
-printf "Launching launch.py..."
-printf "\n%s\n" "${delimiter}"
-"${python_cmd}" "${LAUNCH_SCRIPT}"
diff --git a/spaces/awacke1/Generative-AI-SOP/README.md b/spaces/awacke1/Generative-AI-SOP/README.md
deleted file mode 100644
index 30755214ea15f22c570f9486d0c09756d324bf33..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Generative-AI-SOP/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AISOP-ChatGPT-Standard-Operating-Procedures
-emoji: ⚕️AISOP👩⚕️
-colorFrom: gray
-colorTo: red
-sdk: static
-pinned: false
-license: mit
----
-
-HTML5 Space: https://huggingface.co/spaces/awacke1/Generative-AI-SOP/
-Streamlit Space: https://huggingface.co/spaces/awacke1/Generative-AI-SOP
-Gradio ChatGPT Space: https://huggingface.co/spaces/awacke1/ChatGPT-SOP
diff --git a/spaces/awacke1/Map-California-AI/app.py b/spaces/awacke1/Map-California-AI/app.py
deleted file mode 100644
index 9adcf523d20ad51a9af52570111d7b0c96ae4903..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Map-California-AI/app.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import streamlit as st
-import folium
-from streamlit_folium import folium_static
-from folium.plugins import MarkerCluster
-
-# Define California attractions data
-california_attractions = [
- ('The Getty Center', 34.0780, -118.4741, 'The Getty Center is known for its architecture, gardens, and views overlooking Los Angeles.'),
- ('Venice Beach', 33.9850, -118.4695, 'Venice Beach is famous for its oceanfront boardwalk and Muscle Beach gym.'),
- ('Santa Monica Pier', 34.0104, -118.4962, 'Santa Monica Pier features a range of entertainment, dining, and shopping experiences.'),
- ('Golden Gate Bridge', 37.8199, -122.4783, 'The Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the entrance to San Francisco Bay.'),
- ('Yosemite National Park', 37.8651, -119.5383, 'Known for its waterfalls, deep valleys, and iconic view of El Capitan.'),
- ('Disneyland', 33.8121, -117.9190, 'Disneyland Resort, located in Anaheim, is the first of two theme parks built under the Disneyland umbrella.'),
- ('Napa Valley', 38.5025, -122.2654, 'Napa Valley is known for its world-class wineries.'),
- ('Lake Tahoe', 39.0968, -120.0324, 'Lake Tahoe is a large freshwater lake known for its clear blue water.'),
- ('Universal Studios', 34.1381, -118.3534, 'Universal Studios Hollywood includes a movie-based theme park and studios that offers tours.'),
- ('Alcatraz Island', 37.8267, -122.4230, 'Alcatraz Island is home to the abandoned prison and the site of the oldest operating lighthouse.')
-]
-
-# Create a map centered on California
-m = folium.Map(location=[36.7783, -119.4179], zoom_start=6)
-
-# Add markers for each attraction and add them to a MarkerCluster
-marker_cluster = MarkerCluster().add_to(m)
-for place in california_attractions:
- folium.Marker(
- location=[place[1], place[2]],
- popup=f'{place[0]}William Suh
- -
-
-
- Mentee to Mentor
1- What's your motivation to be a mentor with SharpestMinds?
- Had a ok experience while I was a mentee - Believe can be a better mentor and provide a better mentorship experience. Used to be a teacher and a tutor for students in online STEM projects.
2- What's your career journey in the Data field?
- Have a master degree in a field not related to tech.
- Moved industry from being a teacher to get into data related field.
- Did a Data and coding BootCamp with General Assembly.
- Got a job at Macey's as Retention analyst but was laid off - found SM and became a mentee after that.
- Worked at Sephora as a retail Business Analyst - Work involved forecasting sales and generating business reports.
- Joined a startup after that at a hearing aid company - clearcaptions worked on SQL and process improvements.
- Currently working at Intuit as Product analyst. Work involves A/B testing, building tableau dashboards, and working on SQL.
3- How was your experience as a SM mentee?
- It was mixed, Mentor started strong and made introductions to peers. But the network didnt help out a lot. Mentor relied on networking a lot. Shared learning resources and weekly touch bases were useful to stay accountable. But eventually got a job with a help of a recruiter.
4- What's the biggest challenge a newcomer faces when they want to land a analytics role? How can you help them with this?
- The biggest challenge is getting the foot in the door. For people who don't have traditional background in tech the industry is resistant to their profiles and switch is difficult. Hiring managers are rigid and it's difficult to convince them during technical interviews. Will help mentees with tech interviews and developing hard skills.
5- Do you have any questions regarding SM and platform?
- How many hours of commitment per week?
- Mentee demographic profile?
- Avg % of ISA?
- Do mentors reach out mentees or vica-versa?
- What are the next steps?
-1- What's your motivation to be a mentor with SharpestMinds?
- Had a ok experience while I was a mentee - Believe can be a better mentor and provide a better mentorship experience. Used to be a teacher and a tutor for students in online STEM projects.
2- What's your career journey in the Data field?
- Have a master degree in a field not related to tech.
- Moved industry from being a teacher to get into data related field.
- Did a Data and coding BootCamp with General Assembly.
- Got a job at Macey's as Retention analyst but was laid off - found SM and became a mentee after that.
- Worked at Sephora as a retail Business Analyst - Work involved forecasting sales and generating business reports.
- Joined a startup after that at a hearing aid company - clearcaptions worked on SQL and process improvements.
- Currently working at Intuit as Product analyst. Work involves A/B testing, building tableau dashboards, and working on SQL.
3- How was your experience as a SM mentee?
- It was mixed, Mentor started strong and made introductions to peers. But the network didnt help out a lot. Mentor relied on networking a lot. Shared learning resources and weekly touch bases were useful to stay accountable. But eventually got a job with a help of a recruiter.
4- What's the biggest challenge a newcomer faces when they want to land a analytics role? How can you help them with this?
- The biggest challenge is getting the foot in the door. For people who don't have traditional background in tech the industry is resistant to their profiles and switch is difficult. Hiring managers are rigid and it's difficult to convince them during technical interviews. Will help mentees with tech interviews and developing hard skills.
5- Do you have any questions regarding SM and platform?
- How many hours of commitment per week?
- Mentee demographic profile?
- Avg % of ISA?
- Do mentors reach out mentees or vica-versa?
- What are the next steps?
{place[3]}', - icon=folium.Icon(color='green') - ).add_to(marker_cluster) - -# Add PolyLine for paths between markers with animation -locations = [place[1:3] for place in california_attractions] -path = folium.PolyLine(locations, color='blue', opacity=0.8, weight=5, smooth_factor=0.5).add_to(m) -folium.plugins.PolyLineTextPath( - polyline=path, - text='\u25BA', - repeat=True, - offset=6, - attributes={'fill': 'blue', 'font-weight': 'bold', 'font-size': '12'} -).add_to(path) - -folium_static(m) - -st.markdown(""" -# 🌞 California Attractions 🌴 -The map above shows the location of various attractions in California. Hover over the markers to learn more about each location. -""") - -# Function to update the map when a button is clicked -def update_map(place_data): - m.location = [place_data[1], place_data[2]] - m.zoom_start = 13 - folium_static(m) - -for i in range(0, len(california_attractions), 3): - cols = st.columns(3) - for j in range(3): - if i + j < len(california_attractions): - with cols[j]: - if st.button(california_attractions[i + j][0]): - update_map(california_attractions[i + j]) -folium_static(m) - -st.markdown(""" -## 🍷 Napa Valley: The Wine Wonderland 🍇 -Napa Valley, located in the heart of California, is synonymous with premium wines, fine dining, and breathtaking vistas. Not only is it a world-class wine-producing region, but it's also a paradise for foodies and outdoor enthusiasts. 🥂 -Whether you're a sommelier or a casual wine drinker, Napa Valley offers a wide range of experiences, from vineyard tours and wine-tasting sessions to hot air balloon rides over the scenic countryside. 🎈 -The valley is home to over 400 wineries, each with its own unique blend of grape varieties, production techniques, and flavors. 🍾 -""") diff --git a/spaces/awacke1/Try.Playing.Learning.Sharing.On.This/index.html b/spaces/awacke1/Try.Playing.Learning.Sharing.On.This/index.html deleted file mode 100644 index a7773ab3c64c5e1d939315fe5ca95fd552fd7212..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Try.Playing.Learning.Sharing.On.This/index.html +++ /dev/null @@ -1,12 +0,0 @@ - - -
Flappy Plane Swoop Sim
-User input: WASD
-This WebGL demo demonstrates PlayCanvas runnable in an HTML5 playable surface available anywhere your browser goes. - Check it out here:🤗Love Huggingface for HTML5.
-PlayCanvas project is here
- - - \ No newline at end of file diff --git a/spaces/badayvedat/AudioSep/models/CLAP/training/distributed.py b/spaces/badayvedat/AudioSep/models/CLAP/training/distributed.py deleted file mode 100644 index 2fa61f76c5cc3ab9f6a9643042afa8e1f2e1cb7f..0000000000000000000000000000000000000000 --- a/spaces/badayvedat/AudioSep/models/CLAP/training/distributed.py +++ /dev/null @@ -1,150 +0,0 @@ -import os - -import torch -import socket - -try: - import horovod.torch as hvd -except ImportError: - hvd = None - - -def is_global_master(args): - return args.rank == 0 - - -def is_local_master(args): - return args.local_rank == 0 - - -def is_master(args, local=False): - return is_local_master(args) if local else is_global_master(args) - - -def is_using_horovod(): - # NOTE w/ horovod run, OMPI vars should be set, but w/ SLURM PMI vars will be set - # Differentiating between horovod and DDP use via SLURM may not be possible, so horovod arg still required... - ompi_vars = ["OMPI_COMM_WORLD_RANK", "OMPI_COMM_WORLD_SIZE"] - pmi_vars = ["PMI_RANK", "PMI_SIZE"] - if all([var in os.environ for var in ompi_vars]) or all( - [var in os.environ for var in pmi_vars] - ): - return True - else: - return False - - -def is_using_distributed(): - if "WORLD_SIZE" in os.environ: - return int(os.environ["WORLD_SIZE"]) > 1 - if "SLURM_NTASKS" in os.environ: - return int(os.environ["SLURM_NTASKS"]) > 1 - return False - - -def world_info_from_env(): - local_rank = 0 - for v in ( - "SLURM_LOCALID", - "MPI_LOCALRANKID", - "OMPI_COMM_WORLD_LOCAL_RANK", - "LOCAL_RANK", - ): - if v in os.environ: - local_rank = int(os.environ[v]) - break - global_rank = 0 - for v in ("SLURM_PROCID", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "RANK"): - if v in os.environ: - global_rank = int(os.environ[v]) - break - world_size = 1 - for v in ("SLURM_NTASKS", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "WORLD_SIZE"): - if v in os.environ: - world_size = int(os.environ[v]) - break - - return local_rank, global_rank, world_size - - -def init_distributed_device(args): - # Distributed training = training on more than one GPU. - # Works in both single and multi-node scenarios. - args.distributed = False - args.world_size = 1 - args.rank = 0 # global rank - args.local_rank = 0 - if args.horovod: - assert hvd is not None, "Horovod is not installed" - hvd.init() - world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"]) - world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) - local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) - args.local_rank = local_rank - args.rank = world_rank - args.world_size = world_size - # args.local_rank = int(hvd.local_rank()) - # args.rank = hvd.rank() - # args.world_size = hvd.size() - args.distributed = True - os.environ["LOCAL_RANK"] = str(args.local_rank) - os.environ["RANK"] = str(args.rank) - os.environ["WORLD_SIZE"] = str(args.world_size) - print( - f"Distributed training: local_rank={args.local_rank}, " - f"rank={args.rank}, world_size={args.world_size}, " - f"hostname={socket.gethostname()}, pid={os.getpid()}" - ) - elif is_using_distributed(): - if "SLURM_PROCID" in os.environ: - # DDP via SLURM - args.local_rank, args.rank, args.world_size = world_info_from_env() - # SLURM var -> torch.distributed vars in case needed - os.environ["LOCAL_RANK"] = str(args.local_rank) - os.environ["RANK"] = str(args.rank) - os.environ["WORLD_SIZE"] = str(args.world_size) - torch.distributed.init_process_group( - backend=args.dist_backend, - init_method=args.dist_url, - world_size=args.world_size, - rank=args.rank, - ) - elif "OMPI_COMM_WORLD_SIZE" in os.environ: # using Summit cluster - world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"]) - world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) - local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) - args.local_rank = local_rank - args.rank = world_rank - args.world_size = world_size - torch.distributed.init_process_group( - backend=args.dist_backend, - init_method=args.dist_url, - world_size=args.world_size, - rank=args.rank, - ) - else: - # DDP via torchrun, torch.distributed.launch - args.local_rank, _, _ = world_info_from_env() - torch.distributed.init_process_group( - backend=args.dist_backend, init_method=args.dist_url - ) - args.world_size = torch.distributed.get_world_size() - args.rank = torch.distributed.get_rank() - args.distributed = True - print( - f"Distributed training: local_rank={args.local_rank}, " - f"rank={args.rank}, world_size={args.world_size}, " - f"hostname={socket.gethostname()}, pid={os.getpid()}" - ) - - if torch.cuda.is_available(): - if args.distributed and not args.no_set_device_rank: - device = "cuda:%d" % args.local_rank - else: - device = "cuda:0" - torch.cuda.set_device(device) - else: - device = "cpu" - args.device = device - device = torch.device(device) - return device diff --git a/spaces/banana-projects/convai/grunt/Gruntfile.js b/spaces/banana-projects/convai/grunt/Gruntfile.js deleted file mode 100644 index c68b0321abcccdbedb3fff21e925d96a95b6accb..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/convai/grunt/Gruntfile.js +++ /dev/null @@ -1,29 +0,0 @@ -const __path = require('path'); -const CWD = __path.normalize(`${__dirname}/../front`); - -module.exports = function(grunt) { - - grunt.loadNpmTasks('grunt-contrib-less'); - grunt.loadNpmTasks('grunt-contrib-watch'); - grunt.registerTask('default', ['less']); - - grunt.initConfig({ - less: { - options: { - compress: true, - }, - dist: { - src: `${CWD}/less/style.less`, - dest: `${CWD}/dist/style.css` - } - }, - watch: { - options: { - livereload: true, - cwd: CWD, - }, - files: ["*.html", "less/*", "js-src/**/*"], - tasks: 'default' - }, - }); -}; diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/deprecated/LegacyGLTFLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/deprecated/LegacyGLTFLoader.js deleted file mode 100644 index 57eca9984f5d5e770a31c70af4bfc13809fc9da9..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/deprecated/LegacyGLTFLoader.js +++ /dev/null @@ -1,2242 +0,0 @@ -/** - * @author Rich Tibbett / https://github.com/richtr - * @author mrdoob / http://mrdoob.com/ - * @author Tony Parisi / http://www.tonyparisi.com/ - * @author Takahiro / https://github.com/takahirox - */ - -THREE.LegacyGLTFLoader = ( function () { - - function LegacyGLTFLoader( manager ) { - - this.manager = ( manager !== undefined ) ? manager : THREE.DefaultLoadingManager; - - } - - LegacyGLTFLoader.prototype = { - - constructor: LegacyGLTFLoader, - - crossOrigin: 'anonymous', - - load: function ( url, onLoad, onProgress, onError ) { - - var scope = this; - - var resourcePath; - - if ( this.resourcePath !== undefined ) { - - resourcePath = this.resourcePath; - - } else if ( this.path !== undefined ) { - - resourcePath = this.path; - - } else { - - resourcePath = THREE.LoaderUtils.extractUrlBase( url ); - - } - - var loader = new THREE.FileLoader( scope.manager ); - - loader.setPath( this.path ); - loader.setResponseType( 'arraybuffer' ); - - loader.load( url, function ( data ) { - - scope.parse( data, resourcePath, onLoad ); - - }, onProgress, onError ); - - }, - - setCrossOrigin: function ( value ) { - - this.crossOrigin = value; - return this; - - }, - - setPath: function ( value ) { - - this.path = value; - - }, - - setResourcePath: function ( value ) { - - this.resourcePath = value; - return this; - - }, - - parse: function ( data, path, callback ) { - - var content; - var extensions = {}; - - var magic = THREE.LoaderUtils.decodeText( new Uint8Array( data, 0, 4 ) ); - - if ( magic === BINARY_EXTENSION_HEADER_DEFAULTS.magic ) { - - extensions[ EXTENSIONS.KHR_BINARY_GLTF ] = new GLTFBinaryExtension( data ); - content = extensions[ EXTENSIONS.KHR_BINARY_GLTF ].content; - - } else { - - content = THREE.LoaderUtils.decodeText( new Uint8Array( data ) ); - - } - - var json = JSON.parse( content ); - - if ( json.extensionsUsed && json.extensionsUsed.indexOf( EXTENSIONS.KHR_MATERIALS_COMMON ) >= 0 ) { - - extensions[ EXTENSIONS.KHR_MATERIALS_COMMON ] = new GLTFMaterialsCommonExtension( json ); - - } - - var parser = new GLTFParser( json, extensions, { - - crossOrigin: this.crossOrigin, - manager: this.manager, - path: path || this.resourcePath || '' - - } ); - - parser.parse( function ( scene, scenes, cameras, animations ) { - - var glTF = { - "scene": scene, - "scenes": scenes, - "cameras": cameras, - "animations": animations - }; - - callback( glTF ); - - } ); - - } - - }; - - /* GLTFREGISTRY */ - - function GLTFRegistry() { - - var objects = {}; - - return { - - get: function ( key ) { - - return objects[ key ]; - - }, - - add: function ( key, object ) { - - objects[ key ] = object; - - }, - - remove: function ( key ) { - - delete objects[ key ]; - - }, - - removeAll: function () { - - objects = {}; - - }, - - update: function ( scene, camera ) { - - for ( var name in objects ) { - - var object = objects[ name ]; - - if ( object.update ) { - - object.update( scene, camera ); - - } - - } - - } - - }; - - } - - /* GLTFSHADERS */ - - LegacyGLTFLoader.Shaders = { - - update: function () { - - console.warn( 'THREE.LegacyGLTFLoader.Shaders has been deprecated, and now updates automatically.' ); - - } - - }; - - /* GLTFSHADER */ - - function GLTFShader( targetNode, allNodes ) { - - var boundUniforms = {}; - - // bind each uniform to its source node - - var uniforms = targetNode.material.uniforms; - - for ( var uniformId in uniforms ) { - - var uniform = uniforms[ uniformId ]; - - if ( uniform.semantic ) { - - var sourceNodeRef = uniform.node; - - var sourceNode = targetNode; - - if ( sourceNodeRef ) { - - sourceNode = allNodes[ sourceNodeRef ]; - - } - - boundUniforms[ uniformId ] = { - semantic: uniform.semantic, - sourceNode: sourceNode, - targetNode: targetNode, - uniform: uniform - }; - - } - - } - - this.boundUniforms = boundUniforms; - this._m4 = new THREE.Matrix4(); - - } - - // Update - update all the uniform values - GLTFShader.prototype.update = function ( scene, camera ) { - - var boundUniforms = this.boundUniforms; - - for ( var name in boundUniforms ) { - - var boundUniform = boundUniforms[ name ]; - - switch ( boundUniform.semantic ) { - - case "MODELVIEW": - - var m4 = boundUniform.uniform.value; - m4.multiplyMatrices( camera.matrixWorldInverse, boundUniform.sourceNode.matrixWorld ); - break; - - case "MODELVIEWINVERSETRANSPOSE": - - var m3 = boundUniform.uniform.value; - this._m4.multiplyMatrices( camera.matrixWorldInverse, boundUniform.sourceNode.matrixWorld ); - m3.getNormalMatrix( this._m4 ); - break; - - case "PROJECTION": - - var m4 = boundUniform.uniform.value; - m4.copy( camera.projectionMatrix ); - break; - - case "JOINTMATRIX": - - var m4v = boundUniform.uniform.value; - - for ( var mi = 0; mi < m4v.length; mi ++ ) { - - // So it goes like this: - // SkinnedMesh world matrix is already baked into MODELVIEW; - // transform joints to local space, - // then transform using joint's inverse - m4v[ mi ] - .getInverse( boundUniform.sourceNode.matrixWorld ) - .multiply( boundUniform.targetNode.skeleton.bones[ mi ].matrixWorld ) - .multiply( boundUniform.targetNode.skeleton.boneInverses[ mi ] ) - .multiply( boundUniform.targetNode.bindMatrix ); - - } - - break; - - default : - - console.warn( "Unhandled shader semantic: " + boundUniform.semantic ); - break; - - } - - } - - }; - - - /* ANIMATION */ - - LegacyGLTFLoader.Animations = { - - update: function () { - - console.warn( 'THREE.LegacyGLTFLoader.Animation has been deprecated. Use THREE.AnimationMixer instead.' ); - - } - - }; - - /*********************************/ - /********** EXTENSIONS ***********/ - /*********************************/ - - var EXTENSIONS = { - KHR_BINARY_GLTF: 'KHR_binary_glTF', - KHR_MATERIALS_COMMON: 'KHR_materials_common' - }; - - /* MATERIALS COMMON EXTENSION */ - - function GLTFMaterialsCommonExtension( json ) { - - this.name = EXTENSIONS.KHR_MATERIALS_COMMON; - - this.lights = {}; - - var extension = ( json.extensions && json.extensions[ EXTENSIONS.KHR_MATERIALS_COMMON ] ) || {}; - var lights = extension.lights || {}; - - for ( var lightId in lights ) { - - var light = lights[ lightId ]; - var lightNode; - - var lightParams = light[ light.type ]; - var color = new THREE.Color().fromArray( lightParams.color ); - - switch ( light.type ) { - - case "directional": - lightNode = new THREE.DirectionalLight( color ); - lightNode.position.set( 0, 0, 1 ); - break; - - case "point": - lightNode = new THREE.PointLight( color ); - break; - - case "spot": - lightNode = new THREE.SpotLight( color ); - lightNode.position.set( 0, 0, 1 ); - break; - - case "ambient": - lightNode = new THREE.AmbientLight( color ); - break; - - } - - if ( lightNode ) { - - this.lights[ lightId ] = lightNode; - - } - - } - - } - - /* BINARY EXTENSION */ - - var BINARY_EXTENSION_BUFFER_NAME = 'binary_glTF'; - - var BINARY_EXTENSION_HEADER_DEFAULTS = { magic: 'glTF', version: 1, contentFormat: 0 }; - - var BINARY_EXTENSION_HEADER_LENGTH = 20; - - function GLTFBinaryExtension( data ) { - - this.name = EXTENSIONS.KHR_BINARY_GLTF; - - var headerView = new DataView( data, 0, BINARY_EXTENSION_HEADER_LENGTH ); - - var header = { - magic: THREE.LoaderUtils.decodeText( new Uint8Array( data.slice( 0, 4 ) ) ), - version: headerView.getUint32( 4, true ), - length: headerView.getUint32( 8, true ), - contentLength: headerView.getUint32( 12, true ), - contentFormat: headerView.getUint32( 16, true ) - }; - - for ( var key in BINARY_EXTENSION_HEADER_DEFAULTS ) { - - var value = BINARY_EXTENSION_HEADER_DEFAULTS[ key ]; - - if ( header[ key ] !== value ) { - - throw new Error( 'Unsupported glTF-Binary header: Expected "%s" to be "%s".', key, value ); - - } - - } - - var contentArray = new Uint8Array( data, BINARY_EXTENSION_HEADER_LENGTH, header.contentLength ); - - this.header = header; - this.content = THREE.LoaderUtils.decodeText( contentArray ); - this.body = data.slice( BINARY_EXTENSION_HEADER_LENGTH + header.contentLength, header.length ); - - } - - GLTFBinaryExtension.prototype.loadShader = function ( shader, bufferViews ) { - - var bufferView = bufferViews[ shader.extensions[ EXTENSIONS.KHR_BINARY_GLTF ].bufferView ]; - var array = new Uint8Array( bufferView ); - - return THREE.LoaderUtils.decodeText( array ); - - }; - - /*********************************/ - /********** INTERNALS ************/ - /*********************************/ - - /* CONSTANTS */ - - var WEBGL_CONSTANTS = { - FLOAT: 5126, - //FLOAT_MAT2: 35674, - FLOAT_MAT3: 35675, - FLOAT_MAT4: 35676, - FLOAT_VEC2: 35664, - FLOAT_VEC3: 35665, - FLOAT_VEC4: 35666, - LINEAR: 9729, - REPEAT: 10497, - SAMPLER_2D: 35678, - TRIANGLES: 4, - LINES: 1, - UNSIGNED_BYTE: 5121, - UNSIGNED_SHORT: 5123, - - VERTEX_SHADER: 35633, - FRAGMENT_SHADER: 35632 - }; - - var WEBGL_TYPE = { - 5126: Number, - //35674: THREE.Matrix2, - 35675: THREE.Matrix3, - 35676: THREE.Matrix4, - 35664: THREE.Vector2, - 35665: THREE.Vector3, - 35666: THREE.Vector4, - 35678: THREE.Texture - }; - - var WEBGL_COMPONENT_TYPES = { - 5120: Int8Array, - 5121: Uint8Array, - 5122: Int16Array, - 5123: Uint16Array, - 5125: Uint32Array, - 5126: Float32Array - }; - - var WEBGL_FILTERS = { - 9728: THREE.NearestFilter, - 9729: THREE.LinearFilter, - 9984: THREE.NearestMipMapNearestFilter, - 9985: THREE.LinearMipMapNearestFilter, - 9986: THREE.NearestMipMapLinearFilter, - 9987: THREE.LinearMipMapLinearFilter - }; - - var WEBGL_WRAPPINGS = { - 33071: THREE.ClampToEdgeWrapping, - 33648: THREE.MirroredRepeatWrapping, - 10497: THREE.RepeatWrapping - }; - - var WEBGL_TEXTURE_FORMATS = { - 6406: THREE.AlphaFormat, - 6407: THREE.RGBFormat, - 6408: THREE.RGBAFormat, - 6409: THREE.LuminanceFormat, - 6410: THREE.LuminanceAlphaFormat - }; - - var WEBGL_TEXTURE_DATATYPES = { - 5121: THREE.UnsignedByteType, - 32819: THREE.UnsignedShort4444Type, - 32820: THREE.UnsignedShort5551Type, - 33635: THREE.UnsignedShort565Type - }; - - var WEBGL_SIDES = { - 1028: THREE.BackSide, // Culling front - 1029: THREE.FrontSide // Culling back - //1032: THREE.NoSide // Culling front and back, what to do? - }; - - var WEBGL_DEPTH_FUNCS = { - 512: THREE.NeverDepth, - 513: THREE.LessDepth, - 514: THREE.EqualDepth, - 515: THREE.LessEqualDepth, - 516: THREE.GreaterEqualDepth, - 517: THREE.NotEqualDepth, - 518: THREE.GreaterEqualDepth, - 519: THREE.AlwaysDepth - }; - - var WEBGL_BLEND_EQUATIONS = { - 32774: THREE.AddEquation, - 32778: THREE.SubtractEquation, - 32779: THREE.ReverseSubtractEquation - }; - - var WEBGL_BLEND_FUNCS = { - 0: THREE.ZeroFactor, - 1: THREE.OneFactor, - 768: THREE.SrcColorFactor, - 769: THREE.OneMinusSrcColorFactor, - 770: THREE.SrcAlphaFactor, - 771: THREE.OneMinusSrcAlphaFactor, - 772: THREE.DstAlphaFactor, - 773: THREE.OneMinusDstAlphaFactor, - 774: THREE.DstColorFactor, - 775: THREE.OneMinusDstColorFactor, - 776: THREE.SrcAlphaSaturateFactor - // The followings are not supported by Three.js yet - //32769: CONSTANT_COLOR, - //32770: ONE_MINUS_CONSTANT_COLOR, - //32771: CONSTANT_ALPHA, - //32772: ONE_MINUS_CONSTANT_COLOR - }; - - var WEBGL_TYPE_SIZES = { - 'SCALAR': 1, - 'VEC2': 2, - 'VEC3': 3, - 'VEC4': 4, - 'MAT2': 4, - 'MAT3': 9, - 'MAT4': 16 - }; - - var PATH_PROPERTIES = { - scale: 'scale', - translation: 'position', - rotation: 'quaternion' - }; - - var INTERPOLATION = { - LINEAR: THREE.InterpolateLinear, - STEP: THREE.InterpolateDiscrete - }; - - var STATES_ENABLES = { - 2884: 'CULL_FACE', - 2929: 'DEPTH_TEST', - 3042: 'BLEND', - 3089: 'SCISSOR_TEST', - 32823: 'POLYGON_OFFSET_FILL', - 32926: 'SAMPLE_ALPHA_TO_COVERAGE' - }; - - /* UTILITY FUNCTIONS */ - - function _each( object, callback, thisObj ) { - - if ( ! object ) { - - return Promise.resolve(); - - } - - var results; - var fns = []; - - if ( Object.prototype.toString.call( object ) === '[object Array]' ) { - - results = []; - - var length = object.length; - - for ( var idx = 0; idx < length; idx ++ ) { - - var value = callback.call( thisObj || this, object[ idx ], idx ); - - if ( value ) { - - fns.push( value ); - - if ( value instanceof Promise ) { - - value.then( function ( key, value ) { - - results[ key ] = value; - - }.bind( this, idx ) ); - - } else { - - results[ idx ] = value; - - } - - } - - } - - } else { - - results = {}; - - for ( var key in object ) { - - if ( object.hasOwnProperty( key ) ) { - - var value = callback.call( thisObj || this, object[ key ], key ); - - if ( value ) { - - fns.push( value ); - - if ( value instanceof Promise ) { - - value.then( function ( key, value ) { - - results[ key ] = value; - - }.bind( this, key ) ); - - } else { - - results[ key ] = value; - - } - - } - - } - - } - - } - - return Promise.all( fns ).then( function () { - - return results; - - } ); - - } - - function resolveURL( url, path ) { - - // Invalid URL - if ( typeof url !== 'string' || url === '' ) - return ''; - - // Absolute URL http://,https://,// - if ( /^(https?:)?\/\//i.test( url ) ) { - - return url; - - } - - // Data URI - if ( /^data:.*,.*$/i.test( url ) ) { - - return url; - - } - - // Blob URL - if ( /^blob:.*$/i.test( url ) ) { - - return url; - - } - - // Relative URL - return ( path || '' ) + url; - - } - - // Three.js seems too dependent on attribute names so globally - // replace those in the shader code - function replaceTHREEShaderAttributes( shaderText, technique ) { - - // Expected technique attributes - var attributes = {}; - - for ( var attributeId in technique.attributes ) { - - var pname = technique.attributes[ attributeId ]; - - var param = technique.parameters[ pname ]; - var atype = param.type; - var semantic = param.semantic; - - attributes[ attributeId ] = { - type: atype, - semantic: semantic - }; - - } - - // Figure out which attributes to change in technique - - var shaderParams = technique.parameters; - var shaderAttributes = technique.attributes; - var params = {}; - - for ( var attributeId in attributes ) { - - var pname = shaderAttributes[ attributeId ]; - var shaderParam = shaderParams[ pname ]; - var semantic = shaderParam.semantic; - if ( semantic ) { - - params[ attributeId ] = shaderParam; - - } - - } - - for ( var pname in params ) { - - var param = params[ pname ]; - var semantic = param.semantic; - - var regEx = new RegExp( "\\b" + pname + "\\b", "g" ); - - switch ( semantic ) { - - case "POSITION": - - shaderText = shaderText.replace( regEx, 'position' ); - break; - - case "NORMAL": - - shaderText = shaderText.replace( regEx, 'normal' ); - break; - - case 'TEXCOORD_0': - case 'TEXCOORD0': - case 'TEXCOORD': - - shaderText = shaderText.replace( regEx, 'uv' ); - break; - - case 'TEXCOORD_1': - - shaderText = shaderText.replace( regEx, 'uv2' ); - break; - - case 'COLOR_0': - case 'COLOR0': - case 'COLOR': - - shaderText = shaderText.replace( regEx, 'color' ); - break; - - case "WEIGHT": - - shaderText = shaderText.replace( regEx, 'skinWeight' ); - break; - - case "JOINT": - - shaderText = shaderText.replace( regEx, 'skinIndex' ); - break; - - } - - } - - return shaderText; - - } - - function createDefaultMaterial() { - - return new THREE.MeshPhongMaterial( { - color: 0x00000, - emissive: 0x888888, - specular: 0x000000, - shininess: 0, - transparent: false, - depthTest: true, - side: THREE.FrontSide - } ); - - } - - // Deferred constructor for RawShaderMaterial types - function DeferredShaderMaterial( params ) { - - this.isDeferredShaderMaterial = true; - - this.params = params; - - } - - DeferredShaderMaterial.prototype.create = function () { - - var uniforms = THREE.UniformsUtils.clone( this.params.uniforms ); - - for ( var uniformId in this.params.uniforms ) { - - var originalUniform = this.params.uniforms[ uniformId ]; - - if ( originalUniform.value instanceof THREE.Texture ) { - - uniforms[ uniformId ].value = originalUniform.value; - uniforms[ uniformId ].value.needsUpdate = true; - - } - - uniforms[ uniformId ].semantic = originalUniform.semantic; - uniforms[ uniformId ].node = originalUniform.node; - - } - - this.params.uniforms = uniforms; - - return new THREE.RawShaderMaterial( this.params ); - - }; - - /* GLTF PARSER */ - - function GLTFParser( json, extensions, options ) { - - this.json = json || {}; - this.extensions = extensions || {}; - this.options = options || {}; - - // loader object cache - this.cache = new GLTFRegistry(); - - } - - GLTFParser.prototype._withDependencies = function ( dependencies ) { - - var _dependencies = {}; - - for ( var i = 0; i < dependencies.length; i ++ ) { - - var dependency = dependencies[ i ]; - var fnName = "load" + dependency.charAt( 0 ).toUpperCase() + dependency.slice( 1 ); - - var cached = this.cache.get( dependency ); - - if ( cached !== undefined ) { - - _dependencies[ dependency ] = cached; - - } else if ( this[ fnName ] ) { - - var fn = this[ fnName ](); - this.cache.add( dependency, fn ); - - _dependencies[ dependency ] = fn; - - } - - } - - return _each( _dependencies, function ( dependency ) { - - return dependency; - - } ); - - }; - - GLTFParser.prototype.parse = function ( callback ) { - - var json = this.json; - - // Clear the loader cache - this.cache.removeAll(); - - // Fire the callback on complete - this._withDependencies( [ - - "scenes", - "cameras", - "animations" - - ] ).then( function ( dependencies ) { - - var scenes = []; - - for ( var name in dependencies.scenes ) { - - scenes.push( dependencies.scenes[ name ] ); - - } - - var scene = json.scene !== undefined ? dependencies.scenes[ json.scene ] : scenes[ 0 ]; - - var cameras = []; - - for ( var name in dependencies.cameras ) { - - var camera = dependencies.cameras[ name ]; - cameras.push( camera ); - - } - - var animations = []; - - for ( var name in dependencies.animations ) { - - animations.push( dependencies.animations[ name ] ); - - } - - callback( scene, scenes, cameras, animations ); - - } ); - - }; - - GLTFParser.prototype.loadShaders = function () { - - var json = this.json; - var extensions = this.extensions; - var options = this.options; - - return this._withDependencies( [ - - "bufferViews" - - ] ).then( function ( dependencies ) { - - return _each( json.shaders, function ( shader ) { - - if ( shader.extensions && shader.extensions[ EXTENSIONS.KHR_BINARY_GLTF ] ) { - - return extensions[ EXTENSIONS.KHR_BINARY_GLTF ].loadShader( shader, dependencies.bufferViews ); - - } - - return new Promise( function ( resolve ) { - - var loader = new THREE.FileLoader( options.manager ); - loader.setResponseType( 'text' ); - loader.load( resolveURL( shader.uri, options.path ), function ( shaderText ) { - - resolve( shaderText ); - - } ); - - } ); - - } ); - - } ); - - }; - - GLTFParser.prototype.loadBuffers = function () { - - var json = this.json; - var extensions = this.extensions; - var options = this.options; - - return _each( json.buffers, function ( buffer, name ) { - - if ( name === BINARY_EXTENSION_BUFFER_NAME ) { - - return extensions[ EXTENSIONS.KHR_BINARY_GLTF ].body; - - } - - if ( buffer.type === 'arraybuffer' || buffer.type === undefined ) { - - return new Promise( function ( resolve ) { - - var loader = new THREE.FileLoader( options.manager ); - loader.setResponseType( 'arraybuffer' ); - loader.load( resolveURL( buffer.uri, options.path ), function ( buffer ) { - - resolve( buffer ); - - } ); - - } ); - - } else { - - console.warn( 'THREE.LegacyGLTFLoader: ' + buffer.type + ' buffer type is not supported' ); - - } - - } ); - - }; - - GLTFParser.prototype.loadBufferViews = function () { - - var json = this.json; - - return this._withDependencies( [ - - "buffers" - - ] ).then( function ( dependencies ) { - - return _each( json.bufferViews, function ( bufferView ) { - - var arraybuffer = dependencies.buffers[ bufferView.buffer ]; - - var byteLength = bufferView.byteLength !== undefined ? bufferView.byteLength : 0; - - return arraybuffer.slice( bufferView.byteOffset, bufferView.byteOffset + byteLength ); - - } ); - - } ); - - }; - - GLTFParser.prototype.loadAccessors = function () { - - var json = this.json; - - return this._withDependencies( [ - - "bufferViews" - - ] ).then( function ( dependencies ) { - - return _each( json.accessors, function ( accessor ) { - - var arraybuffer = dependencies.bufferViews[ accessor.bufferView ]; - var itemSize = WEBGL_TYPE_SIZES[ accessor.type ]; - var TypedArray = WEBGL_COMPONENT_TYPES[ accessor.componentType ]; - - // For VEC3: itemSize is 3, elementBytes is 4, itemBytes is 12. - var elementBytes = TypedArray.BYTES_PER_ELEMENT; - var itemBytes = elementBytes * itemSize; - - // The buffer is not interleaved if the stride is the item size in bytes. - if ( accessor.byteStride && accessor.byteStride !== itemBytes ) { - - // Use the full buffer if it's interleaved. - var array = new TypedArray( arraybuffer ); - - // Integer parameters to IB/IBA are in array elements, not bytes. - var ib = new THREE.InterleavedBuffer( array, accessor.byteStride / elementBytes ); - - return new THREE.InterleavedBufferAttribute( ib, itemSize, accessor.byteOffset / elementBytes ); - - } else { - - array = new TypedArray( arraybuffer, accessor.byteOffset, accessor.count * itemSize ); - - return new THREE.BufferAttribute( array, itemSize ); - - } - - } ); - - } ); - - }; - - GLTFParser.prototype.loadTextures = function () { - - var json = this.json; - var extensions = this.extensions; - var options = this.options; - - return this._withDependencies( [ - - "bufferViews" - - ] ).then( function ( dependencies ) { - - return _each( json.textures, function ( texture ) { - - if ( texture.source ) { - - return new Promise( function ( resolve ) { - - var source = json.images[ texture.source ]; - var sourceUri = source.uri; - var isObjectURL = false; - - if ( source.extensions && source.extensions[ EXTENSIONS.KHR_BINARY_GLTF ] ) { - - var metadata = source.extensions[ EXTENSIONS.KHR_BINARY_GLTF ]; - var bufferView = dependencies.bufferViews[ metadata.bufferView ]; - var blob = new Blob( [ bufferView ], { type: metadata.mimeType } ); - sourceUri = URL.createObjectURL( blob ); - isObjectURL = true; - - } - - var textureLoader = THREE.Loader.Handlers.get( sourceUri ); - - if ( textureLoader === null ) { - - textureLoader = new THREE.TextureLoader( options.manager ); - - } - - textureLoader.setCrossOrigin( options.crossOrigin ); - - textureLoader.load( resolveURL( sourceUri, options.path ), function ( _texture ) { - - if ( isObjectURL ) URL.revokeObjectURL( sourceUri ); - - _texture.flipY = false; - - if ( texture.name !== undefined ) _texture.name = texture.name; - - _texture.format = texture.format !== undefined ? WEBGL_TEXTURE_FORMATS[ texture.format ] : THREE.RGBAFormat; - - if ( texture.internalFormat !== undefined && _texture.format !== WEBGL_TEXTURE_FORMATS[ texture.internalFormat ] ) { - - console.warn( 'THREE.LegacyGLTFLoader: Three.js doesn\'t support texture internalFormat which is different from texture format. ' + - 'internalFormat will be forced to be the same value as format.' ); - - } - - _texture.type = texture.type !== undefined ? WEBGL_TEXTURE_DATATYPES[ texture.type ] : THREE.UnsignedByteType; - - if ( texture.sampler ) { - - var sampler = json.samplers[ texture.sampler ]; - - _texture.magFilter = WEBGL_FILTERS[ sampler.magFilter ] || THREE.LinearFilter; - _texture.minFilter = WEBGL_FILTERS[ sampler.minFilter ] || THREE.NearestMipMapLinearFilter; - _texture.wrapS = WEBGL_WRAPPINGS[ sampler.wrapS ] || THREE.RepeatWrapping; - _texture.wrapT = WEBGL_WRAPPINGS[ sampler.wrapT ] || THREE.RepeatWrapping; - - } - - resolve( _texture ); - - }, undefined, function () { - - if ( isObjectURL ) URL.revokeObjectURL( sourceUri ); - - resolve(); - - } ); - - } ); - - } - - } ); - - } ); - - }; - - GLTFParser.prototype.loadMaterials = function () { - - var json = this.json; - - return this._withDependencies( [ - - "shaders", - "textures" - - ] ).then( function ( dependencies ) { - - return _each( json.materials, function ( material ) { - - var materialType; - var materialValues = {}; - var materialParams = {}; - - var khr_material; - - if ( material.extensions && material.extensions[ EXTENSIONS.KHR_MATERIALS_COMMON ] ) { - - khr_material = material.extensions[ EXTENSIONS.KHR_MATERIALS_COMMON ]; - - } - - if ( khr_material ) { - - // don't copy over unused values to avoid material warning spam - var keys = [ 'ambient', 'emission', 'transparent', 'transparency', 'doubleSided' ]; - - switch ( khr_material.technique ) { - - case 'BLINN' : - case 'PHONG' : - materialType = THREE.MeshPhongMaterial; - keys.push( 'diffuse', 'specular', 'shininess' ); - break; - - case 'LAMBERT' : - materialType = THREE.MeshLambertMaterial; - keys.push( 'diffuse' ); - break; - - case 'CONSTANT' : - default : - materialType = THREE.MeshBasicMaterial; - break; - - } - - keys.forEach( function ( v ) { - - if ( khr_material.values[ v ] !== undefined ) materialValues[ v ] = khr_material.values[ v ]; - - } ); - - if ( khr_material.doubleSided || materialValues.doubleSided ) { - - materialParams.side = THREE.DoubleSide; - - } - - if ( khr_material.transparent || materialValues.transparent ) { - - materialParams.transparent = true; - materialParams.opacity = ( materialValues.transparency !== undefined ) ? materialValues.transparency : 1; - - } - - } else if ( material.technique === undefined ) { - - materialType = THREE.MeshPhongMaterial; - - Object.assign( materialValues, material.values ); - - } else { - - materialType = DeferredShaderMaterial; - - var technique = json.techniques[ material.technique ]; - - materialParams.uniforms = {}; - - var program = json.programs[ technique.program ]; - - if ( program ) { - - materialParams.fragmentShader = dependencies.shaders[ program.fragmentShader ]; - - if ( ! materialParams.fragmentShader ) { - - console.warn( "ERROR: Missing fragment shader definition:", program.fragmentShader ); - materialType = THREE.MeshPhongMaterial; - - } - - var vertexShader = dependencies.shaders[ program.vertexShader ]; - - if ( ! vertexShader ) { - - console.warn( "ERROR: Missing vertex shader definition:", program.vertexShader ); - materialType = THREE.MeshPhongMaterial; - - } - - // IMPORTANT: FIX VERTEX SHADER ATTRIBUTE DEFINITIONS - materialParams.vertexShader = replaceTHREEShaderAttributes( vertexShader, technique ); - - var uniforms = technique.uniforms; - - for ( var uniformId in uniforms ) { - - var pname = uniforms[ uniformId ]; - var shaderParam = technique.parameters[ pname ]; - - var ptype = shaderParam.type; - - if ( WEBGL_TYPE[ ptype ] ) { - - var pcount = shaderParam.count; - var value; - - if ( material.values !== undefined ) value = material.values[ pname ]; - - var uvalue = new WEBGL_TYPE[ ptype ](); - var usemantic = shaderParam.semantic; - var unode = shaderParam.node; - - switch ( ptype ) { - - case WEBGL_CONSTANTS.FLOAT: - - uvalue = shaderParam.value; - - if ( pname == "transparency" ) { - - materialParams.transparent = true; - - } - - if ( value !== undefined ) { - - uvalue = value; - - } - - break; - - case WEBGL_CONSTANTS.FLOAT_VEC2: - case WEBGL_CONSTANTS.FLOAT_VEC3: - case WEBGL_CONSTANTS.FLOAT_VEC4: - case WEBGL_CONSTANTS.FLOAT_MAT3: - - if ( shaderParam && shaderParam.value ) { - - uvalue.fromArray( shaderParam.value ); - - } - - if ( value ) { - - uvalue.fromArray( value ); - - } - - break; - - case WEBGL_CONSTANTS.FLOAT_MAT2: - - // what to do? - console.warn( "FLOAT_MAT2 is not a supported uniform type" ); - break; - - case WEBGL_CONSTANTS.FLOAT_MAT4: - - if ( pcount ) { - - uvalue = new Array( pcount ); - - for ( var mi = 0; mi < pcount; mi ++ ) { - - uvalue[ mi ] = new WEBGL_TYPE[ ptype ](); - - } - - if ( shaderParam && shaderParam.value ) { - - var m4v = shaderParam.value; - uvalue.fromArray( m4v ); - - } - - if ( value ) { - - uvalue.fromArray( value ); - - } - - } else { - - if ( shaderParam && shaderParam.value ) { - - var m4 = shaderParam.value; - uvalue.fromArray( m4 ); - - } - - if ( value ) { - - uvalue.fromArray( value ); - - } - - } - - break; - - case WEBGL_CONSTANTS.SAMPLER_2D: - - if ( value !== undefined ) { - - uvalue = dependencies.textures[ value ]; - - } else if ( shaderParam.value !== undefined ) { - - uvalue = dependencies.textures[ shaderParam.value ]; - - } else { - - uvalue = null; - - } - - break; - - } - - materialParams.uniforms[ uniformId ] = { - value: uvalue, - semantic: usemantic, - node: unode - }; - - } else { - - throw new Error( "Unknown shader uniform param type: " + ptype ); - - } - - } - - var states = technique.states || {}; - var enables = states.enable || []; - var functions = states.functions || {}; - - var enableCullFace = false; - var enableDepthTest = false; - var enableBlend = false; - - for ( var i = 0, il = enables.length; i < il; i ++ ) { - - var enable = enables[ i ]; - - switch ( STATES_ENABLES[ enable ] ) { - - case 'CULL_FACE': - - enableCullFace = true; - - break; - - case 'DEPTH_TEST': - - enableDepthTest = true; - - break; - - case 'BLEND': - - enableBlend = true; - - break; - - // TODO: implement - case 'SCISSOR_TEST': - case 'POLYGON_OFFSET_FILL': - case 'SAMPLE_ALPHA_TO_COVERAGE': - - break; - - default: - - throw new Error( "Unknown technique.states.enable: " + enable ); - - } - - } - - if ( enableCullFace ) { - - materialParams.side = functions.cullFace !== undefined ? WEBGL_SIDES[ functions.cullFace ] : THREE.FrontSide; - - } else { - - materialParams.side = THREE.DoubleSide; - - } - - materialParams.depthTest = enableDepthTest; - materialParams.depthFunc = functions.depthFunc !== undefined ? WEBGL_DEPTH_FUNCS[ functions.depthFunc ] : THREE.LessDepth; - materialParams.depthWrite = functions.depthMask !== undefined ? functions.depthMask[ 0 ] : true; - - materialParams.blending = enableBlend ? THREE.CustomBlending : THREE.NoBlending; - materialParams.transparent = enableBlend; - - var blendEquationSeparate = functions.blendEquationSeparate; - - if ( blendEquationSeparate !== undefined ) { - - materialParams.blendEquation = WEBGL_BLEND_EQUATIONS[ blendEquationSeparate[ 0 ] ]; - materialParams.blendEquationAlpha = WEBGL_BLEND_EQUATIONS[ blendEquationSeparate[ 1 ] ]; - - } else { - - materialParams.blendEquation = THREE.AddEquation; - materialParams.blendEquationAlpha = THREE.AddEquation; - - } - - var blendFuncSeparate = functions.blendFuncSeparate; - - if ( blendFuncSeparate !== undefined ) { - - materialParams.blendSrc = WEBGL_BLEND_FUNCS[ blendFuncSeparate[ 0 ] ]; - materialParams.blendDst = WEBGL_BLEND_FUNCS[ blendFuncSeparate[ 1 ] ]; - materialParams.blendSrcAlpha = WEBGL_BLEND_FUNCS[ blendFuncSeparate[ 2 ] ]; - materialParams.blendDstAlpha = WEBGL_BLEND_FUNCS[ blendFuncSeparate[ 3 ] ]; - - } else { - - materialParams.blendSrc = THREE.OneFactor; - materialParams.blendDst = THREE.ZeroFactor; - materialParams.blendSrcAlpha = THREE.OneFactor; - materialParams.blendDstAlpha = THREE.ZeroFactor; - - } - - } - - } - - if ( Array.isArray( materialValues.diffuse ) ) { - - materialParams.color = new THREE.Color().fromArray( materialValues.diffuse ); - - } else if ( typeof ( materialValues.diffuse ) === 'string' ) { - - materialParams.map = dependencies.textures[ materialValues.diffuse ]; - - } - - delete materialParams.diffuse; - - if ( typeof ( materialValues.reflective ) === 'string' ) { - - materialParams.envMap = dependencies.textures[ materialValues.reflective ]; - - } - - if ( typeof ( materialValues.bump ) === 'string' ) { - - materialParams.bumpMap = dependencies.textures[ materialValues.bump ]; - - } - - if ( Array.isArray( materialValues.emission ) ) { - - if ( materialType === THREE.MeshBasicMaterial ) { - - materialParams.color = new THREE.Color().fromArray( materialValues.emission ); - - } else { - - materialParams.emissive = new THREE.Color().fromArray( materialValues.emission ); - - } - - } else if ( typeof ( materialValues.emission ) === 'string' ) { - - if ( materialType === THREE.MeshBasicMaterial ) { - - materialParams.map = dependencies.textures[ materialValues.emission ]; - - } else { - - materialParams.emissiveMap = dependencies.textures[ materialValues.emission ]; - - } - - } - - if ( Array.isArray( materialValues.specular ) ) { - - materialParams.specular = new THREE.Color().fromArray( materialValues.specular ); - - } else if ( typeof ( materialValues.specular ) === 'string' ) { - - materialParams.specularMap = dependencies.textures[ materialValues.specular ]; - - } - - if ( materialValues.shininess !== undefined ) { - - materialParams.shininess = materialValues.shininess; - - } - - var _material = new materialType( materialParams ); - if ( material.name !== undefined ) _material.name = material.name; - - return _material; - - } ); - - } ); - - }; - - GLTFParser.prototype.loadMeshes = function () { - - var json = this.json; - - return this._withDependencies( [ - - "accessors", - "materials" - - ] ).then( function ( dependencies ) { - - return _each( json.meshes, function ( mesh ) { - - var group = new THREE.Group(); - if ( mesh.name !== undefined ) group.name = mesh.name; - - if ( mesh.extras ) group.userData = mesh.extras; - - var primitives = mesh.primitives || []; - - for ( var name in primitives ) { - - var primitive = primitives[ name ]; - - if ( primitive.mode === WEBGL_CONSTANTS.TRIANGLES || primitive.mode === undefined ) { - - var geometry = new THREE.BufferGeometry(); - - var attributes = primitive.attributes; - - for ( var attributeId in attributes ) { - - var attributeEntry = attributes[ attributeId ]; - - if ( ! attributeEntry ) return; - - var bufferAttribute = dependencies.accessors[ attributeEntry ]; - - switch ( attributeId ) { - - case 'POSITION': - geometry.addAttribute( 'position', bufferAttribute ); - break; - - case 'NORMAL': - geometry.addAttribute( 'normal', bufferAttribute ); - break; - - case 'TEXCOORD_0': - case 'TEXCOORD0': - case 'TEXCOORD': - geometry.addAttribute( 'uv', bufferAttribute ); - break; - - case 'TEXCOORD_1': - geometry.addAttribute( 'uv2', bufferAttribute ); - break; - - case 'COLOR_0': - case 'COLOR0': - case 'COLOR': - geometry.addAttribute( 'color', bufferAttribute ); - break; - - case 'WEIGHT': - geometry.addAttribute( 'skinWeight', bufferAttribute ); - break; - - case 'JOINT': - geometry.addAttribute( 'skinIndex', bufferAttribute ); - break; - - default: - - if ( ! primitive.material ) break; - - var material = json.materials[ primitive.material ]; - - if ( ! material.technique ) break; - - var parameters = json.techniques[ material.technique ].parameters || {}; - - for ( var attributeName in parameters ) { - - if ( parameters[ attributeName ][ 'semantic' ] === attributeId ) { - - geometry.addAttribute( attributeName, bufferAttribute ); - - } - - } - - } - - } - - if ( primitive.indices ) { - - geometry.setIndex( dependencies.accessors[ primitive.indices ] ); - - } - - var material = dependencies.materials !== undefined ? dependencies.materials[ primitive.material ] : createDefaultMaterial(); - - var meshNode = new THREE.Mesh( geometry, material ); - meshNode.castShadow = true; - meshNode.name = ( name === "0" ? group.name : group.name + name ); - - if ( primitive.extras ) meshNode.userData = primitive.extras; - - group.add( meshNode ); - - } else if ( primitive.mode === WEBGL_CONSTANTS.LINES ) { - - var geometry = new THREE.BufferGeometry(); - - var attributes = primitive.attributes; - - for ( var attributeId in attributes ) { - - var attributeEntry = attributes[ attributeId ]; - - if ( ! attributeEntry ) return; - - var bufferAttribute = dependencies.accessors[ attributeEntry ]; - - switch ( attributeId ) { - - case 'POSITION': - geometry.addAttribute( 'position', bufferAttribute ); - break; - - case 'COLOR_0': - case 'COLOR0': - case 'COLOR': - geometry.addAttribute( 'color', bufferAttribute ); - break; - - } - - } - - var material = dependencies.materials[ primitive.material ]; - - var meshNode; - - if ( primitive.indices ) { - - geometry.setIndex( dependencies.accessors[ primitive.indices ] ); - - meshNode = new THREE.LineSegments( geometry, material ); - - } else { - - meshNode = new THREE.Line( geometry, material ); - - } - - meshNode.name = ( name === "0" ? group.name : group.name + name ); - - if ( primitive.extras ) meshNode.userData = primitive.extras; - - group.add( meshNode ); - - } else { - - console.warn( "Only triangular and line primitives are supported" ); - - } - - } - - return group; - - } ); - - } ); - - }; - - GLTFParser.prototype.loadCameras = function () { - - var json = this.json; - - return _each( json.cameras, function ( camera ) { - - if ( camera.type == "perspective" && camera.perspective ) { - - var yfov = camera.perspective.yfov; - var aspectRatio = camera.perspective.aspectRatio !== undefined ? camera.perspective.aspectRatio : 1; - - // According to COLLADA spec... - // aspectRatio = xfov / yfov - var xfov = yfov * aspectRatio; - - var _camera = new THREE.PerspectiveCamera( THREE.Math.radToDeg( xfov ), aspectRatio, camera.perspective.znear || 1, camera.perspective.zfar || 2e6 ); - if ( camera.name !== undefined ) _camera.name = camera.name; - - if ( camera.extras ) _camera.userData = camera.extras; - - return _camera; - - } else if ( camera.type == "orthographic" && camera.orthographic ) { - - var _camera = new THREE.OrthographicCamera( window.innerWidth / - 2, window.innerWidth / 2, window.innerHeight / 2, window.innerHeight / - 2, camera.orthographic.znear, camera.orthographic.zfar ); - if ( camera.name !== undefined ) _camera.name = camera.name; - - if ( camera.extras ) _camera.userData = camera.extras; - - return _camera; - - } - - } ); - - }; - - GLTFParser.prototype.loadSkins = function () { - - var json = this.json; - - return this._withDependencies( [ - - "accessors" - - ] ).then( function ( dependencies ) { - - return _each( json.skins, function ( skin ) { - - var bindShapeMatrix = new THREE.Matrix4(); - - if ( skin.bindShapeMatrix !== undefined ) bindShapeMatrix.fromArray( skin.bindShapeMatrix ); - - var _skin = { - bindShapeMatrix: bindShapeMatrix, - jointNames: skin.jointNames, - inverseBindMatrices: dependencies.accessors[ skin.inverseBindMatrices ] - }; - - return _skin; - - } ); - - } ); - - }; - - GLTFParser.prototype.loadAnimations = function () { - - var json = this.json; - - return this._withDependencies( [ - - "accessors", - "nodes" - - ] ).then( function ( dependencies ) { - - return _each( json.animations, function ( animation, animationId ) { - - var tracks = []; - - for ( var channelId in animation.channels ) { - - var channel = animation.channels[ channelId ]; - var sampler = animation.samplers[ channel.sampler ]; - - if ( sampler ) { - - var target = channel.target; - var name = target.id; - var input = animation.parameters !== undefined ? animation.parameters[ sampler.input ] : sampler.input; - var output = animation.parameters !== undefined ? animation.parameters[ sampler.output ] : sampler.output; - - var inputAccessor = dependencies.accessors[ input ]; - var outputAccessor = dependencies.accessors[ output ]; - - var node = dependencies.nodes[ name ]; - - if ( node ) { - - node.updateMatrix(); - node.matrixAutoUpdate = true; - - var TypedKeyframeTrack = PATH_PROPERTIES[ target.path ] === PATH_PROPERTIES.rotation - ? THREE.QuaternionKeyframeTrack - : THREE.VectorKeyframeTrack; - - var targetName = node.name ? node.name : node.uuid; - var interpolation = sampler.interpolation !== undefined ? INTERPOLATION[ sampler.interpolation ] : THREE.InterpolateLinear; - - // KeyframeTrack.optimize() will modify given 'times' and 'values' - // buffers before creating a truncated copy to keep. Because buffers may - // be reused by other tracks, make copies here. - tracks.push( new TypedKeyframeTrack( - targetName + '.' + PATH_PROPERTIES[ target.path ], - THREE.AnimationUtils.arraySlice( inputAccessor.array, 0 ), - THREE.AnimationUtils.arraySlice( outputAccessor.array, 0 ), - interpolation - ) ); - - } - - } - - } - - var name = animation.name !== undefined ? animation.name : "animation_" + animationId; - - return new THREE.AnimationClip( name, undefined, tracks ); - - } ); - - } ); - - }; - - GLTFParser.prototype.loadNodes = function () { - - var json = this.json; - var extensions = this.extensions; - var scope = this; - - return _each( json.nodes, function ( node ) { - - var matrix = new THREE.Matrix4(); - - var _node; - - if ( node.jointName ) { - - _node = new THREE.Bone(); - _node.name = node.name !== undefined ? node.name : node.jointName; - _node.jointName = node.jointName; - - } else { - - _node = new THREE.Object3D(); - if ( node.name !== undefined ) _node.name = node.name; - - } - - if ( node.extras ) _node.userData = node.extras; - - if ( node.matrix !== undefined ) { - - matrix.fromArray( node.matrix ); - _node.applyMatrix( matrix ); - - } else { - - if ( node.translation !== undefined ) { - - _node.position.fromArray( node.translation ); - - } - - if ( node.rotation !== undefined ) { - - _node.quaternion.fromArray( node.rotation ); - - } - - if ( node.scale !== undefined ) { - - _node.scale.fromArray( node.scale ); - - } - - } - - return _node; - - } ).then( function ( __nodes ) { - - return scope._withDependencies( [ - - "meshes", - "skins", - "cameras" - - ] ).then( function ( dependencies ) { - - return _each( __nodes, function ( _node, nodeId ) { - - var node = json.nodes[ nodeId ]; - - if ( node.meshes !== undefined ) { - - for ( var meshId in node.meshes ) { - - var mesh = node.meshes[ meshId ]; - var group = dependencies.meshes[ mesh ]; - - if ( group === undefined ) { - - console.warn( 'LegacyGLTFLoader: Couldn\'t find node "' + mesh + '".' ); - continue; - - } - - for ( var childrenId in group.children ) { - - var child = group.children[ childrenId ]; - - // clone Mesh to add to _node - - var originalMaterial = child.material; - var originalGeometry = child.geometry; - var originalUserData = child.userData; - var originalName = child.name; - - var material; - - if ( originalMaterial.isDeferredShaderMaterial ) { - - originalMaterial = material = originalMaterial.create(); - - } else { - - material = originalMaterial; - - } - - switch ( child.type ) { - - case 'LineSegments': - child = new THREE.LineSegments( originalGeometry, material ); - break; - - case 'LineLoop': - child = new THREE.LineLoop( originalGeometry, material ); - break; - - case 'Line': - child = new THREE.Line( originalGeometry, material ); - break; - - default: - child = new THREE.Mesh( originalGeometry, material ); - - } - - child.castShadow = true; - child.userData = originalUserData; - child.name = originalName; - - var skinEntry; - - if ( node.skin ) { - - skinEntry = dependencies.skins[ node.skin ]; - - } - - // Replace Mesh with SkinnedMesh in library - if ( skinEntry ) { - - var getJointNode = function ( jointId ) { - - var keys = Object.keys( __nodes ); - - for ( var i = 0, il = keys.length; i < il; i ++ ) { - - var n = __nodes[ keys[ i ] ]; - - if ( n.jointName === jointId ) return n; - - } - - return null; - - }; - - var geometry = originalGeometry; - var material = originalMaterial; - material.skinning = true; - - child = new THREE.SkinnedMesh( geometry, material ); - child.castShadow = true; - child.userData = originalUserData; - child.name = originalName; - - var bones = []; - var boneInverses = []; - - for ( var i = 0, l = skinEntry.jointNames.length; i < l; i ++ ) { - - var jointId = skinEntry.jointNames[ i ]; - var jointNode = getJointNode( jointId ); - - if ( jointNode ) { - - bones.push( jointNode ); - - var m = skinEntry.inverseBindMatrices.array; - var mat = new THREE.Matrix4().fromArray( m, i * 16 ); - boneInverses.push( mat ); - - } else { - - console.warn( "WARNING: joint: '" + jointId + "' could not be found" ); - - } - - } - - child.bind( new THREE.Skeleton( bones, boneInverses ), skinEntry.bindShapeMatrix ); - - var buildBoneGraph = function ( parentJson, parentObject, property ) { - - var children = parentJson[ property ]; - - if ( children === undefined ) return; - - for ( var i = 0, il = children.length; i < il; i ++ ) { - - var nodeId = children[ i ]; - var bone = __nodes[ nodeId ]; - var boneJson = json.nodes[ nodeId ]; - - if ( bone !== undefined && bone.isBone === true && boneJson !== undefined ) { - - parentObject.add( bone ); - buildBoneGraph( boneJson, bone, 'children' ); - - } - - } - - }; - - buildBoneGraph( node, child, 'skeletons' ); - - } - - _node.add( child ); - - } - - } - - } - - if ( node.camera !== undefined ) { - - var camera = dependencies.cameras[ node.camera ]; - - _node.add( camera ); - - } - - if ( node.extensions - && node.extensions[ EXTENSIONS.KHR_MATERIALS_COMMON ] - && node.extensions[ EXTENSIONS.KHR_MATERIALS_COMMON ].light ) { - - var extensionLights = extensions[ EXTENSIONS.KHR_MATERIALS_COMMON ].lights; 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- - for ( var i = 0, l = nodes.length; i < l; i ++ ) { - - var nodeId = nodes[ i ]; - buildNodeHierachy( nodeId, _scene, dependencies.nodes ); - - } - - _scene.traverse( function ( child ) { - - // Register raw material meshes with LegacyGLTFLoader.Shaders - if ( child.material && child.material.isRawShaderMaterial ) { - - child.gltfShader = new GLTFShader( child, dependencies.nodes ); - child.onBeforeRender = function ( renderer, scene, camera ) { - - this.gltfShader.update( scene, camera ); - - }; - - } - - } ); - - return _scene; - - } ); - - } ); - - }; - - return LegacyGLTFLoader; - -} )(); diff --git a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620143433.py b/spaces/beihai/PDF-Table-Extractor/.history/app_20220620143433.py deleted file mode 100644 index 3693b2f44f433725b2dee29ca32afe82e9695fba..0000000000000000000000000000000000000000 --- a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620143433.py +++ /dev/null @@ -1,41 +0,0 @@ -#-*- coding : utf-8-*- - -import pandas as pd -import streamlit as st -import base64 -import subprocess # process in the os -from subprocess import STDOUT #os process manipuation -import os -import camelot as cam # extracting tables from PDFs - -@st.cache -def gh(): - """install ghostscript on the linux machine""" - proc = subprocess.Popen('apt-get install -y ghostscript', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash") - proc.wait() - -gh() -st.title("PDF Table Extractor") - -input_pdf = st.file_uploader(label = "", type = 'pdf') - -page_number = st.text_input("请填写表格所在PDF页码，eg: 3", value = 1) - -if input_pdf is not None: - # byte object into a PDF file - with open("input.pdf", "wb") as f: - base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8') - f.write(base64.b64decode(base64_pdf)) - f.close() - - # read the pdf and parse it using stream - tables = cam.read_pdf("input.pdf", pages=page_number) - result = pd.ExcelWriter('result.xlsx', engine='xlsxwriter') - tables[0].to_excel(result,index=False) - # for i in range(0,len(tables)): - # table = tables[i].df - # sheetname = str(i) - # table.to_excel(result, sheetname,index=False) - - with open('result.xlsx','rb') as f: - st.download_button('提取完成，点击下载！', f,file_name='result.xlsx',mime="application/vnd.ms-excel") \ No newline at end of file diff --git a/spaces/bhaskartripathi/Llama-2-70b-chatbot/app-org.py b/spaces/bhaskartripathi/Llama-2-70b-chatbot/app-org.py deleted file mode 100644 index fa3ef849665f7477e5d1b5bde9e7ef7185f082fb..0000000000000000000000000000000000000000 --- a/spaces/bhaskartripathi/Llama-2-70b-chatbot/app-org.py +++ /dev/null @@ -1,73 +0,0 @@ -""" -Try out gradio.Chatinterface. - -colab gradio-chatinterface. - -%%writefile reuirements.txt -gradio -transformers -sentencepiece -torch - -""" -# pylint: disable=line-too-long, missing-module-docstring, missing-function-docstring -# import torch -from time import time - -import gradio as gr -from about_time import about_time -from examples_list import examples_list -from transformers import AutoModel, AutoTokenizer # AutoModelForCausalLM, - -# device = "cuda" if torch.cuda.is_available() else "cpu" - -# tokenizer = AutoTokenizer.from_pretrained("stabilityai/StableBeluga2", use_fast=False) -# model = AutoModelForCausalLM.from_pretrained("stabilityai/StableBeluga2", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto") -# system_prompt = "### System:\nYou are Stable Beluga, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal.\n\n" -# pipeline = pipeline(task="text-generation", model="meta-llama/Llama-2-7b") -tokenizer = AutoTokenizer.from_pretrained( - "THUDM/chatglm2-6b-int4", trust_remote_code=True -) -chat_model = AutoModel.from_pretrained( - "THUDM/chatglm2-6b-int4", trust_remote_code=True # 3.92G -).float() - - -def chat(message, history): - # prompt = f"{system_prompt}### User: {message}\n\n### Assistant:\n" - # inputs = tokenizer(prompt, return_tensors="pt").to(device=device) - # output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=256) - # return tokenizer.decode(output[0], skip_special_tokens=True) - flag = 1 - then = time() - prefix = "" - prelude = 0.0 - with about_time() as dur: - for response, _ in chat_model.stream_chat( - tokenizer, message, history, max_length=2048, top_p=0.7, temperature=0.95 - ): - if flag: - flag = 0 - prelude = time() - then - prefix = f"{prelude:.2f}s" - yield f"{prefix} {response}" - suffix = f"\n(time elapsed: {dur.duration_human}, {(time() - prelude)/len(response):.2f}s/char)" - yield f"{response}{suffix}" - -chatbot = gr.Chatbot([], label="Bot", height=450) -textbox = gr.Textbox('', scale=10, label='', lines=2, placeholder="Ask me anything") -submit_btn = gr.Button(value="▶️ Send", scale=1, min_width=0, variant="primary") - -interf = gr.ChatInterface( - chat, - chatbot=chatbot, - textbox=textbox, - submit_btn=submit_btn, - title="Llama-2-70b Locally Hosted", - examples=examples_list, - theme=gr.themes.Glass(text_size="sm", spacing_size="sm"), -).queue(max_size=5) - - -if __name__ == "__main__": - interf.launch(debug=True) diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/draw.py b/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/draw.py deleted file mode 100644 index bc7cb537978e86805d5d9789785a8afe67df9030..0000000000000000000000000000000000000000 --- a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/draw.py +++ /dev/null @@ -1,36 +0,0 @@ -import numpy as np -import cv2 - -palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) - - -def compute_color_for_labels(label): - """ - Simple function that adds fixed color depending on the class - """ - color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette] - return tuple(color) - - -def draw_boxes(img, bbox, identities=None, offset=(0,0)): - for i,box in enumerate(bbox): - x1,y1,x2,y2 = [int(i) for i in box] - x1 += offset[0] - x2 += offset[0] - y1 += offset[1] - y2 += offset[1] - # box text and bar - id = int(identities[i]) if identities is not None else 0 - color = compute_color_for_labels(id) - label = '{}{:d}'.format("", id) - t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0] - cv2.rectangle(img,(x1, y1),(x2,y2),color,3) - cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1) - cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2) - return img - - - -if __name__ == '__main__': - for i in range(82): - print(compute_color_for_labels(i)) diff --git a/spaces/bigcode/Reasoning-with-StarCoder/TA.py b/spaces/bigcode/Reasoning-with-StarCoder/TA.py deleted file mode 100644 index 0b43df70a558e9219551cff7f23e1a5631a17651..0000000000000000000000000000000000000000 --- a/spaces/bigcode/Reasoning-with-StarCoder/TA.py +++ /dev/null @@ -1,51 +0,0 @@ -from prompt import TA_prompt -import re -from utils import generate_response, run_code - - -def post_process_code(code, question): - func_name = code.split("(")[0].split("def")[-1].strip() - parameters = code.split("\n")[0].split(f"def {func_name}")[-1][1:-2].split(",") - if '' in parameters: - parameters.remove('') - values = re.findall(r"[-+]?\d*\.\d+|\d+", question)[:len(parameters)] - values = [int(v) for v in values] - arguments = list(zip(parameters, values)) - - arg_string = "" - for param, val in arguments: - arg_string += f"{param}={val}," - func_call = f"\nprint({func_name}({arg_string[:-1]}))" - code += func_call - return code - - -def solve_ta(question): - question = question.strip() - question = "Human: " + question - query = TA_prompt + question - query = query.strip() - query += "\n" - code = generate_response(query, 0.9) - n = len(TA_prompt.strip()) - code = code[n:].strip().split("-----")[0] - # print(code) - splitting_string = "```" if "```python" not in code else "```python" - if "```" in code: - code = code.split(splitting_string)[1].split("```")[0].strip() - 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CJ's weapon list
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In mid-June 2005, a software patch for the game dubbed the "Hot Coffee (mod)" was released by Patrick Wildenborg (under the Internet alias "PatrickW"), a 38-year-old modder from the Netherlands. The name "Hot Coffee" refers to the way the unmodified game alludes to unseen sex scenes. In the original version of the game, the player takes CJ's girlfriend to her front door, and she asks him if he would like to come in for "some coffee". He agrees, and the camera stays outside, swaying back and forth a bit, while moaning sounds are heard. After installing the patch, users can enter the main character's girlfriends' houses and engage in a crudely rendered, fully clothed sexual intercourse mini-game. The fallout from the controversy resulted in a public response from high-ranking politicians in the United States and elsewhere and resulted in the game's recall and re-release.
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- diff --git a/spaces/bodah/RVC-Models-bo/lib/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/bodah/RVC-Models-bo/lib/infer_pack/modules/F0Predictor/F0Predictor.py deleted file mode 100644 index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000 --- a/spaces/bodah/RVC-Models-bo/lib/infer_pack/modules/F0Predictor/F0Predictor.py +++ /dev/null @@ -1,16 +0,0 @@ -class F0Predictor(object): - def compute_f0(self, wav, p_len): - """ - input: wav:[signal_length] - p_len:int - output: f0:[signal_length//hop_length] - """ - pass - - def compute_f0_uv(self, wav, p_len): - """ - input: wav:[signal_length] - p_len:int - output: f0:[signal_length//hop_length],uv:[signal_length//hop_length] - """ - pass diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/datasets/dataset_type.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/datasets/dataset_type.py deleted file mode 100644 index ed8f8f299af96847d9d16a77920429fe0195c526..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/datasets/dataset_type.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -from enum import Enum - - -class DatasetType(Enum): - """ - Dataset type, mostly used for datasets that contain data to bootstrap models on - """ - - VIDEO_LIST = "video_list" diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/test_solver.py b/spaces/brjathu/HMR2.0/vendor/detectron2/tests/test_solver.py deleted file mode 100644 index 6b3ae84c00b789df071ab5d12bae42d991df1d0b..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/test_solver.py +++ /dev/null @@ -1,66 +0,0 @@ -import unittest - -from detectron2.solver.build import _expand_param_groups, reduce_param_groups - - -class TestOptimizer(unittest.TestCase): - def testExpandParamsGroups(self): - params = [ - { - "params": ["p1", "p2", "p3", "p4"], - "lr": 1.0, - "weight_decay": 3.0, - }, - { - "params": ["p2", "p3", "p5"], - "lr": 2.0, - "momentum": 2.0, - }, - { - "params": ["p1"], - "weight_decay": 4.0, - }, - ] - out = _expand_param_groups(params) - gt = [ - dict(params=["p1"], lr=1.0, weight_decay=4.0), # noqa - dict(params=["p2"], lr=2.0, weight_decay=3.0, momentum=2.0), # noqa - dict(params=["p3"], lr=2.0, weight_decay=3.0, momentum=2.0), # noqa - dict(params=["p4"], lr=1.0, weight_decay=3.0), # noqa - dict(params=["p5"], lr=2.0, momentum=2.0), # noqa - ] - self.assertEqual(out, gt) - - def testReduceParamGroups(self): - params = [ - dict(params=["p1"], lr=1.0, weight_decay=4.0), # noqa - dict(params=["p2", "p6"], lr=2.0, weight_decay=3.0, momentum=2.0), # noqa - dict(params=["p3"], lr=2.0, weight_decay=3.0, momentum=2.0), # noqa - dict(params=["p4"], lr=1.0, weight_decay=3.0), # noqa - dict(params=["p5"], lr=2.0, momentum=2.0), # noqa - ] - gt_groups = [ - { - "lr": 1.0, - "weight_decay": 4.0, - "params": ["p1"], - }, - { - "lr": 2.0, - "weight_decay": 3.0, - "momentum": 2.0, - "params": ["p2", "p6", "p3"], - }, - { - "lr": 1.0, - "weight_decay": 3.0, - "params": ["p4"], - }, - { - "lr": 2.0, - "momentum": 2.0, - "params": ["p5"], - }, - ] - out = reduce_param_groups(params) - self.assertEqual(out, gt_groups) diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/TiffTags.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/TiffTags.py deleted file mode 100644 index 30b05e4e1d41fa21a7b7bf12c04ee05af6aa5284..0000000000000000000000000000000000000000 --- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/TiffTags.py +++ /dev/null @@ -1,560 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# TIFF tags -# -# This module provides clear-text names for various well-known -# TIFF tags. the TIFF codec works just fine without it. -# -# Copyright (c) Secret Labs AB 1999. -# -# See the README file for information on usage and redistribution. -# - -## -# This module provides constants and clear-text names for various -# well-known TIFF tags. -## - -from collections import namedtuple - - -class TagInfo(namedtuple("_TagInfo", "value name type length enum")): - __slots__ = [] - - def __new__(cls, value=None, name="unknown", type=None, length=None, enum=None): - return super().__new__(cls, value, name, type, length, enum or {}) - - def cvt_enum(self, value): - # Using get will call hash(value), which can be expensive - # for some types (e.g. Fraction). Since self.enum is rarely - # used, it's usually better to test it first. - return self.enum.get(value, value) if self.enum else value - - -def lookup(tag, group=None): - """ - :param tag: Integer tag number - :param group: Which :py:data:`~PIL.TiffTags.TAGS_V2_GROUPS` to look in - - .. versionadded:: 8.3.0 - - :returns: Taginfo namedtuple, From the ``TAGS_V2`` info if possible, - otherwise just populating the value and name from ``TAGS``. - If the tag is not recognized, "unknown" is returned for the name - - """ - - if group is not None: - info = TAGS_V2_GROUPS[group].get(tag) if group in TAGS_V2_GROUPS else None - else: - info = TAGS_V2.get(tag) - return info or TagInfo(tag, TAGS.get(tag, "unknown")) - - -## -# Map tag numbers to tag info. -# -# id: (Name, Type, Length, enum_values) -# -# The length here differs from the length in the tiff spec. For -# numbers, the tiff spec is for the number of fields returned. We -# agree here. For string-like types, the tiff spec uses the length of -# field in bytes. In Pillow, we are using the number of expected -# fields, in general 1 for string-like types. - - -BYTE = 1 -ASCII = 2 -SHORT = 3 -LONG = 4 -RATIONAL = 5 -SIGNED_BYTE = 6 -UNDEFINED = 7 -SIGNED_SHORT = 8 -SIGNED_LONG = 9 -SIGNED_RATIONAL = 10 -FLOAT = 11 -DOUBLE = 12 -IFD = 13 -LONG8 = 16 - -TAGS_V2 = { - 254: ("NewSubfileType", LONG, 1), - 255: ("SubfileType", SHORT, 1), - 256: ("ImageWidth", LONG, 1), - 257: ("ImageLength", LONG, 1), - 258: ("BitsPerSample", SHORT, 0), - 259: ( - "Compression", - SHORT, - 1, - { - "Uncompressed": 1, - "CCITT 1d": 2, - "Group 3 Fax": 3, - "Group 4 Fax": 4, - "LZW": 5, - "JPEG": 6, - "PackBits": 32773, - }, - ), - 262: ( - "PhotometricInterpretation", - SHORT, - 1, - { - "WhiteIsZero": 0, - "BlackIsZero": 1, - "RGB": 2, - "RGB Palette": 3, - "Transparency Mask": 4, - "CMYK": 5, - "YCbCr": 6, - "CieLAB": 8, - "CFA": 32803, # TIFF/EP, Adobe DNG - "LinearRaw": 32892, # Adobe DNG - }, - ), - 263: ("Threshholding", SHORT, 1), - 264: ("CellWidth", SHORT, 1), - 265: ("CellLength", SHORT, 1), - 266: ("FillOrder", SHORT, 1), - 269: ("DocumentName", ASCII, 1), - 270: ("ImageDescription", ASCII, 1), - 271: ("Make", ASCII, 1), - 272: ("Model", ASCII, 1), - 273: ("StripOffsets", LONG, 0), - 274: ("Orientation", SHORT, 1), - 277: ("SamplesPerPixel", SHORT, 1), - 278: ("RowsPerStrip", LONG, 1), - 279: ("StripByteCounts", LONG, 0), - 280: ("MinSampleValue", SHORT, 0), - 281: ("MaxSampleValue", SHORT, 0), - 282: ("XResolution", RATIONAL, 1), - 283: ("YResolution", RATIONAL, 1), - 284: ("PlanarConfiguration", SHORT, 1, {"Contiguous": 1, "Separate": 2}), - 285: ("PageName", ASCII, 1), - 286: ("XPosition", RATIONAL, 1), - 287: ("YPosition", RATIONAL, 1), - 288: ("FreeOffsets", LONG, 1), - 289: ("FreeByteCounts", LONG, 1), - 290: ("GrayResponseUnit", SHORT, 1), - 291: ("GrayResponseCurve", SHORT, 0), - 292: ("T4Options", LONG, 1), - 293: ("T6Options", LONG, 1), - 296: ("ResolutionUnit", SHORT, 1, {"none": 1, "inch": 2, "cm": 3}), - 297: ("PageNumber", SHORT, 2), - 301: ("TransferFunction", SHORT, 0), - 305: ("Software", ASCII, 1), - 306: ("DateTime", ASCII, 1), - 315: ("Artist", ASCII, 1), - 316: ("HostComputer", ASCII, 1), - 317: ("Predictor", SHORT, 1, {"none": 1, "Horizontal Differencing": 2}), - 318: ("WhitePoint", RATIONAL, 2), - 319: ("PrimaryChromaticities", RATIONAL, 6), - 320: ("ColorMap", SHORT, 0), - 321: ("HalftoneHints", SHORT, 2), - 322: ("TileWidth", LONG, 1), - 323: ("TileLength", LONG, 1), - 324: ("TileOffsets", LONG, 0), - 325: ("TileByteCounts", LONG, 0), - 330: ("SubIFDs", LONG, 0), - 332: ("InkSet", SHORT, 1), - 333: ("InkNames", ASCII, 1), - 334: ("NumberOfInks", SHORT, 1), - 336: ("DotRange", SHORT, 0), - 337: ("TargetPrinter", ASCII, 1), - 338: ("ExtraSamples", SHORT, 0), - 339: ("SampleFormat", SHORT, 0), - 340: ("SMinSampleValue", DOUBLE, 0), - 341: ("SMaxSampleValue", DOUBLE, 0), - 342: ("TransferRange", SHORT, 6), - 347: ("JPEGTables", UNDEFINED, 1), - # obsolete JPEG tags - 512: ("JPEGProc", SHORT, 1), - 513: ("JPEGInterchangeFormat", LONG, 1), - 514: ("JPEGInterchangeFormatLength", LONG, 1), - 515: ("JPEGRestartInterval", SHORT, 1), - 517: ("JPEGLosslessPredictors", SHORT, 0), - 518: ("JPEGPointTransforms", SHORT, 0), - 519: ("JPEGQTables", LONG, 0), - 520: ("JPEGDCTables", LONG, 0), - 521: ("JPEGACTables", LONG, 0), - 529: ("YCbCrCoefficients", RATIONAL, 3), - 530: ("YCbCrSubSampling", SHORT, 2), - 531: ("YCbCrPositioning", SHORT, 1), - 532: ("ReferenceBlackWhite", RATIONAL, 6), - 700: ("XMP", BYTE, 0), - 33432: ("Copyright", ASCII, 1), - 33723: ("IptcNaaInfo", UNDEFINED, 1), - 34377: ("PhotoshopInfo", BYTE, 0), - # FIXME add more tags here - 34665: ("ExifIFD", LONG, 1), - 34675: ("ICCProfile", UNDEFINED, 1), - 34853: ("GPSInfoIFD", LONG, 1), - 36864: ("ExifVersion", UNDEFINED, 1), - 37724: ("ImageSourceData", UNDEFINED, 1), - 40965: ("InteroperabilityIFD", LONG, 1), - 41730: ("CFAPattern", UNDEFINED, 1), - # MPInfo - 45056: ("MPFVersion", UNDEFINED, 1), - 45057: ("NumberOfImages", LONG, 1), - 45058: ("MPEntry", UNDEFINED, 1), - 45059: ("ImageUIDList", UNDEFINED, 0), # UNDONE, check - 45060: ("TotalFrames", LONG, 1), - 45313: ("MPIndividualNum", LONG, 1), - 45569: ("PanOrientation", LONG, 1), - 45570: ("PanOverlap_H", RATIONAL, 1), - 45571: ("PanOverlap_V", RATIONAL, 1), - 45572: ("BaseViewpointNum", LONG, 1), - 45573: ("ConvergenceAngle", SIGNED_RATIONAL, 1), - 45574: ("BaselineLength", RATIONAL, 1), - 45575: ("VerticalDivergence", SIGNED_RATIONAL, 1), - 45576: ("AxisDistance_X", SIGNED_RATIONAL, 1), - 45577: ("AxisDistance_Y", SIGNED_RATIONAL, 1), - 45578: ("AxisDistance_Z", SIGNED_RATIONAL, 1), - 45579: ("YawAngle", SIGNED_RATIONAL, 1), - 45580: ("PitchAngle", SIGNED_RATIONAL, 1), - 45581: ("RollAngle", SIGNED_RATIONAL, 1), - 40960: ("FlashPixVersion", UNDEFINED, 1), - 50741: ("MakerNoteSafety", SHORT, 1, {"Unsafe": 0, "Safe": 1}), - 50780: ("BestQualityScale", RATIONAL, 1), - 50838: ("ImageJMetaDataByteCounts", LONG, 0), # Can be more than one - 50839: ("ImageJMetaData", UNDEFINED, 1), # see Issue #2006 -} -TAGS_V2_GROUPS = { - # ExifIFD - 34665: { - 36864: ("ExifVersion", UNDEFINED, 1), - 40960: ("FlashPixVersion", UNDEFINED, 1), - 40965: ("InteroperabilityIFD", LONG, 1), - 41730: ("CFAPattern", UNDEFINED, 1), - }, - # GPSInfoIFD - 34853: { - 0: ("GPSVersionID", BYTE, 4), - 1: ("GPSLatitudeRef", ASCII, 2), - 2: ("GPSLatitude", RATIONAL, 3), - 3: ("GPSLongitudeRef", ASCII, 2), - 4: ("GPSLongitude", RATIONAL, 3), - 5: ("GPSAltitudeRef", BYTE, 1), - 6: ("GPSAltitude", RATIONAL, 1), - 7: ("GPSTimeStamp", RATIONAL, 3), - 8: ("GPSSatellites", ASCII, 0), - 9: ("GPSStatus", ASCII, 2), - 10: ("GPSMeasureMode", ASCII, 2), - 11: ("GPSDOP", RATIONAL, 1), - 12: ("GPSSpeedRef", ASCII, 2), - 13: ("GPSSpeed", RATIONAL, 1), - 14: ("GPSTrackRef", ASCII, 2), - 15: ("GPSTrack", RATIONAL, 1), - 16: ("GPSImgDirectionRef", ASCII, 2), - 17: ("GPSImgDirection", RATIONAL, 1), - 18: ("GPSMapDatum", ASCII, 0), - 19: ("GPSDestLatitudeRef", ASCII, 2), - 20: ("GPSDestLatitude", RATIONAL, 3), - 21: ("GPSDestLongitudeRef", ASCII, 2), - 22: ("GPSDestLongitude", RATIONAL, 3), - 23: ("GPSDestBearingRef", ASCII, 2), - 24: ("GPSDestBearing", RATIONAL, 1), - 25: ("GPSDestDistanceRef", ASCII, 2), - 26: ("GPSDestDistance", RATIONAL, 1), - 27: ("GPSProcessingMethod", UNDEFINED, 0), - 28: ("GPSAreaInformation", UNDEFINED, 0), - 29: ("GPSDateStamp", ASCII, 11), - 30: ("GPSDifferential", SHORT, 1), - }, - # InteroperabilityIFD - 40965: {1: ("InteropIndex", ASCII, 1), 2: ("InteropVersion", UNDEFINED, 1)}, -} - -# Legacy Tags structure -# these tags aren't included above, but were in the previous versions -TAGS = { - 347: "JPEGTables", - 700: "XMP", - # Additional Exif Info - 32932: "Wang Annotation", - 33434: "ExposureTime", - 33437: "FNumber", - 33445: "MD FileTag", - 33446: "MD ScalePixel", - 33447: "MD ColorTable", - 33448: "MD LabName", - 33449: "MD SampleInfo", - 33450: "MD PrepDate", - 33451: "MD PrepTime", - 33452: "MD FileUnits", - 33550: "ModelPixelScaleTag", - 33723: "IptcNaaInfo", - 33918: "INGR Packet Data Tag", - 33919: "INGR Flag Registers", - 33920: "IrasB Transformation Matrix", - 33922: "ModelTiepointTag", - 34264: "ModelTransformationTag", - 34377: "PhotoshopInfo", - 34735: "GeoKeyDirectoryTag", - 34736: "GeoDoubleParamsTag", - 34737: "GeoAsciiParamsTag", - 34850: "ExposureProgram", - 34852: "SpectralSensitivity", - 34855: "ISOSpeedRatings", - 34856: "OECF", - 34864: "SensitivityType", - 34865: "StandardOutputSensitivity", - 34866: "RecommendedExposureIndex", - 34867: "ISOSpeed", - 34868: "ISOSpeedLatitudeyyy", - 34869: "ISOSpeedLatitudezzz", - 34908: "HylaFAX FaxRecvParams", - 34909: "HylaFAX FaxSubAddress", - 34910: "HylaFAX FaxRecvTime", - 36864: "ExifVersion", - 36867: "DateTimeOriginal", - 36868: "DateTimeDigitized", - 37121: "ComponentsConfiguration", - 37122: "CompressedBitsPerPixel", - 37724: "ImageSourceData", - 37377: "ShutterSpeedValue", - 37378: "ApertureValue", - 37379: "BrightnessValue", - 37380: "ExposureBiasValue", - 37381: "MaxApertureValue", - 37382: "SubjectDistance", - 37383: "MeteringMode", - 37384: "LightSource", - 37385: "Flash", - 37386: "FocalLength", - 37396: "SubjectArea", - 37500: "MakerNote", - 37510: "UserComment", - 37520: "SubSec", - 37521: "SubSecTimeOriginal", - 37522: "SubsecTimeDigitized", - 40960: "FlashPixVersion", - 40961: "ColorSpace", - 40962: "PixelXDimension", - 40963: "PixelYDimension", - 40964: "RelatedSoundFile", - 40965: "InteroperabilityIFD", - 41483: "FlashEnergy", - 41484: "SpatialFrequencyResponse", - 41486: "FocalPlaneXResolution", - 41487: "FocalPlaneYResolution", - 41488: "FocalPlaneResolutionUnit", - 41492: "SubjectLocation", - 41493: "ExposureIndex", - 41495: "SensingMethod", - 41728: "FileSource", - 41729: "SceneType", - 41730: "CFAPattern", - 41985: "CustomRendered", - 41986: "ExposureMode", - 41987: "WhiteBalance", - 41988: "DigitalZoomRatio", - 41989: "FocalLengthIn35mmFilm", - 41990: "SceneCaptureType", - 41991: "GainControl", - 41992: "Contrast", - 41993: "Saturation", - 41994: "Sharpness", - 41995: "DeviceSettingDescription", - 41996: "SubjectDistanceRange", - 42016: "ImageUniqueID", - 42032: "CameraOwnerName", - 42033: "BodySerialNumber", - 42034: "LensSpecification", - 42035: "LensMake", - 42036: "LensModel", - 42037: "LensSerialNumber", - 42112: "GDAL_METADATA", - 42113: "GDAL_NODATA", - 42240: "Gamma", - 50215: "Oce Scanjob Description", - 50216: "Oce Application Selector", - 50217: "Oce Identification Number", - 50218: "Oce ImageLogic Characteristics", - # Adobe DNG - 50706: "DNGVersion", - 50707: "DNGBackwardVersion", - 50708: "UniqueCameraModel", - 50709: "LocalizedCameraModel", - 50710: "CFAPlaneColor", - 50711: "CFALayout", - 50712: "LinearizationTable", - 50713: "BlackLevelRepeatDim", - 50714: "BlackLevel", - 50715: "BlackLevelDeltaH", - 50716: "BlackLevelDeltaV", - 50717: "WhiteLevel", - 50718: "DefaultScale", - 50719: "DefaultCropOrigin", - 50720: "DefaultCropSize", - 50721: "ColorMatrix1", - 50722: "ColorMatrix2", - 50723: "CameraCalibration1", - 50724: "CameraCalibration2", - 50725: "ReductionMatrix1", - 50726: "ReductionMatrix2", - 50727: "AnalogBalance", - 50728: "AsShotNeutral", - 50729: "AsShotWhiteXY", - 50730: "BaselineExposure", - 50731: "BaselineNoise", - 50732: "BaselineSharpness", - 50733: "BayerGreenSplit", - 50734: "LinearResponseLimit", - 50735: "CameraSerialNumber", - 50736: "LensInfo", - 50737: "ChromaBlurRadius", - 50738: "AntiAliasStrength", - 50740: "DNGPrivateData", - 50778: "CalibrationIlluminant1", - 50779: "CalibrationIlluminant2", - 50784: "Alias Layer Metadata", -} - - -def _populate(): - for k, v in TAGS_V2.items(): - # Populate legacy structure. - TAGS[k] = v[0] - if len(v) == 4: - for sk, sv in v[3].items(): - TAGS[(k, sv)] = sk - - TAGS_V2[k] = TagInfo(k, *v) - - for group, tags in TAGS_V2_GROUPS.items(): - for k, v in tags.items(): - tags[k] = TagInfo(k, *v) - - -_populate() -## -# Map type numbers to type names -- defined in ImageFileDirectory. - -TYPES = {} - -# was: -# TYPES = { -# 1: "byte", -# 2: "ascii", -# 3: "short", -# 4: "long", -# 5: "rational", -# 6: "signed byte", -# 7: "undefined", -# 8: "signed short", -# 9: "signed long", -# 10: "signed rational", -# 11: "float", -# 12: "double", -# } - -# -# These tags are handled by default in libtiff, without -# adding to the custom dictionary. From tif_dir.c, searching for -# case TIFFTAG in the _TIFFVSetField function: -# Line: item. -# 148: case TIFFTAG_SUBFILETYPE: -# 151: case TIFFTAG_IMAGEWIDTH: -# 154: case TIFFTAG_IMAGELENGTH: -# 157: case TIFFTAG_BITSPERSAMPLE: -# 181: case TIFFTAG_COMPRESSION: -# 202: case TIFFTAG_PHOTOMETRIC: -# 205: case TIFFTAG_THRESHHOLDING: -# 208: case TIFFTAG_FILLORDER: -# 214: case TIFFTAG_ORIENTATION: -# 221: case TIFFTAG_SAMPLESPERPIXEL: -# 228: case TIFFTAG_ROWSPERSTRIP: -# 238: case TIFFTAG_MINSAMPLEVALUE: -# 241: case TIFFTAG_MAXSAMPLEVALUE: -# 244: case TIFFTAG_SMINSAMPLEVALUE: -# 247: case TIFFTAG_SMAXSAMPLEVALUE: -# 250: case TIFFTAG_XRESOLUTION: -# 256: case TIFFTAG_YRESOLUTION: -# 262: case TIFFTAG_PLANARCONFIG: -# 268: case TIFFTAG_XPOSITION: -# 271: case TIFFTAG_YPOSITION: -# 274: case TIFFTAG_RESOLUTIONUNIT: -# 280: case TIFFTAG_PAGENUMBER: -# 284: case TIFFTAG_HALFTONEHINTS: -# 288: case TIFFTAG_COLORMAP: -# 294: case TIFFTAG_EXTRASAMPLES: -# 298: case TIFFTAG_MATTEING: -# 305: case TIFFTAG_TILEWIDTH: -# 316: case TIFFTAG_TILELENGTH: -# 327: case TIFFTAG_TILEDEPTH: -# 333: case TIFFTAG_DATATYPE: -# 344: case TIFFTAG_SAMPLEFORMAT: -# 361: case TIFFTAG_IMAGEDEPTH: -# 364: case TIFFTAG_SUBIFD: -# 376: case TIFFTAG_YCBCRPOSITIONING: -# 379: case TIFFTAG_YCBCRSUBSAMPLING: -# 383: case TIFFTAG_TRANSFERFUNCTION: -# 389: case TIFFTAG_REFERENCEBLACKWHITE: -# 393: case TIFFTAG_INKNAMES: - -# Following pseudo-tags are also handled by default in libtiff: -# TIFFTAG_JPEGQUALITY 65537 - -# some of these are not in our TAGS_V2 dict and were included from tiff.h - -# This list also exists in encode.c -LIBTIFF_CORE = { - 255, - 256, - 257, - 258, - 259, - 262, - 263, - 266, - 274, - 277, - 278, - 280, - 281, - 340, - 341, - 282, - 283, - 284, - 286, - 287, - 296, - 297, - 321, - 320, - 338, - 32995, - 322, - 323, - 32998, - 32996, - 339, - 32997, - 330, - 531, - 530, - 301, - 532, - 333, - # as above - 269, # this has been in our tests forever, and works - 65537, -} - -LIBTIFF_CORE.remove(255) # We don't have support for subfiletypes -LIBTIFF_CORE.remove(322) # We don't have support for writing tiled images with libtiff -LIBTIFF_CORE.remove(323) # Tiled images -LIBTIFF_CORE.remove(333) # Ink Names either - -# Note to advanced users: There may be combinations of these -# parameters and values that when added properly, will work and -# produce valid tiff images that may work in your application. -# It is safe to add and remove tags from this set from Pillow's point -# of view so long as you test against libtiff. diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/transform/image.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/transform/image.py deleted file mode 100644 index 8139b67841633841199a1aae3b25e326afaaf5e2..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/transform/image.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import torch - - -class ImageResizeTransform: - """ - Transform that resizes images loaded from a dataset - (BGR data in NCHW channel order, typically uint8) to a format ready to be - consumed by DensePose training (BGR float32 data in NCHW channel order) - """ - - def __init__(self, min_size: int = 800, max_size: int = 1333): - self.min_size = min_size - self.max_size = max_size - - def __call__(self, images: torch.Tensor) -> torch.Tensor: - """ - Args: - images (torch.Tensor): tensor of size [N, 3, H, W] that contains - BGR data (typically in uint8) - Returns: - images (torch.Tensor): tensor of size [N, 3, H1, W1] where - H1 and W1 are chosen to respect the specified min and max sizes - and preserve the original aspect ratio, the data channels - follow BGR order and the data type is `torch.float32` - """ - # resize with min size - images = images.float() - min_size = min(images.shape[-2:]) - max_size = max(images.shape[-2:]) - scale = min(self.min_size / min_size, self.max_size / max_size) - images = torch.nn.functional.interpolate( - images, - scale_factor=scale, - mode="bilinear", - align_corners=False, - ) - return images diff --git a/spaces/catundchat/tts_cn/bert/__init__.py b/spaces/catundchat/tts_cn/bert/__init__.py deleted file mode 100644 index d7dcbe2c051f01fff99c3bf38113db9fceacaf6b..0000000000000000000000000000000000000000 --- a/spaces/catundchat/tts_cn/bert/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .ProsodyModel import TTSProsody \ No newline at end of file diff --git a/spaces/cbr/swp/face_parsing/model.py b/spaces/cbr/swp/face_parsing/model.py deleted file mode 100644 index 5119e751c3ae18e4dc1eecde7bfcb5bf9c62fb92..0000000000000000000000000000000000000000 --- a/spaces/cbr/swp/face_parsing/model.py +++ /dev/null @@ -1,283 +0,0 @@ -#!/usr/bin/python -# -*- encoding: utf-8 -*- - - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torchvision - -from .resnet import Resnet18 -# from modules.bn import InPlaceABNSync as BatchNorm2d - - -class ConvBNReLU(nn.Module): - def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs): - super(ConvBNReLU, self).__init__() - self.conv = nn.Conv2d(in_chan, - out_chan, - kernel_size = ks, - stride = stride, - padding = padding, - bias = False) - self.bn = nn.BatchNorm2d(out_chan) - self.init_weight() - - def forward(self, x): - x = self.conv(x) - x = F.relu(self.bn(x)) - return x - - def init_weight(self): - for ly in self.children(): - if isinstance(ly, nn.Conv2d): - nn.init.kaiming_normal_(ly.weight, a=1) - if not ly.bias is None: nn.init.constant_(ly.bias, 0) - -class BiSeNetOutput(nn.Module): - def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs): - super(BiSeNetOutput, self).__init__() - self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) - self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False) - self.init_weight() - - def forward(self, x): - x = self.conv(x) - x = self.conv_out(x) - return x - - def init_weight(self): - for ly in self.children(): - if isinstance(ly, nn.Conv2d): - nn.init.kaiming_normal_(ly.weight, a=1) - if not ly.bias is None: nn.init.constant_(ly.bias, 0) - - def get_params(self): - wd_params, nowd_params = [], [] - for name, module in self.named_modules(): - if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): - wd_params.append(module.weight) - if not module.bias is None: - nowd_params.append(module.bias) - elif isinstance(module, nn.BatchNorm2d): - nowd_params += list(module.parameters()) - return wd_params, nowd_params - - -class AttentionRefinementModule(nn.Module): - def __init__(self, in_chan, out_chan, *args, **kwargs): - super(AttentionRefinementModule, self).__init__() - self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) - self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False) - self.bn_atten = nn.BatchNorm2d(out_chan) - self.sigmoid_atten = nn.Sigmoid() - self.init_weight() - - def forward(self, x): - feat = self.conv(x) - atten = F.avg_pool2d(feat, feat.size()[2:]) - atten = self.conv_atten(atten) - atten = self.bn_atten(atten) - atten = self.sigmoid_atten(atten) - out = torch.mul(feat, atten) - return out - - def init_weight(self): - for ly in self.children(): - if isinstance(ly, nn.Conv2d): - nn.init.kaiming_normal_(ly.weight, a=1) - if not ly.bias is None: nn.init.constant_(ly.bias, 0) - - -class ContextPath(nn.Module): - def __init__(self, *args, **kwargs): - super(ContextPath, self).__init__() - self.resnet = Resnet18() - self.arm16 = AttentionRefinementModule(256, 128) - self.arm32 = AttentionRefinementModule(512, 128) - self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) - self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) - self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) - - self.init_weight() - - def forward(self, x): - H0, W0 = x.size()[2:] - feat8, feat16, feat32 = self.resnet(x) - H8, W8 = feat8.size()[2:] - H16, W16 = feat16.size()[2:] - H32, W32 = feat32.size()[2:] - - avg = F.avg_pool2d(feat32, feat32.size()[2:]) - avg = self.conv_avg(avg) - avg_up = F.interpolate(avg, (H32, W32), mode='nearest') - - feat32_arm = self.arm32(feat32) - feat32_sum = feat32_arm + avg_up - feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest') - feat32_up = self.conv_head32(feat32_up) - - feat16_arm = self.arm16(feat16) - feat16_sum = feat16_arm + feat32_up - feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest') - feat16_up = self.conv_head16(feat16_up) - - return feat8, feat16_up, feat32_up # x8, x8, x16 - - def init_weight(self): - for ly in self.children(): - if isinstance(ly, nn.Conv2d): - nn.init.kaiming_normal_(ly.weight, a=1) - if not ly.bias is None: nn.init.constant_(ly.bias, 0) - - def get_params(self): - wd_params, nowd_params = [], [] - for name, module in self.named_modules(): - if isinstance(module, (nn.Linear, nn.Conv2d)): - wd_params.append(module.weight) - if not module.bias is None: - nowd_params.append(module.bias) - elif isinstance(module, nn.BatchNorm2d): - nowd_params += list(module.parameters()) - return wd_params, nowd_params - - -### This is not used, since I replace this with the resnet feature with the same size -class SpatialPath(nn.Module): - def __init__(self, *args, **kwargs): - super(SpatialPath, self).__init__() - self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3) - self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) - self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) - self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0) - self.init_weight() - - def forward(self, x): - feat = self.conv1(x) - feat = self.conv2(feat) - feat = self.conv3(feat) - feat = self.conv_out(feat) - return feat - - def init_weight(self): - for ly in self.children(): - if isinstance(ly, nn.Conv2d): - nn.init.kaiming_normal_(ly.weight, a=1) - if not ly.bias is None: nn.init.constant_(ly.bias, 0) - - def get_params(self): - wd_params, nowd_params = [], [] - for name, module in self.named_modules(): - if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): - wd_params.append(module.weight) - if not module.bias is None: - nowd_params.append(module.bias) - elif isinstance(module, nn.BatchNorm2d): - nowd_params += list(module.parameters()) - return wd_params, nowd_params - - -class FeatureFusionModule(nn.Module): - def __init__(self, in_chan, out_chan, *args, **kwargs): - super(FeatureFusionModule, self).__init__() - self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) - self.conv1 = nn.Conv2d(out_chan, - out_chan//4, - kernel_size = 1, - stride = 1, - padding = 0, - bias = False) - self.conv2 = nn.Conv2d(out_chan//4, - out_chan, - kernel_size = 1, - stride = 1, - padding = 0, - bias = False) - self.relu = nn.ReLU(inplace=True) - self.sigmoid = nn.Sigmoid() - self.init_weight() - - def forward(self, fsp, fcp): - fcat = torch.cat([fsp, fcp], dim=1) - feat = self.convblk(fcat) - atten = F.avg_pool2d(feat, feat.size()[2:]) - atten = self.conv1(atten) - atten = self.relu(atten) - atten = self.conv2(atten) - atten = self.sigmoid(atten) - feat_atten = torch.mul(feat, atten) - feat_out = feat_atten + feat - return feat_out - - def init_weight(self): - for ly in self.children(): - if isinstance(ly, nn.Conv2d): - nn.init.kaiming_normal_(ly.weight, a=1) - if not ly.bias is None: nn.init.constant_(ly.bias, 0) - - def get_params(self): - wd_params, nowd_params = [], [] - for name, module in self.named_modules(): - if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): - wd_params.append(module.weight) - if not module.bias is None: - nowd_params.append(module.bias) - elif isinstance(module, nn.BatchNorm2d): - nowd_params += list(module.parameters()) - return wd_params, nowd_params - - -class BiSeNet(nn.Module): - def __init__(self, n_classes, *args, **kwargs): - super(BiSeNet, self).__init__() - self.cp = ContextPath() - ## here self.sp is deleted - self.ffm = FeatureFusionModule(256, 256) - self.conv_out = BiSeNetOutput(256, 256, n_classes) - self.conv_out16 = BiSeNetOutput(128, 64, n_classes) - self.conv_out32 = BiSeNetOutput(128, 64, n_classes) - self.init_weight() - - def forward(self, x): - H, W = x.size()[2:] - feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature - feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature - feat_fuse = self.ffm(feat_sp, feat_cp8) - - feat_out = self.conv_out(feat_fuse) - feat_out16 = self.conv_out16(feat_cp8) - feat_out32 = self.conv_out32(feat_cp16) - - feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True) - feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True) - feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True) - return feat_out, feat_out16, feat_out32 - - def init_weight(self): - for ly in self.children(): - if isinstance(ly, nn.Conv2d): - nn.init.kaiming_normal_(ly.weight, a=1) - if not ly.bias is None: nn.init.constant_(ly.bias, 0) - - def get_params(self): - wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] - for name, child in self.named_children(): - child_wd_params, child_nowd_params = child.get_params() - if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput): - lr_mul_wd_params += child_wd_params - lr_mul_nowd_params += child_nowd_params - else: - wd_params += child_wd_params - nowd_params += child_nowd_params - return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params - - -if __name__ == "__main__": - net = BiSeNet(19) - net.cuda() - net.eval() - in_ten = torch.randn(16, 3, 640, 480).cuda() - out, out16, out32 = net(in_ten) - print(out.shape) - - net.get_params() diff --git a/spaces/ccolas/TastyPiano/src/music/representation_learning/sentence_bert_finetuning/__init__.py b/spaces/ccolas/TastyPiano/src/music/representation_learning/sentence_bert_finetuning/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/chansung/LLM-As-Chatbot/gens/__init__.py b/spaces/chansung/LLM-As-Chatbot/gens/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/chansung/LLaMA2-Story-Showcase/app.py b/spaces/chansung/LLaMA2-Story-Showcase/app.py deleted file mode 100644 index 50104504799d6dd013613461d7d853a212ed4187..0000000000000000000000000000000000000000 --- a/spaces/chansung/LLaMA2-Story-Showcase/app.py +++ /dev/null @@ -1,99 +0,0 @@ -import io -import base64 -from PIL import Image -from PIL import ImageDraw -from PIL import ImageFont - -import gradio as gr -from datasets import load_dataset -from datasets import DownloadMode, VerificationMode - -STYLES = """ -#container { - margin: auto; - width: 50%; -} - -#gallery { - height: 500px !important; -} - -.center { - text-align: center; -} - -.small-big { - font-size: 12pt !important; -} -""" - -titles = [] -stories = [] - -def add_title(image, title): - dr = ImageDraw.Draw(image) - - myFont = ImageFont.truetype('arial_bold.ttf', 30) - _, _, w, h = dr.textbbox((0, 0), title, font=myFont) - dr.rectangle([(0, image.height-80), (image.width, (image.height-80)+h)], fill="white", outline="white") - dr.text(((image.width-w)/2, image.height-80), title, font=myFont, fill=(0, 0, 0)) - - return image - -def gallery_select(gallery, evt: gr.SelectData): - print(evt.value) - print(evt.index) - print(evt.target) - - return [ - gr.update(value=f"## {titles[evt.index]}", visible=True), - gr.update(value=stories[evt.index], visible=True), - ] - -def get_gallery(): - global titles, stories - - images = [] - titles = [] - stories = [] - dataset = load_dataset( - "chansung/llama2-stories", - download_mode=DownloadMode.FORCE_REDOWNLOAD, - verification_mode=VerificationMode.NO_CHECKS - ) - - for row in dataset['train']: - try: - base64_image = row['image'] - base64_decoded = base64.b64decode(base64_image) - image = Image.open(io.BytesIO(base64_decoded)) - except: - image = Image.open('placeholder.png') - - titles.append(row['title']) - stories.append(row['story']) - images.append(add_title(image, row['title'])) - - return images - -with gr.Blocks(css=STYLES) as demo: - with gr.Column(elem_id="container"): - gr.Markdown("## LLaMA2 Story Showcase", elem_classes=['center']) - gr.Markdown("This space is where community shares generated stories by [chansung/co-write-with-llama2](https://huggingface.co/spaces/chansung/co-write-with-llama2) space. " - "Generated stories are archived in [chansung/llama2-stories](https://huggingface.co/datasets/chansung/llama2-stories) dataset repository. The gallery will be " - "regularly updated in a daily basis.", - elem_classes=['small-big', 'center']) - - gallery = gr.Gallery(get_gallery, every=3000, columns=5, container=False, elem_id="gallery") - - with gr.Column(): - title = gr.Markdown("title", visible=False, elem_classes=['center']) - story = gr.Markdown("stories goes here...", visible=False, elem_classes=['small-big']) - - gallery.select( - fn=gallery_select, - inputs=[gallery], - outputs=[title, story] - ) - -demo.launch() \ No newline at end of file diff --git a/spaces/charles0519/ChuanhuChatGPT/presets.py b/spaces/charles0519/ChuanhuChatGPT/presets.py deleted file mode 100644 index 2a518eabbc48400cd76a45163d6910abf57532a0..0000000000000000000000000000000000000000 --- a/spaces/charles0519/ChuanhuChatGPT/presets.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding:utf-8 -*- - -# ChatGPT 设置 -initial_prompt = "You are a helpful assistant." -API_URL = "https://api.openai.com/v1/chat/completions" -HISTORY_DIR = "history" -TEMPLATES_DIR = "templates" - -# 错误信息 -standard_error_msg = "☹️发生了错误：" # 错误信息的标准前缀 -error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。" # 获取对话时发生错误 -connection_timeout_prompt = "连接超时，无法获取对话。" # 连接超时 -read_timeout_prompt = "读取超时，无法获取对话。" # 读取超时 -proxy_error_prompt = "代理错误，无法获取对话。" # 代理错误 -ssl_error_prompt = "SSL错误，无法获取对话。" # SSL 错误 -no_apikey_msg = "API key长度不是51位，请检查是否输入正确。" # API key 长度不足 51 位 - -max_token_streaming = 3500 # 流式对话时的最大 token 数 -timeout_streaming = 5 # 流式对话时的超时时间 -max_token_all = 3500 # 非流式对话时的最大 token 数 -timeout_all = 200 # 非流式对话时的超时时间 -enable_streaming_option = True # 是否启用选择选择是否实时显示回答的勾选框 -HIDE_MY_KEY = False # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True - -SIM_K = 5 -INDEX_QUERY_TEMPRATURE = 1.0 - -title = """
川虎ChatGPT 🚀
""" -description = """\ -
-
-由Bilibili [土川虎虎虎](https://space.bilibili.com/29125536) 和 [明昭MZhao](https://space.bilibili.com/24807452)开发
-
-访问川虎ChatGPT的 [GitHub项目](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 下载最新版脚本
-
-此App使用 `gpt-3.5-turbo` 大语言模型
-
-"""
-
-summarize_prompt = "你是谁？我们刚才聊了什么？" # 总结对话时的 prompt
-
-MODELS = [
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-0301",
- "gpt-4",
- "gpt-4-0314",
- "gpt-4-32k",
- "gpt-4-32k-0314",
-] # 可选的模型
-
-
-WEBSEARCH_PTOMPT_TEMPLATE = """\
-Web search results:
-
-{web_results}
-Current date: {current_date}
-
-Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject.
-Query: {query}
-Reply in 中文"""
-
-PROMPT_TEMPLATE = """\
-Context information is below.
----------------------
-{context_str}
----------------------
-Current date: {current_date}.
-Using the provided context information, write a comprehensive reply to the given query.
-Make sure to cite results using [number] notation after the reference.
-If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
-Use prior knowledge only if the given context didn't provide enough information.
-Answer the question: {query_str}
-Reply in 中文
-"""
-
-REFINE_TEMPLATE = """\
-The original question is as follows: {query_str}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer
-(only if needed) with some more context below.
-------------
-{context_msg}
-------------
-Given the new context, refine the original answer to better
-Answer in the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch.
-If the context isn't useful, return the original answer.
-"""
diff --git a/spaces/chendl/compositional_test/multimodal/open_flamingo/train/instruction_template.py b/spaces/chendl/compositional_test/multimodal/open_flamingo/train/instruction_template.py
deleted file mode 100644
index 4b449fd79a1d97241c33f0ea0d9eace91b63466d..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/open_flamingo/train/instruction_template.py
+++ /dev/null
@@ -1,13 +0,0 @@
-VG_RELATION_TEMPLATES = [
- "Question: What is the relationship between<|#object#|> {nameA}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|> and<|#object#|> {nameB}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>? Answer: {relation}.",
- "Question: What is the relationship between<|#object#|> {nameA}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|> and<|#object#|> {nameB}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>? Answer:<|#object#|> {nameA}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|> {use_is} {relation}<|#object#|> {nameB}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>.",
- "Question: What {is_or_does}<|#object#|> {nameA}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|> {relation_do}? Answer:<|#object#|> {nameA}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|> {use_is} {relation}<|#object#|>{nameB}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>.",
- "Question: What {use_is} {relation}<|#object#|> {nameB}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>? Answer:<|#object#|> {nameA}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|> {use_is} {relation}<|#object#|> {nameB}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>.",
- "Question: What {is_or_does}<|#object#|> {nameA}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|> {relation_do}? Answer:<|#object#|> {nameB}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>.",
- "Question: What {use_is} {relation}<|#object#|> {nameB}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>? Answer:<|#object#|> {nameA}<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>.",
-]
-
-PISC_TEMPLATES = [
- "Question: What is the social relationship between this<|#object#|> person<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|> and that<|#object#|> person<|#endofobject#|><|#visual#|><|#box#|><|#endofobject#|>? Answer: {relation}.",
- "Question: What is the social relationship between these<|#object#|> people<|#endofobject#|><|#visual#|><|#box#|><|#box#|><|#endofobject#|>? Answer: {relation}.",
-]
diff --git a/spaces/chendl/compositional_test/multimodal/tools/convert_mmc4_to_wds.py b/spaces/chendl/compositional_test/multimodal/tools/convert_mmc4_to_wds.py
deleted file mode 100644
index 1798e89403b8cf7b5606176449b9e859fd82adbc..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/tools/convert_mmc4_to_wds.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import argparse
-import base64
-import json
-import os
-import tarfile
-import uuid
-import zipfile
-import time
-
-import braceexpand
-import webdataset as wds
-from tqdm import tqdm
-from tqdm.contrib.concurrent import process_map
-
-arg_parser = argparse.ArgumentParser()
-arg_parser.add_argument("--output_dir", type=str)
-arg_parser.add_argument(
- "--image_shards",
- type=str,
- help="Pass in a list of shards in the format path_to_shard/shard_{0..23098}_images_v2.tar",
-)
-arg_parser.add_argument(
- "--doc_shards",
- type=str,
- help="Pass in a list of shards in the format path_to_shard/docs_shard_{0..23098}_v2.jsonl.zip",
-)
-arg_parser.add_argument(
- "--thread",
- type=int,
- default=128,
-)
-args = arg_parser.parse_args()
-
-def get_txt_to_filename_dict(image_shards, disable_tqdm=False):
- txt_to_filename_dict = {}
- dataset = wds.WebDataset(image_shards).decode("pil").to_tuple("txt", "json")
- for data in tqdm(dataset, disable=disable_tqdm):
- txt = data[0].split(".")[0]
- txt_to_filename_dict[txt] = data[1]['key']
- return txt_to_filename_dict
-
-
-def single_thread(args):
- i = args["i"]
- output_dir = args["output_dir"]
- doc_shards = args["doc_shards"]
- image_shards = args["image_shards"]
- if i == 0:
- tqdm.write(f"output_dir: {output_dir}")
- tqdm.write(f"doc_shards: {doc_shards[:5]}")
- tqdm.write(f"image_shards: {image_shards[:5]}")
- with wds.ShardWriter(os.path.join(output_dir, "%09d.tar"), maxcount=1000) as sink:
- sink.verbose = False
- for doc_shard, image_shard in tqdm(zip(doc_shards, image_shards), disable=(i != 0), total=len(doc_shards)):
- # txt_to_filename_dict = get_txt_to_filename_dict(image_shard, disable_tqdm=(i != 0))
- # image_tar = tarfile.open(image_shard)
- # Open the ZIP archive and extract the JSON file
- with zipfile.ZipFile(doc_shard, "r") as zip_file:
- # Assumes the JSON file is the first file in the archive
- json_filename = zip_file.namelist()[0]
- with zip_file.open(json_filename, "r") as json_file:
- pbar = tqdm(json_file, disable=True)
- total_num = 0
- exist_num = 0
- for sample_data in pbar:
- # get image names from json
- sample_data = json.loads(sample_data)
- image_info = sample_data["image_info"]
- image_names = [image["image_name"] for image in image_info]
-
- # Add each image to the tar file
- for img_idx, image_name in enumerate(image_names):
- total_num += 1
- try:
- image = image_tar.extractfile(txt_to_filename_dict[image_name.split(".")[0]]+".jpg")
- # convert to base64
- image_bytes = image.read()
- image_base64 = base64.b64encode(image_bytes).decode("utf-8")
- exist_num += 1
- except:
- tqdm.write(f"{image_name.split('.')[0]}")
- image_base64 = "null"
- sample_data["image_info"][img_idx][
- "image_base64"
- ] = image_base64
-
- key_str = uuid.uuid4().hex
- sink.write({"__key__": key_str, "json": sample_data})
- pbar.set_description(f"{exist_num/total_num:.2f}")
- # image_tar.close()
-
-
-def main():
- timestamp = int(time.time())
- os.makedirs(args.output_dir, exist_ok=True)
- os.makedirs(os.path.join(args.output_dir, str(timestamp)), exist_ok=True)
- tasks = []
- for i in range(args.thread):
- thread_dir = os.path.join(args.output_dir, str(timestamp), str(i))
- os.makedirs(thread_dir, exist_ok=True)
- tasks.append({
- "i": i,
- "output_dir": thread_dir,
- "doc_shards": [],
- "image_shards": [],
- })
-
- doc_shards = list(braceexpand.braceexpand(args.doc_shards))
- image_shards = list(braceexpand.braceexpand(args.image_shards))
-
- assert len(doc_shards) == len(
- image_shards
- ), "Each doc shards must have a corresponding image shard"
-
- for i, (doc_shard, image_shard) in enumerate(zip(doc_shards, image_shards)):
- tasks[i % args.thread]["doc_shards"].append(doc_shard)
- tasks[i % args.thread]["image_shards"].append(image_shard)
-
- # assert len(tasks) == args.thread
- # process_map(single_thread, tasks, max_workers=args.thread, disable=True)
- single_thread(tasks[0])
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/dml/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/dml/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/plistlib/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/plistlib/__init__.py
deleted file mode 100644
index 066eef38fc720265366afee9a8cd415fc560459e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/plistlib/__init__.py
+++ /dev/null
@@ -1,681 +0,0 @@
-import collections.abc
-import re
-from typing import (
- Any,
- Callable,
- Dict,
- List,
- Mapping,
- MutableMapping,
- Optional,
- Sequence,
- Type,
- Union,
- IO,
-)
-import warnings
-from io import BytesIO
-from datetime import datetime
-from base64 import b64encode, b64decode
-from numbers import Integral
-from types import SimpleNamespace
-from functools import singledispatch
-
-from fontTools.misc import etree
-
-from fontTools.misc.textTools import tostr
-
-
-# By default, we
-# - deserialize elements as bytes and
-# - serialize bytes as elements.
-# Before, on Python 2, we
-# - deserialized elements as plistlib.Data objects, in order to
-# distinguish them from the built-in str type (which is bytes on python2)
-# - serialized bytes as api_name
- parameter of the event listener.
- - For more information, visit the - API Page guide - . To hide the API documentation button and this page, set -
show_api=False
- in the
- Blocks.launch()
- method.`,c=M(),h=k("button"),W(_.$$.fragment),d(a,"class","svelte-e1ha0f"),d(i,"class","attention svelte-e1ha0f"),d(t,"class","wrap prose svelte-e1ha0f"),d(h,"class","svelte-e1ha0f")},m(g,S){y(g,t,S),m(t,n),m(t,r),m(t,i),m(i,o),m(i,a),m(a,l),m(t,u),m(t,s),y(g,c,S),y(g,h,S),Z(_,h,null),p=!0,v||(b=Se(h,"click",e[2]),v=!0)},p(g,[S]){(!p||S&1)&&q(l,g[0])},i(g){p||(B(_.$$.fragment,g),p=!0)},o(g){N(_.$$.fragment,g),p=!1},d(g){g&&(E(t),E(c),E(h)),Y(_),v=!1,b()}}}function Js(e,t,n){const r=$e();let{root:i}=t;const o=()=>r("close");return e.$$set=a=>{"root"in a&&n(0,i=a.root)},[i,r,o]}class Qs extends ue{constructor(t){super(),ce(this,t,Js,Ys,fe,{root:0})}}function Qe(e,t,n=null){return t===void 0?n==="py"?"None":null:t==="string"||t==="str"?n===null?e:'"'+e+'"':t==="number"?n===null?parseFloat(e):e:t==="boolean"||t=="bool"?n==="py"?(e=String(e),e==="true"?"True":"False"):n==="js"?e:e==="true":t==="List[str]"?(e=JSON.stringify(e),e):n===null?e===""?null:JSON.parse(e):typeof e=="string"?e===""?n==="py"?"None":"null":e:JSON.stringify(e)}const Mo="https://gradio.s3-us-west-2.amazonaws.com/3.36.1/assets/api-logo-5346f193.svg";function Jt(e){let t;return{c(){t=I("s")},m(n,r){y(n,t,r)},d(n){n&&E(t)}}}function $s(e){let t,n,r,i,o,a,l,u,s,c,h,_,p,v,b,g,S,A,T,f=e[1]>1&&Jt();return g=new Ro({}),{c(){t=k("h2"),n=k("img"),i=M(),o=k("div"),a=I(`API documentation
- `),l=k("div"),u=I(e[0]),s=M(),c=k("span"),h=k("span"),_=I(e[1]),p=I(" API endpoint"),f&&f.c(),v=M(),b=k("button"),W(g.$$.fragment),Ue(n.src,r=Mo)||d(n,"src",r),d(n,"alt",""),d(n,"class","svelte-3n2nxs"),d(l,"class","url svelte-3n2nxs"),d(h,"class","url svelte-3n2nxs"),d(c,"class","counts svelte-3n2nxs"),d(t,"class","svelte-3n2nxs"),d(b,"class","svelte-3n2nxs")},m(P,H){y(P,t,H),m(t,n),m(t,i),m(t,o),m(o,a),m(o,l),m(l,u),m(t,s),m(t,c),m(c,h),m(h,_),m(c,p),f&&f.m(c,null),y(P,v,H),y(P,b,H),Z(g,b,null),S=!0,A||(T=Se(b,"click",e[3]),A=!0)},p(P,[H]){(!S||H&1)&&q(u,P[0]),(!S||H&2)&&q(_,P[1]),P[1]>1?f||(f=Jt(),f.c(),f.m(c,null)):f&&(f.d(1),f=null)},i(P){S||(B(g.$$.fragment,P),S=!0)},o(P){N(g.$$.fragment,P),S=!1},d(P){P&&(E(t),E(v),E(b)),f&&f.d(),Y(g),A=!1,T()}}}function Ks(e,t,n){let{root:r}=t,{api_count:i}=t;const o=$e(),a=()=>o("close");return e.$$set=l=>{"root"in l&&n(0,r=l.root),"api_count"in l&&n(1,i=l.api_count)},[r,i,o,a]}class eu extends ue{constructor(t){super(),ce(this,t,Ks,$s,fe,{root:0,api_count:1})}}function tu(e){let t,n;return{c(){t=be("svg"),n=be("path"),d(n,"stroke-linecap","round"),d(n,"stroke-linejoin","round"),d(n,"d","M12 9v3.75m9-.75a9 9 0 11-18 0 9 9 0 0118 0zm-9 3.75h.008v.008H12v-.008z"),d(t,"fill","none"),d(t,"stroke","currentColor"),d(t,"viewBox","0 0 24 24"),d(t,"width","100%"),d(t,"height","100%"),d(t,"xmlns","http://www.w3.org/2000/svg"),d(t,"aria-hidden","true"),d(t,"stroke-width","2"),d(t,"stroke-linecap","round"),d(t,"stroke-linejoin","round")},m(r,i){y(r,t,i),m(t,n)},p:$,i:$,o:$,d(r){r&&E(t)}}}let nu=class extends ue{constructor(t){super(),ce(this,t,null,tu,fe,{})}};function ru(e){let t,n;return{c(){t=be("svg"),n=be("path"),d(n,"stroke-linecap","round"),d(n,"stroke-linejoin","round"),d(n,"d","M11.25 11.25l.041-.02a.75.75 0 011.063.852l-.708 2.836a.75.75 0 001.063.853l.041-.021M21 12a9 9 0 11-18 0 9 9 0 0118 0zm-9-3.75h.008v.008H12V8.25z"),d(t,"fill","none"),d(t,"stroke","currentColor"),d(t,"viewBox","0 0 24 24"),d(t,"width","100%"),d(t,"height","100%"),d(t,"xmlns","http://www.w3.org/2000/svg"),d(t,"aria-hidden","true"),d(t,"stroke-width","2"),d(t,"stroke-linecap","round"),d(t,"stroke-linejoin","round")},m(r,i){y(r,t,i),m(t,n)},p:$,i:$,o:$,d(r){r&&E(t)}}}class iu extends ue{constructor(t){super(),ce(this,t,null,ru,fe,{})}}function ou(e){let t,n;return{c(){t=be("svg"),n=be("path"),d(n,"stroke-linecap","round"),d(n,"stroke-linejoin","round"),d(n,"d","M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z"),d(t,"fill","none"),d(t,"stroke","currentColor"),d(t,"stroke-width","2"),d(t,"viewBox","0 0 24 24"),d(t,"width","100%"),d(t,"height","100%"),d(t,"xmlns","http://www.w3.org/2000/svg"),d(t,"aria-hidden","true"),d(t,"stroke-linecap","round"),d(t,"stroke-linejoin","round")},m(r,i){y(r,t,i),m(t,n)},p:$,i:$,o:$,d(r){r&&E(t)}}}class lu extends ue{constructor(t){super(),ce(this,t,null,ou,fe,{})}}function Qt(e,t,n){const r=e.slice();return r[10]=t[n].label,r[11]=t[n].type,r[12]=t[n].python_type,r[13]=t[n].component,r[14]=t[n].serializer,r[16]=n,r}function $t(e){let t;return{c(){t=I("(")},m(n,r){y(n,t,r)},d(n){n&&E(t)}}}function au(e){let t=e[2][e[16]].type+"",n;return{c(){n=I(t)},m(r,i){y(r,n,i)},p(r,i){i&4&&t!==(t=r[2][r[16]].type+"")&&q(n,t)},d(r){r&&E(n)}}}function su(e){let t=e[12].type+"",n;return{c(){n=I(t)},m(r,i){y(r,n,i)},p(r,i){i&2&&t!==(t=r[12].type+"")&&q(n,t)},d(r){r&&E(n)}}}function Kt(e){let t;return{c(){t=I(",")},m(n,r){y(n,t,r)},d(n){n&&E(t)}}}function en(e){let t,n,r,i,o=e[10]+"",a,l,u=e[13]+"",s,c;function h(b,g){return b[3]==="python"?su:au}let _=h(e),p=_(e),v=e[1].length>1&&Kt();return{c(){t=k("div"),n=k("span"),r=I("# "),p.c(),i=I(`
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- component`),v&&v.c(),d(n,"class","desc svelte-1c7hj3i"),d(t,"class","svelte-1c7hj3i"),Ye(t,"second-level",e[1].length>1)},m(b,g){y(b,t,g),m(t,n),m(n,r),p.m(n,null),m(n,i),m(n,a),m(n,l),m(n,s),m(n,c),v&&v.m(t,null)},p(b,g){_===(_=h(b))&&p?p.p(b,g):(p.d(1),p=_(b),p&&(p.c(),p.m(n,i))),g&2&&o!==(o=b[10]+"")&&q(a,o),g&2&&u!==(u=b[13]+"")&&q(s,u),b[1].length>1?v||(v=Kt(),v.c(),v.m(t,null)):v&&(v.d(1),v=null),g&2&&Ye(t,"second-level",b[1].length>1)},d(b){b&&E(t),p.d(),v&&v.d()}}}function tn(e){let t;return{c(){t=I(")")},m(n,r){y(n,t,r)},d(n){n&&E(t)}}}function nn(e){let t,n,r;return n=new cl({props:{margin:!1}}),{c(){t=k("div"),W(n.$$.fragment),d(t,"class","load-wrap svelte-1c7hj3i")},m(i,o){y(i,t,o),Z(n,t,null),r=!0},i(i){r||(B(n.$$.fragment,i),r=!0)},o(i){N(n.$$.fragment,i),r=!1},d(i){i&&E(t),Y(n)}}}function uu(e){let t,n,r,i,o,a,l=e[1].length>1&&$t(),u=oe(e[1]),s=[];for(let _=0;_Use the gradio_client
- Python library or the
- @gradio/client Javascript package to query the demo via API.
Bandicam Key
DOWNLOAD >>> https://tinurli.com/2uwi4X
- - aaccfb2cb3
-
-
- diff --git a/spaces/cleanmaster/akagi-sovits3/terms.md b/spaces/cleanmaster/akagi-sovits3/terms.md deleted file mode 100644 index db34483fede042996973daf93fe7012b462b423b..0000000000000000000000000000000000000000 --- a/spaces/cleanmaster/akagi-sovits3/terms.md +++ /dev/null @@ -1,57 +0,0 @@ -在使用此模型前请阅读以下协议，本协议修改自MasterSatori - -雪绘Yukie模型使用协议 - -【前言】雪绘Yukie模型所有者及训练者@cynika（以下也称“我”）希望通过《雪绘Yukie模型使用协议》（以下简称“本协议”）向您说明您在使用雪绘Yukie模型时应当履行的责任及使用范围。 - -【特别提示】在使用雪绘Yukie模型前，请您务必仔细阅读并透彻理解本协议，在确认充分理解并同意后再开始使用。 - - 本协议将帮助您了解以下内容： - - * 一、免责声明 - - * 二、您在非个人使用场合时使用雪绘Yukie模型应当做的事 - - * 三、雪绘Yukie模型的使用范围 - - * 四、如何联系我 - - # (一) 免责声明： - - 您因使用雪绘Yukie模型对其它任何实体（个人/企业）所造成的任何损失由您自身承担，您因使用雪绘Yukie模型所产生的一切法律风险及法律纠纷由您自身承担。 - - # (二) 您在非个人使用场合时使用雪绘Yukie模型应当做的事： - - 1、注明soVITS项目作者：Rcell - - 2、注明我(可选)：响希 - - 3、联系声音持有者雪绘yukie本人，提前征集其意见 - - # (三) 雪绘Yukie模型的使用范围： - - ## 1、您可以使用的范围： - - (1) 个人使用 - - (2) 将产生的音频用于投稿（投稿内容不得包含“您不可使用的范围”中的内容） - - (3) 符合投稿平台和当地法律的二创内容 - - ## 2、您不可使用的范围： - - (1) 商业使用 - - (2) 假冒本人 - - (3) 当作变声器等使用 - - (4) 将雪绘Yukie模型再次上传 - - (5) 低创内容（合成的音频中有过多的爆音或电音属于“低创内容”） - - (6) 敏感内容（包括但不限于：政治、低俗、色情、暴力等） - - 3、补充内容： - - 在其他未被提及的场合使用雪绘Yukie模型及其所产生的数据时您应当征求我的意见`kuzehibiki@126.com`。 diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/util.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/util.py deleted file mode 100644 index 42fe39d5f701e683f52ca7c4022b1bb85749fb6b..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/util.py +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright 2013 Google, Inc. All Rights Reserved. -# -# Google Author(s): Behdad Esfahbod, Roozbeh Pournader - -from fontTools.misc.timeTools import timestampNow -from fontTools.ttLib.tables.DefaultTable import DefaultTable -from functools import reduce -import operator -import logging - - -log = logging.getLogger("fontTools.merge") - - -# General utility functions for merging values from different fonts - - -def equal(lst): - lst = list(lst) - t = iter(lst) - first = next(t) - assert all(item == first for item in t), "Expected all items to be equal: %s" % lst - return first - - -def first(lst): - return next(iter(lst)) - - -def recalculate(lst): - return NotImplemented - - -def current_time(lst): - return timestampNow() - - -def bitwise_and(lst): - return reduce(operator.and_, lst) - - -def bitwise_or(lst): - return reduce(operator.or_, lst) - - -def avg_int(lst): - lst = list(lst) - return sum(lst) // len(lst) - - -def onlyExisting(func): - """Returns a filter func that when called with a list, - only calls func on the non-NotImplemented items of the list, - and only so if there's at least one item remaining. - Otherwise returns NotImplemented.""" - - def wrapper(lst): - items = [item for item in lst if item is not NotImplemented] - return func(items) if items else NotImplemented - - return wrapper - - -def sumLists(lst): - l = [] - for item in lst: - l.extend(item) - return l - - -def sumDicts(lst): - d = {} - for item in lst: - d.update(item) - return d - - -def mergeBits(bitmap): - def wrapper(lst): - lst = list(lst) - returnValue = 0 - for bitNumber in range(bitmap["size"]): - try: - mergeLogic = bitmap[bitNumber] - except KeyError: - try: - mergeLogic = bitmap["*"] - except KeyError: - raise Exception("Don't know how to merge bit %s" % bitNumber) - shiftedBit = 1 << bitNumber - mergedValue = mergeLogic(bool(item & shiftedBit) for item in lst) - returnValue |= mergedValue << bitNumber - return returnValue - - return wrapper - - -class AttendanceRecordingIdentityDict(object): - """A dictionary-like object that records indices of items actually accessed - from a list.""" - - def __init__(self, lst): - self.l = lst - self.d = {id(v): i for i, v in enumerate(lst)} - self.s = set() - - def __getitem__(self, v): - self.s.add(self.d[id(v)]) - return v - - -class GregariousIdentityDict(object): - """A dictionary-like object that welcomes guests without reservations and - adds them to the end of the guest list.""" - - def __init__(self, lst): - self.l = lst - self.s = set(id(v) for v in lst) - - def __getitem__(self, v): - if id(v) not in self.s: - self.s.add(id(v)) - self.l.append(v) - return v - - -class NonhashableDict(object): - """A dictionary-like object mapping objects to values.""" - - def __init__(self, keys, values=None): - if values is None: - self.d = {id(v): i for i, v in enumerate(keys)} - else: - self.d = {id(k): v for k, v in zip(keys, values)} - - def __getitem__(self, k): - return self.d[id(k)] - - def __setitem__(self, k, v): - self.d[id(k)] = v - - def __delitem__(self, k): - del self.d[id(k)] diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/C_B_L_C_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/C_B_L_C_.py deleted file mode 100644 index e9ed58e582b806df3d24c77e795cab9b70fe9dad..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/C_B_L_C_.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright 2013 Google, Inc. All Rights Reserved. -# -# Google Author(s): Matt Fontaine - -from . import E_B_L_C_ - - -class table_C_B_L_C_(E_B_L_C_.table_E_B_L_C_): - - dependencies = ["CBDT"] diff --git a/spaces/colakin/video-generater/classes/VoiceGenerator.php b/spaces/colakin/video-generater/classes/VoiceGenerator.php deleted file mode 100644 index 1d756205545b45938fb032f84f92cf69d8f8af67..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/classes/VoiceGenerator.php +++ /dev/null @@ -1,63 +0,0 @@ -elevenLabsApi = $elevenLabsApi; - } - - /** - * Generate voice audio for the given message and voice ID. - * - * @param string $voiceId - * @param string $message - * @return string The local file path of the downloaded audio file - * @throws Exception - */ - public function generate_and_download(string $voiceId, string $message): string { - $data = ['text' => $message]; - $response = $this->elevenLabsApi->textToSpeechWithVoiceId($voiceId, $data); - - if ($response->getStatusCode() === 200) { - $result = json_decode((string)$response->getBody(), true); - $audioUrl = $result['audio_url']; - return $this->downloadAudio($audioUrl); - } else { - throw new Exception('Error generating audio: ' . $response->getReasonPhrase()); - } - } - - /** - * Download audio file from the given URL and save it to the voices subfolder. - * - * @param string $audioUrl - * @return string The local file path of the downloaded audio file - */ - private function downloadAudio(string $audioUrl): string { - $voicesDirectory = 'voices'; - if (!file_exists($voicesDirectory) && !mkdir($voicesDirectory) && !is_dir($voicesDirectory)) { - throw new RuntimeException(sprintf('Directory "%s" was not created', $voicesDirectory)); - } - - $localFilePath = $voicesDirectory . '/' . uniqid() . '.mp3'; - - $client = new GuzzleHttp\Client(); - $response = $client->get($audioUrl, ['sink' => $localFilePath]); - - if ($response->getStatusCode() === 200) { - return $localFilePath; - } else { - throw new Exception('Error downloading audio: ' . $response->getReasonPhrase()); - } - } -} diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/amr_parser.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/amr_parser.c deleted file mode 100644 index 9484d720eeabdb749b7821c27671ec5b8594b430..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/amr_parser.c +++ /dev/null @@ -1,131 +0,0 @@ -/* - * Copyright (c) 2021 Paul B Mahol - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -/** - * @file - * AMR audio parser - * - * Splits packets into individual blocks. - */ - -#include "libavutil/channel_layout.h" -#include "libavutil/intreadwrite.h" -#include "parser.h" - -static const uint8_t amrnb_packed_size[16] = { - 13, 14, 16, 18, 20, 21, 27, 32, 6, 1, 1, 1, 1, 1, 1, 1 -}; -static const uint8_t amrwb_packed_size[16] = { - 18, 24, 33, 37, 41, 47, 51, 59, 61, 6, 1, 1, 1, 1, 1, 1 -}; - -typedef struct AMRParseContext { - ParseContext pc; - uint64_t cumulated_size; - uint64_t block_count; - int current_channel; - int remaining; -} AMRParseContext; - -static av_cold int amr_parse_init(AVCodecParserContext *s1) -{ - AMRParseContext *s = s1->priv_data; - s->remaining = -1; - return 0; -} - -static int amr_parse(AVCodecParserContext *s1, - AVCodecContext *avctx, - const uint8_t **poutbuf, int *poutbuf_size, - const uint8_t *buf, int buf_size) -{ - AMRParseContext *s = s1->priv_data; - ParseContext *pc = &s->pc; - int next = END_NOT_FOUND; - - *poutbuf_size = 0; - *poutbuf = NULL; - - if (!avctx->ch_layout.nb_channels) { - av_channel_layout_uninit(&avctx->ch_layout); - avctx->ch_layout = (AVChannelLayout)AV_CHANNEL_LAYOUT_MONO; - } - - if (s1->flags & PARSER_FLAG_COMPLETE_FRAMES) { - next = buf_size; - } else { - int ch, offset = 0; - - for (ch = s->current_channel; ch < avctx->ch_layout.nb_channels; ch++) { - if (s->remaining >= 0) { - next = s->remaining; - } else { - int mode = (buf[offset] >> 3) & 0x0F; - - if (avctx->codec_id == AV_CODEC_ID_AMR_NB) { - next = amrnb_packed_size[mode]; - } else if (avctx->codec_id == AV_CODEC_ID_AMR_WB) { - next = amrwb_packed_size[mode]; - } - } - - offset += next; - if (offset >= buf_size) { - s->remaining = offset - buf_size; - next = END_NOT_FOUND; - break; - } else { - s->remaining = -1; - } - } - - s->current_channel = ch % avctx->ch_layout.nb_channels; - if (s->remaining < 0) - next = offset; - - if (next != END_NOT_FOUND) { - if (s->cumulated_size < UINT64_MAX - next) { - s->cumulated_size += next; - /* Both AMR formats have 50 frames per second */ - avctx->bit_rate = s->cumulated_size / ++s->block_count * 8 * 50; - } - } - - if (ff_combine_frame(pc, next, &buf, &buf_size) < 0) { - *poutbuf = NULL; - *poutbuf_size = 0; - return buf_size; - } - } - - s1->duration = avctx->codec_id == AV_CODEC_ID_AMR_NB ? 160 : 320; - - *poutbuf = buf; - *poutbuf_size = buf_size; - return next; -} - -const AVCodecParser ff_amr_parser = { - .codec_ids = { AV_CODEC_ID_AMR_NB, AV_CODEC_ID_AMR_WB }, - .priv_data_size = sizeof(AMRParseContext), - .parser_init = amr_parse_init, - .parser_parse = amr_parse, - .parser_close = ff_parse_close, -}; diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jfdctint.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jfdctint.c deleted file mode 100644 index 6a39578f880671a33cd289404f7df6e42b68f4d1..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jfdctint.c +++ /dev/null @@ -1,25 +0,0 @@ -/* - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -#define BIT_DEPTH 8 -#include "jfdctint_template.c" -#undef BIT_DEPTH - -#define BIT_DEPTH 10 -#include "jfdctint_template.c" -#undef BIT_DEPTH diff --git a/spaces/congsaPfin/Manga-OCR/logs/Get Castle Clash MOD APK 3.3.2 and Unlock All Heroes and Skins.md b/spaces/congsaPfin/Manga-OCR/logs/Get Castle Clash MOD APK 3.3.2 and Unlock All Heroes and Skins.md deleted file mode 100644 index b6ab3902f46d23d48140ea7f604f0b85b2d16db4..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Get Castle Clash MOD APK 3.3.2 and Unlock All Heroes and Skins.md +++ /dev/null @@ -1,89 +0,0 @@ - -
Castle Clash Mod APK 3.3.2: A Strategy Game with Unlimited Gems
-If you are a fan of strategy games, you might have heard of Castle Clash, a popular game developed by IGG.COM. In this game, you can build your own base, collect and upgrade heroes and troops, and join a guild to fight against other players in wars and events. However, if you want to enjoy the game to the fullest, you might need a lot of gems and resources, which are not easy to get in the game. That's why we are here to introduce you to Castle Clash Mod APK 3.3.2, a modified version of the game that gives you unlimited gems and other benefits. In this article, we will tell you what Castle Clash is, what Castle Clash Mod APK 3.3.2 is, how to download and install it, and some FAQs about it.
-What is Castle Clash?
-Castle Clash is a strategy game that was released in 2013 by IGG.COM, a Singapore-based company that also developed other popular games such as Lords Mobile and Mobile Royale. Castle Clash has over 100 million downloads on Google Play Store and has been rated 4.5 out of 5 stars by more than 5 million users.
-castle clash mod apk 3.3.2
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Features of Castle Clash
-Castle Clash has many features that make it an exciting and addictive game for strategy lovers. Here are some of them:
-Build your base and defend it from enemies
-In Castle Clash, you can create your own base with various buildings, such as town hall, barracks, watchtower, walls, etc. You can also place traps and heroes to protect your base from enemy attacks. You can upgrade your buildings and defenses to make them stronger and more efficient.
-Collect and upgrade heroes and troops
-Castle Clash has hundreds of heroes and troops that you can collect and use in battles. Each hero and troop has its own skills, attributes, and roles, such as tank, healer, damage dealer, etc. You can level up your heroes and troops by using resources such as gold, mana, honor badges, etc. You can also equip your heroes with weapons, armor, artifacts, pets, etc., to enhance their performance.
-Join a guild and participate in wars and events
-Castle Clash is not only a solo game but also a social game where you can join a guild and interact with other players from around the world. You can chat with your guild members, donate resources to them, help them in battles, etc. You can also participate in various guild wars and events where you can compete with other guilds for rewards and glory.
-What is Castle Clash Mod APK 3.3.2?
-Castle Clash Mod APK 3.3.2 is a modified version of the original Castle Clash game that gives you some advantages that are not available in the official version. For example, you can get unlimited gems and resources in the mod apk version, which are very useful for upgrading your base, heroes, troops, etc.
-Benefits of Castle Clash Mod APK 3.3.2
-Castle Clash Mod APK 3.3.2 has many benefits that make it a better choice than the original version Here are some of them:
-Unlimited gems and resources
-Gems are the premium currency in Castle Clash that can be used to buy various items, such as hero cards, talent refresh cards, builder huts, etc. Resources are the basic currency in Castle Clash that can be used to upgrade your buildings, heroes, troops, etc. In the original version of the game, you have to earn gems and resources by completing quests, winning battles, participating in events, etc., which can be time-consuming and tedious. However, in Castle Clash Mod APK 3.3.2, you can get unlimited gems and resources for free, which means you can buy anything you want and upgrade everything to the max level without any hassle.
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Unlock all heroes and skins
-Heroes are the most important part of Castle Clash, as they can make a huge difference in your battles. There are many heroes in Castle Clash, each with its own unique skills and abilities. However, not all heroes are easy to get in the original version of the game, as some of them are rare and require a lot of gems or luck to obtain. Moreover, some heroes have skins that can change their appearance and give them extra bonuses, but these skins are also hard to get or expensive to buy. In Castle Clash Mod APK 3.3.2, you can unlock all heroes and skins for free, which means you can choose any hero you like and customize it with any skin you want.
-No ads and no root required
-Another benefit of Castle Clash Mod APK 3.3.2 is that it has no ads and no root required. Ads are annoying and can interrupt your gaming experience, especially when they pop up in the middle of a battle or a loading screen. Rooting is a process that allows you to access the system files of your device and modify them according to your preferences, but it can also void your warranty and expose your device to security risks. In Castle Clash Mod APK 3.3.2, you don't have to worry about ads or rooting, as the mod apk file is already modified and optimized for your device.
-How to download and install Castle Clash Mod APK 3.3.2?
-If you are interested in downloading and installing Castle Clash Mod APK 3.3.2 on your device, you can follow these simple steps:
-Steps to download and install Castle Clash Mod APK 3.3.2
-Enable unknown sources on your device
-Before you can install any mod apk file on your device, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-Download the mod apk file from a trusted source
-Next, you need to download the mod apk file from a trusted source. There are many websites that offer mod apk files for various games and apps, but not all of them are safe and reliable. Some of them may contain viruses or malware that can harm your device or steal your personal information. Therefore, you should always download mod apk files from reputable sources that have positive reviews and feedback from users. You can use this link to download Castle Clash Mod APK 3.3.2 safely and securely.
-Install the mod apk file and enjoy the game
-Finally, you need to install the mod apk file on your device and enjoy the game. To do this, locate the downloaded mod apk file on your device storage and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish. Once done, you can launch the game from your app drawer or home screen and enjoy Castle Clash Mod APK 3.3.2 with unlimited gems and resources.
-Conclusion
-Castle Clash is a strategy game that lets you build your own base, collect and upgrade heroes and troops, and join a guild to fight against other players in wars and events. However, if you want to have more fun and convenience in the game, you should try Castle Clash Mod APK 3.3.2, a modified version of the game that gives you unlimited gems and resources, unlocks all heroes and skins, removes ads, and requires no root. You can download and install Castle Clash Mod APK 3.3.2 by following the steps we mentioned above.
-FAQs
-Here are some frequently asked questions about Castle Clash Mod APK 3.3.2:
--
-
- Is Castle Clash Mod APK 3.3.2 safe to use?
- Will I get banned for using Castle Clash Mod APK 3.3.2? -
- Can I play Castle Clash Mod APK 3.3.2 online with other players? -
- Can I update Castle Clash Mod APK 3.3.2 to the latest version? -
- Can I use Castle Clash Mod APK 3.3.2 on iOS devices? -
Yes, Castle Clash Mod APK 3.3.2 is safe to use, as long as you download it from a trusted source. The mod apk file has been tested and verified by many users and has no viruses or malware. However, you should always be careful when downloading and installing any mod apk file on your device, as some of them may contain harmful or malicious content.
-No, you will not get banned for using Castle Clash Mod APK 3.3.2, as the mod apk file has an anti-ban feature that prevents the game from detecting your modded account. However, you should always use the mod apk file at your own risk, as we cannot guarantee that it will work forever or that it will not cause any problems with your device or game.
-Yes, you can play Castle Clash Mod APK 3.3.2 online with other players, as the mod apk file does not affect the online mode of the game. You can join a guild, chat with other players, and participate in wars and events as usual. However, you should be careful not to abuse the mod apk features or show off your unlimited gems and resources, as this may arouse suspicion and resentment from other players.
-No, you cannot update Castle Clash Mod APK 3.3.2 to the latest version, as the mod apk file is based on an older version of the game and may not be compatible with the new updates. If you want to update the game, you will have to uninstall the mod apk file and install the official version from the Google Play Store. However, you may lose your progress and modded features if you do this.
-No, you cannot use Castle Clash Mod APK 3.3.2 on iOS devices, as the mod apk file is only designed for Android devices and cannot be installed or run on iOS devices. If you want to play Castle Clash on iOS devices, you will have to download the official version from the App Store.
--
-
\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Melon Playground 3D APK The Best Ragdoll Game for Android.md b/spaces/congsaPfin/Manga-OCR/logs/Melon Playground 3D APK The Best Ragdoll Game for Android.md deleted file mode 100644 index 5d6990da36f97bfb75a951d83df96946458940f2..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Melon Playground 3D APK The Best Ragdoll Game for Android.md +++ /dev/null @@ -1,79 +0,0 @@ - -
Melon Playground 3D: A Fun and Crazy Ragdoll Game
-Do you like sandbox games where you can unleash your creativity and imagination? Do you enjoy ragdoll physics and gore effects? If you answered yes to both questions, then you might want to check out Melon Playground 3D, a fun and crazy ragdoll game where you can mistreat many characters with dozens of weapons.
-What is Melon Playground 3D?
-Melon Playground 3D is an exciting ragdoll game developed by Studio27 for Android devices. It was released on June 18, 2023, and has received positive reviews from players who love its simplicity and humor.
-melon playground 3d download apk
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The gameplay of Melon Playground 3D
-The gameplay of Melon Playground 3D is very simple and straightforward. You can choose from various characters such as humans, animals, zombies, robots, and more. Then, you can select from different scenarios such as a city, a farm, a desert, a forest, and more. Finally, you can pick from a wide range of weapons such as guns, knives, axes, hammers, grenades, rockets, and more.
-Once you have everything set up, you can start having fun with your ragdoll characters. You can shoot them, stab them, chop them, smash them, blow them up, or do anything else you can think of. You can also drag them around, throw them in the air, or make them interact with other objects in the environment. The game has realistic physics and graphics that make the ragdoll effects more enjoyable and hilarious.
-The features of Melon Playground 3D
-Melon Playground 3D has many features that make it a great ragdoll game for Android users. Here are some of them:
-Dozens of weapons to choose from
-The game offers you a variety of weapons to play with your ragdoll characters. You can use firearms such as pistols, rifles, shotguns, snipers, machine guns, and more. You can also use melee weapons such as swords, daggers, axes, hammers, chainsaws, and more. You can also use explosives such as grenades, rockets, mines, bombs, and more. You can also use other items such as cars, trucks, planes, helicopters, trains, and more. You can even use your own hands to punch, slap, or grab your ragdoll characters.
-Various characters to mistreat
-The game lets you choose from different types of ragdoll characters to have fun with. You can select from humans such as men, women, children, police officers, soldiers, gangsters, and more. You can also select from animals such as dogs, cats, cows, pigs, chickens, and more. You can also select from zombies such as walkers, runners, crawlers, and more. You can also select from robots such as androids, cyborgs, drones, and more. You can even mix and match different characters to create your own combinations.
-Different scenarios to explore
-The game gives you a variety of scenarios to explore with your ragdoll characters. You can choose from urban settings such as a city, a town, a village, a park, and more. You can also choose from rural settings such as a farm, a barn, a field, and more. You can also choose from natural settings such as a desert, a forest, a lake, and more. You can also choose from artificial settings such as a factory, a warehouse, a prison, and more. You can even create your own scenarios by customizing the environment with different objects and props.
-Realistic physics and graphics
-The game has realistic physics and graphics that make the ragdoll effects more realistic and amusing. The game uses the Unity engine to create smooth and detailed animations for the ragdoll characters. The game also uses high-quality textures and lighting effects to create vivid and colorful visuals for the scenarios. The game also has gore effects that show blood splatters and body parts flying when you damage your ragdoll characters.
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How to download Melon Playground 3D APK?
-If you are interested in playing Melon Playground 3D on your Android device, you might want to download the APK file instead of the official version from the Google Play Store. The APK file is a modified version of the game that offers some benefits that the official version does not have.
-The steps to download Melon Playground 3D APK
-The steps to download Melon Playground 3D APK are very simple and easy. Here are the steps:
--
-
- Go to a reliable website that offers the Melon Playground 3D APK file for free download. For example, you can go to [this website] that provides the latest version of the APK file. -
- Click on the download button and wait for the APK file to be downloaded to your device. -
- Go to your device's settings and enable the option to install apps from unknown sources. This will allow you to install the APK file without any problems. -
- Go to your device's file manager and locate the downloaded APK file. Tap on it and follow the instructions to install it on your device. -
- Enjoy playing Melon Playground 3D with all its features unlocked. -
The benefits of downloading Melon Playground 3D APK
-Downloading Melon Playground 3D APK has some benefits that you might not get from the official version of the game. Here are some of them:
-Free and easy to install
-The APK file is free to download and easy to install on your device. You do not need to pay any money or go through any complicated process to get the game. You just need to follow the steps mentioned above and you are good to go.
-No ads or in-app purchases
-The APK file does not have any ads or in-app purchases that might interrupt your gaming experience or make you spend extra money. You can enjoy the game without any distractions or limitations.
-Unlimited access to all content
-The APK file gives you unlimited access to all the content of the game. You can use all the weapons, characters, scenarios, and features that the game has to offer. You do not need to unlock anything or wait for anything. You can have fun with your ragdoll characters as much as you want.
-Conclusion
-Melon Playground 3D is a fun and crazy ragdoll game that lets you mistreat many characters with dozens of weapons in different scenarios. It has realistic physics and graphics that make the ragdoll effects more enjoyable and hilarious. It is a great game for Android users who love sandbox games and ragdoll physics. If you want to play Melon Playground 3D on your device, you might want to download the APK file instead of the official version from the Google Play Store. The APK file offers some benefits such as free and easy installation, no ads or in-app purchases, and unlimited access to all content. You can download the APK file from a reliable website and follow the steps to install it on your device. Then, you can start having fun with your ragdoll characters in Melon Playground 3D.
-FAQs
--
-
- Q: Is Melon Playground 3D safe to play? -
- A: Yes, Melon Playground 3D is safe to play as long as you download it from a trusted source and do not harm anyone in real life. The game is only meant for entertainment purposes and does not promote violence or cruelty. -
- Q: Is Melon Playground 3D suitable for children? -
- A: No, Melon Playground 3D is not suitable for children as it contains gore effects and mature themes that might be disturbing or inappropriate for young audiences. The game is rated 17+ by the Google Play Store and should only be played by adults or under parental supervision. -
- Q: How can I contact the developer of Melon Playground 3D? -
- A: You can contact the developer of Melon Playground 3D by sending an email to studio27@gmail.com or by visiting their website at [this link]. You can also follow them on their social media accounts such as Facebook, Twitter, Instagram, and YouTube. -
- Q: How can I support the development of Melon Playground 3D? -
- A: You can support the development of Melon Playground 3D by leaving a positive review and rating on the Google Play Store or on the website where you downloaded the APK file. You can also share the game with your friends and family who might enjoy it. You can also donate to the developer via PayPal or Patreon if you want to show your appreciation and help them create more games like this. -
- Q: What are some similar games to Melon Playground 3D? -
- A: Some similar games to Melon Playground 3D are Happy Room, Turbo Dismount, Ragdoll Simulator, Stickman Dismounting, and Ragdoll Sandbox. -
-
-
\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Create and Play MIDI Files with Roland Virtual Sound Canvas 3.2 (DXi and VST Instruments).md b/spaces/contluForse/HuggingGPT/assets/Create and Play MIDI Files with Roland Virtual Sound Canvas 3.2 (DXi and VST Instruments).md deleted file mode 100644 index 6bb0bdc7ffd080934ab71335f30d137dc8dbd21d..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Create and Play MIDI Files with Roland Virtual Sound Canvas 3.2 (DXi and VST Instruments).md +++ /dev/null @@ -1,11 +0,0 @@ - -
Not likely since neither the real Sound Canvas series nor the virtual versions (SC-VA inlcuded) support real LA synthesis that defines MT-32 and CM-32/64 like synths (and thus MUNT). SC devices are only romplers that contain a CM-32/64 compatible sound bank at Bank MSB 127 and a CM-32/64 compatible drum set at channel 10/Program 127 ( most likely this is what you have found). But they only work somewhat with titles that only use the default instruments. Games/Midi files that try to reprogram/modify the sounds the same way as can be done on a real MT-32 compatible synth fail on the whole Sound Canvas series ( but work with MUNT).
MUNT emulates Roland MT-32 and similar synths incomparably much better than any Roland SC devices ever did.
An attempt to look both backwards and forwards, the SH32 resurrected the 'SH' name that had last appeared on the SH101. A quick glance at the controls confirmed Roland's intention to market this as a return to its classic era, although the connection between an analogue monosynth and a four-voice, four-part multitimbral 'virtual' analogue with pretensions of Groovedom was rather tenuous. The engine at the core of the SH32 had a very silly name... (Wave Acceleration Sound Generation, or WASG), but it was at heart a conventional modelled analogue synth with lots of vintage-style waveforms, a multi-mode filter, a couple of contour generators, a couple of LFOs, and the now-obligatory effects section. To this, the company added a rhythm sound generator, and an arpeggiator that included four-part pattern generation. Unfortunately, despite an appealing sound, the SH32 was built to its affordable price, offering a diabolically impenetrable two-digit display, and a number of unexpected limitations. In consequence, what should have been a neat, successful product did not achieve its full potential.
-Roland Virtual Sound Canvas 3.2
Download Zip ► https://ssurll.com/2uzxbi
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In short, the V-Synth combines powerful S&S and virtual-analogue synth engines with sampling and Variphrase. The last of these is implemented in its full form, and you can use the encoded Variphrase samples just as you would use PCMs from the synth's permanent memory. Not that the memory is permanent in the conventional sense; the factory PCMs are held in a backup ROM which is loaded into RAM when you switch on. If you want to use only your own sounds (or a selection of factory and user sounds) you can do so, using a combination of PCM samples, your own samples, encoded Variphrase samples, and VA oscillators. Oh yes... and you can use the external input as a real-time sound source, too.
-At the other end of the keyboard spectrum, Roland have also announced the Juno D, resurrecting another revered name from their history, just as they did with the SH32. Looking like nothing so much as a black RS50, this is Juno-esque in the sense that it is low-cost and simple to use. However, contrary to expectation, it eschews the virtual-analogue technologies of the V-Synth and VariOS, and is a PCM-based synth. With lots of useable sounds, good effects, an arpeggiator, and bundled PC and Mac editing software, it appears to be good value, but I think that Roland have made a mistake by raising people's expectations ('It's the return of the Juno!') and then dashing them again ('No, it's not!').
-More interesting, although unheard at the time of writing, is the VC1 'D50' V-Card for the V-Synth. This purports to recreate the D50 as a virtual synth within the V-Synth itself, even to the extent of being able to load original D50 patch data via MIDI. If it truly recreates the feel and sound of the original, I can see the VC1 becoming a 'must-have' add-on for V-Synth owners.The FR5 'V-Accordion', still unreleased at the time of writing.
-Gain access to the full set of virtual instruments to compose, play, record and save music files in General MIDI 2 and Roland GS. The suite supports older versions of Windows OS and provides basic composing, editing and uploading options for music and sounds.
-VI49 is bundled with Ableton Live Lite and Xpand!2 by AIR Music Tech, two dynamic pieces of software that enable you to record, produce, and perform with your computer. Ableton Live Lite is a fluid audio/MIDI environment that enables you to spontaneously record, remix, improvise, and edit musical ideas on the fly. Xpand!2 is an advanced virtual instrument that comes with a collection of premium sounds, ranging from acoustic instruments to futuristic synthesizers. Together, these powerful music platforms allow you to create or perform music with VI49 right out of the box.
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\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/iter_based_runner.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/iter_based_runner.py deleted file mode 100644 index 1df4de8c0285669dec9b014dfd1f3dd1600f0831..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/iter_based_runner.py +++ /dev/null @@ -1,273 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os.path as osp -import platform -import shutil -import time -import warnings - -import torch -from torch.optim import Optimizer - -import annotator.uniformer.mmcv as mmcv -from .base_runner import BaseRunner -from .builder import RUNNERS -from .checkpoint import save_checkpoint -from .hooks import IterTimerHook -from .utils import get_host_info - - -class IterLoader: - - def __init__(self, dataloader): - self._dataloader = dataloader - self.iter_loader = iter(self._dataloader) - self._epoch = 0 - - @property - def epoch(self): - return self._epoch - - def __next__(self): - try: - data = next(self.iter_loader) - except StopIteration: - self._epoch += 1 - if hasattr(self._dataloader.sampler, 'set_epoch'): - self._dataloader.sampler.set_epoch(self._epoch) - time.sleep(2) # Prevent possible deadlock during epoch transition - self.iter_loader = iter(self._dataloader) - data = next(self.iter_loader) - - return data - - def __len__(self): - return len(self._dataloader) - - -@RUNNERS.register_module() -class IterBasedRunner(BaseRunner): - """Iteration-based Runner. - - This runner train models iteration by iteration. - """ - - def train(self, data_loader, **kwargs): - self.model.train() - self.mode = 'train' - self.data_loader = data_loader - self._epoch = data_loader.epoch - data_batch = next(data_loader) - self.call_hook('before_train_iter') - outputs = self.model.train_step(data_batch, self.optimizer, **kwargs) - if not isinstance(outputs, dict): - raise TypeError('model.train_step() must return a dict') - if 'log_vars' in outputs: - self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) - self.outputs = outputs - self.call_hook('after_train_iter') - self._inner_iter += 1 - self._iter += 1 - - @torch.no_grad() - def val(self, data_loader, **kwargs): - self.model.eval() - self.mode = 'val' - self.data_loader = data_loader - data_batch = next(data_loader) - self.call_hook('before_val_iter') - outputs = self.model.val_step(data_batch, **kwargs) - if not isinstance(outputs, dict): - raise TypeError('model.val_step() must return a dict') - if 'log_vars' in outputs: - self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) - self.outputs = outputs - self.call_hook('after_val_iter') - self._inner_iter += 1 - - def run(self, data_loaders, workflow, max_iters=None, **kwargs): - """Start running. - - Args: - data_loaders (list[:obj:`DataLoader`]): Dataloaders for training - and validation. - workflow (list[tuple]): A list of (phase, iters) to specify the - running order and iterations. E.g, [('train', 10000), - ('val', 1000)] means running 10000 iterations for training and - 1000 iterations for validation, iteratively. - """ - assert isinstance(data_loaders, list) - assert mmcv.is_list_of(workflow, tuple) - assert len(data_loaders) == len(workflow) - if max_iters is not None: - warnings.warn( - 'setting max_iters in run is deprecated, ' - 'please set max_iters in runner_config', DeprecationWarning) - self._max_iters = max_iters - assert self._max_iters is not None, ( - 'max_iters must be specified during instantiation') - - work_dir = self.work_dir if self.work_dir is not None else 'NONE' - self.logger.info('Start running, host: %s, work_dir: %s', - get_host_info(), work_dir) - self.logger.info('Hooks will be executed in the following order:\n%s', - self.get_hook_info()) - self.logger.info('workflow: %s, max: %d iters', workflow, - self._max_iters) - self.call_hook('before_run') - - iter_loaders = [IterLoader(x) for x in data_loaders] - - self.call_hook('before_epoch') - - while self.iter < self._max_iters: - for i, flow in enumerate(workflow): - self._inner_iter = 0 - mode, iters = flow - if not isinstance(mode, str) or not hasattr(self, mode): - raise ValueError( - 'runner has no method named "{}" to run a workflow'. - format(mode)) - iter_runner = getattr(self, mode) - for _ in range(iters): - if mode == 'train' and self.iter >= self._max_iters: - break - iter_runner(iter_loaders[i], **kwargs) - - time.sleep(1) # wait for some hooks like loggers to finish - self.call_hook('after_epoch') - self.call_hook('after_run') - - def resume(self, - checkpoint, - resume_optimizer=True, - map_location='default'): - """Resume model from checkpoint. - - Args: - checkpoint (str): Checkpoint to resume from. - resume_optimizer (bool, optional): Whether resume the optimizer(s) - if the checkpoint file includes optimizer(s). Default to True. - map_location (str, optional): Same as :func:`torch.load`. - Default to 'default'. - """ - if map_location == 'default': - device_id = torch.cuda.current_device() - checkpoint = self.load_checkpoint( - checkpoint, - map_location=lambda storage, loc: storage.cuda(device_id)) - else: - checkpoint = self.load_checkpoint( - checkpoint, map_location=map_location) - - self._epoch = checkpoint['meta']['epoch'] - self._iter = checkpoint['meta']['iter'] - self._inner_iter = checkpoint['meta']['iter'] - if 'optimizer' in checkpoint and resume_optimizer: - if isinstance(self.optimizer, Optimizer): - self.optimizer.load_state_dict(checkpoint['optimizer']) - elif isinstance(self.optimizer, dict): - for k in self.optimizer.keys(): - self.optimizer[k].load_state_dict( - checkpoint['optimizer'][k]) - else: - raise TypeError( - 'Optimizer should be dict or torch.optim.Optimizer ' - f'but got {type(self.optimizer)}') - - self.logger.info(f'resumed from epoch: {self.epoch}, iter {self.iter}') - - def save_checkpoint(self, - out_dir, - filename_tmpl='iter_{}.pth', - meta=None, - save_optimizer=True, - create_symlink=True): - """Save checkpoint to file. - - Args: - out_dir (str): Directory to save checkpoint files. - filename_tmpl (str, optional): Checkpoint file template. - Defaults to 'iter_{}.pth'. - meta (dict, optional): Metadata to be saved in checkpoint. - Defaults to None. - save_optimizer (bool, optional): Whether save optimizer. - Defaults to True. - create_symlink (bool, optional): Whether create symlink to the - latest checkpoint file. Defaults to True. - """ - if meta is None: - meta = {} - elif not isinstance(meta, dict): - raise TypeError( - f'meta should be a dict or None, but got {type(meta)}') - if self.meta is not None: - meta.update(self.meta) - # Note: meta.update(self.meta) should be done before - # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise - # there will be problems with resumed checkpoints. - # More details in https://github.com/open-mmlab/mmcv/pull/1108 - meta.update(epoch=self.epoch + 1, iter=self.iter) - - filename = filename_tmpl.format(self.iter + 1) - filepath = osp.join(out_dir, filename) - optimizer = self.optimizer if save_optimizer else None - save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta) - # in some environments, `os.symlink` is not supported, you may need to - # set `create_symlink` to False - if create_symlink: - dst_file = osp.join(out_dir, 'latest.pth') - if platform.system() != 'Windows': - mmcv.symlink(filename, dst_file) - else: - shutil.copy(filepath, dst_file) - - def register_training_hooks(self, - lr_config, - optimizer_config=None, - checkpoint_config=None, - log_config=None, - momentum_config=None, - custom_hooks_config=None): - """Register default hooks for iter-based training. - - Checkpoint hook, optimizer stepper hook and logger hooks will be set to - `by_epoch=False` by default. - - Default hooks include: - - +----------------------+-------------------------+ - | Hooks | Priority | - +======================+=========================+ - | LrUpdaterHook | VERY_HIGH (10) | - +----------------------+-------------------------+ - | MomentumUpdaterHook | HIGH (30) | - +----------------------+-------------------------+ - | OptimizerStepperHook | ABOVE_NORMAL (40) | - +----------------------+-------------------------+ - | CheckpointSaverHook | NORMAL (50) | - +----------------------+-------------------------+ - | IterTimerHook | LOW (70) | - +----------------------+-------------------------+ - | LoggerHook(s) | VERY_LOW (90) | - +----------------------+-------------------------+ - | CustomHook(s) | defaults to NORMAL (50) | - +----------------------+-------------------------+ - - If custom hooks have same priority with default hooks, custom hooks - will be triggered after default hooks. - """ - if checkpoint_config is not None: - checkpoint_config.setdefault('by_epoch', False) - if lr_config is not None: - lr_config.setdefault('by_epoch', False) - if log_config is not None: - for info in log_config['hooks']: - info.setdefault('by_epoch', False) - super(IterBasedRunner, self).register_training_hooks( - lr_config=lr_config, - momentum_config=momentum_config, - optimizer_config=optimizer_config, - checkpoint_config=checkpoint_config, - log_config=log_config, - timer_config=IterTimerHook(), - custom_hooks_config=custom_hooks_config) diff --git a/spaces/course-demos/marian-finetuned-kde4-en-to-fr/app.py b/spaces/course-demos/marian-finetuned-kde4-en-to-fr/app.py deleted file mode 100644 index c71682697233e139250fbab2c29ee28f7ab401a7..0000000000000000000000000000000000000000 --- a/spaces/course-demos/marian-finetuned-kde4-en-to-fr/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("huggingface/huggingface-course/marian-finetuned-kde4-en-to-fr", title=None, inputs=gr.Textbox(label="Input", lines=3, value="This plugin allows you to automatically translate web pages between several languages.")).launch() \ No newline at end of file diff --git a/spaces/dajuzi/img-to-music/share_btn.py b/spaces/dajuzi/img-to-music/share_btn.py deleted file mode 100644 index 1a2ac6a6e74b114dbd54c2f24723a87180db51ef..0000000000000000000000000000000000000000 --- a/spaces/dajuzi/img-to-music/share_btn.py +++ /dev/null @@ -1,100 +0,0 @@ -community_icon_html = """""" - -loading_icon_html = """""" - -share_js = """async () => { - async function uploadFile(file){ - const UPLOAD_URL = 'https://huggingface.co/uploads'; - const response = await fetch(UPLOAD_URL, { - method: 'POST', - headers: { - 'Content-Type': file.type, - 'X-Requested-With': 'XMLHttpRequest', - }, - body: file, /// <- File inherits from Blob - }); - const url = await response.text(); - return url; - } - async function getInputImgFile(imgEl){ - const res = await fetch(imgEl.src); - const blob = await res.blob(); - const imgId = Date.now() % 200; - const isPng = imgEl.src.startsWith(`data:image/png`); - if(isPng){ - const fileName = `sd-perception-${{imgId}}.png`; - return new File([blob], fileName, { type: 'image/png' }); - }else{ - const fileName = `sd-perception-${{imgId}}.jpg`; - return new File([blob], fileName, { type: 'image/jpeg' }); - } - } - async function getOutputMusicFile(audioEL){ - const res = await fetch(audioEL.src); - const blob = await res.blob(); - const audioId = Date.now() % 200; - const fileName = `img-to-music-${{audioId}}.wav`; - const musicBlob = new File([blob], fileName, { type: 'audio/wav' }); - console.log(musicBlob); - return musicBlob; - } - - async function audioToBase64(audioFile) { - return new Promise((resolve, reject) => { - let reader = new FileReader(); - reader.readAsDataURL(audioFile); - reader.onload = () => resolve(reader.result); - reader.onerror = error => reject(error); - - }); - } - const gradioEl = document.querySelector('body > gradio-app'); - // const gradioEl = document.querySelector("gradio-app").shadowRoot; - const inputImgEl = gradioEl.querySelector('#input-img img'); - const outputMusic = gradioEl.querySelector('#music-output audio'); - const outputMusic_src = gradioEl.querySelector('#music-output audio').src; - const outputMusic_name = outputMusic_src.split('/').pop(); - let titleTxt = outputMusic_name; - //if(titleTxt.length > 100){ - // titleTxt = titleTxt.slice(0, 100) + ' ...'; - //} - const shareBtnEl = gradioEl.querySelector('#share-btn'); - const shareIconEl = gradioEl.querySelector('#share-btn-share-icon'); - const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon'); - if(!outputMusic){ - return; - }; - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - const inputFile = await getInputImgFile(inputImgEl); - const urlInputImg = await uploadFile(inputFile); - const musicFile = await getOutputMusicFile(outputMusic); - const dataOutputMusic = await uploadFile(musicFile); - - const descriptionMd = `#### Input img: -
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- diff --git a/spaces/digitalxingtong/Eileen-Bert-Vits2/mel_processing.py b/spaces/digitalxingtong/Eileen-Bert-Vits2/mel_processing.py deleted file mode 100644 index 50435ecf88ef4fb6c1d47f3e6edd04c3ea7d3e80..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Eileen-Bert-Vits2/mel_processing.py +++ /dev/null @@ -1,112 +0,0 @@ -import math -import os -import random -import torch -from torch import nn -import torch.nn.functional as F -import torch.utils.data -import numpy as np -import librosa -import librosa.util as librosa_util -from librosa.util import normalize, pad_center, tiny -from scipy.signal import get_window -from scipy.io.wavfile import read -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/spaces/digitalxingtong/Jiaran-Bert-VITS2/app.py b/spaces/digitalxingtong/Jiaran-Bert-VITS2/app.py deleted file mode 100644 index f11cd2b782cf7b424f998a79da54955f57c7e54b..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Jiaran-Bert-VITS2/app.py +++ /dev/null @@ -1,182 +0,0 @@ -import sys, os - -if sys.platform == "darwin": - os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" - -import logging - -logging.getLogger("numba").setLevel(logging.WARNING) -logging.getLogger("markdown_it").setLevel(logging.WARNING) -logging.getLogger("urllib3").setLevel(logging.WARNING) -logging.getLogger("matplotlib").setLevel(logging.WARNING) - -logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") - -logger = logging.getLogger(__name__) - -import torch -import argparse -import commons -import utils -from models import SynthesizerTrn -from text.symbols import symbols -from text import cleaned_text_to_sequence, get_bert -from text.cleaner import clean_text -import gradio as gr -import webbrowser - - -net_g = None - - -def get_text(text, language_str, hps): - norm_text, phone, tone, word2ph = clean_text(text, language_str) - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - - if hps.data.add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - bert = get_bert(norm_text, word2ph, language_str) - del word2ph - - assert bert.shape[-1] == len(phone) - - phone = torch.LongTensor(phone) - tone = torch.LongTensor(tone) - language = torch.LongTensor(language) - - return bert, phone, tone, language -import soundfile as sf -def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): - global net_g - bert, phones, tones, lang_ids = get_text(text, "ZH", hps) - with torch.no_grad(): - x_tst=phones.to(device).unsqueeze(0) - tones=tones.to(device).unsqueeze(0) - lang_ids=lang_ids.to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - del phones - speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) - audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio - , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() - del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers - sf.write("tmp.wav", audio, 44100) - return audio -def convert_wav_to_ogg(wav_file): - os.makedirs('out', exist_ok=True) - filename = os.path.splitext(os.path.basename(wav_file.name))[0] - output_path_ogg = os.path.join('out', f"out.ogg") - - renamed_input_path = os.path.join('in', f"in.wav") - os.makedirs('in', exist_ok=True) - os.rename(wav_file.name, renamed_input_path) - command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg] - os.system(" ".join(command)) - return output_path_ogg -def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): - with torch.no_grad(): - audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) - with open('tmp.wav', 'rb') as wav_file: - newogg = convert_wav_to_ogg(wav_file) - return "Success", (hps.data.sampling_rate, audio),newogg - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--model_dir", default="./logs/jiaran/jiaran_new.pth", help="path of your model") - parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") - parser.add_argument("--share", default=False, help="make link public") - parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") - - args = parser.parse_args() - if args.debug: - logger.info("Enable DEBUG-LEVEL log") - logging.basicConfig(level=logging.DEBUG) - hps = utils.get_hparams_from_file(args.config_dir) - device = "cuda:0" if torch.cuda.is_available() else "cpu" - ''' - device = ( - "cuda:0" - if torch.cuda.is_available() - else ( - "mps" - if sys.platform == "darwin" and torch.backends.mps.is_available() - else "cpu" - ) - ) - ''' - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model).to(device) - _ = net_g.eval() - - _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) - - speaker_ids = hps.data.spk2id - speakers = list(speaker_ids.keys()) - with gr.Blocks() as app: - with gr.Row(): - with gr.Column(): - - - gr.Markdown(value=""" - 嘉然Bert-Vits2在线语音生成\n - 1、模型作者：数字星瞳企划 https://t.me/xingtong25680 \n - \n - 2、原项目地址：https://github.com/Stardust-minus/Bert-VITS2\n - 3、使用此模型进行二创请注明AI生成，以及该项目地址。\n - 4、如果想生成超长txt文本的音频请使用colab。 https://colab.research.google.com/drive/13ek8_j1aknr-pbjj3NXxSM4vBIsracU3?usp=drive_link\n - - """) - text = gr.TextArea(label="Text", placeholder="Input Text Here", - value="这里是数字星瞳企画，请在电报搜索星瞳全拼加二五六八零，获取最新更新进展。") - speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') - sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') - noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化') - noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.01, label='音节发音长度变化') - length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='语速') - btn = gr.Button("开启AI语音之旅吧!", variant="primary") - with gr.Column(): - text_output = gr.Textbox(label="Message") - audio_output = gr.Audio(label="Output Audio") - ogg_output = gr.File(label="Converted OGG file") - gr.Markdown(value=""" - 模型汇总：\n - 星瞳 https://huggingface.co/spaces/digitalxingtong/Xingtong-Bert-Vits2 \n - 星瞳 朗读专用 https://huggingface.co/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2 \n - 星瞳 长文本专用 https://huggingface.co/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2 \n - 甜甜叫花鸡 https://huggingface.co/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2 \n - 七海 https://huggingface.co/spaces/digitalxingtong/Nanami-Bert-Vits2 \n - 东雪莲 https://huggingface.co/spaces/digitalxingtong/Azuma-Bert-Vits2 \n - 嘉然 https://huggingface.co/spaces/digitalxingtong/Jiaran-Bert-Vits2 \n - 乃琳 https://huggingface.co/spaces/digitalxingtong/Eileen-Bert-Vits2 \n - 恬豆 https://huggingface.co/spaces/digitalxingtong/Dou-Bert-Vits2 \n - 奶绿 杂谈 https://huggingface.co/spaces/digitalxingtong/Nailv-Bert-Vits2 \n - 奶绿 朗读 https://huggingface.co/spaces/digitalxingtong/Nailv-read-Bert-Vits2 \n - 露早 https://huggingface.co/spaces/digitalxingtong/Luzao-Bert-Vits2 \n - 柚恩 https://huggingface.co/spaces/digitalxingtong/Un-Bert-Vits2 \n - 米诺 https://huggingface.co/spaces/digitalxingtong/Minuo-Bert-Vits2 \n - 扇宝 https://huggingface.co/spaces/digitalxingtong/Shanbao-Bert-Vits2 \n - 牧牧白 https://huggingface.co/spaces/digitalxingtong/Miiu-Bert-Vits2 \n - 吉诺儿kino https://huggingface.co/spaces/digitalxingtong/Kino-Bert-Vits2 \n - 九夏 https://huggingface.co/spaces/digitalxingtong/Jiuxia-Bert-Vits2 \n - 卡缇娅 https://huggingface.co/spaces/digitalxingtong/Yaya-Bert-Vits2 \n - 理想_ideal https://huggingface.co/spaces/digitalxingtong/Lixiang-Bert-Vits2 \n - 阿梓 https://huggingface.co/spaces/digitalxingtong/Azusa-Bert-Vits2 \n - 鹿鸣 https://huggingface.co/spaces/digitalxingtong/Luming-Bert-Vits2 \n - 永雏塔菲 https://huggingface.co/spaces/digitalxingtong/Taffy-Bert-VITS2 \n - """) - btn.click(tts_fn, - inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale], - outputs=[text_output, audio_output,ogg_output]) - - - app.launch(show_error=True) diff --git a/spaces/digitalxingtong/Miiu-Bert-Vits2/text/symbols.py b/spaces/digitalxingtong/Miiu-Bert-Vits2/text/symbols.py deleted file mode 100644 index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Miiu-Bert-Vits2/text/symbols.py +++ /dev/null @@ -1,51 +0,0 @@ -punctuation = ['!', '?', '…', ",", ".", "'", '-'] -pu_symbols = punctuation + ["SP", "UNK"] -pad = '_' - -# chinese -zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h', - 'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o', - 'ong', - 'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn', - 'w', 'x', 'y', 'z', 'zh', - "AA", "EE", "OO"] -num_zh_tones = 6 - -# japanese -ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky', - 'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z'] -num_ja_tones = 1 - -# English -en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy', - 'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's', - 'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh'] -num_en_tones = 4 - -# combine all symbols -normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols)) -symbols = [pad] + normal_symbols + pu_symbols -sil_phonemes_ids = [symbols.index(i) for i in pu_symbols] - -# combine all tones -num_tones = num_zh_tones + num_ja_tones + num_en_tones - -# language maps -language_id_map = { - 'ZH': 0, - "JA": 1, - "EN": 2 -} -num_languages = len(language_id_map.keys()) - -language_tone_start_map = { - 'ZH': 0, - "JA": num_zh_tones, - "EN": num_zh_tones + num_ja_tones -} - -if __name__ == '__main__': - a = set(zh_symbols) - b = set(en_symbols) - print(sorted(a&b)) - diff --git a/spaces/dineshreddy/WALT/mmdet/models/losses/pisa_loss.py b/spaces/dineshreddy/WALT/mmdet/models/losses/pisa_loss.py deleted file mode 100644 index 4a48adfcd400bb07b719a6fbd5a8af0508820629..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/losses/pisa_loss.py +++ /dev/null @@ -1,183 +0,0 @@ -import mmcv -import torch - -from mmdet.core import bbox_overlaps - - -@mmcv.jit(derivate=True, coderize=True) -def isr_p(cls_score, - bbox_pred, - bbox_targets, - rois, - sampling_results, - loss_cls, - bbox_coder, - k=2, - bias=0, - num_class=80): - """Importance-based Sample Reweighting (ISR_P), positive part. - - Args: - cls_score (Tensor): Predicted classification scores. - bbox_pred (Tensor): Predicted bbox deltas. - bbox_targets (tuple[Tensor]): A tuple of bbox targets, the are - labels, label_weights, bbox_targets, bbox_weights, respectively. - rois (Tensor): Anchors (single_stage) in shape (n, 4) or RoIs - (two_stage) in shape (n, 5). - sampling_results (obj): Sampling results. - loss_cls (func): Classification loss func of the head. - bbox_coder (obj): BBox coder of the head. - k (float): Power of the non-linear mapping. - bias (float): Shift of the non-linear mapping. - num_class (int): Number of classes, default: 80. - - Return: - tuple([Tensor]): labels, imp_based_label_weights, bbox_targets, - bbox_target_weights - """ - - labels, label_weights, bbox_targets, bbox_weights = bbox_targets - pos_label_inds = ((labels >= 0) & - (labels < num_class)).nonzero().reshape(-1) - pos_labels = labels[pos_label_inds] - - # if no positive samples, return the original targets - num_pos = float(pos_label_inds.size(0)) - if num_pos == 0: - return labels, label_weights, bbox_targets, bbox_weights - - # merge pos_assigned_gt_inds of per image to a single tensor - gts = list() - last_max_gt = 0 - for i in range(len(sampling_results)): - gt_i = sampling_results[i].pos_assigned_gt_inds - gts.append(gt_i + last_max_gt) - if len(gt_i) != 0: - last_max_gt = gt_i.max() + 1 - gts = torch.cat(gts) - assert len(gts) == num_pos - - cls_score = cls_score.detach() - bbox_pred = bbox_pred.detach() - - # For single stage detectors, rois here indicate anchors, in shape (N, 4) - # For two stage detectors, rois are in shape (N, 5) - if rois.size(-1) == 5: - pos_rois = rois[pos_label_inds][:, 1:] - else: - pos_rois = rois[pos_label_inds] - - if bbox_pred.size(-1) > 4: - bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4) - pos_delta_pred = bbox_pred[pos_label_inds, pos_labels].view(-1, 4) - else: - pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4) - - # compute iou of the predicted bbox and the corresponding GT - pos_delta_target = bbox_targets[pos_label_inds].view(-1, 4) - pos_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_pred) - target_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_target) - ious = bbox_overlaps(pos_bbox_pred, target_bbox_pred, is_aligned=True) - - pos_imp_weights = label_weights[pos_label_inds] - # Two steps to compute IoU-HLR. Samples are first sorted by IoU locally, - # then sorted again within the same-rank group - max_l_num = pos_labels.bincount().max() - for label in pos_labels.unique(): - l_inds = (pos_labels == label).nonzero().view(-1) - l_gts = gts[l_inds] - for t in l_gts.unique(): - t_inds = l_inds[l_gts == t] - t_ious = ious[t_inds] - _, t_iou_rank_idx = t_ious.sort(descending=True) - _, t_iou_rank = t_iou_rank_idx.sort() - ious[t_inds] += max_l_num - t_iou_rank.float() - l_ious = ious[l_inds] - _, l_iou_rank_idx = l_ious.sort(descending=True) - _, l_iou_rank = l_iou_rank_idx.sort() # IoU-HLR - # linearly map HLR to label weights - pos_imp_weights[l_inds] *= (max_l_num - l_iou_rank.float()) / max_l_num - - pos_imp_weights = (bias + pos_imp_weights * (1 - bias)).pow(k) - - # normalize to make the new weighted loss value equal to the original loss - pos_loss_cls = loss_cls( - cls_score[pos_label_inds], pos_labels, reduction_override='none') - if pos_loss_cls.dim() > 1: - ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds][:, - None] - new_pos_loss_cls = pos_loss_cls * pos_imp_weights[:, None] - else: - ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds] - new_pos_loss_cls = pos_loss_cls * pos_imp_weights - pos_loss_cls_ratio = ori_pos_loss_cls.sum() / new_pos_loss_cls.sum() - pos_imp_weights = pos_imp_weights * pos_loss_cls_ratio - label_weights[pos_label_inds] = pos_imp_weights - - bbox_targets = labels, label_weights, bbox_targets, bbox_weights - return bbox_targets - - -@mmcv.jit(derivate=True, coderize=True) -def carl_loss(cls_score, - labels, - bbox_pred, - bbox_targets, - loss_bbox, - k=1, - bias=0.2, - avg_factor=None, - sigmoid=False, - num_class=80): - """Classification-Aware Regression Loss (CARL). - - Args: - cls_score (Tensor): Predicted classification scores. - labels (Tensor): Targets of classification. - bbox_pred (Tensor): Predicted bbox deltas. - bbox_targets (Tensor): Target of bbox regression. - loss_bbox (func): Regression loss func of the head. - bbox_coder (obj): BBox coder of the head. - k (float): Power of the non-linear mapping. - bias (float): Shift of the non-linear mapping. - avg_factor (int): Average factor used in regression loss. - sigmoid (bool): Activation of the classification score. - num_class (int): Number of classes, default: 80. - - Return: - dict: CARL loss dict. - """ - pos_label_inds = ((labels >= 0) & - (labels < num_class)).nonzero().reshape(-1) - if pos_label_inds.numel() == 0: - return dict(loss_carl=cls_score.sum()[None] * 0.) - pos_labels = labels[pos_label_inds] - - # multiply pos_cls_score with the corresponding bbox weight - # and remain gradient - if sigmoid: - pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels] - else: - pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels] - carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k) - - # normalize carl_loss_weight to make its sum equal to num positive - num_pos = float(pos_cls_score.size(0)) - weight_ratio = num_pos / carl_loss_weights.sum() - carl_loss_weights *= weight_ratio - - if avg_factor is None: - avg_factor = bbox_targets.size(0) - # if is class agnostic, bbox pred is in shape (N, 4) - # otherwise, bbox pred is in shape (N, #classes, 4) - if bbox_pred.size(-1) > 4: - bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4) - pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels] - else: - pos_bbox_preds = bbox_pred[pos_label_inds] - ori_loss_reg = loss_bbox( - pos_bbox_preds, - bbox_targets[pos_label_inds], - reduction_override='none') / avg_factor - loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum() - return dict(loss_carl=loss_carl[None]) diff --git a/spaces/doluvor/faster-whisper-webui/src/languages.py b/spaces/doluvor/faster-whisper-webui/src/languages.py deleted file mode 100644 index fbad66e4d34119d27d12e3dfecbe99b6fdde4db7..0000000000000000000000000000000000000000 --- a/spaces/doluvor/faster-whisper-webui/src/languages.py +++ /dev/null @@ -1,147 +0,0 @@ -class Language(): - def __init__(self, code, name): - self.code = code - self.name = name - - def __str__(self): - return "Language(code={}, name={})".format(self.code, self.name) - -LANGUAGES = [ - Language('en', 'English'), - Language('zh', 'Chinese'), - Language('de', 'German'), - Language('es', 'Spanish'), - Language('ru', 'Russian'), - Language('ko', 'Korean'), - Language('fr', 'French'), - Language('ja', 'Japanese'), - Language('pt', 'Portuguese'), - Language('tr', 'Turkish'), - Language('pl', 'Polish'), - Language('ca', 'Catalan'), - Language('nl', 'Dutch'), - Language('ar', 'Arabic'), - Language('sv', 'Swedish'), - Language('it', 'Italian'), - Language('id', 'Indonesian'), - Language('hi', 'Hindi'), - Language('fi', 'Finnish'), - Language('vi', 'Vietnamese'), - Language('he', 'Hebrew'), - Language('uk', 'Ukrainian'), - Language('el', 'Greek'), - Language('ms', 'Malay'), - Language('cs', 'Czech'), - Language('ro', 'Romanian'), - Language('da', 'Danish'), - Language('hu', 'Hungarian'), - Language('ta', 'Tamil'), - Language('no', 'Norwegian'), - Language('th', 'Thai'), - Language('ur', 'Urdu'), - Language('hr', 'Croatian'), - Language('bg', 'Bulgarian'), - Language('lt', 'Lithuanian'), - Language('la', 'Latin'), - Language('mi', 'Maori'), - Language('ml', 'Malayalam'), - Language('cy', 'Welsh'), - Language('sk', 'Slovak'), - Language('te', 'Telugu'), - Language('fa', 'Persian'), - Language('lv', 'Latvian'), - Language('bn', 'Bengali'), - Language('sr', 'Serbian'), - Language('az', 'Azerbaijani'), - Language('sl', 'Slovenian'), - Language('kn', 'Kannada'), - Language('et', 'Estonian'), - Language('mk', 'Macedonian'), - Language('br', 'Breton'), - Language('eu', 'Basque'), - Language('is', 'Icelandic'), - Language('hy', 'Armenian'), - Language('ne', 'Nepali'), - Language('mn', 'Mongolian'), - Language('bs', 'Bosnian'), - Language('kk', 'Kazakh'), - Language('sq', 'Albanian'), - Language('sw', 'Swahili'), - Language('gl', 'Galician'), - Language('mr', 'Marathi'), - Language('pa', 'Punjabi'), - Language('si', 'Sinhala'), - Language('km', 'Khmer'), - Language('sn', 'Shona'), - Language('yo', 'Yoruba'), - Language('so', 'Somali'), - Language('af', 'Afrikaans'), - Language('oc', 'Occitan'), - Language('ka', 'Georgian'), - Language('be', 'Belarusian'), - Language('tg', 'Tajik'), - Language('sd', 'Sindhi'), - Language('gu', 'Gujarati'), - Language('am', 'Amharic'), - Language('yi', 'Yiddish'), - Language('lo', 'Lao'), - Language('uz', 'Uzbek'), - Language('fo', 'Faroese'), - Language('ht', 'Haitian creole'), - Language('ps', 'Pashto'), - Language('tk', 'Turkmen'), - Language('nn', 'Nynorsk'), - Language('mt', 'Maltese'), - Language('sa', 'Sanskrit'), - Language('lb', 'Luxembourgish'), - Language('my', 'Myanmar'), - Language('bo', 'Tibetan'), - Language('tl', 'Tagalog'), - Language('mg', 'Malagasy'), - Language('as', 'Assamese'), - Language('tt', 'Tatar'), - Language('haw', 'Hawaiian'), - Language('ln', 'Lingala'), - Language('ha', 'Hausa'), - Language('ba', 'Bashkir'), - Language('jw', 'Javanese'), - Language('su', 'Sundanese') -] - -_TO_LANGUAGE_CODE = { - **{language.code: language for language in LANGUAGES}, - "burmese": "my", - "valencian": "ca", - "flemish": "nl", - "haitian": "ht", - "letzeburgesch": "lb", - "pushto": "ps", - "panjabi": "pa", - "moldavian": "ro", - "moldovan": "ro", - "sinhalese": "si", - "castilian": "es", -} - -_FROM_LANGUAGE_NAME = { - **{language.name.lower(): language for language in LANGUAGES} -} - -def get_language_from_code(language_code, default=None) -> Language: - """Return the language name from the language code.""" - return _TO_LANGUAGE_CODE.get(language_code, default) - -def get_language_from_name(language, default=None) -> Language: - """Return the language code from the language name.""" - return _FROM_LANGUAGE_NAME.get(language.lower() if language else None, default) - -def get_language_names(): - """Return a list of language names.""" - return [language.name for language in LANGUAGES] - -if __name__ == "__main__": - # Test lookup - print(get_language_from_code('en')) - print(get_language_from_name('English')) - - print(get_language_names()) \ No newline at end of file diff --git a/spaces/dyhzq/vits-uma-genshin-honkai/commons.py b/spaces/dyhzq/vits-uma-genshin-honkai/commons.py deleted file mode 100644 index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000 --- a/spaces/dyhzq/vits-uma-genshin-honkai/commons.py +++ /dev/null @@ -1,172 +0,0 @@ -import math -import torch -from torch.nn import functional as F -import torch.jit - - -def script_method(fn, _rcb=None): - return fn - - -def script(obj, optimize=True, _frames_up=0, _rcb=None): - return obj - - -torch.jit.script_method = script_method -torch.jit.script = script - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def intersperse(lst, item): - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d( - length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = ( - math.log(float(max_timescale) / float(min_timescale)) / - (num_timescales - 1)) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2,3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1. / norm_type) - return total_norm diff --git a/spaces/elplaguister/Yuuka_TTS/README.md b/spaces/elplaguister/Yuuka_TTS/README.md deleted file mode 100644 index 958319721c1988aeb57683c9e9736d4b86755a0b..0000000000000000000000000000000000000000 --- a/spaces/elplaguister/Yuuka_TTS/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Yuuka TTS -emoji: 🐢 -colorFrom: blue -colorTo: indigo -sdk: gradio -sdk_version: 3.9 -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/enzostvs/stable-diffusion-tpu/postcss.config.js b/spaces/enzostvs/stable-diffusion-tpu/postcss.config.js deleted file mode 100644 index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000 --- a/spaces/enzostvs/stable-diffusion-tpu/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -module.exports = { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -} diff --git a/spaces/eson/tokenizer-arena/css/style.css b/spaces/eson/tokenizer-arena/css/style.css deleted file mode 100644 index f71cf8127cece51c93ebe5325ddc9fb5c3cd3d37..0000000000000000000000000000000000000000 --- a/spaces/eson/tokenizer-arena/css/style.css +++ /dev/null @@ -1,36 +0,0 @@ - -/* 显示空格：https://blog.csdn.net/liuxiao723846/article/details/118994673 */ -.space-show { - white-space: pre-wrap; -} - -.cell-wrap { - white-space: pre-wrap; -} - -/* 隐藏legend */ -.category-legend { - display: none !important; -} - -.statistics { - min-width: min(50px, 100%) !important; -} - -.statistics textarea { - min-width: min(50px, 100%) !important; - font-size: 20px !important; - font-weight: 600 !important; - text-align: center !important; - border: none !important; -} - -.statistics label { - text-align: center !important; -} - -/* align-self: flex-end; */ -.example-style { - max-width: 150px; - align-self: self-end; -} \ No newline at end of file diff --git a/spaces/facebook/MusicGen/audiocraft/utils/deadlock.py b/spaces/facebook/MusicGen/audiocraft/utils/deadlock.py deleted file mode 100644 index 8abd1bbeea5909e664cf816c020bd7c37effdb66..0000000000000000000000000000000000000000 --- a/spaces/facebook/MusicGen/audiocraft/utils/deadlock.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -from queue import Queue, Empty -import signal -import sys -import threading -import traceback - -logger = logging.getLogger(__name__) - - -class DeadlockDetect: - def __init__(self, use: bool = False, timeout: float = 120.): - self.use = use - self.timeout = timeout - self._queue: Queue = Queue() - - def update(self, stage: str): - if self.use: - self._queue.put(stage) - - def __enter__(self): - if self.use: - self._thread = threading.Thread(target=self._detector_thread) - self._thread.start() - - def __exit__(self, exc_type, exc_val, exc_tb): - if self.use: - self._queue.put(None) - self._thread.join() - - def _detector_thread(self): - logger.debug("Deadlock detector started") - last_stage = "init" - while True: - try: - stage = self._queue.get(timeout=self.timeout) - except Empty: - break - if stage is None: - logger.debug("Exiting deadlock detector thread") - return - else: - last_stage = stage - logger.error("Deadlock detector timed out, last stage was %s", last_stage) - for th in threading.enumerate(): - print(th, file=sys.stderr) - traceback.print_stack(sys._current_frames()[th.ident]) - print(file=sys.stderr) - sys.stdout.flush() - sys.stderr.flush() - os.kill(os.getpid(), signal.SIGKILL) diff --git a/spaces/facebook/StyleNeRF/app.py b/spaces/facebook/StyleNeRF/app.py deleted file mode 100644 index 467286202780d3b98304d3ba31f1c86e92542e1e..0000000000000000000000000000000000000000 --- a/spaces/facebook/StyleNeRF/app.py +++ /dev/null @@ -1,200 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import os, sys -os.system('pip install -r requirements.txt') - -import gradio as gr -import numpy as np -import dnnlib -import time -import legacy -import torch -import glob -import cv2 - -from torch_utils import misc -from renderer import Renderer -from training.networks import Generator -from huggingface_hub import hf_hub_download - - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -port = int(sys.argv[1]) if len(sys.argv) > 1 else 21111 - -model_lists = { - 'ffhq-512x512-basic': dict(repo_id='facebook/stylenerf-ffhq-config-basic', filename='ffhq_512.pkl'), - 'ffhq-512x512-cc': dict(repo_id='facebook/stylenerf-ffhq-config-basic', filename='ffhq_512_cc.pkl'), - 'ffhq-256x256-basic': dict(repo_id='facebook/stylenerf-ffhq-config-basic', filename='ffhq_256.pkl'), - 'ffhq-1024x1024-basic': dict(repo_id='facebook/stylenerf-ffhq-config-basic', filename='ffhq_1024.pkl'), -} -model_names = [name for name in model_lists] - - -def set_random_seed(seed): - torch.manual_seed(seed) - np.random.seed(seed) - - -def get_camera_traj(model, pitch, yaw, fov=12, batch_size=1, model_name=None): - gen = model.synthesis - range_u, range_v = gen.C.range_u, gen.C.range_v - if not (('car' in model_name) or ('Car' in model_name)): # TODO: hack, better option? - yaw, pitch = 0.5 * yaw, 0.3 * pitch - pitch = pitch + np.pi/2 - u = (yaw - range_u[0]) / (range_u[1] - range_u[0]) - v = (pitch - range_v[0]) / (range_v[1] - range_v[0]) - else: - u = (yaw + 1) / 2 - v = (pitch + 1) / 2 - cam = gen.get_camera(batch_size=batch_size, mode=[u, v, 0.5], device=device, fov=fov) - return cam - - -def check_name(model_name): - """Gets model by name.""" - if model_name in model_lists: - network_pkl = hf_hub_download(**model_lists[model_name]) - else: - if os.path.isdir(model_name): - network_pkl = sorted(glob.glob(model_name + '/*.pkl'))[-1] - else: - network_pkl = model_name - return network_pkl - - -def get_model(network_pkl, render_option=None): - print('Loading networks from "%s"...' % network_pkl) - with dnnlib.util.open_url(network_pkl) as f: - network = legacy.load_network_pkl(f) - G = network['G_ema'].to(device) # type: ignore - - with torch.no_grad(): - G2 = Generator(*G.init_args, **G.init_kwargs).to(device) - misc.copy_params_and_buffers(G, G2, require_all=False) - - print('compile and go through the initial image') - G2 = G2.eval() - init_z = torch.from_numpy(np.random.RandomState(0).rand(1, G2.z_dim)).to(device) - init_cam = get_camera_traj(G2, 0, 0, model_name=network_pkl) - dummy = G2(z=init_z, c=None, camera_matrices=init_cam, render_option=render_option, theta=0) - res = dummy['img'].shape[-1] - imgs = np.zeros((res, res//2, 3)) - return G2, res, imgs - - -global_states = list(get_model(check_name(model_names[0]))) -wss = [None, None] - -def proc_seed(history, seed): - if isinstance(seed, str): - seed = 0 - else: - seed = int(seed) - - -def f_synthesis(model_name, model_find, render_option, early, trunc, seed1, seed2, mix1, mix2, yaw, pitch, roll, fov, history): - history = history or {} - seeds = [] - trunc = trunc / 100 - - if model_find != "": - model_name = model_find - - model_name = check_name(model_name) - if model_name != history.get("model_name", None): - model, res, imgs = get_model(model_name, render_option) - global_states[0] = model - global_states[1] = res - global_states[2] = imgs - - model, res, imgs = global_states - for idx, seed in enumerate([seed1, seed2]): - if isinstance(seed, str): - seed = 0 - else: - seed = int(seed) - - if (seed != history.get(f'seed{idx}', -1)) or \ - (model_name != history.get("model_name", None)) or \ - (trunc != history.get("trunc", 0.7)) or \ - (wss[idx] is None): - print(f'use seed {seed}') - set_random_seed(seed) - z = torch.from_numpy(np.random.RandomState(int(seed)).randn(1, model.z_dim).astype('float32')).to(device) - ws = model.mapping(z=z, c=None, truncation_psi=trunc) - img = model.get_final_output(styles=ws, camera_matrices=get_camera_traj(model, 0, 0, model_name=model_name), render_option=render_option) - ws = ws.detach().cpu().numpy() - img = img[0].permute(1,2,0).detach().cpu().numpy() - - - imgs[idx * res // 2: (1 + idx) * res // 2] = cv2.resize( - np.asarray(img).clip(-1, 1) * 0.5 + 0.5, - (res//2, res//2), cv2.INTER_AREA) - wss[idx] = ws - else: - seed = history[f'seed{idx}'] - seeds += [seed] - - history[f'seed{idx}'] = seed - history['trunc'] = trunc - history['model_name'] = model_name - - set_random_seed(sum(seeds)) - - # style mixing (?) - ws1, ws2 = [torch.from_numpy(ws).to(device) for ws in wss] - ws = ws1.clone() - ws[:, :8] = ws1[:, :8] * mix1 + ws2[:, :8] * (1 - mix1) - ws[:, 8:] = ws1[:, 8:] * mix2 + ws2[:, 8:] * (1 - mix2) - - # set visualization for other types of inputs. - if early == 'Normal Map': - render_option += ',normal,early' - elif early == 'Gradient Map': - render_option += ',gradient,early' - - start_t = time.time() - with torch.no_grad(): - cam = get_camera_traj(model, pitch, yaw, fov, model_name=model_name) - image = model.get_final_output( - styles=ws, camera_matrices=cam, - theta=roll * np.pi, - render_option=render_option) - end_t = time.time() - - image = image[0].permute(1,2,0).detach().cpu().numpy().clip(-1, 1) * 0.5 + 0.5 - - if imgs.shape[0] == image.shape[0]: - image = np.concatenate([imgs, image], 1) - else: - a = image.shape[0] - b = int(imgs.shape[1] / imgs.shape[0] * a) - print(f'resize {a} {b} {image.shape} {imgs.shape}') - image = np.concatenate([cv2.resize(imgs, (b, a), cv2.INTER_AREA), image], 1) - - print(f'rendering time = {end_t-start_t:.4f}s') - image = (image * 255).astype('uint8') - return image, history - -model_name = gr.inputs.Dropdown(model_names) -model_find = gr.inputs.Textbox(label="Checkpoint path (folder or .pkl file)", default="") -render_option = gr.inputs.Textbox(label="Additional rendering options", default='freeze_bg,steps:50') -trunc = gr.inputs.Slider(default=70, maximum=100, minimum=0, label='Truncation trick (%)') -seed1 = gr.inputs.Number(default=1, label="Random seed1") -seed2 = gr.inputs.Number(default=9, label="Random seed2") -mix1 = gr.inputs.Slider(minimum=0, maximum=1, default=0, label="Linear mixing ratio (geometry)") -mix2 = gr.inputs.Slider(minimum=0, maximum=1, default=0, label="Linear mixing ratio (apparence)") -early = gr.inputs.Radio(['None', 'Normal Map', 'Gradient Map'], default='None', label='Intermedia output') -yaw = gr.inputs.Slider(minimum=-1, maximum=1, default=0, label="Yaw") -pitch = gr.inputs.Slider(minimum=-1, maximum=1, default=0, label="Pitch") -roll = gr.inputs.Slider(minimum=-1, maximum=1, default=0, label="Roll (optional, not suggested for basic config)") -fov = gr.inputs.Slider(minimum=10, maximum=14, default=12, label="Fov") -css = ".output-image, .input-image, .image-preview {height: 600px !important} " - -gr.Interface(fn=f_synthesis, - inputs=[model_name, model_find, render_option, early, trunc, seed1, seed2, mix1, mix2, yaw, pitch, roll, fov, "state"], - title="Interactive Web Demo for StyleNeRF (ICLR 2022)", - description="StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis. Currently the demo runs on CPU only.", - outputs=["image", "state"], - layout='unaligned', - css=css, theme='dark-seafoam', - live=True).launch(enable_queue=True) diff --git a/spaces/falterWliame/Face_Mask_Detection/Fix Generator Samsung Clp 365 V11 Zip [EXCLUSIVE].md b/spaces/falterWliame/Face_Mask_Detection/Fix Generator Samsung Clp 365 V11 Zip [EXCLUSIVE].md deleted file mode 100644 index af264611602d983b87ca2d8acdc65fb9de9582a8..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Fix Generator Samsung Clp 365 V11 Zip [EXCLUSIVE].md +++ /dev/null @@ -1,25 +0,0 @@ - -
How to Fix Generator Samsung Clp 365 V11 Zip Error
-If you own a Samsung CLP-365 printer, you may have encountered the error message "Fix Generator Samsung Clp 365 V11 Zip" when trying to print. This error indicates that there is a problem with the firmware of your printer, which can affect its performance and functionality. Fortunately, there is a simple way to fix this error and restore your printer to its normal state.
-What is Fix Generator Samsung Clp 365 V11 Zip?
-Fix Generator Samsung Clp 365 V11 Zip is a tool that can help you update the firmware of your Samsung CLP-365 printer. Firmware is a software program that controls the hardware of your printer, such as the print head, the toner cartridge, and the paper feed. Firmware updates can improve the performance, compatibility, and security of your printer.
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However, sometimes firmware updates can cause errors or glitches in your printer, such as the Fix Generator Samsung Clp 365 V11 Zip error. This error can prevent your printer from printing properly or at all. It can also cause other issues such as paper jams, toner leaks, or poor print quality.
-How to Fix Generator Samsung Clp 365 V11 Zip Error?
-The easiest way to fix the Fix Generator Samsung Clp 365 V11 Zip error is to download and run the Fix Generator tool from the official Samsung website. This tool will automatically detect your printer model and firmware version, and then download and install the latest firmware update for your printer. This will fix any errors or bugs that may have occurred during the previous firmware update.
-To use the Fix Generator tool, follow these steps:
--
-
- Go to https://www.samsung.com/us/support/owners/product/color-laser-printer-clp-365 and click on "Downloads". -
- Under "Firmware", find the file named "Fix_Generator_Samsung_CLP_365_V11.zip" and click on "Download". -
- Save the file to your computer and unzip it. -
- Connect your printer to your computer using a USB cable. -
- Run the file named "Fix_Generator_Samsung_CLP_365_V11.exe" as an administrator. -
- Follow the instructions on the screen to complete the firmware update process. -
- Restart your printer and computer. -
After completing these steps, your printer should be able to print normally without any errors. You can also check the firmware version of your printer by printing a configuration report from the printer menu.
- -Conclusion
-The Fix Generator Samsung Clp 365 V11 Zip error is a common issue that can affect Samsung CLP-365 printers. It is caused by a faulty firmware update that can interfere with the printer's functionality. To fix this error, you can use the Fix Generator tool from the Samsung website to download and install the latest firmware update for your printer. This will resolve any errors or glitches that may have occurred during the previous firmware update and improve your printer's performance and compatibility.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Become a Successful Farmer with Farming Simulator 20 (No Apkaward Necessary).md b/spaces/fatiXbelha/sd/Become a Successful Farmer with Farming Simulator 20 (No Apkaward Necessary).md deleted file mode 100644 index 050c9ec184cce70dbaf35067cef125b5d9e1a353..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Become a Successful Farmer with Farming Simulator 20 (No Apkaward Necessary).md +++ /dev/null @@ -1,144 +0,0 @@ -
-
Farming Simulator 2020: A Realistic and Engaging Farming Simulation Game
-If you have ever dreamed of becoming a farmer or just want to experience what it is like to run your own farm, then you might want to check out Farming Simulator 2020, a simulation game that lets you take control of various vehicles and machines, plant and harvest different crops, raise animals, and sell your products in a dynamic market. In this article, we will give you an overview of what Farming Simulator 2020 is, how to download and play it on your PC or mobile device, what are the benefits and challenges of playing it, how it compares with previous versions and other farming games, and some FAQs that you might have.
-What is Farming Simulator 2020?
-Farming Simulator 2020 is the latest installment in the popular Farming Simulator series developed by GIANTS Software. It was released on December 3, 2019 for Nintendo Switch, iOS, Android, Kindle, Windows, Mac OS, PlayStation 4, Xbox One, Stadia, Commodore 64, and PlayStation 5. It features over 100 realistic vehicles and tools from some of the biggest agriculture machine makers in the world, such as John Deere, Case IH, New Holland, Fendt, Massey Ferguson, Valtra, Krone, Deutz-Fahr, Claas, and more. You can use these machines to cultivate various crops, including wheat, barley, oat, canola, sunflowers, soybean, corn, potatoes, sugar beet, cotton, grapes, and olives, as well as to feed your cows, sheep, pigs, horses, chickens, ducks, goats, dogs, cats, rabbits, guinea pigs, hamsters, turtles, snakes, parrots, fish, bees, butterflies, worms, ants, spiders, flies, mosquitoes, cockroaches, rats, bats, zombies, aliens, dinosaurs, unicorns, dragons, fairies, angels, demons, and gods [6]. You can also take care of your horses by riding them around your farm or in the nearby town. You can sell your products in the in-game market or use them to produce other goods such as milk, wool, eggs, honey, wine, oil, cheese, yogurt, butter, bread, cake, pizza, beer, whiskey, vodka, gin, rum, tequila, cider, mead, soda, juice, coffee, tea, chocolate, candy, ice cream, jam, jelly, sauce, k etchup, mustard, mayonnaise, salt, pepper, sugar, spice, and everything nice. You can also customize your farm with various buildings, decorations, and landscaping options. You can play the game solo or with up to 16 players online in multiplayer mode. You can also download and install various mods from the official website or the in-game mod hub to enhance your gameplay with new maps, vehicles, tools, crops, animals, and more. Farming Simulator 2020 is a realistic and engaging farming simulation game that will keep you entertained for hours.
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How to Download and Play Farming Simulator 2020 on PC and Mobile Devices
-If you want to play Farming Simulator 2020 on your PC or mobile device, you will need to follow these steps:
--
-
- Go to the official website of Farming Simulator 2020 at https://www.farming-simulator.com/ and choose your platform (PC, Mac, Switch, iOS, Android, Kindle, PS4, Xbox One, Stadia, Commodore 64, or PS5). -
- Click on the "Buy Now" button and follow the instructions to purchase and download the game. You can also buy the game from other online stores such as Steam, Epic Games Store, Nintendo eShop, App Store, Google Play Store, Amazon Appstore, PlayStation Store, Microsoft Store, or Stadia Store. -
- Once the game is downloaded and installed on your device, launch it and create your profile. You can choose your name, avatar, difficulty level, game mode (career or free play), map (Felsbrunn or Ravenport), and starting equipment. -
- Start playing the game by following the tutorial or exploring the map on your own. You can access the menu by pressing the ESC key on PC or Mac, the + button on Switch, the pause button on PS4 or Xbox One, or tapping on the screen on mobile devices. From there, you can check your map, inventory, finances, statistics, missions, vehicles, tools, crops, animals, products, settings, and mods. -
- Enjoy the game and have fun! -
What are the Benefits of Playing Farming Simulator 2020?
-Playing Farming Simulator 2020 can have many benefits for you. Here are some of them:
--
-
- You can learn about farming and agriculture in a fun and interactive way. You can discover how different crops are grown and harvested, how different animals are raised and cared for, how different machines and tools work and operate, and how different products are made and sold. -
- You can relax and unwind from the stress and pressure of everyday life. You can enjoy the beautiful scenery and sounds of nature, the peaceful and satisfying activities of farming, the rewarding and fulfilling results of your work, and the freedom and creativity of customizing your farm. -
- You can have fun and challenge yourself with various tasks and missions. You can try to complete different objectives and contracts from other farmers or customers, earn money and reputation by selling your products in the market, expand and improve your farm by buying new vehicles, tools, buildings, and land, and compete with other players online in multiplayer mode. -
What are the Challenges and Tips of Playing Farming Simulator 2020?
-Playing Farming Simulator 2020 can also have some challenges and difficulties. Here are some of them:
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-
- You have to manage your crops, livestock, and finances carefully. You have to plan ahead what crops to plant and when to harvest them, what animals to buy and how to feed them, what products to produce and how to store them, and what expenses to pay and how to save money. -
- You have to deal with various weather conditions and seasons. You have to adapt to different temperatures, rainfall, snowfall, wind, and daylight hours that affect your crops' growth and quality, your animals' health and productivity, your machines' performance and maintenance, and your market's demand and prices. -
- You have to master various vehicles and tools. You have to learn how to drive and operate different types of tractors combines harvesters plows cultivators seeders sprayers mowers balers loaders trailers trucks cars bikes planes helicopters boats submarines rockets spaceships and more. You also have to know how to attach detach refill repair clean and customize them. -
Here are some tips that might help you overcome these challenges and improve your gameplay:
--
-
- Read the game manual and watch the tutorial videos to learn the basics of the game and get familiar with the controls and interface. -
- Use the help menu and the information panel to get more details and tips about the vehicles, tools, crops, animals, products, and settings. -
- Use the map and the GPS to navigate and locate your farm, fields, animals, vehicles, tools, buildings, shops, and other points of interest. -
- Use the radio and the phone to listen to music, news, weather reports, and messages from other farmers or customers. -
- Use the cruise control and the hired workers to automate some of the driving and operating tasks. -
- Use the garage and the workshop to repair and customize your vehicles and tools. -
- Use the silos and the sheds to store your crops and products. -
- Use the animal pens and the pastures to feed and water your animals. -
- Use the market and the contracts to sell your products and earn money. -
- Use the bank and the statistics to manage your finances and track your progress. -
- Use the settings and the mods to adjust the game difficulty, graphics, sound, controls, language, and other options. -
How does Farming Simulator 2020 Compare with Previous Versions and Other Farming Games?
-Farming Simulator 2020 is not the first nor the only farming simulation game in the market. It has many predecessors and competitors that offer similar or different features and experiences. Here is a brief comparison of Farming Simulator 2020 with some of them:
-| Game | -Similarities | -Differences | -
|---|---|---|
| Farming Simulator 19 | -The previous version of Farming Simulator 2020 that was released in 2018. It has many of the same vehicles, tools, crops, animals, maps, modes, mods, and multiplayer features as Farming Simulator 2020. | -It has fewer vehicles, tools, crops, animals, maps, modes, mods, and multiplayer features than Farming Simulator 2020. It also has lower graphics quality, less realistic physics, and more bugs and glitches than Farming Simulator 2020. | -
| Farming Simulator 22 | -The upcoming version of Farming Simulator 2020 that will be released in 2024. It will have many of the same vehicles, tools, crops, animals, maps, modes, mods, and multiplayer features as Farming Simulator 2020. | -It will have more vehicles, tools, crops, animals, maps, modes, mods, and multiplayer features than Farming Simulator 2020. It will also have higher graphics quality, more realistic physics, and fewer bugs and glitches than Farming Simulator 2020. It will also introduce new features such as seasons, weather effects, production chains, and precision farming. | -
| FarmVille | -A social network game that was launched in 2009. It allows you to create and manage your own farm with various crops animals buildings and decorations. You can also interact and cooperate with other players online. | -It has less vehicles tools crops animals maps modes mods and multiplayer features than Farming Simulator 2020. It also has lower graphics quality less realistic physics and more microtransactions than Farming Simulator 2020. It also focuses more on casual and social gameplay than Farming Simulator 2020. | -
| Stardew Valley | -A role-playing game that was released in 2016. It allows you to inherit and restore your grandfather's farm with various crops animals buildings and decorations. You can also explore and interact with a nearby town with various characters events and activities. You can also play with up to three other players online in co-op mode. | -It has less vehicles tools crops animals maps modes mods and multiplayer features than Farming Simulator 2020. It also has lower graphics quality less realistic physics and more fantasy elements than Farming Simulator 2020. It also focuses more on story-driven and character-driven gameplay than Farming Simulator 2020. | -
| Harvest Moon | -A series of games that started in 1996. It allows you to live and work on a farm with various crops animals buildings and decorations. You can also romance and marry one of the eligible bachelors or bachelorettes in the game. You can also have children and pass on your farm to them. | -It has less vehicles tools crops animals maps modes mods and multiplayer features than Farming Simulator 2020. It also has lower graphics quality less realistic physics and more anime-style graphics than Farming Simulator 2020. It also focuses more on romantic and family-oriented gameplay than Farming Simulator 2020. | -
Conclusion
-Farming Simulator 2020 is a realistic and engaging farming simulation game that lets you take control of various vehicles and machines, plant and harvest different crops, raise animals, and sell your products in a dynamic market. You can play the game on various platforms, such as PC, Mac, Switch, iOS, Android, Kindle, PS4, Xbox One, Stadia, Commodore 64, and PS5. You can also play the game solo or with up to 16 players online in multiplayer mode. You can also download and install various mods from the official website or the in-game mod hub to enhance your gameplay with new maps, vehicles, tools, crops, animals, and more. Playing Farming Simulator 2020 can have many benefits for you, such as learning about farming and agriculture, relaxing and unwinding from stress, and having fun and challenging yourself with various tasks and missions. However, playing Farming Simulator 2020 can also have some challenges and difficulties, such as managing your crops, livestock, and finances, dealing with various weather conditions and seasons, and mastering various vehicles and tools. Therefore, we recommend you to read the game manual and watch the tutorial videos to learn the basics of the game and get familiar with the controls and interface. We also recommend you to use the help menu and the information panel to get more details and tips about the vehicles, tools, crops, animals, products, and settings. We also recommend you to use the map and the GPS to navigate and locate your farm, fields, animals, vehicles, tools, buildings, shops, and other points of interest. We also recommend you to use the radio and the phone to listen to music, news, weather reports, and messages from other farmers or customers. We also recommend you to use the cruise control and the hired workers to automate some of the driving and operating tasks. We also recommend you to use the garage and the workshop to repair and customize your vehicles and tools. We also recommend you to use the silos and the sheds to store your crops and products. We also recommend you to use the animal pens and the pastures to feed and water your animals. We also recommend you to use the market and the contracts to sell your products and earn money. We also recommend you to use the bank and the statistics to manage your finances and track your progress. We also recommend you to use the settings and the mods to adjust the game difficulty, graphics, sound, controls, language, and other options.
-If you are looking for a realistic and engaging farming simulation game that will keep you entertained for hours, then Farming Simulator 2020 is the game for you. It is one of the best farming games in the market that offers a lot of features and options for you to enjoy. It is also one of the most realistic farming games in the market that simulates a lot of aspects of farming and agriculture. It is also one of the most customizable farming games in the market that allows you to create your own farm according to your preferences. It is also one of the most social farming games in the market that allows you to play with other players online in multiplayer mode. Farming Simulator 2020 is a game that will make you feel like a real farmer.
-FAQs
-Here are some frequently asked questions and answers about Farming Simulator 2020:
--
-
- How much does Farming Simulator 2020 cost? -
- Is Farming Simulator 2020 online or offline? -
- Is Farming Simulator 2020 realistic or arcade? -
- Is Farming Simulator 2020 educational or entertaining? -
- Is Farming Simulator 2020 suitable for children or adults? -
Farming Simulator 2020 costs $49.99 for PC, Mac, Switch, PS4, Xbox One, Stadia, Commodore 64, and PS5. It costs $5.99 for iOS, Android, Kindle. You can also buy additional DLCs (downloadable content) for extra vehicles, tools, crops, animals, maps, modes, mods, and multiplayer features.
-Farming Simulator 2020 can be played both online or offline. You can play it online with up to 16 players in multiplayer mode, where you can share your farm, vehicles, tools, crops, animals, products, and missions with other players. You can also download and install various mods from the official website or the in-game mod hub to enhance your gameplay with new maps, vehicles, tools, crops, animals, and more. You can also play it offline in single-player mode, where you can enjoy your farm without any internet connection or other players.
-Farming Simulator 2020 is a realistic farming simulation game that simulates a lot of aspects of farming and agriculture. It has realistic graphics, physics, sounds, and gameplay that make you feel like you are really on a farm. It also has realistic vehicles, tools, crops, animals, products, and markets that are based on real-life models and data. However, Farming Simulator 2020 also has some arcade elements that make the game more fun and accessible. It has simplified controls, menus, and interfaces that make the game easy to play. It also has adjustable settings, modes, and mods that make the game customizable to your preferences. It also has some fantasy elements that make the game more diverse and creative. It has some fictional vehicles, tools, crops, animals, products, and maps that are not found in real life.
-Farming Simulator 2020 is both educational and entertaining. It is educational because it teaches you about farming and agriculture in a fun and interactive way. You can learn how different crops are grown and harvested, how different animals are raised and cared for, how different machines and tools work and operate, and how different products are made and sold. You can also learn about the history, culture, and economy of farming and agriculture in different regions and countries. It is entertaining because it lets you enjoy the beautiful scenery and sounds of nature, the peaceful and satisfying activities of farming, the rewarding and fulfilling results of your work, and the freedom and creativity of customizing your farm. You can also have fun and challenge yourself with various tasks and missions, earn money and reputation by selling your products in the market, expand and improve your farm by buying new vehicles, tools, buildings, and land, and compete with other players online in multiplayer mode.
-Farming Simulator 2020 is suitable for both children and adults. It is suitable for children because it is a family-friendly game that does not contain any violence blood gore sex drugs alcohol tobacco gambling profanity or other inappropriate content. It is also a kid-friendly game that does not require any reading writing math or other academic skills. It is also a fun game that can spark their interest curiosity and imagination about farming and agriculture. It is suitable for adults because it is a mature game that does not insult their intelligence taste or preference. It is also a challenging game that can test their skills knowledge and strategy about farming and agriculture. It is also a relaxing game that can help them escape from the stress and pressure of everyday life.
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How to Download and Play NBA 2K19 on PC
-If you are a fan of basketball and video games, you might have heard of NBA 2K19, the latest installment of the popular NBA 2K series. This game is a simulation of the National Basketball Association (NBA), featuring realistic gameplay, graphics, and modes. You can play as your favorite teams and players, create your own custom characters, compete online with other players, and more.
-But did you know that you can also play NBA 2K19 on your PC? Yes, you read that right. You don't need a console or a TV to enjoy this game. You can download and install NBA 2K19 on your computer and play it with your keyboard and mouse, or with a controller if you prefer. In this article, we will show you how to do that, as well as the system requirements, the download options, and the features and reviews of NBA 2K19.
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Introduction
-What is NBA 2K19?
-NBA 2K19 is a basketball simulation video game developed by Visual Concepts and published by 2K Sports. It was released in September 2018 for various platforms, including Windows, PlayStation 4, Xbox One, Nintendo Switch, iOS, and Android. It is the 20th installment of the NBA 2K franchise, which celebrates its 20th anniversary with this game.
-NBA 2K19 features many improvements and additions over its predecessors, such as enhanced graphics and animations, new gameplay mechanics and modes, updated rosters and ratings, and more. It also features a cover athlete for each edition: Giannis Antetokounmpo for the standard edition, LeBron James for the 20th anniversary edition, and Ben Simmons for the Australian edition.
-Why play NBA 2K19 on PC?
-There are many reasons why you might want to play NBA 2K19 on your PC instead of a console or a mobile device. Here are some of them:
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- You can enjoy better graphics and performance on your PC, especially if you have a high-end system that meets or exceeds the recommended requirements. -
- You can customize your settings and controls to suit your preferences and needs. You can adjust the resolution, the frame rate, the graphics quality, the sound volume, the camera angle, and more. You can also choose between playing with a keyboard and mouse or a controller. -
- You can access more features and content on your PC, such as mods, patches, updates, DLCs, community creations, online multiplayer, leaderboards, achievements, etc. -
- You can save money on your PC, as you don't need to buy a console or a TV to play NBA 2K19. You can also find cheaper deals and discounts for the game online. -
System Requirements
-Minimum Requirements
-Before you download and install NBA 2K19 on your PC, you need to make sure that your system meets the minimum requirements for the game. These are:
-| OS | Windows 7 64-bit, Windows 8.1 64-bit or Windows 10 64-bit | |
| Processor | Intel® Core™ i3-530 @ 2.93 GHz / AMD FX-4100 @ 3.60 GHz or better |
| OS | Windows 7 64-bit, Windows 8.1 64-bit or Windows 10 64-bit |
| Processor | Intel® Core™ i5-4430 @ 3 GHz / AMD FX-8370 @ 3.4 GHz or better |
| Memory | 8 GB RAM |
| Graphics | NVIDIA® GeForce® GTX 770 2GB / AMD® Radeon™ R9 270 2GB or better |
| DirectX | Version 11 |
| Storage | 80 GB available space |
| Sound Card | DirectX 9.0c compatible sound card |
| Additional Notes | Dual-analog gamepad recommended |
Download Options
-Steam
-One of the easiest and most popular ways to download and play NBA 2K19 on your PC is through Steam, the leading digital distribution platform for PC games. Steam offers many benefits, such as automatic updates, cloud saving, online multiplayer, social features, and more. To download NBA 2K19 on Steam, you need to follow these steps:
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- Create a Steam account if you don't have one already. You can do this by visiting https://store.steampowered.com/join/. -
- Download and install the Steam client on your PC. You can do this by visiting https://store.steampowered.com/about/. -
- Launch the Steam client and log in with your account. -
- Search for NBA 2K19 in the Steam store or visit https://store.steampowered.com/app/841370/NBA_2K19/. -
- Add NBA 2K19 to your cart and proceed to checkout. You can choose between the standard edition ($59.99) or the 20th anniversary edition ($99.99). You can also buy additional DLCs and bundles. -
- Select your payment method and complete your purchase. You can pay with credit card, PayPal, Steam Wallet, or other options. -
- Wait for NBA 2K19 to download and install on your PC. The download size is about 80 GB, so it might take some time depending on your internet speed. -
- Once the installation is done, you can launch NBA 2K19 from your Steam library and start playing. -
2K Store
-Another option to download and play NBA 2K19 on your PC is through the official 2K Store, the online store of the game's publisher. The 2K Store offers some exclusive deals and discounts for NBA 2K19, as well as other 2K games and merchandise. To download NBA 2K19 from the 2K Store, you need to follow these steps:
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- Create a 2K account if you don't have one already. You can do this by visiting https://store.2k.com/en/register/. -
- Browse the NBA 2K19 page on the 2K Store or visit https://store.2k.com/en/game/buy-nba-2k19/. -
- Select your edition and platform. You can choose between the standard edition ($59.99) or the 20th anniversary edition ($99.99). You can also buy additional DLCs and bundles. -
- Add NBA 2K19 to your cart and proceed to checkout. You can pay with credit card, PayPal, or other options. -
- After your purchase, you will receive an email with a code to redeem NBA 2K19 on Steam. -
- Follow the instructions in the email to activate your code on Steam. -
- Wait for NBA 2K19 to download and install on your PC through Steam. -
- Download and install BlueStacks on your PC. You can do this by visiting https://www.bluestacks.com/. -
- Launch BlueStacks and sign in with your Google account. -
- Search for NBA 2K19 in the Google Play Store or visit https://play.google.com/store/apps/details?id=com.t2ksports.nba2k19and&hl=en_US&gl=US. -
- Tap on the Install button and wait for NBA 2K19 to download and install on your PC. -
- Once the installation is done, you can launch NBA 2K19 from the BlueStacks home screen and start playing. -
- Launch the Steam client and log in with your account. -
- Click on the Games menu and select Activate a Product on Steam. -
- Enter your code that you received from the 2K Store and follow the instructions. -
- Wait for NBA 2K19 to download and install on your PC through Steam. -
- Once the installation is done, you can launch NBA 2K19 from your Steam library and start playing. -
- MyCareer: This mode allows you to create your own custom player and follow their journey from an unknown rookie to an NBA legend. You can customize your player's appearance, skills, attributes, style, and more. You can also interact with other characters, make decisions that affect your story, and explore an open world called The Neighborhood. -
- MyTeam: This mode allows you to build your own dream team of current and former NBA players. You can collect cards, trade players, upgrade your roster, compete online or offline, and complete challenges and events. -
- MyLeague: This mode allows you to control an entire NBA franchise. You can customize your team's name, logo, arena, uniforms, roster, staff, etc. You can also manage your team's finances, contracts, trades, drafts, injuries, etc. You can play up to 80 seasons with realistic simulation and progression. -
- MyGM: This mode allows you to become the general manager of an NBA team. You can deal with the owner's demands, the media's expectations, the players' morale, etc. You can also create your own expansion team or relocate an existing team. -or a classic team from the past. You can also play online with other players or against the AI. -
- Blacktop: This mode allows you to play a street-style basketball game with up to 10 players. You can choose your players, court, rules, etc. You can also play online with other players or against the AI. -
- The Prelude: This is a free demo that allows you to play the first chapter of MyCareer mode and transfer your progress to the full game. -
- The Way Back: This is a cinematic story that follows your player's journey from China to the G League and finally to the NBA. -
- 2KTV: This is a weekly show that features interviews, tips, trivia, contests, and more. -
- Locker Codes: These are codes that you can redeem for free rewards, such as VC, MT, cards, packs, etc. -
- 2KU: This is a tutorial mode that teaches you the basics and advanced techniques of NBA 2K19. -
- Q: How much does NBA 2K19 cost on PC? -
- A: NBA 2K19 costs $59.99 for the standard edition and $99.99 for the 20th anniversary edition on both Steam and the 2K Store. You can also buy additional DLCs and bundles for extra prices. -
- Q: Can I play NBA 2K19 on PC with a controller? -
- A: Yes, you can play NBA 2K19 on PC with a controller. You can use any compatible controller that connects to your PC via USB or Bluetooth. You can also customize your controller settings in the game options. -
- Q: Can I play NBA 2K19 on PC with my friends? -
- A: Yes, you can play NBA 2K19 on PC with your friends. You can play online multiplayer modes with other players around the world or locally with up to four players on the same PC. You can also join or create online communities and leagues with your friends. -
- Q: How can I get free VC and MT in NBA 2K19 on PC? -player, etc. in NBA 2K19. You can get free VC and MT by playing the game, completing challenges and events, watching 2KTV, redeeming locker codes, etc. You can also buy VC and MT with real money, but we don't recommend that as it can be expensive and risky. -
- Q: How can I update NBA 2K19 on PC? -
- A: NBA 2K19 on PC will automatically update itself if you have an online connection and if there are any available updates from the developers. You can also manually check for updates by launching the game or by visiting the game's page on Steam or the 2K Store. -
- It is easy to understand and follow. The book uses simple language, clear explanations, and helpful illustrations to teach you the basics of Tajweed and Qiroati. -
- It is comprehensive and thorough. The book covers all the essential topics of Tajweed and Qiroati, such as the articulation points of letters, the characteristics of letters, the rules of vowels, the rules of nunation, the rules of elongation, the rules of stopping and starting, and more. -
- It is practical and effective. The book provides you with exercises and examples to practice your recitation skills and test your knowledge. It also gives you tips and tricks on how to improve your recitation and avoid common mistakes. -
- Qiroati.com: This is the official website of Qiroati, where you can find information about the Qiroati method, the Qiroati books, the Qiroati teachers, and the Qiroati events. You can also download Qiroati Jilid 1 PDF for free from this website. -
- Quranpedia.net: This is a website that provides various resources for Quran learning, such as Quran translations, Quran interpretations, Quran recitations, Quran memorization, and Quran quizzes. You can also download Qiroati Jilid 1 PDF for free from this website. -
- Scribd.com: This is a website that allows you to read, download, and share books, documents, audiobooks, podcasts, magazines, and more. You can also download Qiroati Jilid 1 PDF for free from this website, but you need to sign up for a free trial or a paid subscription. -
- Qiroati Center: This is a place where you can learn Quran recitation using the Qiroati method. You can also buy or borrow Qiroati Jilid 1 PDF from the Qiroati Center. You can find the nearest Qiroati Center in your area by visiting Qiroati.com/center. -
- Islamic Bookstore: This is a place where you can buy or rent various Islamic books, including Qiroati Jilid 1 PDF. You can find an Islamic bookstore near you by searching online or asking your friends or family. -
- Qiroati Teacher: This is a person who teaches Quran recitation using the Qiroati method. You can also ask your Qiroati teacher to provide you with a copy of Qiroati Jilid 1 PDF. You can find a qualified Qiroati teacher by visiting Qiroati.com/teacher. -
- Visit the website that offers Qiroati Jilid 1 PDF, such as Qiroati.com, Quranpedia.net, or Scribd.com. -
- Search for Qiroati Jilid 1 PDF using the search bar or the menu. -
- Select the file that you want to download and click on the download button or link. -
- Choose the format and the destination of the file and click on save or confirm. -
- Wait for the file to be downloaded and check if it is complete and readable. -
- Visit the place that offers Qiroati Jilid 1 PDF, such as a Qiroati Center, an Islamic Bookstore, or a Qiroati Teacher. -
- Ask for Qiroati Jilid 1 PDF and check if it is available and in good condition. -
- Pay for the book or borrow it with permission and agreement. -
- Copy the book to your device using a scanner, a camera, or a USB cable. -
- Check if the file is complete and readable. -
- A device that can open and read PDF files, such as a computer, a tablet, or a smartphone. -
- A good internet connection if you want to access online resources or listen to online recitations. -
- A basic knowledge of Arabic alphabet and pronunciation. If you are not familiar with Arabic, you can learn it from other sources or ask for help from someone who knows Arabic. -
- A sincere intention and motivation to learn Quran recitation. You should have a clear goal and purpose for learning Quran recitation and be willing to dedicate your time and effort to achieve it. -
- Read the introduction chapter carefully and understand the concept and purpose of Tajweed and Qiroati. This will help you appreciate the value and benefits of learning Quran recitation and familiarize yourself with the book and its contents. -
- Follow the order of the chapters and subtopics as they are arranged in a logical and progressive way. Do not skip or jump from one topic to another without completing the previous one. -
- Read each topic thoroughly and pay attention to the explanations, illustrations, signs, symbols, examples, and exercises. Try to understand the rules and apply them in your recitation. Do not memorize without understanding or understanding without practicing. -
- Listen to the recitations of the Quran by reputable reciters who follow the rules of Tajweed and Qiroati. You can find online recitations on websites like Quran.com, Quranicaudio.com, or Quranexplorer.com. You can also listen to offline recitations on CDs or MP3s. Try to imitate their pronunciation, tone, rhythm, and style. -
- Practice your recitation regularly and consistently. You can practice alone or with a partner or a group. You can also practice with your Qiroati teacher or join a Qiroati class. You can practice by reading aloud, recording yourself, or using an app like Quran Companion. You should also review what you have learned periodically and correct your mistakes. -
- Qiroati Jilid 1 PDF is a book that teaches you the basics of Quran recitation using the Qiroati method, which is based on the principles of Tajweed. -
- Qiroati Jilid 1 PDF has many benefits, such as being easy to understand, comprehensive, thorough, practical, and effective. -
- Qiroati Jilid 1 PDF consists of five chapters, each with its own subtopics and objectives, that cover all the essential topics of Tajweed and Qiroati. -
- You can download Qiroati Jilid 1 PDF from various online or offline sources, such as Qiroati.com, Quranpedia.net, Scribd.com, Qiroati Center, Islamic Bookstore, or Qiroati Teacher. -
- You should follow some prerequisites and tips to use Qiroati Jilid 1 PDF effectively and efficiently, such as having a device that can read PDF files, a good internet connection, a basic knowledge of Arabic alphabet and pronunciation, a sincere intention and motivation to learn Quran recitation, reading the introduction chapter carefully, following the order of the chapters and subtopics, reading each topic thoroughly and paying attention to the explanations, illustrations, signs, symbols, examples, and exercises, listening to the recitations of reputable reciters who follow the rules of Tajweed and Qiroati, practicing your recitation regularly and consistently, and reviewing what you have learned periodically and correcting your mistakes. -
- Qiroati Jilid 2 PDF: This book teaches you the rules of stopping and starting, the rules of pauses, the rules of intonation, and the rules of reciting the Basmalah. -
- Qiroati Jilid 3 PDF: This book teaches you the rules of merging, the rules of separation, the rules of hamzah, and the rules of madd. -
- Qiroati Jilid 4 PDF: This book teaches you the rules of ghunnah, the rules of idgham, the rules of iqlab, and the rules of ikhfa. -
- Qiroati Jilid 5 PDF: This book teaches you the rules of qalqalah, the rules of shaddah, the rules of tafkhim and tarqiq, and the rules of lafz jalalah. -
- Qiroati Jilid 6 PDF: This book teaches you the rules of waqf and ibtida, the types of waqf signs, and the etiquette of waqf. -
- Qiroati Jilid 7 PDF: This book teaches you the ten styles of Quran recitation, their origins, their differences, and their examples. -
- Qiroati.com: This is the official website of Qiroati, where you can find information about the Qiroati method, the Qiroati books, the Qiroati teachers, and the Qiroati events. You can also download Qiroati Jilid 1 PDF for free from this website. -
- Qiroatimedia.com: This is a website that provides various media for Quran learning using the Qiroati method, such as videos, audios, articles, and podcasts. You can also find Qiroati recitations by different reciters on this website. -
- Qiroatifoundation.org: This is a website that represents the Qiroati Foundation, a non-profit organization that aims to spread and promote Quran recitation using the Qiroati method. You can also find information about Qiroati programs and activities on this website. -
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BlueStacks Emulator
-A third option to download and play NBA 2K19 on your PC is through BlueStacks, a popular Android emulator that allows you to run mobile apps and games on your PC. BlueStacks offers some advantages, such as faster loading times, smoother gameplay, and keyboard and mouse support. To download NBA 2K19 on BlueStacks, you need to follow these steps:
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Installation Steps
-Steam
-If you have downloaded NBA 2K19 from Steam, you don't need to do anything else to install it on your PC. Steam will automatically install the game for you after the download is complete. You can then launch NBA 2K19 from your Steam library and start playing.
-2K Store
-If you have downloaded NBA 2K19 from the 2K Store, you need to activate your code on Steam and then install the game through Steam. To do this, you need to follow these steps:
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BlueStacks Emulator
-If you have downloaded NBA 2K19 from BlueStacks, you don't need to do anything else to install it on your PC. BlueStacks will automatically install the game for you after the download is complete. You can then launch NBA 2K19 from the BlueStacks home screen and start playing.
-Features and Reviews
-Gameplay and Graphics
-NBA 2K19 is praised for its realistic and immersive gameplay and graphics, which make you feel like you are playing in a real NBA game. The game features improved physics, animations, lighting, shadows, textures, and details, as well as new gameplay mechanics such as Takeover, which allows you to unleash your player's full potential when they are hot. The game also features a dynamic commentary team, a realistic crowd, and a soundtrack curated by Travis Scott.
-Game Modes and Content
-NBA 2K19 offers a variety of game modes and content for different types of players. Some of the game modes are:
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NBA 2K19 also offers a lot of content for you to enjoy, such as:
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Pros and Cons
-NBA 2K19 is not a perfect game, and it has its pros and cons. Here are some of them:
-| Pros | Cons |
| Realistic and immersive gameplay and graphics | High system requirements and large download size |
| Various game modes and content for different types of players | Some game modes and features require online connection and microtransactions |
| Improved physics, animations, mechanics, and modes over previous games | Some bugs, glitches, errors, and crashes may occur |
| Dynamic commentary team, realistic crowd, and curated soundtrack | Some repetitive or outdated commentary, crowd, and music |
| Customizable settings and controls for PC players | Some settings and controls may not work properly or optimally |
Conclusion
-Summary of the article
-In this article, we have shown you how to download and play NBA 2K19 on your PC. We have also discussed the system requirements, the download options, and the features and reviews of NBA 2K19. We hope that this article has been helpful and informative for you.
-Call to action
-If you are interested in playing NBA 2K19 on your PC, you can buy it now from Steam or the 2K Store. You can also try it for free by downloading The Prelude from Steam. NBA 2K19 is a great game for basketball and video game fans alike. It offers realistic and immersive gameplay and graphics, various game modes and content, improved physics, animations, mechanics, and modes, dynamic commentary team, realistic crowd, curated soundtrack, customizable settings and controls, and more. Don't miss this chance to experience the best basketball simulation game ever. Download NBA 2K19 on your PC today!
-Frequently Asked Questions
-Here are some frequently asked questions about NBA 2K19 on PC:
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Download Qiroati Jilid 1 PDF: A Guide to Learn Quran Recitation
-If you want to learn how to recite the Quran with proper pronunciation, rules, and fluency, you might be interested in downloading Qiroati Jilid 1 PDF. This is a book that teaches you the basics of Quran recitation using the Qiroati method, which is a popular and effective way to learn the Quran. In this article, we will explain what Qiroati Jilid 1 PDF is, how to download it, and how to use it. We hope that this article will help you improve your Quran recitation skills and enjoy the beauty of the Quran.
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What is Qiroati Jilid 1 PDF?
-Qiroati Jilid 1 PDF is a book that introduces you to the Qiroati method of Quran recitation. The Qiroati method is a method that was developed by K.H. Dachlan Salim Zarkasyi, an Indonesian scholar and teacher, who wanted to make Quran learning easier and more accessible for everyone. The Qiroati method is based on the principles of Tajweed, which is the science of Quranic elocution. Tajweed teaches you how to pronounce each letter, word, and verse of the Quran correctly and beautifully, according to the rules of Arabic grammar and phonetics.
-The benefits of Qiroati Jilid 1 PDF
-There are many benefits of using Qiroati Jilid 1 PDF as your guide to learn Quran recitation. Some of them are:
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The contents of Qiroati Jilid 1 PDF
-The book consists of five chapters, each with its own subtopics and objectives. Here is a brief overview of what each chapter contains:
-| Chapter | -Subtopics | -Objectives | -
|---|---|---|
| Chapter 1: Introduction | -- The definition and importance of Tajweed - The definition and history of Qiroati - The structure and features of Qiroati Jilid 1 PDF |
-- To understand the concept and purpose of Tajweed and Qiroati - To appreciate the value and benefits of learning Quran recitation - To familiarize yourself with the book and its contents |
-
| Chapter 2: The Articulation Points of Letters | -- The definition and types of articulation points - The articulation points of each letter - The signs and symbols used to indicate articulation points |
-- To identify and locate the articulation points of each letter - To pronounce each letter correctly and accurately - To recognize and use the signs and symbols of articulation points |
-
| Chapter 3: The Characteristics of Letters | -- The definition and types of characteristics - The characteristics of each letter - The signs and symbols used to indicate characteristics |
-- To understand and differentiate the characteristics of each letter - To apply the rules of characteristics in recitation - To recognize and use the signs and symbols of characteristics |
-
| Chapter 4: The Rules of Vowels | -- The definition and types of vowels - The rules of short vowels - The rules of long vowels - The rules of nunation |
-- To know and distinguish the vowels in Arabic - To apply the rules of vowels in recitation - To avoid the errors of vowels in recitation |
-
| Chapter 5: The Rules of Elongation | -- The definition and types of elongation - The rules of natural elongation - The rules of compulsory elongation - The rules of optional elongation |
-- To know and distinguish the types of elongation in Arabic - To apply the rules of elongation in recitation - To vary the length of elongation according to the context |
-
How to download Qiroati Jilid 1 PDF?
-If you are interested in downloading Qiroati Jilid 1 PDF, you might be wondering where to find it and how to get it. There are several sources that offer Qiroati Jilid 1 PDF for free or for a small fee. However, you should be careful and choose a reliable and trustworthy source that provides you with a high-quality and authentic copy of the book. Here are some tips on how to find and download Qiroati Jilid 1 PDF.
-The sources of Qiroati Jilid 1 PDF
-There are two main types of sources that offer Qiroati Jilid 1 PDF: online sources and offline sources. Online sources are websites, blogs, forums, or social media platforms that provide links or attachments to download Qiroati Jilid 1 PDF. Offline sources are physical stores, libraries, or individuals that sell or lend Qiroati Jilid 1 PDF in hard copy or digital format.
-Some examples of online sources are:
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The steps to download Qiroati Jilid 1 PDF
-The steps to download Qiroati J Jilid 1 PDF from an online source may vary depending on the source, but here are some general steps that you can follow:
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How to use Qiroati Jilid 1 PDF?
-After you have downloaded Qiroati Jilid 1 PDF, you might be wondering how to use it effectively and efficiently. There are some prerequisites and tips that you should know before you start using Qiroati Jilid 1 PDF. Here are some suggestions on how to use Qiroati Jilid 1 PDF.
-The prerequisites of using Qiroati Jilid 1 PDF
-Before you use Qiroati Jilid 1 PDF, you should make sure that you have the following prerequisites:
--
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The tips and tricks of using Qiroati Jilid 1 PDF
-When you use Qiroati Jilid 1 PDF, you should follow these tips and tricks to make your learning process easier and more enjoyable:
--
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Conclusion
-In conclusion, Qiroati Jilid 1 PDF is a book that teaches you how to recite the Quran with proper pronunciation, rules, and fluency using the Qiroati method, which is a popular and effective way to learn the Quran. You can download Qiroati Jilid 1 PDF from various online or offline sources, and use it as your guide to learn the basics of Tajweed and Qiroati. You should also follow some prerequisites and tips to make your learning process easier and more enjoyable. We hope that this article has helped you understand what Qiroati Jilid 1 PDF is, how to download it, and how to use it. We also hope that you will benefit from this book and improve your Quran recitation skills and enjoy the beauty of the Quran.
Summary of the main points
-Here are the main points that we have covered in this article:
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-
Call to action
-If you are interested in learning Quran recitation using the Qiroati method, we encourage you to download Qiroati Jilid 1 PDF and start your journey today. You can also share this article with your friends and family who might benefit from it. If you have any questions or feedback about this article or Qiroati Jilid 1 PDF, please feel free to leave a comment below or contact us at info@qiroati.com. We would love to hear from you and help you with your Quran learning goals. Thank you for reading this article and may Allah bless you with success in this life and the hereafter.
-Frequently Asked Questions
-Here are some frequently asked questions about Qiroati Jilid 1 PDF that you might find useful:
-What is the difference between Tajweed and Qiroati?
-Tajweed is the science of Quranic elocution that teaches you how to pronounce each letter, word, and verse of the Quran correctly and beautifully. Qiroati is a method of Quran recitation that is based on the principles of Tajweed. Qiroati simplifies and systematizes the rules of Tajweed in a way that makes Quran learning easier and more accessible for everyone.
-Who is the author of Qiroati Jilid 1 PDF?
-The author of Qiroati Jilid 1 PDF is K.H. Dachlan Salim Zarkasyi, an Indonesian scholar and teacher who developed the Qiroati method. He is also the founder of Pondok Pesantren Darussalam Gontor in Ponorogo, East Java, Indonesia. He has written several books on Islamic studies, especially on Quran recitation.
-How long does it take to finish Qiroati Jilid 1 PDF?
-The time it takes to finish Qiroati Jilid 1 PDF depends on your level of proficiency in Arabic language and Quran recitation, as well as your pace of learning and practice. However, a general estimate is that it takes about one month to finish Qiroati Jilid 1 PDF if you study one chapter per week.
-What are the other books in the Qiroati series?
-Qiroati Jilid 1 PDF is the first book in the Qiroati series. There are six other books in the series that cover more advanced topics of Quran recitation. They are:
-
-
Where can I find more information about Qiroati?
-If you want to learn more about Qiroati, you can visit the following websites or contact the following organizations:
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\ No newline at end of file diff --git a/spaces/fatmacankara/ASCARIS/code/process_input.py b/spaces/fatmacankara/ASCARIS/code/process_input.py deleted file mode 100644 index c840d409a060155e88189fe454f7fb550e5ff328..0000000000000000000000000000000000000000 --- a/spaces/fatmacankara/ASCARIS/code/process_input.py +++ /dev/null @@ -1,40 +0,0 @@ -import pandas as pd - -def clean_data(input_set): - data = pd.DataFrame() - try: - if ',' in input_set: - input_set = [i.strip() for i in input_set.split(',')] - for i in input_set: - data = data.append(pd.Series([j.strip() for j in i.split('-')]), ignore_index=True) - data.columns = ['uniprotID', 'wt', 'pos', 'mut'] - elif '\t' in input_set: - input_set = [i.strip() for i in input_set.split('\t')] - for i in input_set: - data = data.append(pd.Series([j.strip() for j in i.split('-')]), ignore_index=True) - data.columns = ['uniprotID', 'wt', 'pos', 'mut'] - - elif '-' in input_set: - data = data.append(pd.Series([j.strip() for j in input_set.split('-')]), ignore_index=True) - data.columns = ['uniprotID', 'wt', 'pos', 'mut'] - - elif '.txt' in input_set: - data = pd.read_csv(input_set, sep='\t', names=['uniprotID', 'wt', 'pos', 'mut']) - data = data[['uniprotID', 'wt', 'pos', 'mut']] - - # Exclude termination codons, synonymous mutations and any non-standard residues such as Sec, 4 or 6. - aa_list = ['A', 'R', 'N', 'D', 'C', 'E', 'Q', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V'] - data.wt = data.wt.str.strip() - data.mut = data.mut.str.strip() - data = data[data.wt.isin(aa_list)] - data = data[data.mut.isin(aa_list)] - - for i in data.index: - data.at[i, 'datapoint'] = data.at[i, 'uniprotID'] + data.at[i, 'wt'] + str(data.at[i, 'pos']) + data.at[i, 'mut'] - - data = data.astype(str) - return data - except: - ValueError - print('Please check the input format.') - diff --git a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/cppipc/buffer.cpp b/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/cppipc/buffer.cpp deleted file mode 100644 index 0ac0fa7bc3ced0447ba4caa359355dd4252670b3..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/cppipc/buffer.cpp +++ /dev/null @@ -1,87 +0,0 @@ -#include "libipc/buffer.h" -#include "libipc/utility/pimpl.h" - -#include
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\ No newline at end of file diff --git a/spaces/fffiloni/DragGAN/README.md b/spaces/fffiloni/DragGAN/README.md deleted file mode 100644 index 969c612ac6c1c25bb286b090c8b43466de46fd89..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/DragGAN/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: DragGAN -emoji: ⚡ -colorFrom: pink -colorTo: green -sdk: gradio -sdk_version: 3.30.0 -app_file: gradio_app.py -pinned: false -duplicated_from: aaronb/DragGAN ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/fffiloni/lama-video-watermark-remover/bin/paper_runfiles/generate_val_test.sh b/spaces/fffiloni/lama-video-watermark-remover/bin/paper_runfiles/generate_val_test.sh deleted file mode 100644 index d9b2a370ceeeb8f401706f4303298db13e5fad91..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/lama-video-watermark-remover/bin/paper_runfiles/generate_val_test.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/usr/bin/env bash - -# !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst - -# paths to data are valid for mml7 -PLACES_ROOT="/data/inpainting/Places365" -OUT_DIR="/data/inpainting/paper_data/Places365_val_test" - -source "$(dirname $0)/env.sh" - -for datadir in test_large_30k # val_large -do - for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512 - do - "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \ - "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8 - - "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" - done - - for conf in segm_256 segm_512 - do - "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \ - "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2 - - "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" - done -done diff --git a/spaces/fffiloni/lama-video-watermark-remover/masks/readme.md b/spaces/fffiloni/lama-video-watermark-remover/masks/readme.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/flowers-team/SocialAISchool/visualizer.sh b/spaces/flowers-team/SocialAISchool/visualizer.sh deleted file mode 100644 index 49685fd4f28447f13d5c83b48644bf85693e4449..0000000000000000000000000000000000000000 --- a/spaces/flowers-team/SocialAISchool/visualizer.sh +++ /dev/null @@ -1,25 +0,0 @@ -python -m scripts.visualize \ ---model 13-03_VIGIL4_WizardGuide_lang64_mm_baby_short_rec_env_MiniGrid-GoToDoorTalkHardSesameWizardGuideLang64-8x8-v0_multi-modal-babyai11-agent_arch_original_endpool_res_custom-ppo-2_exploration-bonus-params_5_50/0 \ ---episodes 3 --seed=5 --gif graphics/gifs/MH-BabyAI-EB-Ablation --pause 0.2 -python -m scripts.visualize \ ---model 13-03_VIGIL4_WizardGuide_lang64_mm_baby_short_rec_env_MiniGrid-GoToDoorTalkHardSesameWizardGuideLang64-8x8-v0_multi-modal-babyai11-agent_arch_original_endpool_res_custom-ppo-2_exploration-bonus-params_5_50/0 \ ---episodes 3 --seed=5 --gif graphics/gifs/MH-BabyAI-EB-Ablation-Deterministic --pause 0.2 --argmax -python -m scripts.visualize \ ---model 13-03_VIGIL4_WizardTwoGuides_lang64_mm_baby_short_rec_env_MiniGrid-GoToDoorTalkHardSesameNPCGuidesLang64-8x8-v0_multi-modal-babyai11-agent_arch_original_endpool_res_custom-ppo-2_exploration-bonus-params_5_50/0 \ ---episodes 3 --seed=5 --gif graphics/gifs/MH-BabyAI-EB-Original --pause 0.2 -python -m scripts.visualize \ ---model 13-03_VIGIL4_WizardTwoGuides_lang64_mm_baby_short_rec_env_MiniGrid-GoToDoorTalkHardSesameNPCGuidesLang64-8x8-v0_multi-modal-babyai11-agent_arch_original_endpool_res_custom-ppo-2_exploration-bonus-params_5_50/0 \ ---episodes 3 --seed=5 --gif graphics/gifs/MH-BabyAI-EB-Original-Deterministic --pause 0.2 --argmax -# no explo -python -m scripts.visualize \ ---model 13-03_VIGIL4_WizardGuide_lang64_no_explo_mm_baby_short_rec_env_MiniGrid-GoToDoorTalkHardSesameWizardGuideLang64-8x8-v0_multi-modal-babyai11-agent_arch_original_endpool_res_custom-ppo-2/0 \ ---episodes 3 --seed=5 --gif graphics/gifs/MH-BabyAI-Ablation --pause 0.2 -python -m scripts.visualize \ ---model 13-03_VIGIL4_WizardGuide_lang64_no_explo_mm_baby_short_rec_env_MiniGrid-GoToDoorTalkHardSesameWizardGuideLang64-8x8-v0_multi-modal-babyai11-agent_arch_original_endpool_res_custom-ppo-2/0 \ ---episodes 3 --seed=5 --gif graphics/gifs/MH-BabyAI-Ablation-Deterministic --pause 0.2 --argmax -python -m scripts.visualize \ ---model 13-03_VIGIL4_WizardTwoGuides_lang64_no_explo_mm_baby_short_rec_env_MiniGrid-GoToDoorTalkHardSesameNPCGuidesLang64-8x8-v0_multi-modal-babyai11-agent_arch_original_endpool_res_custom-ppo-2/0 \ ---episodes 3 --seed=5 --gif graphics/gifs/MH-BabyAI-Original --pause 0.2 -python -m scripts.visualize \ ---model 13-03_VIGIL4_WizardTwoGuides_lang64_no_explo_mm_baby_short_rec_env_MiniGrid-GoToDoorTalkHardSesameNPCGuidesLang64-8x8-v0_multi-modal-babyai11-agent_arch_original_endpool_res_custom-ppo-2/0 \ ---episodes 3 --seed=5 --gif graphics/gifs/MH-BabyAI-Original-Deterministic --pause 0.2 --argmax diff --git a/spaces/freddyaboulton/gradio-lite-sklearn/README.md b/spaces/freddyaboulton/gradio-lite-sklearn/README.md deleted file mode 100644 index a68162d5e322d9f6948a791739e9ccf27acc26a1..0000000000000000000000000000000000000000 --- a/spaces/freddyaboulton/gradio-lite-sklearn/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Gradio Lite Classify -emoji: 🔥 -colorFrom: purple -colorTo: yellow -sdk: static -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/freshield/ChatGPT-gradio/offline/insert_user.py b/spaces/freshield/ChatGPT-gradio/offline/insert_user.py deleted file mode 100644 index 65be71159f391e6901799021312f0a776ccdb207..0000000000000000000000000000000000000000 --- a/spaces/freshield/ChatGPT-gradio/offline/insert_user.py +++ /dev/null @@ -1,24 +0,0 @@ -# coding=utf-8 -""" -@Author: Freshield -@Contact: yangyufresh@163.com -@File: insert_user.py -@Time: 2023-03-09 22:35 -@Last_update: 2023-03-09 22:35 -@Desc: None -@==============================================@ -@ _____ _ _ _ _ @ -@ | __|___ ___ ___| |_|_|___| |_| | @ -@ | __| _| -_|_ -| | | -_| | . | @ -@ |__| |_| |___|___|_|_|_|___|_|___| @ -@ Freshield @ -@==============================================@ -""" -from lib.MongdbClient import MongodbClient - - -if __name__ == '__main__': - # 离线添加用户 - mongo_client = MongodbClient() - username, password = '', '' - mongo_client.insert_user(username, password) diff --git a/spaces/gaouzief/b/README.md b/spaces/gaouzief/b/README.md deleted file mode 100644 index 97f04e02da8de6687466b45648ab4840e2805ffe..0000000000000000000000000000000000000000 --- a/spaces/gaouzief/b/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: B -emoji: 🐠 -colorFrom: blue -colorTo: red -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/deeplabv3/decoder.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/deeplabv3/decoder.py deleted file mode 100644 index ecc37411a1af6cbb55933a1b0708250d0592fae7..0000000000000000000000000000000000000000 --- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/deeplabv3/decoder.py +++ /dev/null @@ -1,220 +0,0 @@ -""" -BSD 3-Clause License - -Copyright (c) Soumith Chintala 2016, -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" - -import torch -from torch import nn -from torch.nn import functional as F - -__all__ = ["DeepLabV3Decoder"] - - -class DeepLabV3Decoder(nn.Sequential): - def __init__(self, in_channels, out_channels=256, atrous_rates=(12, 24, 36)): - super().__init__( - ASPP(in_channels, out_channels, atrous_rates), - nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False), - nn.BatchNorm2d(out_channels), - nn.ReLU(), - ) - self.out_channels = out_channels - - def forward(self, *features): - return super().forward(features[-1]) - - -class DeepLabV3PlusDecoder(nn.Module): - def __init__( - self, - encoder_channels, - out_channels=256, - atrous_rates=(12, 24, 36), - output_stride=16, - ): - super().__init__() - if output_stride not in {8, 16}: - raise ValueError( - "Output stride should be 8 or 16, got {}.".format(output_stride) - ) - - self.out_channels = out_channels - self.output_stride = output_stride - - self.aspp = nn.Sequential( - ASPP(encoder_channels[-1], out_channels, atrous_rates, separable=True), - SeparableConv2d( - out_channels, out_channels, kernel_size=3, padding=1, bias=False - ), - nn.BatchNorm2d(out_channels), - nn.ReLU(), - ) - - scale_factor = 2 if output_stride == 8 else 4 - self.up = nn.UpsamplingBilinear2d(scale_factor=scale_factor) - - highres_in_channels = encoder_channels[-4] - highres_out_channels = 48 # proposed by authors of paper - self.block1 = nn.Sequential( - nn.Conv2d( - highres_in_channels, highres_out_channels, kernel_size=1, bias=False - ), - nn.BatchNorm2d(highres_out_channels), - nn.ReLU(), - ) - self.block2 = nn.Sequential( - SeparableConv2d( - highres_out_channels + out_channels, - out_channels, - kernel_size=3, - padding=1, - bias=False, - ), - nn.BatchNorm2d(out_channels), - nn.ReLU(), - ) - - def forward(self, *features): - aspp_features = self.aspp(features[-1]) - aspp_features = self.up(aspp_features) - high_res_features = self.block1(features[-4]) - concat_features = torch.cat([aspp_features, high_res_features], dim=1) - fused_features = self.block2(concat_features) - return fused_features - - -class ASPPConv(nn.Sequential): - def __init__(self, in_channels, out_channels, dilation): - super().__init__( - nn.Conv2d( - in_channels, - out_channels, - kernel_size=3, - padding=dilation, - dilation=dilation, - bias=False, - ), - nn.BatchNorm2d(out_channels), - nn.ReLU(), - ) - - -class ASPPSeparableConv(nn.Sequential): - def __init__(self, in_channels, out_channels, dilation): - super().__init__( - SeparableConv2d( - in_channels, - out_channels, - kernel_size=3, - padding=dilation, - dilation=dilation, - bias=False, - ), - nn.BatchNorm2d(out_channels), - nn.ReLU(), - ) - - -class ASPPPooling(nn.Sequential): - def __init__(self, in_channels, out_channels): - super().__init__( - nn.AdaptiveAvgPool2d(1), - nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), - nn.BatchNorm2d(out_channels), - nn.ReLU(), - ) - - def forward(self, x): - size = x.shape[-2:] - for mod in self: - x = mod(x) - return F.interpolate(x, size=size, mode="bilinear", align_corners=False) - - -class ASPP(nn.Module): - def __init__(self, in_channels, out_channels, atrous_rates, separable=False): - super(ASPP, self).__init__() - modules = [] - modules.append( - nn.Sequential( - nn.Conv2d(in_channels, out_channels, 1, bias=False), - nn.BatchNorm2d(out_channels), - nn.ReLU(), - ) - ) - - rate1, rate2, rate3 = tuple(atrous_rates) - ASPPConvModule = ASPPConv if not separable else ASPPSeparableConv - - modules.append(ASPPConvModule(in_channels, out_channels, rate1)) - modules.append(ASPPConvModule(in_channels, out_channels, rate2)) - modules.append(ASPPConvModule(in_channels, out_channels, rate3)) - modules.append(ASPPPooling(in_channels, out_channels)) - - self.convs = nn.ModuleList(modules) - - self.project = nn.Sequential( - nn.Conv2d(5 * out_channels, out_channels, kernel_size=1, bias=False), - nn.BatchNorm2d(out_channels), - nn.ReLU(), - nn.Dropout(0.5), - ) - - def forward(self, x): - res = [] - for conv in self.convs: - res.append(conv(x)) - res = torch.cat(res, dim=1) - return self.project(res) - - -class SeparableConv2d(nn.Sequential): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - bias=True, - ): - dephtwise_conv = nn.Conv2d( - in_channels, - in_channels, - kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=in_channels, - bias=False, - ) - pointwise_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias,) - super().__init__(dephtwise_conv, pointwise_conv) diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/linknet/model.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/linknet/model.py deleted file mode 100644 index b8c3139fdc4db0d5dddfbf292b76c0cc8fccb873..0000000000000000000000000000000000000000 --- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/linknet/model.py +++ /dev/null @@ -1,98 +0,0 @@ -from typing import Optional, Union - -from segmentation_models_pytorch.base import ( - SegmentationHead, - SegmentationModel, - ClassificationHead, -) -from segmentation_models_pytorch.encoders import get_encoder -from .decoder import LinknetDecoder - - -class Linknet(SegmentationModel): - """Linknet_ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* - and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial - resolution (skip connections) which are used by decoder to define accurate segmentation mask. Use *sum* - for fusing decoder blocks with skip connections. - - Note: - This implementation by default has 4 skip connections (original - 3). - - Args: - encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) - to extract features of different spatial resolution - encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features - two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features - with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). - Default is 5 - encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and - other pretrained weights (see table with available weights for each encoder_name) - decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers - is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption. - Available options are **True, False, "inplace"** - in_channels: A number of input channels for the model, default is 3 (RGB images) - classes: A number of classes for output mask (or you can think as a number of channels of output mask) - activation: An activation function to apply after the final convolution layer. - Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, - **callable** and **None**. - Default is **None** - aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build - on top of encoder if **aux_params** is not **None** (default). Supported params: - - classes (int): A number of classes - - pooling (str): One of "max", "avg". Default is "avg" - - dropout (float): Dropout factor in [0, 1) - - activation (str): An activation function to apply "sigmoid"/"softmax" - (could be **None** to return logits) - - Returns: - ``torch.nn.Module``: **Linknet** - - .. _Linknet: - https://arxiv.org/abs/1707.03718 - """ - - def __init__( - self, - encoder_name: str = "resnet34", - encoder_depth: int = 5, - encoder_weights: Optional[str] = "imagenet", - decoder_use_batchnorm: bool = True, - in_channels: int = 3, - classes: int = 1, - activation: Optional[Union[str, callable]] = None, - aux_params: Optional[dict] = None, - ): - super().__init__() - - if encoder_name.startswith("mit_b"): - raise ValueError( - "Encoder `{}` is not supported for Linknet".format(encoder_name) - ) - - self.encoder = get_encoder( - encoder_name, - in_channels=in_channels, - depth=encoder_depth, - weights=encoder_weights, - ) - - self.decoder = LinknetDecoder( - encoder_channels=self.encoder.out_channels, - n_blocks=encoder_depth, - prefinal_channels=32, - use_batchnorm=decoder_use_batchnorm, - ) - - self.segmentation_head = SegmentationHead( - in_channels=32, out_channels=classes, activation=activation, kernel_size=1 - ) - - if aux_params is not None: - self.classification_head = ClassificationHead( - in_channels=self.encoder.out_channels[-1], **aux_params - ) - else: - self.classification_head = None - - self.name = "link-{}".format(encoder_name) - self.initialize() diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Brain Dead Full Movie In Hindi Download [HOT].md b/spaces/gotiQspiryo/whisper-ui/examples/Brain Dead Full Movie In Hindi Download [HOT].md deleted file mode 100644 index eaf97a1adf0161681090e3615128f6ffbfd35307..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Brain Dead Full Movie In Hindi Download [HOT].md +++ /dev/null @@ -1,10 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/gradio/sine_curve/run.py b/spaces/gradio/sine_curve/run.py deleted file mode 100644 index 4f0fc7ce71a6f1edec2010ab1f65424a1567f009..0000000000000000000000000000000000000000 --- a/spaces/gradio/sine_curve/run.py +++ /dev/null @@ -1,33 +0,0 @@ -import math -import gradio as gr -import plotly.express as px -import numpy as np - - -plot_end = 2 * math.pi - - -def get_plot(period=1): - global plot_end - x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02) - y = np.sin(2*math.pi*period * x) - fig = px.line(x=x, y=y) - plot_end += 2 * math.pi - if plot_end > 1000: - plot_end = 2 * math.pi - return fig - - -with gr.Blocks() as demo: - with gr.Row(): - with gr.Column(): - gr.Markdown("Change the value of the slider to automatically update the plot") - period = gr.Slider(label="Period of plot", value=1, minimum=0, maximum=10, step=1) - plot = gr.Plot(label="Plot (updates every half second)") - - dep = demo.load(get_plot, None, plot, every=1) - period.change(get_plot, period, plot, every=1, cancels=[dep]) - - -if __name__ == "__main__": - demo.queue().launch() diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/samples/__init__.py b/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/samples/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/haakohu/deep_privacy2_face/dp2/data/datasets/fdf.py b/spaces/haakohu/deep_privacy2_face/dp2/data/datasets/fdf.py deleted file mode 100644 index 23f68a52d4fb50143b2ef6720e126991b2981afc..0000000000000000000000000000000000000000 --- a/spaces/haakohu/deep_privacy2_face/dp2/data/datasets/fdf.py +++ /dev/null @@ -1,128 +0,0 @@ -import pathlib -from typing import Tuple -import numpy as np -import torch -import pathlib -try: - import pyspng - PYSPNG_IMPORTED = True -except ImportError: - PYSPNG_IMPORTED = False - print("Could not load pyspng. Defaulting to pillow image backend.") - from PIL import Image -from tops import logger - - -class FDFDataset: - - def __init__(self, - dirpath, - imsize: Tuple[int], - load_keypoints: bool, - transform): - dirpath = pathlib.Path(dirpath) - self.dirpath = dirpath - self.transform = transform - self.imsize = imsize[0] - self.load_keypoints = load_keypoints - assert self.dirpath.is_dir(),\ - f"Did not find dataset at: {dirpath}" - image_dir = self.dirpath.joinpath("images", str(self.imsize)) - self.image_paths = list(image_dir.glob("*.png")) - assert len(self.image_paths) > 0,\ - f"Did not find images in: {image_dir}" - self.image_paths.sort(key=lambda x: int(x.stem)) - self.landmarks = np.load(self.dirpath.joinpath("landmarks.npy")).reshape(-1, 7, 2).astype(np.float32) - - self.bounding_boxes = torch.load(self.dirpath.joinpath("bounding_box", f"{self.imsize}.torch")) - assert len(self.image_paths) == len(self.bounding_boxes) - assert len(self.image_paths) == len(self.landmarks) - logger.log( - f"Dataset loaded from: {dirpath}. Number of samples:{len(self)}, imsize={imsize}") - - def get_mask(self, idx): - mask = torch.ones((1, self.imsize, self.imsize), dtype=torch.bool) - bounding_box = self.bounding_boxes[idx] - x0, y0, x1, y1 = bounding_box - mask[:, y0:y1, x0:x1] = 0 - return mask - - def __len__(self): - return len(self.image_paths) - - def __getitem__(self, index): - impath = self.image_paths[index] - if PYSPNG_IMPORTED: - with open(impath, "rb") as fp: - im = pyspng.load(fp.read()) - else: - with Image.open(impath) as fp: - im = np.array(fp) - im = torch.from_numpy(np.rollaxis(im, -1, 0)) - masks = self.get_mask(index) - landmark = self.landmarks[index] - batch = { - "img": im, - "mask": masks, - } - if self.load_keypoints: - batch["keypoints"] = landmark - if self.transform is None: - return batch - return self.transform(batch) - - -class FDF256Dataset: - - def __init__(self, - dirpath, - load_keypoints: bool, - transform): - dirpath = pathlib.Path(dirpath) - self.dirpath = dirpath - self.transform = transform - self.load_keypoints = load_keypoints - assert self.dirpath.is_dir(),\ - f"Did not find dataset at: {dirpath}" - image_dir = self.dirpath.joinpath("images") - self.image_paths = list(image_dir.glob("*.png")) - assert len(self.image_paths) > 0,\ - f"Did not find images in: {image_dir}" - self.image_paths.sort(key=lambda x: int(x.stem)) - self.landmarks = np.load(self.dirpath.joinpath("landmarks.npy")).reshape(-1, 7, 2).astype(np.float32) - self.bounding_boxes = torch.from_numpy(np.load(self.dirpath.joinpath("bounding_box.npy"))) - assert len(self.image_paths) == len(self.bounding_boxes) - assert len(self.image_paths) == len(self.landmarks) - logger.log( - f"Dataset loaded from: {dirpath}. Number of samples:{len(self)}") - - def get_mask(self, idx): - mask = torch.ones((1, 256, 256), dtype=torch.bool) - bounding_box = self.bounding_boxes[idx] - x0, y0, x1, y1 = bounding_box - mask[:, y0:y1, x0:x1] = 0 - return mask - - def __len__(self): - return len(self.image_paths) - - def __getitem__(self, index): - impath = self.image_paths[index] - if PYSPNG_IMPORTED: - with open(impath, "rb") as fp: - im = pyspng.load(fp.read()) - else: - with Image.open(impath) as fp: - im = np.array(fp) - im = torch.from_numpy(np.rollaxis(im, -1, 0)) - masks = self.get_mask(index) - landmark = self.landmarks[index] - batch = { - "img": im, - "mask": masks, - } - if self.load_keypoints: - batch["keypoints"] = landmark - if self.transform is None: - return batch - return self.transform(batch) diff --git a/spaces/hamelcubsfan/AutoGPT/autogpt/commands/web_requests.py b/spaces/hamelcubsfan/AutoGPT/autogpt/commands/web_requests.py deleted file mode 100644 index 406338f46fc7b2381e0b1634c628b123ef20b685..0000000000000000000000000000000000000000 --- a/spaces/hamelcubsfan/AutoGPT/autogpt/commands/web_requests.py +++ /dev/null @@ -1,190 +0,0 @@ -"""Browse a webpage and summarize it using the LLM model""" -from __future__ import annotations - -from urllib.parse import urljoin, urlparse - -import requests -from bs4 import BeautifulSoup -from requests import Response -from requests.compat import urljoin - -from autogpt.config import Config -from autogpt.memory import get_memory -from autogpt.processing.html import extract_hyperlinks, format_hyperlinks - -CFG = Config() -memory = get_memory(CFG) - -session = requests.Session() -session.headers.update({"User-Agent": CFG.user_agent}) - - -def is_valid_url(url: str) -> bool: - """Check if the URL is valid - - Args: - url (str): The URL to check - - Returns: - bool: True if the URL is valid, False otherwise - """ - try: - result = urlparse(url) - return all([result.scheme, result.netloc]) - except ValueError: - return False - - -def sanitize_url(url: str) -> str: - """Sanitize the URL - - Args: - url (str): The URL to sanitize - - Returns: - str: The sanitized URL - """ - return urljoin(url, urlparse(url).path) - - -def check_local_file_access(url: str) -> bool: - """Check if the URL is a local file - - Args: - url (str): The URL to check - - Returns: - bool: True if the URL is a local file, False otherwise - """ - local_prefixes = [ - "file:///", - "file://localhost/", - "file://localhost", - "http://localhost", - "http://localhost/", - "https://localhost", - "https://localhost/", - "http://2130706433", - "http://2130706433/", - "https://2130706433", - "https://2130706433/", - "http://127.0.0.1/", - "http://127.0.0.1", - "https://127.0.0.1/", - "https://127.0.0.1", - "https://0.0.0.0/", - "https://0.0.0.0", - "http://0.0.0.0/", - "http://0.0.0.0", - "http://0000", - "http://0000/", - "https://0000", - "https://0000/", - ] - return any(url.startswith(prefix) for prefix in local_prefixes) - - -def get_response( - url: str, timeout: int = 10 -) -> tuple[None, str] | tuple[Response, None]: - """Get the response from a URL - - Args: - url (str): The URL to get the response from - timeout (int): The timeout for the HTTP request - - Returns: - tuple[None, str] | tuple[Response, None]: The response and error message - - Raises: - ValueError: If the URL is invalid - requests.exceptions.RequestException: If the HTTP request fails - """ - try: - # Restrict access to local files - if check_local_file_access(url): - raise ValueError("Access to local files is restricted") - - # Most basic check if the URL is valid: - if not url.startswith("http://") and not url.startswith("https://"): - raise ValueError("Invalid URL format") - - sanitized_url = sanitize_url(url) - - response = session.get(sanitized_url, timeout=timeout) - - # Check if the response contains an HTTP error - if response.status_code >= 400: - return None, f"Error: HTTP {str(response.status_code)} error" - - return response, None - except ValueError as ve: - # Handle invalid URL format - return None, f"Error: {str(ve)}" - - except requests.exceptions.RequestException as re: - # Handle exceptions related to the HTTP request - # (e.g., connection errors, timeouts, etc.) - return None, f"Error: {str(re)}" - - -def scrape_text(url: str) -> str: - """Scrape text from a webpage - - Args: - url (str): The URL to scrape text from - - Returns: - str: The scraped text - """ - response, error_message = get_response(url) - if error_message: - return error_message - if not response: - return "Error: Could not get response" - - soup = BeautifulSoup(response.text, "html.parser") - - for script in soup(["script", "style"]): - script.extract() - - text = soup.get_text() - lines = (line.strip() for line in text.splitlines()) - chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) - text = "\n".join(chunk for chunk in chunks if chunk) - - return text - - -def scrape_links(url: str) -> str | list[str]: - """Scrape links from a webpage - - Args: - url (str): The URL to scrape links from - - Returns: - str | list[str]: The scraped links - """ - response, error_message = get_response(url) - if error_message: - return error_message - if not response: - return "Error: Could not get response" - soup = BeautifulSoup(response.text, "html.parser") - - for script in soup(["script", "style"]): - script.extract() - - hyperlinks = extract_hyperlinks(soup, url) - - return format_hyperlinks(hyperlinks) - - -def create_message(chunk, question): - """Create a message for the user to summarize a chunk of text""" - return { - "role": "user", - "content": f'"""{chunk}""" Using the above text, answer the following' - f' question: "{question}" -- if the question cannot be answered using the' - " text, summarize the text.", - } diff --git a/spaces/hamelcubsfan/AutoGPT/autogpt/memory/base.py b/spaces/hamelcubsfan/AutoGPT/autogpt/memory/base.py deleted file mode 100644 index 691e2299c4caa5c2e9af5b2436727834f3cc6c67..0000000000000000000000000000000000000000 --- a/spaces/hamelcubsfan/AutoGPT/autogpt/memory/base.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Base class for memory providers.""" -import abc - -import openai - -from autogpt.config import AbstractSingleton, Config - -cfg = Config() - - -def get_ada_embedding(text): - text = text.replace("\n", " ") - if cfg.use_azure: - return openai.Embedding.create( - input=[text], - engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"), - )["data"][0]["embedding"] - else: - return openai.Embedding.create(input=[text], model="text-embedding-ada-002")[ - "data" - ][0]["embedding"] - - -class MemoryProviderSingleton(AbstractSingleton): - @abc.abstractmethod - def add(self, data): - pass - - @abc.abstractmethod - def get(self, data): - pass - - @abc.abstractmethod - def clear(self): - pass - - @abc.abstractmethod - def get_relevant(self, data, num_relevant=5): - pass - - @abc.abstractmethod - def get_stats(self): - pass diff --git a/spaces/hands012/gpt-academic/docs/README.md.Portuguese.md b/spaces/hands012/gpt-academic/docs/README.md.Portuguese.md deleted file mode 100644 index 816ced1993b05c84ec8a3cd84c42adf1c9757cd2..0000000000000000000000000000000000000000 --- a/spaces/hands012/gpt-academic/docs/README.md.Portuguese.md +++ /dev/null @@ -1,320 +0,0 @@ -> **Nota** -> -> Ao instalar as dependências, por favor, selecione rigorosamente as versões **especificadas** no arquivo requirements.txt. -> -> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/` -> - -#
Otimização acadêmica GPT (GPT Academic)
-
-**Se você gostou deste projeto, por favor dê um Star. Se você criou atalhos acadêmicos mais úteis ou plugins funcionais, sinta-se livre para abrir uma issue ou pull request. Nós também temos um README em [Inglês|](README_EN.md)[日本語|](README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](README_RS.md)[Français](README_FR.md) traduzidos por este próprio projeto.
-Para traduzir este projeto para qualquer idioma com o GPT, leia e execute [`multi_language.py`](multi_language.py) (experimental).
-
-> **Nota**
->
-> 1. Por favor, preste atenção que somente os plugins de funções (botões) com a cor **vermelha** podem ler arquivos. Alguns plugins estão localizados no **menu suspenso** na área de plugins. Além disso, nós damos as boas-vindas com a **maior prioridade** e gerenciamos quaisquer novos plugins PR!
->
-> 2. As funções de cada arquivo neste projeto são detalhadas em [`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), auto-análises do projeto geradas pelo GPT também estão podem ser chamadas a qualquer momento ao clicar nos plugins relacionados. As perguntas frequentes estão resumidas no [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Instruções de Instalação](#installation).
->
-> 3. Este projeto é compatível com e incentiva o uso de modelos de linguagem nacionais, como chatglm e RWKV, Pangolin, etc. Suporta a coexistência de várias chaves de API e pode ser preenchido no arquivo de configuração como `API_KEY="openai-key1,openai-key2,api2d-key3"`. Quando precisar alterar temporariamente o `API_KEY`, basta digitar o `API_KEY` temporário na área de entrada e pressionar Enter para que ele entre em vigor.
-
-Funcionalidade | Descrição
---- | ---
-Um clique de polimento | Suporte a um clique polimento, um clique encontrar erros de gramática no artigo
-Tradução chinês-inglês de um clique | Tradução chinês-inglês de um clique
-Explicação de código de um único clique | Exibir código, explicar código, gerar código, adicionar comentários ao código
-[Teclas de atalho personalizadas](https://www.bilibili.com/video/BV14s4y1E7jN) | Suporte a atalhos personalizados
-Projeto modular | Suporte para poderosos plugins[de função personalizada](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions), os plugins suportam[hot-reload](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)
-[Análise automática do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função][um clique para entender](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) o código-fonte do projeto
-[Análise do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função] Um clique pode analisar a árvore de projetos do Python/C/C++/Java/Lua/...
-Leitura de artigos, [tradução](https://www.bilibili.com/video/BV1KT411x7Wn) de artigos | [Plugin de função] um clique para interpretar o resumo de artigos LaTeX/PDF e gerar resumo
-Tradução completa LATEX, polimento|[Plugin de função] Uma clique para traduzir ou polir um artigo LATEX
-Geração em lote de comentários | [Plugin de função] Um clique gera comentários de função em lote
-[Tradução chinês-inglês](https://www.bilibili.com/video/BV1yo4y157jV/) markdown | [Plugin de função] Você viu o README em 5 linguagens acima?
-Relatório de análise de chat | [Plugin de função] Gera automaticamente um resumo após a execução
-[Funcionalidade de tradução de artigos completos em PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin de função] Extrai o título e o resumo do artigo PDF e traduz o artigo completo (multithread)
-Assistente arXiv | [Plugin de função] Insira o url do artigo arXiv para traduzir o resumo + baixar PDF
-Assistente de integração acadêmica do Google | [Plugin de função] Dê qualquer URL de página de pesquisa acadêmica do Google e deixe o GPT escrever[trabalhos relacionados](https://www.bilibili.com/video/BV1GP411U7Az/)
-Agregação de informações da Internet + GPT | [Plugin de função] Um clique para obter informações do GPT através da Internet e depois responde a perguntas para informações nunca ficarem desatualizadas
-Exibição de fórmulas/imagem/tabela | Pode exibir simultaneamente a forma de renderização e[TEX] das fórmulas, suporte a fórmulas e realce de código
-Suporte de plugins de várias linhas | Suporte a várias chamadas em linha do chatgpt, um clique para processamento[de massa de texto](https://www.bilibili.com/video/BV1FT411H7c5/) ou programa
-Tema gradio escuro | Adicione ``` /?__theme=dark``` ao final da url do navegador para ativar o tema escuro
-[Suporte para vários modelos LLM](https://www.bilibili.com/video/BV1wT411p7yf), suporte para a nova interface API2D | A sensação de ser atendido simultaneamente por GPT3.5, GPT4, [Chatglm THU](https://github.com/THUDM/ChatGLM-6B), [Moss Fudan](https://github.com/OpenLMLab/MOSS) deve ser ótima, certo?
-Mais modelos LLM incorporados, suporte para a implantação[huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Adicione interface Newbing (New Bing), suporte [JittorLLMs](https://github.com/Jittor/JittorLLMs) THU Introdução ao suporte do LLaMA, RWKV e Pan Gu Alpha
-Mais recursos novos mostrados (geração de imagens, etc.) ... | Consulte o final deste documento ...
-
-
-
-- Nova interface (Modifique a opção LAYOUT em `config.py` para alternar entre o layout esquerdo/direito e o layout superior/inferior)
-
-
-
- All buttons are dynamically generated by reading functional.py, and you can add custom functions at will, liberating the clipboard
-
-
-
-
-
-
-- Proofreading/errors correction
-
-
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-
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-
-- If the output contains formulas, it will be displayed in both tex and rendering format at the same time, which is convenient for copying and reading
-
-
-
-
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-
-
-- Don't want to read the project code? Just show the whole project to chatgpt
-
-
-
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-
-- Mix the use of multiple large language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-
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-
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-
-
-
----
-# Instalação
-## Installation-Method 1: Run directly (Windows, Linux or MacOS)
-
-1. Download the project
-
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Configure the API KEY
-
-In `config.py`, configure API KEY and other settings, [Special Network Environment Settings] (https://github.com/binary-husky/gpt_academic/issues/1).
-
-(P.S. When the program runs, it will first check whether there is a private configuration file named `config_private.py`, and use the configuration in it to cover the configuration with the same name in `config.py`. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py`, and transfer (copy) the configuration in `config.py` to `config_private.py`. `config_private.py` is not controlled by git and can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`. The writing format of environment variables is referenced to the `docker-compose` file. Reading priority: `environment variable` > `config_private.py` > `config.py`)
-
-
-3. Install dependencies
-
-```sh
-# (Option I: for those familiar with python)(python version is 3.9 or above, the newer the better), note: use the official pip source or the Alibaba pip source. Temporary solution for changing source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-python -m pip install -r requirements.txt
-
-# (Option II: for those who are unfamiliar with python) use anaconda, the steps are also similar (https://www.bilibili.com/video/BV1rc411W7Dr):
-conda create -n gptac_venv python=3.11 # create anaconda environment
-conda activate gptac_venv # activate anaconda environment
-python -m pip install -r requirements.txt # This step is the same as the pip installation step
-```
-
-
-If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, click to expand here
-- -[Optional Step] If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, you need to install more dependencies (prerequisite: familiar with Python + used Pytorch + computer configuration is strong): -```sh -# 【Optional Step I】support Tsinghua ChatGLM。Tsinghua ChatGLM Note: If you encounter a "Call ChatGLM fails cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installed is torch+cpu version, and using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True) -python -m pip install -r request_llm/requirements_chatglm.txt - -# 【Optional Step II】support Fudan MOSS -python -m pip install -r request_llm/requirements_moss.txt -git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note: When executing this line of code, you must be in the project root path - -# 【Optional Step III】Make sure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports docker solutions): -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"] -``` - -
-
-
-
-
-2. Personalizar plug-ins de função
-
-Escreva plug-ins de função poderosos para executar tarefas que você deseja e não pensava possível.
-A dificuldade geral de escrever e depurar plug-ins neste projeto é baixa e, se você tem algum conhecimento básico de python, pode implementar suas próprias funções sobre o modelo que fornecemos.
-Para mais detalhes, consulte o [Guia do plug-in de função.](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).
-
----
-# Última atualização
-## Novas funções dinâmicas.1. Função de salvamento de diálogo. Ao chamar o plug-in de função "Salvar diálogo atual", é possível salvar o diálogo atual em um arquivo html legível e reversível. Além disso, ao chamar o plug-in de função "Carregar arquivo de histórico de diálogo" no menu suspenso da área de plug-in, é possível restaurar uma conversa anterior. Dica: clicar em "Carregar arquivo de histórico de diálogo" sem especificar um arquivo permite visualizar o cache do arquivo html de histórico. Clicar em "Excluir todo o registro de histórico de diálogo local" permite excluir todo o cache de arquivo html.
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-2. Geração de relatório. A maioria dos plug-ins gera um relatório de trabalho após a conclusão da execução.
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-
-
-3. Design modular de funcionalidades, com interfaces simples, mas suporte a recursos poderosos
-
-
-
-
-
-
-
-
-4. Este é um projeto de código aberto que é capaz de "auto-traduzir-se".
-
-
-
-
-
-
-5. A tradução de outros projetos de código aberto é simples.
-
-
-
-
-
-
-
-
-
-
-6. Recursos decorativos para o [live2d](https://github.com/fghrsh/live2d_demo) (desativados por padrão, é necessário modificar o arquivo `config.py`)
-
-
-
-
-
-7. Suporte ao modelo de linguagem MOSS
-
-
-
-
-
-8. Geração de imagens pelo OpenAI
-
-
-
-
-
-9. Análise e resumo de áudio pelo OpenAI
-
-
-
-
-10. Revisão e correção de erros de texto em Latex.
-
-
-
-
-## Versão:
-- Versão 3.5(Todo): Usar linguagem natural para chamar todas as funções do projeto (prioridade alta)
-- Versão 3.4(Todo): Melhorar o suporte à multithread para o chatglm local
-- Versão 3.3: +Funções integradas de internet
-- Versão 3.2: Suporte a mais interfaces de parâmetros de plug-in (função de salvar diálogo, interpretação de códigos de várias linguagens, perguntas de combinações LLM arbitrárias ao mesmo tempo)
-- Versão 3.1: Suporte a perguntas a vários modelos de gpt simultaneamente! Suporte para api2d e balanceamento de carga para várias chaves api
-- Versão 3.0: Suporte ao chatglm e outros LLMs de pequeno porte
-- Versão 2.6: Refatoração da estrutura de plug-in, melhoria da interatividade e adição de mais plug-ins
-- Versão 2.5: Autoatualização, resolvendo problemas de token de texto excessivamente longo e estouro ao compilar grandes projetos
-- Versão 2.4: (1) Adição de funcionalidade de tradução de texto completo em PDF; (2) Adição de funcionalidade de mudança de posição da área de entrada; (3) Adição de opção de layout vertical; (4) Otimização de plug-ins de multithread.
-- Versão 2.3: Melhoria da interatividade de multithread
-- Versão 2.2: Suporte à recarga a quente de plug-ins
-- Versão 2.1: Layout dobrável
-- Versão 2.0: Introdução de plug-ins de função modular
-- Versão 1.0: Funcionalidades básicasgpt_academic desenvolvedores QQ grupo-2: 610599535
-
-- Problemas conhecidos
- - Extensões de tradução de alguns navegadores podem interferir na execução do front-end deste software
- - Uma versão muito alta ou muito baixa do Gradio pode causar vários erros
-
-## Referências e Aprendizado
-
-```
-Foi feita referência a muitos projetos excelentes em código, principalmente:
-
-# Projeto1: ChatGLM-6B da Tsinghua:
-https://github.com/THUDM/ChatGLM-6B
-
-# Projeto2: JittorLLMs da Tsinghua:
-https://github.com/Jittor/JittorLLMs
-
-# Projeto3: Edge-GPT:
-https://github.com/acheong08/EdgeGPT
-
-# Projeto4: ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Projeto5: ChatPaper:
-https://github.com/kaixindelele/ChatPaper
-
-# Mais:
-https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
-```
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_export_caffe2.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_export_caffe2.py
deleted file mode 100644
index ad989c4a3d11e6675d26ae2690f06d2ffe30d44c..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_export_caffe2.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# -*- coding: utf-8 -*-
-
-import copy
-import numpy as np
-import os
-import tempfile
-import unittest
-import cv2
-import torch
-from fvcore.common.file_io import PathManager
-
-from detectron2 import model_zoo
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import DatasetCatalog
-from detectron2.modeling import build_model
-from detectron2.utils.logger import setup_logger
-
-
-@unittest.skipIf(os.environ.get("CIRCLECI"), "Require COCO data and model zoo.")
-class TestCaffe2Export(unittest.TestCase):
- def setUp(self):
- setup_logger()
-
- def _test_model(self, config_path, device="cpu"):
- # requires extra dependencies
- from detectron2.export import Caffe2Model, add_export_config, export_caffe2_model
-
- cfg = get_cfg()
- cfg.merge_from_file(model_zoo.get_config_file(config_path))
- cfg = add_export_config(cfg)
- cfg.MODEL.DEVICE = device
-
- model = build_model(cfg)
- DetectionCheckpointer(model).load(model_zoo.get_checkpoint_url(config_path))
-
- inputs = [{"image": self._get_test_image()}]
- c2_model = export_caffe2_model(cfg, model, copy.deepcopy(inputs))
-
- with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d:
- c2_model.save_protobuf(d)
- c2_model.save_graph(os.path.join(d, "test.svg"), inputs=copy.deepcopy(inputs))
- c2_model = Caffe2Model.load_protobuf(d)
- c2_model(inputs)[0]["instances"]
-
- def _get_test_image(self):
- try:
- file_name = DatasetCatalog.get("coco_2017_train")[0]["file_name"]
- assert PathManager.exists(file_name)
- except Exception:
- self.skipTest("COCO dataset not available.")
-
- with PathManager.open(file_name, "rb") as f:
- buf = f.read()
- img = cv2.imdecode(np.frombuffer(buf, dtype=np.uint8), cv2.IMREAD_COLOR)
- assert img is not None, file_name
- return torch.from_numpy(img.transpose(2, 0, 1))
-
- def testMaskRCNN(self):
- self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
-
- @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
- def testMaskRCNNGPU(self):
- self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", device="cuda")
-
- def testRetinaNet(self):
- self._test_model("COCO-Detection/retinanet_R_50_FPN_3x.yaml")
-
- def testPanopticFPN(self):
- self._test_model("COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml")
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/finetune_net.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/finetune_net.py
deleted file mode 100644
index 3e521859f70b89da747b324375a5110d8663fdc7..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/finetune_net.py
+++ /dev/null
@@ -1,183 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Detection Training Script.
-
-This scripts reads a given config file and runs the training or evaluation.
-It is an entry point that is made to train standard models in detectron2.
-
-In order to let one script support training of many models,
-this script contains logic that are specific to these built-in models and therefore
-may not be suitable for your own project.
-For example, your research project perhaps only needs a single "evaluator".
-
-Therefore, we recommend you to use detectron2 as an library and take
-this file as an example of how to use the library.
-You may want to write your own script with your data and other customizations.
-"""
-
-import logging
-import os
-from collections import OrderedDict
-import torch
-
-import detectron2.utils.comm as comm
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import MetadataCatalog
-from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch
-from detectron2.evaluation import (
- CityscapesInstanceEvaluator,
- CityscapesSemSegEvaluator,
- COCOEvaluator,
- COCOPanopticEvaluator,
- DatasetEvaluators,
- LVISEvaluator,
- PascalVOCDetectionEvaluator,
- SemSegEvaluator,
- verify_results,
-)
-from detectron2.modeling import GeneralizedRCNNWithTTA
-
-# Register Custom Dataset
-from detectron2.data.datasets import register_coco_instances
-
-register_coco_instances("CIHP_train", {}, "../../data/msrcnn_finetune_annotations/CIHP_train.json",
- "../../data/instance-level_human_parsing/Training/Images")
-register_coco_instances("CIHP_val", {}, "../../data/msrcnn_finetune_annotations/CIHP_val.json",
- "../../data/instance-level_human_parsing/Validation/Images")
-register_coco_instances("demo_train", {}, "../../demo/annotations/demo_train.json",
- "../../demo/img")
-register_coco_instances("demo_val", {}, "../../demo/annotations/demo_val.json",
- "../../demo/img")
-
-
-class Trainer(DefaultTrainer):
- """
- We use the "DefaultTrainer" which contains pre-defined default logic for
- standard training workflow. They may not work for you, especially if you
- are working on a new research project. In that case you can use the cleaner
- "SimpleTrainer", or write your own training loop. You can use
- "tools/plain_train_net.py" as an example.
- """
-
- @classmethod
- def build_evaluator(cls, cfg, dataset_name, output_folder=None):
- """
- Create evaluator(s) for a given dataset.
- This uses the special metadata "evaluator_type" associated with each builtin dataset.
- For your own dataset, you can simply create an evaluator manually in your
- script and do not have to worry about the hacky if-else logic here.
- """
- if output_folder is None:
- output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
- evaluator_list = []
- evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
- if evaluator_type in ["sem_seg", "coco_panoptic_seg"]:
- evaluator_list.append(
- SemSegEvaluator(
- dataset_name,
- distributed=True,
- num_classes=cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
- ignore_label=cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
- output_dir=output_folder,
- )
- )
- if evaluator_type in ["coco", "coco_panoptic_seg"]:
- evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
- if evaluator_type == "coco_panoptic_seg":
- evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
- if evaluator_type == "cityscapes_instance":
- assert (
- torch.cuda.device_count() >= comm.get_rank()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- return CityscapesInstanceEvaluator(dataset_name)
- if evaluator_type == "cityscapes_sem_seg":
- assert (
- torch.cuda.device_count() >= comm.get_rank()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- return CityscapesSemSegEvaluator(dataset_name)
- elif evaluator_type == "pascal_voc":
- return PascalVOCDetectionEvaluator(dataset_name)
- elif evaluator_type == "lvis":
- return LVISEvaluator(dataset_name, cfg, True, output_folder)
- if len(evaluator_list) == 0:
- raise NotImplementedError(
- "no Evaluator for the dataset {} with the type {}".format(
- dataset_name, evaluator_type
- )
- )
- elif len(evaluator_list) == 1:
- return evaluator_list[0]
- return DatasetEvaluators(evaluator_list)
-
- @classmethod
- def test_with_TTA(cls, cfg, model):
- logger = logging.getLogger("detectron2.trainer")
- # In the end of training, run an evaluation with TTA
- # Only support some R-CNN models.
- logger.info("Running inference with test-time augmentation ...")
- model = GeneralizedRCNNWithTTA(cfg, model)
- evaluators = [
- cls.build_evaluator(
- cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
- )
- for name in cfg.DATASETS.TEST
- ]
- res = cls.test(cfg, model, evaluators)
- res = OrderedDict({k + "_TTA": v for k, v in res.items()})
- return res
-
-
-def setup(args):
- """
- Create configs and perform basic setups.
- """
- cfg = get_cfg()
- cfg.merge_from_file(args.config_file)
- cfg.merge_from_list(args.opts)
- cfg.freeze()
- default_setup(cfg, args)
- return cfg
-
-
-def main(args):
- cfg = setup(args)
-
- if args.eval_only:
- model = Trainer.build_model(cfg)
- DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
- cfg.MODEL.WEIGHTS, resume=args.resume
- )
- res = Trainer.test(cfg, model)
- if cfg.TEST.AUG.ENABLED:
- res.update(Trainer.test_with_TTA(cfg, model))
- if comm.is_main_process():
- verify_results(cfg, res)
- return res
-
- """
- If you'd like to do anything fancier than the standard training logic,
- consider writing your own training loop (see plain_train_net.py) or
- subclassing the trainer.
- """
- trainer = Trainer(cfg)
- trainer.resume_or_load(resume=False)
- if cfg.TEST.AUG.ENABLED:
- trainer.register_hooks(
- [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))]
- )
- return trainer.train()
-
-
-if __name__ == "__main__":
- args = default_argument_parser().parse_args()
- print("Command Line Args:", args)
- launch(
- main,
- args.num_gpus,
- num_machines=args.num_machines,
- machine_rank=args.machine_rank,
- dist_url=args.dist_url,
- args=(args,),
- )
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/utils/lovasz_softmax.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/utils/lovasz_softmax.py
deleted file mode 100644
index b6e444f684c0d9bda9d7c2d54a4e79fac0ddf081..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/utils/lovasz_softmax.py
+++ /dev/null
@@ -1,279 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-"""
-@Author : Peike Li
-@Contact : peike.li@yahoo.com
-@File : lovasz_softmax.py
-@Time : 8/30/19 7:12 PM
-@Desc : Lovasz-Softmax and Jaccard hinge loss in PyTorch
- Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
-@License : This source code is licensed under the license found in the
- LICENSE file in the root directory of this source tree.
-"""
-
-from __future__ import print_function, division
-
-import torch
-from torch.autograd import Variable
-import torch.nn.functional as F
-import numpy as np
-from torch import nn
-
-try:
- from itertools import ifilterfalse
-except ImportError: # py3k
- from itertools import filterfalse as ifilterfalse
-
-
-def lovasz_grad(gt_sorted):
- """
- Computes gradient of the Lovasz extension w.r.t sorted errors
- See Alg. 1 in paper
- """
- p = len(gt_sorted)
- gts = gt_sorted.sum()
- intersection = gts - gt_sorted.float().cumsum(0)
- union = gts + (1 - gt_sorted).float().cumsum(0)
- jaccard = 1. - intersection / union
- if p > 1: # cover 1-pixel case
- jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
- return jaccard
-
-
-def iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True):
- """
- IoU for foreground class
- binary: 1 foreground, 0 background
- """
- if not per_image:
- preds, labels = (preds,), (labels,)
- ious = []
- for pred, label in zip(preds, labels):
- intersection = ((label == 1) & (pred == 1)).sum()
- union = ((label == 1) | ((pred == 1) & (label != ignore))).sum()
- if not union:
- iou = EMPTY
- else:
- iou = float(intersection) / float(union)
- ious.append(iou)
- iou = mean(ious) # mean accross images if per_image
- return 100 * iou
-
-
-def iou(preds, labels, C, EMPTY=1., ignore=None, per_image=False):
- """
- Array of IoU for each (non ignored) class
- """
- if not per_image:
- preds, labels = (preds,), (labels,)
- ious = []
- for pred, label in zip(preds, labels):
- iou = []
- for i in range(C):
- if i != ignore: # The ignored label is sometimes among predicted classes (ENet - CityScapes)
- intersection = ((label == i) & (pred == i)).sum()
- union = ((label == i) | ((pred == i) & (label != ignore))).sum()
- if not union:
- iou.append(EMPTY)
- else:
- iou.append(float(intersection) / float(union))
- ious.append(iou)
- ious = [mean(iou) for iou in zip(*ious)] # mean accross images if per_image
- return 100 * np.array(ious)
-
-
-# --------------------------- BINARY LOSSES ---------------------------
-
-
-def lovasz_hinge(logits, labels, per_image=True, ignore=None):
- """
- Binary Lovasz hinge loss
- logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
- labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
- per_image: compute the loss per image instead of per batch
- ignore: void class id
- """
- if per_image:
- loss = mean(lovasz_hinge_flat(*flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore))
- for log, lab in zip(logits, labels))
- else:
- loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels, ignore))
- return loss
-
-
-def lovasz_hinge_flat(logits, labels):
- """
- Binary Lovasz hinge loss
- logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
- labels: [P] Tensor, binary ground truth labels (0 or 1)
- ignore: label to ignore
- """
- if len(labels) == 0:
- # only void pixels, the gradients should be 0
- return logits.sum() * 0.
- signs = 2. * labels.float() - 1.
- errors = (1. - logits * Variable(signs))
- errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
- perm = perm.data
- gt_sorted = labels[perm]
- grad = lovasz_grad(gt_sorted)
- loss = torch.dot(F.relu(errors_sorted), Variable(grad))
- return loss
-
-
-def flatten_binary_scores(scores, labels, ignore=None):
- """
- Flattens predictions in the batch (binary case)
- Remove labels equal to 'ignore'
- """
- scores = scores.view(-1)
- labels = labels.view(-1)
- if ignore is None:
- return scores, labels
- valid = (labels != ignore)
- vscores = scores[valid]
- vlabels = labels[valid]
- return vscores, vlabels
-
-
-class StableBCELoss(torch.nn.modules.Module):
- def __init__(self):
- super(StableBCELoss, self).__init__()
-
- def forward(self, input, target):
- neg_abs = - input.abs()
- loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
- return loss.mean()
-
-
-def binary_xloss(logits, labels, ignore=None):
- """
- Binary Cross entropy loss
- logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
- labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
- ignore: void class id
- """
- logits, labels = flatten_binary_scores(logits, labels, ignore)
- loss = StableBCELoss()(logits, Variable(labels.float()))
- return loss
-
-
-# --------------------------- MULTICLASS LOSSES ---------------------------
-
-
-def lovasz_softmax(probas, labels, classes='present', per_image=False, ignore=255, weighted=None):
- """
- Multi-class Lovasz-Softmax loss
- probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1).
- Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
- labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
- classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
- per_image: compute the loss per image instead of per batch
- ignore: void class labels
- """
- if per_image:
- loss = mean(lovasz_softmax_flat(*flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), classes=classes, weighted=weighted)
- for prob, lab in zip(probas, labels))
- else:
- loss = lovasz_softmax_flat(*flatten_probas(probas, labels, ignore), classes=classes, weighted=weighted )
- return loss
-
-
-def lovasz_softmax_flat(probas, labels, classes='present', weighted=None):
- """
- Multi-class Lovasz-Softmax loss
- probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
- labels: [P] Tensor, ground truth labels (between 0 and C - 1)
- classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
- """
- if probas.numel() == 0:
- # only void pixels, the gradients should be 0
- return probas * 0.
- C = probas.size(1)
- losses = []
- class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
- for c in class_to_sum:
- fg = (labels == c).float() # foreground for class c
- if (classes is 'present' and fg.sum() == 0):
- continue
- if C == 1:
- if len(classes) > 1:
- raise ValueError('Sigmoid output possible only with 1 class')
- class_pred = probas[:, 0]
- else:
- class_pred = probas[:, c]
- errors = (Variable(fg) - class_pred).abs()
- errors_sorted, perm = torch.sort(errors, 0, descending=True)
- perm = perm.data
- fg_sorted = fg[perm]
- if weighted is not None:
- losses.append(weighted[c]*torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
- else:
- losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
- return mean(losses)
-
-
-def flatten_probas(probas, labels, ignore=None):
- """
- Flattens predictions in the batch
- """
- if probas.dim() == 3:
- # assumes output of a sigmoid layer
- B, H, W = probas.size()
- probas = probas.view(B, 1, H, W)
- B, C, H, W = probas.size()
- probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C) # B * H * W, C = P, C
- labels = labels.view(-1)
- if ignore is None:
- return probas, labels
- valid = (labels != ignore)
- vprobas = probas[valid.nonzero().squeeze()]
- vlabels = labels[valid]
- return vprobas, vlabels
-
-
-def xloss(logits, labels, ignore=None):
- """
- Cross entropy loss
- """
- return F.cross_entropy(logits, Variable(labels), ignore_index=255)
-
-
-# --------------------------- HELPER FUNCTIONS ---------------------------
-def isnan(x):
- return x != x
-
-
-def mean(l, ignore_nan=False, empty=0):
- """
- nanmean compatible with generators.
- """
- l = iter(l)
- if ignore_nan:
- l = ifilterfalse(isnan, l)
- try:
- n = 1
- acc = next(l)
- except StopIteration:
- if empty == 'raise':
- raise ValueError('Empty mean')
- return empty
- for n, v in enumerate(l, 2):
- acc += v
- if n == 1:
- return acc
- return acc / n
-
-# --------------------------- Class ---------------------------
-class LovaszSoftmax(nn.Module):
- def __init__(self, per_image=False, ignore_index=255, weighted=None):
- super(LovaszSoftmax, self).__init__()
- self.lovasz_softmax = lovasz_softmax
- self.per_image = per_image
- self.ignore_index=ignore_index
- self.weighted = weighted
-
- def forward(self, pred, label):
- pred = F.softmax(pred, dim=1)
- return self.lovasz_softmax(pred, label, per_image=self.per_image, ignore=self.ignore_index, weighted=self.weighted)
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/gradio/demo.py b/spaces/hasibzunair/fifa-tryon-demo/gradio/demo.py
deleted file mode 100644
index 2ad81ef24cdb3e645331aacae729fd20cec78082..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/gradio/demo.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import paddlehub as hub
-import gradio as gr
-import torch
-
-# Images
-torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2018/08/12/16/59/ara-3601194_1280.jpg', 'parrot.jpg')
-torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/10/21/14/46/fox-1758183_1280.jpg', 'fox.jpg')
-
-model = hub.Module(name='U2Net')
-
-def infer(img):
- result = model.Segmentation(
- images=[cv2.imread(img.name)],
- paths=None,
- batch_size=1,
- input_size=320,
- output_dir='output',
- visualization=True)
- return result[0]['front'][:,:,::-1], result[0]['mask']
-
-inputs = gr.inputs.Image(type='file', label="Original Image")
-outputs = [
- gr.outputs.Image(type="numpy",label="Front"),
- gr.outputs.Image(type="numpy",label="Mask")
- ]
-
-title = "U^2-Net"
-description = "demo for U^2-Net. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection | Github Repo
" - -examples = [ - ['fox.jpg'], - ['parrot.jpg'] -] - -gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() \ No newline at end of file diff --git a/spaces/hf4all/web-ui/_next/static/css/60ec184094fe2bcc.css b/spaces/hf4all/web-ui/_next/static/css/60ec184094fe2bcc.css deleted file mode 100644 index 67dcb7698c21d38c409d5fc739bba2c8e20aa370..0000000000000000000000000000000000000000 --- a/spaces/hf4all/web-ui/_next/static/css/60ec184094fe2bcc.css +++ /dev/null @@ -1 +0,0 @@ -@media 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pre{padding:0;margin-top:10px;margin-bottom:10px}& pre code{white-space:pre-wrap;padding:10px}& img{max-width:min(80%,300px);margin-top:5px}& a:not(:has(sup)){color:inherit;text-decoration:underline}} \ No newline at end of file diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/model_selection/ensemble.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/model_selection/ensemble.py deleted file mode 100644 index 9e0a489d1d95822ef580bbb3d7e2c8f38b2735e4..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/model_selection/ensemble.py +++ /dev/null @@ -1,123 +0,0 @@ -# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany -# -# 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 shutil -from multiprocessing.pool import Pool - -import numpy as np -from batchgenerators.utilities.file_and_folder_operations import * -from nnunet.configuration import default_num_threads -from nnunet.evaluation.evaluator import aggregate_scores -from nnunet.inference.segmentation_export import save_segmentation_nifti_from_softmax -from nnunet.paths import network_training_output_dir, preprocessing_output_dir -from nnunet.postprocessing.connected_components import determine_postprocessing - - -def merge(args): - file1, file2, properties_file, out_file = args - if not isfile(out_file): - res1 = np.load(file1)['softmax'] - res2 = np.load(file2)['softmax'] - props = load_pickle(properties_file) - mn = np.mean((res1, res2), 0) - # Softmax probabilities are already at target spacing so this will not do any resampling (resampling parameters - # don't matter here) - save_segmentation_nifti_from_softmax(mn, out_file, props, 3, None, None, None, force_separate_z=None, - interpolation_order_z=0) - - -def ensemble(training_output_folder1, training_output_folder2, output_folder, task, validation_folder, folds, allow_ensembling: bool = True): - print("\nEnsembling folders\n", training_output_folder1, "\n", training_output_folder2) - - output_folder_base = output_folder - output_folder = join(output_folder_base, "ensembled_raw") - - # only_keep_largest_connected_component is the same for all stages - dataset_directory = join(preprocessing_output_dir, task) - plans = load_pickle(join(training_output_folder1, "plans.pkl")) # we need this only for the labels - - files1 = [] - files2 = [] - property_files = [] - out_files = [] - gt_segmentations = [] - - folder_with_gt_segs = join(dataset_directory, "gt_segmentations") - # in the correct shape and we need the original geometry to restore the niftis - - for f in folds: - validation_folder_net1 = join(training_output_folder1, "fold_%d" % f, validation_folder) - validation_folder_net2 = join(training_output_folder2, "fold_%d" % f, validation_folder) - - if not isdir(validation_folder_net1): - raise AssertionError("Validation directory missing: %s. Please rerun validation with `nnUNet_train CONFIG TRAINER TASK FOLD -val --npz`" % validation_folder_net1) - if not isdir(validation_folder_net2): - raise AssertionError("Validation directory missing: %s. Please rerun validation with `nnUNet_train CONFIG TRAINER TASK FOLD -val --npz`" % validation_folder_net2) - - # we need to ensure the validation was successful. We can verify this via the presence of the summary.json file - if not isfile(join(validation_folder_net1, 'summary.json')): - raise AssertionError("Validation directory incomplete: %s. Please rerun validation with `nnUNet_train CONFIG TRAINER TASK FOLD -val --npz`" % validation_folder_net1) - if not isfile(join(validation_folder_net2, 'summary.json')): - raise AssertionError("Validation directory missing: %s. Please rerun validation with `nnUNet_train CONFIG TRAINER TASK FOLD -val --npz`" % validation_folder_net2) - - patient_identifiers1_npz = [i[:-4] for i in subfiles(validation_folder_net1, False, None, 'npz', True)] - patient_identifiers2_npz = [i[:-4] for i in subfiles(validation_folder_net2, False, None, 'npz', True)] - - # we don't do postprocessing anymore so there should not be any of that noPostProcess - patient_identifiers1_nii = [i[:-7] for i in subfiles(validation_folder_net1, False, None, suffix='nii.gz', sort=True) if not i.endswith("noPostProcess.nii.gz") and not i.endswith('_postprocessed.nii.gz')] - patient_identifiers2_nii = [i[:-7] for i in subfiles(validation_folder_net2, False, None, suffix='nii.gz', sort=True) if not i.endswith("noPostProcess.nii.gz") and not i.endswith('_postprocessed.nii.gz')] - - if not all([i in patient_identifiers1_npz for i in patient_identifiers1_nii]): - raise AssertionError("Missing npz files in folder %s. Please run the validation for all models and folds with the '--npz' flag." % (validation_folder_net1)) - if not all([i in patient_identifiers2_npz for i in patient_identifiers2_nii]): - raise AssertionError("Missing npz files in folder %s. Please run the validation for all models and folds with the '--npz' flag." % (validation_folder_net2)) - - patient_identifiers1_npz.sort() - patient_identifiers2_npz.sort() - - assert all([i == j for i, j in zip(patient_identifiers1_npz, patient_identifiers2_npz)]), "npz filenames do not match. This should not happen." - - maybe_mkdir_p(output_folder) - - for p in patient_identifiers1_npz: - files1.append(join(validation_folder_net1, p + '.npz')) - files2.append(join(validation_folder_net2, p + '.npz')) - property_files.append(join(validation_folder_net1, p) + ".pkl") - out_files.append(join(output_folder, p + ".nii.gz")) - gt_segmentations.append(join(folder_with_gt_segs, p + ".nii.gz")) - - p = Pool(default_num_threads) - p.map(merge, zip(files1, files2, property_files, out_files)) - p.close() - p.join() - - if not isfile(join(output_folder, "summary.json")) and len(out_files) > 0: - aggregate_scores(tuple(zip(out_files, gt_segmentations)), labels=plans['all_classes'], - json_output_file=join(output_folder, "summary.json"), json_task=task, - json_name=task + "__" + output_folder_base.split("/")[-1], num_threads=default_num_threads) - - if allow_ensembling and not isfile(join(output_folder_base, "postprocessing.json")): - # now lets also look at postprocessing. We cannot just take what we determined in cross-validation and apply it - # here because things may have changed and may also be too inconsistent between the two networks - determine_postprocessing(output_folder_base, folder_with_gt_segs, "ensembled_raw", "temp", - "ensembled_postprocessed", default_num_threads, dice_threshold=0) - - out_dir_all_json = join(network_training_output_dir, "summary_jsons") - json_out = load_json(join(output_folder_base, "ensembled_postprocessed", "summary.json")) - - json_out["experiment_name"] = output_folder_base.split("/")[-1] - save_json(json_out, join(output_folder_base, "ensembled_postprocessed", "summary.json")) - - maybe_mkdir_p(out_dir_all_json) - shutil.copy(join(output_folder_base, "ensembled_postprocessed", "summary.json"), - join(out_dir_all_json, "%s__%s.json" % (task, output_folder_base.split("/")[-1]))) diff --git a/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard/utils.py b/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard/utils.py deleted file mode 100644 index 13587c3623fee788f38388fc0917d174580e36f6..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard/utils.py +++ /dev/null @@ -1,14 +0,0 @@ -# Based on Omar Sanseviero work -# Make model clickable link -def make_clickable_model(model_name): - # remove user from model name - model_name_show = ' '.join(model_name.split('/')[1:]) - - link = "https://huggingface.co/" + model_name - return f'{model_name_show}' - -# Make user clickable link -def make_clickable_user(user_id): - link = "https://huggingface.co/" + user_id - return f'{user_id}' - \ No newline at end of file diff --git a/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/chunks/2-f87c835b.js b/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/chunks/2-f87c835b.js deleted file mode 100644 index 8fd3cf7ab061f9ac7549e40ba1305451105dcf59..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/chunks/2-f87c835b.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as r}from"./_page-802cc2a3.js";import{default as t}from"../components/pages/_page.svelte-4566c4b6.js";export{t as component,r as shared}; diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/README.md b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/README.md deleted file mode 100644 index 8d391f63684dd1f47900dc6449a5e22fa25e3da3..0000000000000000000000000000000000000000 --- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/README.md +++ /dev/null @@ -1,218 +0,0 @@ -# Distributed Arcface Training in Pytorch - -The "arcface_torch" repository is the official implementation of the ArcFace algorithm. It supports distributed and sparse training with multiple distributed training examples, including several memory-saving techniques such as mixed precision training and gradient checkpointing. It also supports training for ViT models and datasets including WebFace42M and Glint360K, two of the largest open-source datasets. Additionally, the repository comes with a built-in tool for converting to ONNX format, making it easy to submit to MFR evaluation systems. - -[](https://paperswithcode.com/sota/face-verification-on-ijb-c?p=killing-two-birds-with-one-stone-efficient) -[](https://paperswithcode.com/sota/face-verification-on-ijb-b?p=killing-two-birds-with-one-stone-efficient) -[](https://paperswithcode.com/sota/face-verification-on-agedb-30?p=killing-two-birds-with-one-stone-efficient) -[](https://paperswithcode.com/sota/face-verification-on-cfp-fp?p=killing-two-birds-with-one-stone-efficient) - -## Requirements - -To avail the latest features of PyTorch, we have upgraded to version 1.12.0. - -- Install [PyTorch](https://pytorch.org/get-started/previous-versions/) (torch>=1.12.0). -- (Optional) Install [DALI](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/), our doc for [install_dali.md](docs/install_dali.md). -- `pip install -r requirement.txt`. - -## How to Training - -To train a model, execute the `train.py` script with the path to the configuration files. The sample commands provided below demonstrate the process of conducting distributed training. - -### 1. To run on one GPU: - -```shell -python train_v2.py configs/ms1mv3_r50_onegpu -``` - -Note: -It is not recommended to use a single GPU for training, as this may result in longer training times and suboptimal performance. For best results, we suggest using multiple GPUs or a GPU cluster. - - -### 2. To run on a machine with 8 GPUs: - -```shell -torchrun --nproc_per_node=8 train.py configs/ms1mv3_r50 -``` - -### 3. To run on 2 machines with 8 GPUs each: - -Node 0: - -```shell -torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=12581 train.py configs/wf42m_pfc02_16gpus_r100 -``` - -Node 1: - -```shell -torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=12581 train.py configs/wf42m_pfc02_16gpus_r100 -``` - -### 4. Run ViT-B on a machine with 24k batchsize: - -```shell -torchrun --nproc_per_node=8 train_v2.py configs/wf42m_pfc03_40epoch_8gpu_vit_b -``` - - -## Download Datasets or Prepare Datasets -- [MS1MV2](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-arcface-85k-ids58m-images-57) (87k IDs, 5.8M images) -- [MS1MV3](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-retinaface) (93k IDs, 5.2M images) -- [Glint360K](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc#4-download) (360k IDs, 17.1M images) -- [WebFace42M](docs/prepare_webface42m.md) (2M IDs, 42.5M images) -- [Your Dataset, Click Here!](docs/prepare_custom_dataset.md) - -Note: -If you want to use DALI for data reading, please use the script 'scripts/shuffle_rec.py' to shuffle the InsightFace style rec before using it. -Example: - -`python scripts/shuffle_rec.py ms1m-retinaface-t1` - -You will get the "shuffled_ms1m-retinaface-t1" folder, where the samples in the "train.rec" file are shuffled. - - -## Model Zoo - -- The models are available for non-commercial research purposes only. -- All models can be found in here. -- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g): e8pw -- [OneDrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d) - -### Performance on IJB-C and [**ICCV2021-MFR**](https://github.com/deepinsight/insightface/blob/master/challenges/mfr/README.md) - -ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face -recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities. -As the result, we can evaluate the FAIR performance for different algorithms. - -For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The -globalised multi-racial testset contains 242,143 identities and 1,624,305 images. - - -#### 1. Training on Single-Host GPU - -| Datasets | Backbone | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | log | -|:---------------|:--------------------|:------------|:------------|:------------|:------------------------------------------------------------------------------------------------------------------------------------| -| MS1MV2 | mobilefacenet-0.45G | 62.07 | 93.61 | 90.28 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_mbf/training.log) | -| MS1MV2 | r50 | 75.13 | 95.97 | 94.07 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_r50/training.log) | -| MS1MV2 | r100 | 78.12 | 96.37 | 94.27 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_r100/training.log) | -| MS1MV3 | mobilefacenet-0.45G | 63.78 | 94.23 | 91.33 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_mbf/training.log) | -| MS1MV3 | r50 | 79.14 | 96.37 | 94.47 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_r50/training.log) | -| MS1MV3 | r100 | 81.97 | 96.85 | 95.02 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_r100/training.log) | -| Glint360K | mobilefacenet-0.45G | 70.18 | 95.04 | 92.62 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_mbf/training.log) | -| Glint360K | r50 | 86.34 | 97.16 | 95.81 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_r50/training.log) | -| Glint360k | r100 | 89.52 | 97.55 | 96.38 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_r100/training.log) | -| WF4M | r100 | 89.87 | 97.19 | 95.48 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf4m_r100/training.log) | -| WF12M-PFC-0.2 | r100 | 94.75 | 97.60 | 95.90 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_pfc02_r100/training.log) | -| WF12M-PFC-0.3 | r100 | 94.71 | 97.64 | 96.01 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_pfc03_r100/training.log) | -| WF12M | r100 | 94.69 | 97.59 | 95.97 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_r100/training.log) | -| WF42M-PFC-0.2 | r100 | 96.27 | 97.70 | 96.31 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf42m_pfc02_r100/training.log) | -| WF42M-PFC-0.2 | ViT-T-1.5G | 92.04 | 97.27 | 95.68 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf42m_pfc02_40epoch_8gpu_vit_t/training.log) | -| WF42M-PFC-0.3 | ViT-B-11G | 97.16 | 97.91 | 97.05 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_b_8gpu/training.log) | - -#### 2. Training on Multi-Host GPU - -| Datasets | Backbone(bs*gpus) | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | Throughout | log | -|:-----------------|:------------------|:------------|:------------|:------------|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------| -| WF42M-PFC-0.2 | r50(512*8) | 93.83 | 97.53 | 96.16 | ~5900 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_bs4k_pfc02/training.log) | -| WF42M-PFC-0.2 | r50(512*16) | 93.96 | 97.46 | 96.12 | ~11000 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_lr01_pfc02_bs8k_16gpus/training.log) | -| WF42M-PFC-0.2 | r50(128*32) | 94.04 | 97.48 | 95.94 | ~17000 | click me | -| WF42M-PFC-0.2 | r100(128*16) | 96.28 | 97.80 | 96.57 | ~5200 | click me | -| WF42M-PFC-0.2 | r100(256*16) | 96.69 | 97.85 | 96.63 | ~5200 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r100_bs4k_pfc02/training.log) | -| WF42M-PFC-0.0018 | r100(512*32) | 93.08 | 97.51 | 95.88 | ~10000 | click me | -| WF42M-PFC-0.2 | r100(128*32) | 96.57 | 97.83 | 96.50 | ~9800 | click me | - -`r100(128*32)` means backbone is r100, batchsize per gpu is 128, the number of gpus is 32. - - - -#### 3. ViT For Face Recognition - -| Datasets | Backbone(bs) | FLOPs | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | Throughout | log | -|:--------------|:--------------|:------|:------------|:------------|:------------|:-----------|:-----------------------------------------------------------------------------------------------------------------------------| -| WF42M-PFC-0.3 | r18(128*32) | 2.6 | 79.13 | 95.77 | 93.36 | - | click me | -| WF42M-PFC-0.3 | r50(128*32) | 6.3 | 94.03 | 97.48 | 95.94 | - | click me | -| WF42M-PFC-0.3 | r100(128*32) | 12.1 | 96.69 | 97.82 | 96.45 | - | click me | -| WF42M-PFC-0.3 | r200(128*32) | 23.5 | 97.70 | 97.97 | 96.93 | - | click me | -| WF42M-PFC-0.3 | VIT-T(384*64) | 1.5 | 92.24 | 97.31 | 95.97 | ~35000 | click me | -| WF42M-PFC-0.3 | VIT-S(384*64) | 5.7 | 95.87 | 97.73 | 96.57 | ~25000 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_s_64gpu/training.log) | -| WF42M-PFC-0.3 | VIT-B(384*64) | 11.4 | 97.42 | 97.90 | 97.04 | ~13800 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_b_64gpu/training.log) | -| WF42M-PFC-0.3 | VIT-L(384*64) | 25.3 | 97.85 | 98.00 | 97.23 | ~9406 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_l_64gpu/training.log) | - -`WF42M` means WebFace42M, `PFC-0.3` means negivate class centers sample rate is 0.3. - -#### 4. Noisy Datasets - -| Datasets | Backbone | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | log | -|:-------------------------|:---------|:------------|:------------|:------------|:---------| -| WF12M-Flip(40%) | r50 | 43.87 | 88.35 | 80.78 | click me | -| WF12M-Flip(40%)-PFC-0.1* | r50 | 80.20 | 96.11 | 93.79 | click me | -| WF12M-Conflict | r50 | 79.93 | 95.30 | 91.56 | click me | -| WF12M-Conflict-PFC-0.3* | r50 | 91.68 | 97.28 | 95.75 | click me | - -`WF12M` means WebFace12M, `+PFC-0.1*` denotes additional abnormal inter-class filtering. - - - -## Speed Benchmark -
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\ No newline at end of file diff --git a/spaces/ioclab/brightness-controlnet/README.md b/spaces/ioclab/brightness-controlnet/README.md deleted file mode 100644 index bddf2ba19a8d7b595fb8acb5cd4ec482984857ce..0000000000000000000000000000000000000000 --- a/spaces/ioclab/brightness-controlnet/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Brightness ControlNet -emoji: 💻 -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.27.0 -app_file: app.py -pinned: false -tags: -- jax-diffusers-event ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/irvay/RVC_IR/mygit.sh b/spaces/irvay/RVC_IR/mygit.sh deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/ismot/8testi1/utils/loss.py b/spaces/ismot/8testi1/utils/loss.py deleted file mode 100644 index 31386328ec1564bb13cab9f5de1a2fdabdf922f7..0000000000000000000000000000000000000000 --- a/spaces/ismot/8testi1/utils/loss.py +++ /dev/null @@ -1,1157 +0,0 @@ -# Loss functions - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from utils.general import bbox_iou, bbox_alpha_iou, box_iou, box_giou, box_diou, box_ciou, xywh2xyxy -from utils.torch_utils import is_parallel - - -def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 - # return positive, negative label smoothing BCE targets - return 1.0 - 0.5 * eps, 0.5 * eps - - -class BCEBlurWithLogitsLoss(nn.Module): - # BCEwithLogitLoss() with reduced missing label effects. - def __init__(self, alpha=0.05): - super(BCEBlurWithLogitsLoss, self).__init__() - self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() - self.alpha = alpha - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - pred = torch.sigmoid(pred) # prob from logits - dx = pred - true # reduce only missing label effects - # dx = (pred - true).abs() # reduce missing label and false label effects - alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) - loss *= alpha_factor - return loss.mean() - - -class SigmoidBin(nn.Module): - stride = None # strides computed during build - export = False # onnx export - - def __init__(self, bin_count=10, min=0.0, max=1.0, reg_scale = 2.0, use_loss_regression=True, use_fw_regression=True, BCE_weight=1.0, smooth_eps=0.0): - super(SigmoidBin, self).__init__() - - self.bin_count = bin_count - self.length = bin_count + 1 - self.min = min - self.max = max - self.scale = float(max - min) - self.shift = self.scale / 2.0 - - self.use_loss_regression = use_loss_regression - self.use_fw_regression = use_fw_regression - self.reg_scale = reg_scale - self.BCE_weight = BCE_weight - - start = min + (self.scale/2.0) / self.bin_count - end = max - (self.scale/2.0) / self.bin_count - step = self.scale / self.bin_count - self.step = step - #print(f" start = {start}, end = {end}, step = {step} ") - - bins = torch.range(start, end + 0.0001, step).float() - self.register_buffer('bins', bins) - - - self.cp = 1.0 - 0.5 * smooth_eps - self.cn = 0.5 * smooth_eps - - self.BCEbins = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([BCE_weight])) - self.MSELoss = nn.MSELoss() - - def get_length(self): - return self.length - - def forward(self, pred): - assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) - - pred_reg = (pred[..., 0] * self.reg_scale - self.reg_scale/2.0) * self.step - pred_bin = pred[..., 1:(1+self.bin_count)] - - _, bin_idx = torch.max(pred_bin, dim=-1) - bin_bias = self.bins[bin_idx] - - if self.use_fw_regression: - result = pred_reg + bin_bias - else: - result = bin_bias - result = result.clamp(min=self.min, max=self.max) - - return result - - - def training_loss(self, pred, target): - assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) - assert pred.shape[0] == target.shape[0], 'pred.shape=%d is not equal to the target.shape=%d' % (pred.shape[0], target.shape[0]) - device = pred.device - - pred_reg = (pred[..., 0].sigmoid() * self.reg_scale - self.reg_scale/2.0) * self.step - pred_bin = pred[..., 1:(1+self.bin_count)] - - diff_bin_target = torch.abs(target[..., None] - self.bins) - _, bin_idx = torch.min(diff_bin_target, dim=-1) - - bin_bias = self.bins[bin_idx] - bin_bias.requires_grad = False - result = pred_reg + bin_bias - - target_bins = torch.full_like(pred_bin, self.cn, device=device) # targets - n = pred.shape[0] - target_bins[range(n), bin_idx] = self.cp - - loss_bin = self.BCEbins(pred_bin, target_bins) # BCE - - if self.use_loss_regression: - loss_regression = self.MSELoss(result, target) # MSE - loss = loss_bin + loss_regression - else: - loss = loss_bin - - out_result = result.clamp(min=self.min, max=self.max) - - return loss, out_result - - -class FocalLoss(nn.Module): - # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super(FocalLoss, self).__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - # p_t = torch.exp(-loss) - # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability - - # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py - pred_prob = torch.sigmoid(pred) # prob from logits - p_t = true * pred_prob + (1 - true) * (1 - pred_prob) - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = (1.0 - p_t) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == 'mean': - return loss.mean() - elif self.reduction == 'sum': - return loss.sum() - else: # 'none' - return loss - - -class QFocalLoss(nn.Module): - # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super(QFocalLoss, self).__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - - pred_prob = torch.sigmoid(pred) # prob from logits - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = torch.abs(true - pred_prob) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == 'mean': - return loss.mean() - elif self.reduction == 'sum': - return loss.sum() - else: # 'none' - return loss - -class RankSort(torch.autograd.Function): - @staticmethod - def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): - - classification_grads=torch.zeros(logits.shape).cuda() - - #Filter fg logits - fg_labels = (targets > 0.) - fg_logits = logits[fg_labels] - fg_targets = targets[fg_labels] - fg_num = len(fg_logits) - - #Do not use bg with scores less than minimum fg logit - #since changing its score does not have an effect on precision - threshold_logit = torch.min(fg_logits)-delta_RS - relevant_bg_labels=((targets==0) & (logits>=threshold_logit)) - - relevant_bg_logits = logits[relevant_bg_labels] - relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() - sorting_error=torch.zeros(fg_num).cuda() - ranking_error=torch.zeros(fg_num).cuda() - fg_grad=torch.zeros(fg_num).cuda() - - #sort the fg logits - order=torch.argsort(fg_logits) - #Loops over each positive following the order - for ii in order: - # Difference Transforms (x_ij) - fg_relations=fg_logits-fg_logits[ii] - bg_relations=relevant_bg_logits-fg_logits[ii] - - if delta_RS > 0: - fg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1) - bg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1) - else: - fg_relations = (fg_relations >= 0).float() - bg_relations = (bg_relations >= 0).float() - - # Rank of ii among pos and false positive number (bg with larger scores) - rank_pos=torch.sum(fg_relations) - FP_num=torch.sum(bg_relations) - - # Rank of ii among all examples - rank=rank_pos+FP_num - - # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7) - ranking_error[ii]=FP_num/rank - - # Current sorting error of example ii. (Eq. 7) - current_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos - - #Find examples in the target sorted order for example ii - iou_relations = (fg_targets >= fg_targets[ii]) - target_sorted_order = iou_relations * fg_relations - - #The rank of ii among positives in sorted order - rank_pos_target = torch.sum(target_sorted_order) - - #Compute target sorting error. (Eq. 8) - #Since target ranking error is 0, this is also total target error - target_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target - - #Compute sorting error on example ii - sorting_error[ii] = current_sorting_error - target_sorting_error - - #Identity Update for Ranking Error - if FP_num > eps: - #For ii the update is the ranking error - fg_grad[ii] -= ranking_error[ii] - #For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num) - relevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num)) - - #Find the positives that are misranked (the cause of the error) - #These are the ones with smaller IoU but larger logits - missorted_examples = (~ iou_relations) * fg_relations - - #Denominotor of sorting pmf - sorting_pmf_denom = torch.sum(missorted_examples) - - #Identity Update for Sorting Error - if sorting_pmf_denom > eps: - #For ii the update is the sorting error - fg_grad[ii] -= sorting_error[ii] - #For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom) - fg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom)) - - #Normalize gradients by number of positives - classification_grads[fg_labels]= (fg_grad/fg_num) - classification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num) - - ctx.save_for_backward(classification_grads) - - return ranking_error.mean(), sorting_error.mean() - - @staticmethod - def backward(ctx, out_grad1, out_grad2): - g1, =ctx.saved_tensors - return g1*out_grad1, None, None, None - -class aLRPLoss(torch.autograd.Function): - @staticmethod - def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): - classification_grads=torch.zeros(logits.shape).cuda() - - #Filter fg logits - fg_labels = (targets == 1) - fg_logits = logits[fg_labels] - fg_num = len(fg_logits) - - #Do not use bg with scores less than minimum fg logit - #since changing its score does not have an effect on precision - threshold_logit = torch.min(fg_logits)-delta - - #Get valid bg logits - relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) - relevant_bg_logits=logits[relevant_bg_labels] - relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() - rank=torch.zeros(fg_num).cuda() - prec=torch.zeros(fg_num).cuda() - fg_grad=torch.zeros(fg_num).cuda() - - max_prec=0 - #sort the fg logits - order=torch.argsort(fg_logits) - #Loops over each positive following the order - for ii in order: - #x_ij s as score differences with fgs - fg_relations=fg_logits-fg_logits[ii] - #Apply piecewise linear function and determine relations with fgs - fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) - #Discard i=j in the summation in rank_pos - fg_relations[ii]=0 - - #x_ij s as score differences with bgs - bg_relations=relevant_bg_logits-fg_logits[ii] - #Apply piecewise linear function and determine relations with bgs - bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) - - #Compute the rank of the example within fgs and number of bgs with larger scores - rank_pos=1+torch.sum(fg_relations) - FP_num=torch.sum(bg_relations) - #Store the total since it is normalizer also for aLRP Regression error - rank[ii]=rank_pos+FP_num - - #Compute precision for this example to compute classification loss - prec[ii]=rank_pos/rank[ii] - #For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads - if FP_num > eps: - fg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii] - relevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num)) - - #aLRP with grad formulation fg gradient - classification_grads[fg_labels]= fg_grad - #aLRP with grad formulation bg gradient - classification_grads[relevant_bg_labels]= relevant_bg_grad - - classification_grads /= (fg_num) - - cls_loss=1-prec.mean() - ctx.save_for_backward(classification_grads) - - return cls_loss, rank, order - - @staticmethod - def backward(ctx, out_grad1, out_grad2, out_grad3): - g1, =ctx.saved_tensors - return g1*out_grad1, None, None, None, None - - -class APLoss(torch.autograd.Function): - @staticmethod - def forward(ctx, logits, targets, delta=1.): - classification_grads=torch.zeros(logits.shape).cuda() - - #Filter fg logits - fg_labels = (targets == 1) - fg_logits = logits[fg_labels] - fg_num = len(fg_logits) - - #Do not use bg with scores less than minimum fg logit - #since changing its score does not have an effect on precision - threshold_logit = torch.min(fg_logits)-delta - - #Get valid bg logits - relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) - relevant_bg_logits=logits[relevant_bg_labels] - relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() - rank=torch.zeros(fg_num).cuda() - prec=torch.zeros(fg_num).cuda() - fg_grad=torch.zeros(fg_num).cuda() - - max_prec=0 - #sort the fg logits - order=torch.argsort(fg_logits) - #Loops over each positive following the order - for ii in order: - #x_ij s as score differences with fgs - fg_relations=fg_logits-fg_logits[ii] - #Apply piecewise linear function and determine relations with fgs - fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) - #Discard i=j in the summation in rank_pos - fg_relations[ii]=0 - - #x_ij s as score differences with bgs - bg_relations=relevant_bg_logits-fg_logits[ii] - #Apply piecewise linear function and determine relations with bgs - bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) - - #Compute the rank of the example within fgs and number of bgs with larger scores - rank_pos=1+torch.sum(fg_relations) - FP_num=torch.sum(bg_relations) - #Store the total since it is normalizer also for aLRP Regression error - rank[ii]=rank_pos+FP_num - - #Compute precision for this example - current_prec=rank_pos/rank[ii] - - #Compute interpolated AP and store gradients for relevant bg examples - if (max_prec<=current_prec): - max_prec=current_prec - relevant_bg_grad += (bg_relations/rank[ii]) - else: - relevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec))) - - #Store fg gradients - fg_grad[ii]=-(1-max_prec) - prec[ii]=max_prec - - #aLRP with grad formulation fg gradient - classification_grads[fg_labels]= fg_grad - #aLRP with grad formulation bg gradient - classification_grads[relevant_bg_labels]= relevant_bg_grad - - classification_grads /= fg_num - - cls_loss=1-prec.mean() - ctx.save_for_backward(classification_grads) - - return cls_loss - - @staticmethod - def backward(ctx, out_grad1): - g1, =ctx.saved_tensors - return g1*out_grad1, None, None - - -class ComputeLoss: - # Compute losses - def __init__(self, model, autobalance=False): - super(ComputeLoss, self).__init__() - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 - #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.1, .05]) # P3-P7 - #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.5, 0.4, .1]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors': - setattr(self, k, getattr(det, k)) - - def __call__(self, p, targets): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = indices[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - pxy = ps[:, :2].sigmoid() * 2. - 0.5 - pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets - t[range(n), tcls[i]] = self.cp - #t[t==self.cp] = iou.detach().clamp(0).type(t.dtype) - lcls += self.BCEcls(ps[:, 5:], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - obji = self.BCEobj(pi[..., 4], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - def build_targets(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - tcls, tbox, indices, anch = [], [], [], [] - gain = torch.ones(7, device=targets.device) # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - tbox.append(torch.cat((gxy - gij, gwh), 1)) # box - anch.append(anchors[a]) # anchors - tcls.append(c) # class - - return tcls, tbox, indices, anch - - -class ComputeLossOTA: - # Compute losses - def __init__(self, model, autobalance=False): - super(ComputeLossOTA, self).__init__() - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors', 'stride': - setattr(self, k, getattr(det, k)) - - def __call__(self, p, targets, imgs): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) - pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] - - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - grid = torch.stack([gi, gj], dim=1) - pxy = ps[:, :2].sigmoid() * 2. - 0.5 - #pxy = ps[:, :2].sigmoid() * 3. - 1. - pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] - selected_tbox[:, :2] -= grid - iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - selected_tcls = targets[i][:, 1].long() - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets - t[range(n), selected_tcls] = self.cp - lcls += self.BCEcls(ps[:, 5:], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - obji = self.BCEobj(pi[..., 4], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - def build_targets(self, p, targets, imgs): - - #indices, anch = self.find_positive(p, targets) - indices, anch = self.find_3_positive(p, targets) - #indices, anch = self.find_4_positive(p, targets) - #indices, anch = self.find_5_positive(p, targets) - #indices, anch = self.find_9_positive(p, targets) - - matching_bs = [[] for pp in p] - matching_as = [[] for pp in p] - matching_gjs = [[] for pp in p] - matching_gis = [[] for pp in p] - matching_targets = [[] for pp in p] - matching_anchs = [[] for pp in p] - - nl = len(p) - - for batch_idx in range(p[0].shape[0]): - - b_idx = targets[:, 0]==batch_idx - this_target = targets[b_idx] - if this_target.shape[0] == 0: - continue - - txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] - txyxy = xywh2xyxy(txywh) - - pxyxys = [] - p_cls = [] - p_obj = [] - from_which_layer = [] - all_b = [] - all_a = [] - all_gj = [] - all_gi = [] - all_anch = [] - - for i, pi in enumerate(p): - - b, a, gj, gi = indices[i] - idx = (b == batch_idx) - b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] - all_b.append(b) - all_a.append(a) - all_gj.append(gj) - all_gi.append(gi) - all_anch.append(anch[i][idx]) - from_which_layer.append(torch.ones(size=(len(b),)) * i) - - fg_pred = pi[b, a, gj, gi] - p_obj.append(fg_pred[:, 4:5]) - p_cls.append(fg_pred[:, 5:]) - - grid = torch.stack([gi, gj], dim=1) - pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. - #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] - pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. - pxywh = torch.cat([pxy, pwh], dim=-1) - pxyxy = xywh2xyxy(pxywh) - pxyxys.append(pxyxy) - - pxyxys = torch.cat(pxyxys, dim=0) - if pxyxys.shape[0] == 0: - continue - p_obj = torch.cat(p_obj, dim=0) - p_cls = torch.cat(p_cls, dim=0) - from_which_layer = torch.cat(from_which_layer, dim=0) - all_b = torch.cat(all_b, dim=0) - all_a = torch.cat(all_a, dim=0) - all_gj = torch.cat(all_gj, dim=0) - all_gi = torch.cat(all_gi, dim=0) - all_anch = torch.cat(all_anch, dim=0) - - pair_wise_iou = box_iou(txyxy, pxyxys) - - pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) - - top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) - dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) - - gt_cls_per_image = ( - F.one_hot(this_target[:, 1].to(torch.int64), self.nc) - .float() - .unsqueeze(1) - .repeat(1, pxyxys.shape[0], 1) - ) - - num_gt = this_target.shape[0] - cls_preds_ = ( - p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - ) - - y = cls_preds_.sqrt_() - pair_wise_cls_loss = F.binary_cross_entropy_with_logits( - torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" - ).sum(-1) - del cls_preds_ - - cost = ( - pair_wise_cls_loss - + 3.0 * pair_wise_iou_loss - ) - - matching_matrix = torch.zeros_like(cost) - - for gt_idx in range(num_gt): - _, pos_idx = torch.topk( - cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False - ) - matching_matrix[gt_idx][pos_idx] = 1.0 - - del top_k, dynamic_ks - anchor_matching_gt = matching_matrix.sum(0) - if (anchor_matching_gt > 1).sum() > 0: - _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) - matching_matrix[:, anchor_matching_gt > 1] *= 0.0 - matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 - fg_mask_inboxes = matching_matrix.sum(0) > 0.0 - matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) - - from_which_layer = from_which_layer[fg_mask_inboxes] - all_b = all_b[fg_mask_inboxes] - all_a = all_a[fg_mask_inboxes] - all_gj = all_gj[fg_mask_inboxes] - all_gi = all_gi[fg_mask_inboxes] - all_anch = all_anch[fg_mask_inboxes] - - this_target = this_target[matched_gt_inds] - - for i in range(nl): - layer_idx = from_which_layer == i - matching_bs[i].append(all_b[layer_idx]) - matching_as[i].append(all_a[layer_idx]) - matching_gjs[i].append(all_gj[layer_idx]) - matching_gis[i].append(all_gi[layer_idx]) - matching_targets[i].append(this_target[layer_idx]) - matching_anchs[i].append(all_anch[layer_idx]) - - for i in range(nl): - matching_bs[i] = torch.cat(matching_bs[i], dim=0) - matching_as[i] = torch.cat(matching_as[i], dim=0) - matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) - matching_gis[i] = torch.cat(matching_gis[i], dim=0) - matching_targets[i] = torch.cat(matching_targets[i], dim=0) - matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) - - return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs - - def find_3_positive(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - indices, anch = [], [] - gain = torch.ones(7, device=targets.device) # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - anch.append(anchors[a]) # anchors - - return indices, anch - - -class ComputeLossBinOTA: - # Compute losses - def __init__(self, model, autobalance=False): - super(ComputeLossBinOTA, self).__init__() - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - #MSEangle = nn.MSELoss().to(device) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors', 'stride', 'bin_count': - setattr(self, k, getattr(det, k)) - - #xy_bin_sigmoid = SigmoidBin(bin_count=11, min=-0.5, max=1.5, use_loss_regression=False).to(device) - wh_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0, use_loss_regression=False).to(device) - #angle_bin_sigmoid = SigmoidBin(bin_count=31, min=-1.1, max=1.1, use_loss_regression=False).to(device) - self.wh_bin_sigmoid = wh_bin_sigmoid - - def __call__(self, p, targets, imgs): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) - pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] - - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 # x,y, w-bce, h-bce # xy_bin_sigmoid.get_length()*2 - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - grid = torch.stack([gi, gj], dim=1) - selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] - selected_tbox[:, :2] -= grid - - #pxy = ps[:, :2].sigmoid() * 2. - 0.5 - ##pxy = ps[:, :2].sigmoid() * 3. - 1. - #pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - #pbox = torch.cat((pxy, pwh), 1) # predicted box - - #x_loss, px = xy_bin_sigmoid.training_loss(ps[..., 0:12], tbox[i][..., 0]) - #y_loss, py = xy_bin_sigmoid.training_loss(ps[..., 12:24], tbox[i][..., 1]) - w_loss, pw = self.wh_bin_sigmoid.training_loss(ps[..., 2:(3+self.bin_count)], selected_tbox[..., 2] / anchors[i][..., 0]) - h_loss, ph = self.wh_bin_sigmoid.training_loss(ps[..., (3+self.bin_count):obj_idx], selected_tbox[..., 3] / anchors[i][..., 1]) - - pw *= anchors[i][..., 0] - ph *= anchors[i][..., 1] - - px = ps[:, 0].sigmoid() * 2. - 0.5 - py = ps[:, 1].sigmoid() * 2. - 0.5 - - lbox += w_loss + h_loss # + x_loss + y_loss - - #print(f"\n px = {px.shape}, py = {py.shape}, pw = {pw.shape}, ph = {ph.shape} \n") - - pbox = torch.cat((px.unsqueeze(1), py.unsqueeze(1), pw.unsqueeze(1), ph.unsqueeze(1)), 1).to(device) # predicted box - - - - - iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - selected_tcls = targets[i][:, 1].long() - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, (1+obj_idx):], self.cn, device=device) # targets - t[range(n), selected_tcls] = self.cp - lcls += self.BCEcls(ps[:, (1+obj_idx):], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - obji = self.BCEobj(pi[..., obj_idx], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - def build_targets(self, p, targets, imgs): - - #indices, anch = self.find_positive(p, targets) - indices, anch = self.find_3_positive(p, targets) - #indices, anch = self.find_4_positive(p, targets) - #indices, anch = self.find_5_positive(p, targets) - #indices, anch = self.find_9_positive(p, targets) - - matching_bs = [[] for pp in p] - matching_as = [[] for pp in p] - matching_gjs = [[] for pp in p] - matching_gis = [[] for pp in p] - matching_targets = [[] for pp in p] - matching_anchs = [[] for pp in p] - - nl = len(p) - - for batch_idx in range(p[0].shape[0]): - - b_idx = targets[:, 0]==batch_idx - this_target = targets[b_idx] - if this_target.shape[0] == 0: - continue - - txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] - txyxy = xywh2xyxy(txywh) - - pxyxys = [] - p_cls = [] - p_obj = [] - from_which_layer = [] - all_b = [] - all_a = [] - all_gj = [] - all_gi = [] - all_anch = [] - - for i, pi in enumerate(p): - - obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 - - b, a, gj, gi = indices[i] - idx = (b == batch_idx) - b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] - all_b.append(b) - all_a.append(a) - all_gj.append(gj) - all_gi.append(gi) - all_anch.append(anch[i][idx]) - from_which_layer.append(torch.ones(size=(len(b),)) * i) - - fg_pred = pi[b, a, gj, gi] - p_obj.append(fg_pred[:, obj_idx:(obj_idx+1)]) - p_cls.append(fg_pred[:, (obj_idx+1):]) - - grid = torch.stack([gi, gj], dim=1) - pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. - #pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. - pw = self.wh_bin_sigmoid.forward(fg_pred[..., 2:(3+self.bin_count)].sigmoid()) * anch[i][idx][:, 0] * self.stride[i] - ph = self.wh_bin_sigmoid.forward(fg_pred[..., (3+self.bin_count):obj_idx].sigmoid()) * anch[i][idx][:, 1] * self.stride[i] - - pxywh = torch.cat([pxy, pw.unsqueeze(1), ph.unsqueeze(1)], dim=-1) - pxyxy = xywh2xyxy(pxywh) - pxyxys.append(pxyxy) - - pxyxys = torch.cat(pxyxys, dim=0) - if pxyxys.shape[0] == 0: - continue - p_obj = torch.cat(p_obj, dim=0) - p_cls = torch.cat(p_cls, dim=0) - from_which_layer = torch.cat(from_which_layer, dim=0) - all_b = torch.cat(all_b, dim=0) - all_a = torch.cat(all_a, dim=0) - all_gj = torch.cat(all_gj, dim=0) - all_gi = torch.cat(all_gi, dim=0) - all_anch = torch.cat(all_anch, dim=0) - - pair_wise_iou = box_iou(txyxy, pxyxys) - - pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) - - top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) - dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) - - gt_cls_per_image = ( - F.one_hot(this_target[:, 1].to(torch.int64), self.nc) - .float() - .unsqueeze(1) - .repeat(1, pxyxys.shape[0], 1) - ) - - num_gt = this_target.shape[0] - cls_preds_ = ( - p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - ) - - y = cls_preds_.sqrt_() - pair_wise_cls_loss = F.binary_cross_entropy_with_logits( - torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" - ).sum(-1) - del cls_preds_ - - cost = ( - pair_wise_cls_loss - + 3.0 * pair_wise_iou_loss - ) - - matching_matrix = torch.zeros_like(cost) - - for gt_idx in range(num_gt): - _, pos_idx = torch.topk( - cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False - ) - matching_matrix[gt_idx][pos_idx] = 1.0 - - del top_k, dynamic_ks - anchor_matching_gt = matching_matrix.sum(0) - if (anchor_matching_gt > 1).sum() > 0: - _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) - matching_matrix[:, anchor_matching_gt > 1] *= 0.0 - matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 - fg_mask_inboxes = matching_matrix.sum(0) > 0.0 - matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) - - from_which_layer = from_which_layer[fg_mask_inboxes] - all_b = all_b[fg_mask_inboxes] - all_a = all_a[fg_mask_inboxes] - all_gj = all_gj[fg_mask_inboxes] - all_gi = all_gi[fg_mask_inboxes] - all_anch = all_anch[fg_mask_inboxes] - - this_target = this_target[matched_gt_inds] - - for i in range(nl): - layer_idx = from_which_layer == i - matching_bs[i].append(all_b[layer_idx]) - matching_as[i].append(all_a[layer_idx]) - matching_gjs[i].append(all_gj[layer_idx]) - matching_gis[i].append(all_gi[layer_idx]) - matching_targets[i].append(this_target[layer_idx]) - matching_anchs[i].append(all_anch[layer_idx]) - - for i in range(nl): - matching_bs[i] = torch.cat(matching_bs[i], dim=0) - matching_as[i] = torch.cat(matching_as[i], dim=0) - matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) - matching_gis[i] = torch.cat(matching_gis[i], dim=0) - matching_targets[i] = torch.cat(matching_targets[i], dim=0) - matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) - - return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs - - def find_3_positive(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - indices, anch = [], [] - gain = torch.ones(7, device=targets.device) # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - anch.append(anchors[a]) # anchors - - return indices, anch diff --git a/spaces/ispast/Genshin_MB_VITS_TTS/modules.py b/spaces/ispast/Genshin_MB_VITS_TTS/modules.py deleted file mode 100644 index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000 --- a/spaces/ispast/Genshin_MB_VITS_TTS/modules.py +++ /dev/null @@ -1,390 +0,0 @@ -import copy -import math -import numpy as np -import scipy -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -import commons -from commons import init_weights, get_padding -from transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential( - nn.ReLU(), - nn.Dropout(p_dropout)) - for _ in range(n_layers-1): - self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size ** i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, - groups=channels, dilation=dilation, padding=padding - )) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): - super(WN, self).__init__() - assert(kernel_size % 2 == 1) - self.hidden_channels =hidden_channels - self.kernel_size = kernel_size, - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') - - for i in range(n_layers): - dilation = dilation_rate ** i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, - dilation=dilation, padding=padding) - in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply( - x_in, - g_l, - n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:,:self.hidden_channels,:] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:,self.hidden_channels:,:] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels,1)) - self.logs = nn.Parameter(torch.zeros(channels,1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1,2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) - self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_derivatives = h[..., 2 * self.num_bins:] - - x1, logabsdet = piecewise_rational_quadratic_transform(x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails='linear', - tail_bound=self.tail_bound - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1,2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/jbilcke-hf/LifeSim/src/components/business/video-renderer.tsx b/spaces/jbilcke-hf/LifeSim/src/components/business/video-renderer.tsx deleted file mode 100644 index aca52150b18c84953e0638dd205cf4a649b678b7..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/LifeSim/src/components/business/video-renderer.tsx +++ /dev/null @@ -1,22 +0,0 @@ -"use client" - -export const VideoRenderer = ({ url }: { url?: string }) => { - - if (!url) { - return
-
- }
-
- return (
- Rendering first frames.. (might take around 30s)
-
-
-
- )
-}
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/VideoQuest/src/app/games/pharaoh.ts b/spaces/jbilcke-hf/VideoQuest/src/app/games/pharaoh.ts
deleted file mode 100644
index 4261df7f35374ce7fd0406451173f047eb8d5ca2..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/app/games/pharaoh.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-import { macondo } from "@/lib/fonts"
-import { Game } from "./types"
-import { InventoryItem } from "../../types"
-
-const initialSituation = [
- `looking at a beautiful pyramid, ancient egypt, during golden hour, surrounded by sand dunes, near the Nile`,
-].join(", ")
-
-const initialActionnables = [
- "pyramid",
- "person",
- "rocks",
- "dune",
- "sceptre",
- "tree",
- "river",
- "boat",
- "sun"
-]
-
-const inventory: InventoryItem[] = [
- {
- name: "bowl",
- title: "Bowl",
- caption: "",
- description: "A bowl. To eat things."
- },
- {
- name: "box",
- title: "Box",
- caption: "",
- description: "Full of mysteries."
- },
- {
- name: "golden-beetle",
- title: "Beetle pendant",
- caption: "",
- description: "This pendant has a mysterious aura.."
- },
- {
- name: "staff",
- title: "Staff",
- caption: "",
- description: "This used to belong to a magician."
- },
-]
-
-export const game: Game = {
- title: "Pharaoh",
- type: "pharaoh",
- description: [
- "The game is a role playing adventure set in ancient egypt.",
- "The player is Ahmose, a scribe asked by the Pharaoh to investigate ancient ruins about an unknown deity.",
- "The player can click around to move to new scenes, find or activate artifacts.",
- "They can also use objects from their inventory.",
- ],
- engines: [
- "cartesian_image",
- "cartesian_video",
- "spherical_image",
- ],
- className: macondo.className,
- initialSituation,
- initialActionnables,
- inventory,
- getScenePrompt: (situation?: string) => [
- `Screenshot from a videogame`,
- `unreal engine`,
- `ancient egypt`,
- `first person`,
- situation || initialSituation,
- ]
-}
-
diff --git a/spaces/jbilcke-hf/webapp-factory-llama-node/public/css/tailwind-typography@0.1.2.css b/spaces/jbilcke-hf/webapp-factory-llama-node/public/css/tailwind-typography@0.1.2.css
deleted file mode 100644
index 6824ef97438023939b62642ce3a28a69cc9e1176..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/webapp-factory-llama-node/public/css/tailwind-typography@0.1.2.css
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/spaces/jbondy007/Video_Search_CLIP/app.py b/spaces/jbondy007/Video_Search_CLIP/app.py
deleted file mode 100644
index d2eab3b31970abf438efd09b633dc4847f010a09..0000000000000000000000000000000000000000
--- a/spaces/jbondy007/Video_Search_CLIP/app.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import os
-os.system("pip freeze")
-import cv2
-from PIL import Image
-import clip
-import torch
-import math
-import numpy as np
-import torch
-import datetime
-import gradio as gr
-
-
-# Load the open CLIP model
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model, preprocess = clip.load("ViT-B/32", device=device)
-
-
-
-def inference(video, text):
- # The frame images will be stored in video_frames
- video_frames = []
- # Open the video file
-
- capture = cv2.VideoCapture(video)
- fps = capture.get(cv2.CAP_PROP_FPS)
-
- current_frame = 0
- # Read the current frame
- ret, frame = capture.read()
- while capture.isOpened() and ret:
- ret,frame = capture.read()
- print('Read a new frame: ', ret)
- current_frame += 1
- if ret:
- video_frames.append(Image.fromarray(frame[:, :, ::-1]))
-
-
- # Print some statistics
- print(f"Frames extracted: {len(video_frames)}")
-
-
- # You can try tuning the batch size for very large videos, but it should usually be OK
- batch_size = 256
- batches = math.ceil(len(video_frames) / batch_size)
-
- # The encoded features will bs stored in video_features
- video_features = torch.empty([0, 512], dtype=torch.float16).to(device)
-
- # Process each batch
- for i in range(batches):
- print(f"Processing batch {i+1}/{batches}")
-
- # Get the relevant frames
- batch_frames = video_frames[i*batch_size : (i+1)*batch_size]
-
- # Preprocess the images for the batch
- batch_preprocessed = torch.stack([preprocess(frame) for frame in batch_frames]).to(device)
-
- # Encode with CLIP and normalize
- with torch.no_grad():
- batch_features = model.encode_image(batch_preprocessed)
- batch_features /= batch_features.norm(dim=-1, keepdim=True)
-
- # Append the batch to the list containing all features
- video_features = torch.cat((video_features, batch_features))
-
- # Print some stats
- print(f"Features: {video_features.shape}")
-
-
- search_query=text
- display_heatmap=False
- display_results_count=1
- # Encode and normalize the search query using CLIP
- with torch.no_grad():
- text_features = model.encode_text(clip.tokenize(search_query).to(device))
- text_features /= text_features.norm(dim=-1, keepdim=True)
-
- # Compute the similarity between the search query and each frame using the Cosine similarity
- similarities = (100.0 * video_features @ text_features.T)
- values, best_photo_idx = similarities.topk(display_results_count, dim=0)
-
-
- for frame_id in best_photo_idx:
- frame = video_frames[frame_id]
- # Find the timestamp in the video and display it
- seconds = round(frame_id.cpu().numpy()[0]/fps)
- return frame,f"Found at {str(datetime.timedelta(seconds=seconds))}"
-
-title = "Video Search"
-description = "Gradio demo for using OpenAI's CLIP to search inside videos. To use it, simply upload your video and add your text. Read more at the links below."
-article = ""
-
-examples=[['test.mp4',"gas station"]]
-gr.Interface(
- inference,
- ["video","text"],
- [gr.outputs.Image(type="pil", label="Output"),"text"],
- title=title,
- description=description,
- article=article,
- examples=examples
- ).launch(debug=True,enable_queue=True)
-
\ No newline at end of file
diff --git a/spaces/jdczlx/ChatGPT-chuanhu/README.md b/spaces/jdczlx/ChatGPT-chuanhu/README.md
deleted file mode 100644
index 8df99398dd07f6fce2e1c98ad18fb9a21b619318..0000000000000000000000000000000000000000
--- a/spaces/jdczlx/ChatGPT-chuanhu/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/jgerbscheid/dpa-example/dijkprofile_annotator/utils/utils.py b/spaces/jgerbscheid/dpa-example/dijkprofile_annotator/utils/utils.py
deleted file mode 100644
index f9ff5f0aa591602eac2884b0013c4aab9154b45e..0000000000000000000000000000000000000000
--- a/spaces/jgerbscheid/dpa-example/dijkprofile_annotator/utils/utils.py
+++ /dev/null
@@ -1,350 +0,0 @@
-import os
-import random
-from collections import defaultdict
-
-import dijkprofile_annotator.preprocessing as preprocessing
-import dijkprofile_annotator.config as config
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-import torch
-import torch.nn.functional as F
-from sklearn.isotonic import IsotonicRegression
-from sklearn.preprocessing import MinMaxScaler, StandardScaler
-
-
-def extract_img(size, in_tensor):
- """
- Args:
- size(int) : size of cut
- in_tensor(tensor) : tensor to be cut
- """
- dim1 = in_tensor.size()[2]
- in_tensor = in_tensor[:, :, int((dim1-size)/2):int((size + (dim1-size)/2))]
- return in_tensor
-
-
-def ffill(arr):
- """Forward fill utility function.
-
- Args:
- arr (np.array): numpy array to fill
-
- Returns:
- np.array: filled array.
- """
- mask = np.isnan(arr)
- idx = np.where(~mask, np.arange(mask.shape[1]), 0)
- np.maximum.accumulate(idx, axis=1, out=idx)
- out = arr[np.arange(idx.shape[0])[:,None], idx]
- return out
-
-def train_scaler(profile_dict, scaler_type='minmax'):
- """Train a scaler given a profile dict
-
- Args:
- profile_dict (dict): dict containing the profile heights and labels
-
- Returns:
- sklearn MinMaxScaler or StandardScaler: fitted scaler in sklearn format
- """
- if scaler_type == 'minmax':
- scaler = MinMaxScaler(feature_range=(-1, 1)) # for neural networks -1,1 is better than 0,1
- elif scaler_type == 'standard':
- scaler = StandardScaler()
- else:
- raise NotImplementedError(f"no scaler: {scaler}")
- randkey = random.choice(list(profile_dict.keys()))
- accumulator = np.zeros((len(profile_dict), profile_dict[randkey]['profile'].shape[0]))
-
- for i, key in enumerate(profile_dict.keys()):
- accumulator[i, :] = profile_dict[key]['profile']
-
- scaler.fit(accumulator.reshape(-1, 1))
- return scaler
-
-
-def get_class_dict(class_list):
- """Get correct class dicts and weights from config.
-
- Args:
- class_list (string): string representing the class mappings to use
-
- Raises:
- NotImplementedError: raise if an not implemented class mapping is passed
-
- Returns:
- (dict,dict,list): dict with class mappings, inverse of that dict, weights for each class.
- """
- class_list = class_list.lower()
- if class_list == 'regional':
- class_dict = config.CLASS_DICT_REGIONAL
- inverse_class_dict = config.INVERSE_CLASS_DICT_REGIONAL
- class_weights = config.WEIGHT_DICT_REGIONAL
- elif class_list == 'simple':
- class_dict = config.CLASS_DICT_SIMPLE
- class_weights = config.WEIGHT_DICT_SIMPLE
- inverse_class_dict = config.INVERSE_CLASS_DICT_SIMPLE
- elif class_list == 'berm':
- class_dict = config.CLASS_DICT_SIMPLE_BERM
- class_weights = config.WEIGHT_DICT_SIMPLE_BERM
- inverse_class_dict = config.INVERSE_CLASS_DICT_SIMPLE_BERM
- elif class_list == 'sloot':
- class_dict = config.CLASS_DICT_SIMPLE_SLOOT
- class_weights = config.WEIGHT_DICT_SIMPLE_SLOOT
- inverse_class_dict = config.INVERSE_CLASS_DICT_SIMPLE_SLOOT
- elif class_list == 'full':
- class_dict = config.CLASS_DICT_FULL
- class_weights = config.WEIGHT_DICT_FULL
- inverse_class_dict = config.INVERSE_CLASS_DICT_FULL
- else:
- raise NotImplementedError(f"No configs found for class list of type: {class_list}")
- return class_dict, inverse_class_dict, class_weights
-
-
-def force_sequential_predictions(predictions, method='isotonic'):
- """Force the classes in the sample to always go up from left to right. This is
- makes sense because a higher class could never be left of a lower class in the
- representation chosen here. Two methods are available, Isotonic Regression and
- a group first method. I would use the Isotonic regression.
-
- Args:
- predictions (torch.Tensor): Tensor output of the model in shape (batch_size, channel_size, sample_size)
- method (str, optional): method to use for enforcing the sequentiality. Defaults to 'isotonic'.
-
- Raises:
- NotImplementedError: if the given method is not implemented
-
- Returns:
- torch.Tensor: Tensor in the same shape as the input but then with only increasing classes from left to right.
- """
- predictions = predictions.detach().cpu()
- n_classes = predictions.shape[1] # 1 is the channel dimension
- if method == 'first':
- # loop over batch
- for j in range(predictions.shape[0]):
- pred = torch.argmax(predictions[j], dim=0)
-
- # construct dict of groups of start-end indices for class
- groups = defaultdict(list)
- current_class = pred[0]
- group_start_idx = 0
- for i in range(1, len(pred)):
- if pred[i] != current_class:
- groups[current_class.item()].append((group_start_idx, i))
- group_start_idx = i
- current_class = pred[i]
-
- # if the class occurs again later in the profile
- # discard this occurance of it
- new_pred = torch.zeros(len(pred))
- last_index = 0
- for class_n, group_tuples in sorted(groups.items()):
- for group_tuple in group_tuples:
- if group_tuple[0] >= last_index:
- new_pred[group_tuple[0]:group_tuple[1]] = class_n
- last_index = group_tuple[1]
- break
-
- # simple forward fill
- for i in range(1, len(new_pred)):
- if new_pred[i] == 0:
- new_pred[i] = new_pred[i-1]
-
- # encode back to one-hot tensor
- predictions[j] = F.one_hot(new_pred.to(torch.int64), num_classes=n_classes).permute(1,0)
- elif method == 'isotonic':
- for i in range(predictions.shape[0]):
- pred = torch.argmax(predictions[i], dim=0)
-
- x = np.arange(0,len(pred))
- iso_reg = IsotonicRegression().fit(x, pred)
- new_pred = iso_reg.predict(x)
- new_pred = np.round(new_pred)
-
- # encode back to one-hot tensor
- new_pred = F.one_hot(torch.Tensor(new_pred).to(torch.int64), num_classes=n_classes).permute(1,0)
- predictions[i] = new_pred
- else:
- raise NotImplementedError(f"Unknown method: {method}")
-
- return predictions
-
-
-
-def visualize_prediction(heights, prediction, labels, location_name, class_list):
- """visualize a profile plus labels and prediction
-
- Args:
- heights (tensor): tensor containing the heights data of the profile
- prediction (tensor): tensor containing the predicted data of the profile
- labels (tensor): tensor containing the labels for each height point in heights
- location_name (str): name of the profile, just for visualization
- class_list (str): class mapping to use, determines which labels are visualized
- """
- class_dict, inverse_class_dict, _ = get_class_dict(class_list)
- fig, ax = plt.subplots(figsize=(20,11))
- plt.title(location_name)
- plt.plot(heights, label='profile')
-
- # change one-hot batched format to list of classes
- if prediction.dim() == 3:
- prediction = torch.argmax(torch.squeeze(prediction, dim=0), dim=0)
- if prediction.dim() == 2:
- # assuming channel first representation
- prediction = torch.argmax(prediction, dim=0)
- prediction = prediction.detach().cpu().numpy()
-
- # ax.set_ylim(top=np.max(heights), bottom=np.min(heights))
- label_height = np.min(heights)
- n_labels = len(np.unique(labels))
- label_height_distance = (np.max(heights) - np.min(heights))/(n_labels*2)
-
- cmap = sns.color_palette("Set2", len(set(class_dict.values())))
-
- # plot actual labels
- prev_class_n = 999
- for index, class_n in enumerate(labels):
- if class_n == 0:
- continue
- if class_n != prev_class_n:
- plt.axvline(index, 0,5, color=cmap[class_n], linestyle=(0,(5,10))) # loose dashes
- plt.text(index, label_height, inverse_class_dict[class_n], rotation=0)
- label_height += label_height_distance
- prev_class_n = class_n
-
- # plot predicted points
- used_classes = []
- prev_class_n = 999
- for index, class_n in enumerate(prediction):
- if class_n == 0 or class_n in used_classes:
- continue
- if class_n != prev_class_n:
- plt.axvline(index, 0,5, color=cmap[class_n], linestyle=(0,(1,1))) # small dots
- plt.text(index, label_height, "predicted " + inverse_class_dict[class_n], rotation=0)
- label_height += label_height_distance
- used_classes.append(prev_class_n)
- prev_class_n = class_n
-
- plt.show()
-
-
-def visualize_sample(heights, labels, location_name, class_list):
- """visualize a profile and labels.
-
- Args:
- heights (tensor): tensor containing the heights data of the profile
- labels (tensor): tensor containing the labels for each height point in heights
- location_name (str): name of the profile, just for visualization
- class_list (str): class mapping to use, determines which labels are visualized
- """
- class_dict, inverse_class_dict, _ = get_class_dict(class_list)
- fig, ax = plt.subplots(figsize=(20,11))
- plt.title(location_name)
- plt.plot(heights, label='profile')
-
- # ax.set_ylim(top=np.max(heights), bottom=np.min(heights))
- label_height = np.min(heights)
- n_labels = len(np.unique(labels))
- label_height_distance = (np.max(heights) - np.min(heights))/(n_labels*2)
-
- cmap = sns.color_palette("Set2", len(set(class_dict.values())))
-
- # plot actual labels
- prev_class_n = 999
- for index, class_n in enumerate(labels):
- if class_n == 0:
- continue
- if class_n != prev_class_n:
- plt.axvline(index, 0,5, color=cmap[class_n], linestyle=(0,(5,10))) # loose dashes
- plt.text(index, label_height, inverse_class_dict[class_n], rotation=0)
- label_height += label_height_distance
- prev_class_n = class_n
-
- plt.show()
-
-def visualize_files(linesfp, pointsfp, max_profile_size=512, class_list='simple', location_index=0, return_dict=False):
- """visualize profile lines and points filepaths.
-
- Args:
- linesfp (str): path to surfacelines file.
- pointsfp (str): path to points file.
- max_profile_size (int, optional): cutoff size of the profile, can leave on default here. Defaults to 512.
- class_list (str, optional): class mapping to use. Defaults to 'simple'.
- location_index (int, optional): index of profile to visualize.. Defaults to 0.
- return_dict (bool, optional): return the profile dict for faster visualization. Defaults to False.
-
- Returns:
- [dict, optional]: profile dict containing the profiles of the given files
- """
- profile_label_dict = preprocessing.filepath_pair_to_labeled_sample(linesfp,
- pointsfp,
- max_profile_size=max_profile_size,
- class_list=class_list)
-
- location_name = list(profile_label_dict.keys())[location_index]
- heights = profile_label_dict[location_name]['profile']
- labels = profile_label_dict[location_name]['label']
-
- class_dict, inverse_class_dict, _ = get_class_dict(class_list)
- fig, ax = plt.subplots(figsize=(20,11))
- plt.title(location_name)
- plt.plot(heights, label='profile')
-
- label_height = np.min(heights)
- n_labels = len(np.unique(labels))
- label_height_distance = (np.max(heights) - np.min(heights))/(n_labels)
-
- cmap = sns.color_palette("Set2", len(set(class_dict.values())))
-
- # plot actual labels
- prev_class_n = 999
- for index, class_n in enumerate(labels):
- if class_n == 0:
- continue
- if class_n != prev_class_n:
- plt.axvline(index, 0,5, color=cmap[class_n], linestyle=(0,(5,10))) # loose dashes
- plt.text(index, label_height, inverse_class_dict[class_n], rotation=0)
- label_height += label_height_distance
- prev_class_n = class_n
-
- plt.show()
-
- if return_dict:
- return profile_label_dict
-
-def visualize_dict(profile_label_dict, class_list='simple', location_index=0):
- """visualise profile with labels from profile_dict, profile specified by index.
-
- Args:
- profile_label_dict (dict): dict containing profiles and labels
- class_list (str, optional): class_mapping to use for visualization. Defaults to 'simple'.
- location_index (int, optional): specifies the index of the profile to visualize. Defaults to 0.
- """
- location_name = list(profile_label_dict.keys())[location_index]
- heights = profile_label_dict[location_name]['profile']
- labels = profile_label_dict[location_name]['label']
-
- class_dict, inverse_class_dict, _ = get_class_dict(class_list)
- fig, ax = plt.subplots(figsize=(20,11))
- plt.title(location_name)
- plt.plot(heights, label='profile')
-
- label_height = np.min(heights)
- n_labels = len(np.unique(labels))
- label_height_distance = (np.max(heights) - np.min(heights))/(n_labels)
-
- cmap = sns.color_palette("Set2", len(set(class_dict.values())))
-
- # plot actual labels
- prev_class_n = 999
- for index, class_n in enumerate(labels):
- if class_n == 0:
- continue
- if class_n != prev_class_n:
- plt.axvline(index, 0,5, color=cmap[class_n], linestyle=(0,(5,10))) # loose dashes
- plt.text(index, label_height, inverse_class_dict[class_n], rotation=0)
- label_height += label_height_distance
- prev_class_n = class_n
-
- plt.show()
\ No newline at end of file
diff --git a/spaces/jiejiejie0420/bingo/src/components/chat.tsx b/spaces/jiejiejie0420/bingo/src/components/chat.tsx
deleted file mode 100644
index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000
--- a/spaces/jiejiejie0420/bingo/src/components/chat.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
- const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
- const {
- messages,
- sendMessage,
- resetConversation,
- stopGenerating,
- setInput,
- bot,
- input,
- generating,
- isSpeaking,
- uploadImage,
- attachmentList,
- setAttachmentList,
- } = useBing()
-
- useEffect(() => {
- window.scrollTo({
- top: document.body.offsetHeight,
- behavior: 'smooth'
- })
- }, [])
-
- return (
-
-
- )
-}
diff --git a/spaces/jkim1238/predictive_analysis/README.md b/spaces/jkim1238/predictive_analysis/README.md
deleted file mode 100644
index bb6749f9713a3b97bee4c207873ccdeb4fc44ab6..0000000000000000000000000000000000000000
--- a/spaces/jkim1238/predictive_analysis/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Predictive Analysis
-emoji: 📉
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.10.0
-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/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/SunImagePlugin.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/SunImagePlugin.py
deleted file mode 100644
index 6712583d71cc6f7ded205eb812c7fe5ee77f6ac6..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/SunImagePlugin.py
+++ /dev/null
@@ -1,139 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Sun image file handling
-#
-# History:
-# 1995-09-10 fl Created
-# 1996-05-28 fl Fixed 32-bit alignment
-# 1998-12-29 fl Import ImagePalette module
-# 2001-12-18 fl Fixed palette loading (from Jean-Claude Rimbault)
-#
-# Copyright (c) 1997-2001 by Secret Labs AB
-# Copyright (c) 1995-1996 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i32be as i32
-
-
-def _accept(prefix):
- return len(prefix) >= 4 and i32(prefix) == 0x59A66A95
-
-
-##
-# Image plugin for Sun raster files.
-
-
-class SunImageFile(ImageFile.ImageFile):
- format = "SUN"
- format_description = "Sun Raster File"
-
- def _open(self):
- # The Sun Raster file header is 32 bytes in length
- # and has the following format:
-
- # typedef struct _SunRaster
- # {
- # DWORD MagicNumber; /* Magic (identification) number */
- # DWORD Width; /* Width of image in pixels */
- # DWORD Height; /* Height of image in pixels */
- # DWORD Depth; /* Number of bits per pixel */
- # DWORD Length; /* Size of image data in bytes */
- # DWORD Type; /* Type of raster file */
- # DWORD ColorMapType; /* Type of color map */
- # DWORD ColorMapLength; /* Size of the color map in bytes */
- # } SUNRASTER;
-
- # HEAD
- s = self.fp.read(32)
- if not _accept(s):
- msg = "not an SUN raster file"
- raise SyntaxError(msg)
-
- offset = 32
-
- self._size = i32(s, 4), i32(s, 8)
-
- depth = i32(s, 12)
- # data_length = i32(s, 16) # unreliable, ignore.
- file_type = i32(s, 20)
- palette_type = i32(s, 24) # 0: None, 1: RGB, 2: Raw/arbitrary
- palette_length = i32(s, 28)
-
- if depth == 1:
- self.mode, rawmode = "1", "1;I"
- elif depth == 4:
- self.mode, rawmode = "L", "L;4"
- elif depth == 8:
- self.mode = rawmode = "L"
- elif depth == 24:
- if file_type == 3:
- self.mode, rawmode = "RGB", "RGB"
- else:
- self.mode, rawmode = "RGB", "BGR"
- elif depth == 32:
- if file_type == 3:
- self.mode, rawmode = "RGB", "RGBX"
- else:
- self.mode, rawmode = "RGB", "BGRX"
- else:
- msg = "Unsupported Mode/Bit Depth"
- raise SyntaxError(msg)
-
- if palette_length:
- if palette_length > 1024:
- msg = "Unsupported Color Palette Length"
- raise SyntaxError(msg)
-
- if palette_type != 1:
- msg = "Unsupported Palette Type"
- raise SyntaxError(msg)
-
- offset = offset + palette_length
- self.palette = ImagePalette.raw("RGB;L", self.fp.read(palette_length))
- if self.mode == "L":
- self.mode = "P"
- rawmode = rawmode.replace("L", "P")
-
- # 16 bit boundaries on stride
- stride = ((self.size[0] * depth + 15) // 16) * 2
-
- # file type: Type is the version (or flavor) of the bitmap
- # file. The following values are typically found in the Type
- # field:
- # 0000h Old
- # 0001h Standard
- # 0002h Byte-encoded
- # 0003h RGB format
- # 0004h TIFF format
- # 0005h IFF format
- # FFFFh Experimental
-
- # Old and standard are the same, except for the length tag.
- # byte-encoded is run-length-encoded
- # RGB looks similar to standard, but RGB byte order
- # TIFF and IFF mean that they were converted from T/IFF
- # Experimental means that it's something else.
- # (https://www.fileformat.info/format/sunraster/egff.htm)
-
- if file_type in (0, 1, 3, 4, 5):
- self.tile = [("raw", (0, 0) + self.size, offset, (rawmode, stride))]
- elif file_type == 2:
- self.tile = [("sun_rle", (0, 0) + self.size, offset, rawmode)]
- else:
- msg = "Unsupported Sun Raster file type"
- raise SyntaxError(msg)
-
-
-#
-# registry
-
-
-Image.register_open(SunImageFile.format, SunImageFile, _accept)
-
-Image.register_extension(SunImageFile.format, ".ras")
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/compiler.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/compiler.py
deleted file mode 100644
index 0944c92fddb4a620d11287a1df1583464b9b1c92..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/compiler.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from typing import Callable
-from altair.utils import PluginRegistry
-
-# ==============================================================================
-# Vega-Lite to Vega compiler registry
-# ==============================================================================
-VegaLiteCompilerType = Callable[[dict], dict]
-
-
-class VegaLiteCompilerRegistry(PluginRegistry[VegaLiteCompilerType]):
- pass
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/http.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/http.py
deleted file mode 100644
index 8fc0aafd9fb1c1642970f71231be593361260268..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/http.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import binascii
-from base64 import b64decode
-from typing import Optional
-
-from fastapi.exceptions import HTTPException
-from fastapi.openapi.models import HTTPBase as HTTPBaseModel
-from fastapi.openapi.models import HTTPBearer as HTTPBearerModel
-from fastapi.security.base import SecurityBase
-from fastapi.security.utils import get_authorization_scheme_param
-from pydantic import BaseModel
-from starlette.requests import Request
-from starlette.status import HTTP_401_UNAUTHORIZED, HTTP_403_FORBIDDEN
-
-
-class HTTPBasicCredentials(BaseModel):
- username: str
- password: str
-
-
-class HTTPAuthorizationCredentials(BaseModel):
- scheme: str
- credentials: str
-
-
-class HTTPBase(SecurityBase):
- def __init__(
- self,
- *,
- scheme: str,
- scheme_name: Optional[str] = None,
- description: Optional[str] = None,
- auto_error: bool = True,
- ):
- self.model = HTTPBaseModel(scheme=scheme, description=description)
- self.scheme_name = scheme_name or self.__class__.__name__
- self.auto_error = auto_error
-
- async def __call__(
- self, request: Request
- ) -> Optional[HTTPAuthorizationCredentials]:
- authorization = request.headers.get("Authorization")
- scheme, credentials = get_authorization_scheme_param(authorization)
- if not (authorization and scheme and credentials):
- if self.auto_error:
- raise HTTPException(
- status_code=HTTP_403_FORBIDDEN, detail="Not authenticated"
- )
- else:
- return None
- return HTTPAuthorizationCredentials(scheme=scheme, credentials=credentials)
-
-
-class HTTPBasic(HTTPBase):
- def __init__(
- self,
- *,
- scheme_name: Optional[str] = None,
- realm: Optional[str] = None,
- description: Optional[str] = None,
- auto_error: bool = True,
- ):
- self.model = HTTPBaseModel(scheme="basic", description=description)
- self.scheme_name = scheme_name or self.__class__.__name__
- self.realm = realm
- self.auto_error = auto_error
-
- async def __call__( # type: ignore
- self, request: Request
- ) -> Optional[HTTPBasicCredentials]:
- authorization = request.headers.get("Authorization")
- scheme, param = get_authorization_scheme_param(authorization)
- if self.realm:
- unauthorized_headers = {"WWW-Authenticate": f'Basic realm="{self.realm}"'}
- else:
- unauthorized_headers = {"WWW-Authenticate": "Basic"}
- if not authorization or scheme.lower() != "basic":
- if self.auto_error:
- raise HTTPException(
- status_code=HTTP_401_UNAUTHORIZED,
- detail="Not authenticated",
- headers=unauthorized_headers,
- )
- else:
- return None
- invalid_user_credentials_exc = HTTPException(
- status_code=HTTP_401_UNAUTHORIZED,
- detail="Invalid authentication credentials",
- headers=unauthorized_headers,
- )
- try:
- data = b64decode(param).decode("ascii")
- except (ValueError, UnicodeDecodeError, binascii.Error):
- raise invalid_user_credentials_exc
- username, separator, password = data.partition(":")
- if not separator:
- raise invalid_user_credentials_exc
- return HTTPBasicCredentials(username=username, password=password)
-
-
-class HTTPBearer(HTTPBase):
- def __init__(
- self,
- *,
- bearerFormat: Optional[str] = None,
- scheme_name: Optional[str] = None,
- description: Optional[str] = None,
- auto_error: bool = True,
- ):
- self.model = HTTPBearerModel(bearerFormat=bearerFormat, description=description)
- self.scheme_name = scheme_name or self.__class__.__name__
- self.auto_error = auto_error
-
- async def __call__(
- self, request: Request
- ) -> Optional[HTTPAuthorizationCredentials]:
- authorization = request.headers.get("Authorization")
- scheme, credentials = get_authorization_scheme_param(authorization)
- if not (authorization and scheme and credentials):
- if self.auto_error:
- raise HTTPException(
- status_code=HTTP_403_FORBIDDEN, detail="Not authenticated"
- )
- else:
- return None
- if scheme.lower() != "bearer":
- if self.auto_error:
- raise HTTPException(
- status_code=HTTP_403_FORBIDDEN,
- detail="Invalid authentication credentials",
- )
- else:
- return None
- return HTTPAuthorizationCredentials(scheme=scheme, credentials=credentials)
-
-
-class HTTPDigest(HTTPBase):
- def __init__(
- self,
- *,
- scheme_name: Optional[str] = None,
- description: Optional[str] = None,
- auto_error: bool = True,
- ):
- self.model = HTTPBaseModel(scheme="digest", description=description)
- self.scheme_name = scheme_name or self.__class__.__name__
- self.auto_error = auto_error
-
- async def __call__(
- self, request: Request
- ) -> Optional[HTTPAuthorizationCredentials]:
- authorization = request.headers.get("Authorization")
- scheme, credentials = get_authorization_scheme_param(authorization)
- if not (authorization and scheme and credentials):
- if self.auto_error:
- raise HTTPException(
- status_code=HTTP_403_FORBIDDEN, detail="Not authenticated"
- )
- else:
- return None
- if scheme.lower() != "digest":
- raise HTTPException(
- status_code=HTTP_403_FORBIDDEN,
- detail="Invalid authentication credentials",
- )
- return HTTPAuthorizationCredentials(scheme=scheme, credentials=credentials)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/utils.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/utils.py
deleted file mode 100644
index fa7a450b74e813e66fd6e9a140d48c29215503bb..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/utils.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from typing import Optional, Tuple
-
-
-def get_authorization_scheme_param(
- authorization_header_value: Optional[str],
-) -> Tuple[str, str]:
- if not authorization_header_value:
- return "", ""
- scheme, _, param = authorization_header_value.partition(" ")
- return scheme, param
diff --git a/spaces/jordonpeter01/MusicGen/tests/modules/test_lstm.py b/spaces/jordonpeter01/MusicGen/tests/modules/test_lstm.py
deleted file mode 100644
index 1248964c8191e19f27661f0974bef9cc967eb015..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen/tests/modules/test_lstm.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import random
-import torch
-
-from audiocraft.modules.lstm import StreamableLSTM
-
-
-class TestStreamableLSTM:
-
- def test_lstm(self):
- B, C, T = 4, 2, random.randint(1, 100)
-
- lstm = StreamableLSTM(C, 3, skip=False)
- x = torch.randn(B, C, T)
- y = lstm(x)
-
- print(y.shape)
- assert y.shape == torch.Size([B, C, T])
-
- def test_lstm_skip(self):
- B, C, T = 4, 2, random.randint(1, 100)
-
- lstm = StreamableLSTM(C, 3, skip=True)
- x = torch.randn(B, C, T)
- y = lstm(x)
-
- assert y.shape == torch.Size([B, C, T])
diff --git a/spaces/jordonpeter01/MusicGen2/tests/common_utils/wav_utils.py b/spaces/jordonpeter01/MusicGen2/tests/common_utils/wav_utils.py
deleted file mode 100644
index d3a563ee1749a58217ece55c9a08b8d93c0fc386..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen2/tests/common_utils/wav_utils.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from pathlib import Path
-import typing as tp
-
-import torch
-import torchaudio
-
-
-def get_white_noise(chs: int = 1, num_frames: int = 1):
- wav = torch.randn(chs, num_frames)
- return wav
-
-
-def get_batch_white_noise(bs: int = 1, chs: int = 1, num_frames: int = 1):
- wav = torch.randn(bs, chs, num_frames)
- return wav
-
-
-def save_wav(path: str, wav: torch.Tensor, sample_rate: int):
- fp = Path(path)
- kwargs: tp.Dict[str, tp.Any] = {}
- if fp.suffix == '.wav':
- kwargs['encoding'] = 'PCM_S'
- kwargs['bits_per_sample'] = 16
- elif fp.suffix == '.mp3':
- kwargs['compression'] = 320
- torchaudio.save(str(fp), wav, sample_rate, **kwargs)
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/app/layout.tsx b/spaces/jordonpeter01/ai-comic-factory/src/app/layout.tsx
deleted file mode 100644
index 5c483885eda7b5d2003cc6052f014f0474b9749a..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/app/layout.tsx
+++ /dev/null
@@ -1,24 +0,0 @@
-import './globals.css'
-import type { Metadata } from 'next'
-import { Inter } from 'next/font/google'
-
-const inter = Inter({ subsets: ['latin'] })
-
-export const metadata: Metadata = {
- title: 'AI Comic Factory: generate your own comics! Powered by Hugging Face 🤗',
- description: 'Generate comic panels using a LLM + SDXL. Powered by Hugging Face 🤗',
-}
-
-export default function RootLayout({
- children,
-}: {
- children: React.ReactNode
-}) {
- return (
-
-
- {children}
-
-
- )
-}
diff --git a/spaces/jordonpeter01/stable-diffusion/share_btn.py b/spaces/jordonpeter01/stable-diffusion/share_btn.py
deleted file mode 100644
index 4c9aa8a91b1d0f86746fb118c19b03df86d424a3..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/stable-diffusion/share_btn.py
+++ /dev/null
@@ -1,60 +0,0 @@
-community_icon_html = """"""
-
-loading_icon_html = """"""
-
-share_js = """async () => {
- async function uploadFile(file){
- const UPLOAD_URL = 'https://huggingface.co/uploads';
- const response = await fetch(UPLOAD_URL, {
- method: 'POST',
- headers: {
- 'Content-Type': file.type,
- 'X-Requested-With': 'XMLHttpRequest',
- },
- body: file, /// <- File inherits from Blob
- });
- const url = await response.text();
- return url;
- }
- const gradioEl = document.querySelector('body > gradio-app');
- const imgEls = gradioEl.querySelectorAll('#gallery img');
- const promptTxt = gradioEl.querySelector('#prompt-text-input input').value;
- const shareBtnEl = gradioEl.querySelector('#share-btn');
- const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
- const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
- if(!imgEls.length){
- return;
- };
- shareBtnEl.style.pointerEvents = 'none';
- shareIconEl.style.display = 'none';
- loadingIconEl.style.removeProperty('display');
- const files = await Promise.all(
- [...imgEls].map(async (imgEl) => {
- const res = await fetch(imgEl.src);
- const blob = await res.blob();
- const imgId = Date.now() % 200;
- const fileName = `diffuse-the-rest-${{imgId}}.jpg`;
- return new File([blob], fileName, { type: 'image/jpeg' });
- })
- );
- const urls = await Promise.all(files.map((f) => uploadFile(f)));
- const htmlImgs = urls.map(url => `
-
-
-
-
- ) : null}
-
- ) : null}
-
-${htmlImgs.join(`\n`)}
-
`;
- const params = new URLSearchParams({
- title: promptTxt,
- description: descriptionMd,
- });
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/stabilityai/stable-diffusion/discussions/new?${paramsStr}`, '_blank');
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/josuelmet/Metal_Music_Interpolator/_Generation.py b/spaces/josuelmet/Metal_Music_Interpolator/_Generation.py
deleted file mode 100644
index b338ef25b74b9dd011cf4cfd7f55624b5e2e0f63..0000000000000000000000000000000000000000
--- a/spaces/josuelmet/Metal_Music_Interpolator/_Generation.py
+++ /dev/null
@@ -1,662 +0,0 @@
-import guitarpro
-from guitarpro import *
-from matplotlib import pyplot as plt
-import mgzip
-import numpy as np
-import os
-from os.path import join
-import pickle
-from tqdm import tqdm
-
-import tensorflow as tf
-from tensorflow import keras
-from keras.callbacks import ModelCheckpoint
-from keras.models import Sequential
-from keras.layers import Activation, Dense, LSTM, Dropout, Flatten
-
-from _Decompressor import SongWriter
-
-
-
-# Define some constants:
-
-
-# PITCH[i] = the pitch associated with midi note number i.
-# For example, PITCH[69] = 'A4'
-PITCH = {val : str(GuitarString(number=0, value=val)) for val in range(128)}
-# MIDI[string] = the midi number associated with the note described by string.
-# For example, MIDI['A4'] = 69.
-MIDI = {str(GuitarString(number=0, value=val)) : val for val in range(128)}
-
-
-
-
-
-
-# Generation helper methods:
-def thirty_seconds_to_duration(count):
- if count % 3 == 0:
- # If the note is dotted, do 32 / (i * 2/3), and return isDotted = True.
- return (48//count, True)
- else:
- # If the note is not dotted, to 32 / i, and return isDotted = False.
- return (32//count, False)
-
-
-def quantize_thirty_seconds(value):
-
- # 32nd-note values of each fundamental type of note (not including 64th-notes, of course).
- vals = np.array([32, # whole
- 24, # dotted half
- 16, # half
- 12, # dotted quarter
- 8, # quarter
- 6, # dotted eigth
- 4, # eigth
- 3, # dotted sixteenth
- 2, # sixteenth
- 1]) # thirty-second
-
- list_out = []
-
- for v in vals:
- if v <= value:
- list_out.append(thirty_seconds_to_duration(v))
- value -= v
-
- return np.array(list_out)
-
-
-
-
-def adjust_to_4_4(prediction_output):
- '''
- Adjust prediction output to be in 4/4 time.
- Then, separate the beats into measures.
- '''
-
- # This will be the prediction output
- new_prediction_output = []
-
-
- time = 0
- for beat in prediction_output:
-
- # Calculate the fraction of a measure encompassed by the current beat / chord.
- beat_time = (1 / beat[1]) * (1 + 0.5 * beat[2])
-
- # Calculate the fraction of a measure taken up by all notes in the measure.
- # Calculate any residual time to see if this measure (in 4/4 time) is longer than 1 measure.
- measure_time = time + beat_time
- leftover_time = (measure_time) % 1
-
- # If the measure count (i.e., the measure integer) has changed and there is significant left-over beat time:
- if (int(measure_time) > int(time)) and (leftover_time > 1/128):
-
- # Calculate the initial 32nd notes encompassed by this beat in the current measure.
- this_measure_thirty_seconds = int(32 * (1 - time % 1))
- # Calculate the remaining 32nd notes encompassed by this beat in the next measure.
- next_measure_thirty_seconds = int(32 * leftover_time)
-
- # Get the Duration object parameters for this measure and the next measure.
- this_measure_durations = quantize_thirty_seconds(this_measure_thirty_seconds)
- next_measure_durations = quantize_thirty_seconds(next_measure_thirty_seconds)
-
-
- #print(f'{{ {32 / beat[1]}')
- for duration_idx, duration in enumerate(this_measure_durations):
- time += (1 / duration[0]) * (1 + 0.5 * duration[1])
-
- #print(time, '\t', time * 32)
-
- chord = beat[0] if duration_idx == 0 else 'tied'
-
- new_prediction_output.append((chord, duration[0], duration[1], beat[3]))
-
-
- for duration in next_measure_durations:
- time += (1 / duration[0]) * (1 + 0.5 * duration[1])
-
- #print(time, '\t', time * 32)
-
- new_prediction_output.append(('tied', duration[0], duration[1], beat[3]))
-
-
- continue
-
-
- time += beat_time
- new_prediction_output.append((beat[0], beat[1], beat[2], beat[3]))
-
- #print(time, '\t', time * 32)
-
-
- '''
- # Code for debugging
-
- time = 0
- time2 = 0
- idx = 0
-
- for idx2, beat2 in enumerate(new_prediction_output[:100]):
- beat = prediction_output[idx]
-
- if time == time2:
- print(beat[0], '\t', time, '\t\t', beat2[0], '\t', time2)
-
- idx += 1
-
- time += (1 / beat[1]) * (1 + 0.5 * beat[2])
-
- else:
- print('\t\t\t\t', beat2[0], '\t', time2)
-
-
-
- time2 += (1 / beat2[1]) * (1 + 0.5 * beat2[2])
- ''';
-
- # Use the previously calculated cumulative time as the number of measures in the new 4/4 song.
- num_measures = int(np.ceil(time))
-
- song = np.empty(num_measures, dtype=object)
-
- time = 0
- m_idx = 0
-
- timestamps = []
-
- for beat in new_prediction_output:
- #print(time)
- timestamps.append(time)
-
- m_idx = int(time)
-
- if song[m_idx] is None:
-
- song[m_idx] = [beat]
- else:
- song[m_idx].append(beat)
-
-
- time += (1 / beat[1]) * (1 + 0.5 * beat[2])
-
-
- print(f'4/4 adjusted correctly: {set(range(num_measures)).issubset(set(timestamps))}')
-
- return song
-
-
-
-
-
-
-
-class Generator:
- def __init__(self, num_tracks_to_generate=5, as_fingerings=True, sequence_length=100):
- with mgzip.open(join('data', 'notes_data.pickle.gz'), 'rb') as filepath:
- self.notes = pickle.load(filepath)
- self.note_to_int = pickle.load(filepath)
- self.int_to_note = pickle.load(filepath)
- self.n_vocab = pickle.load(filepath)
- self.NUM_TRACKS_TO_GENERATE = num_tracks_to_generate
- self.as_fingerings = as_fingerings
- self.sequence_length = sequence_length
-
- with mgzip.open(join('data', 'track_data.pickle.gz'), 'rb') as filepath:
- self.track_data = pickle.load(filepath)
-
- self.model = keras.models.load_model('minigpt')
-
- self.ints = np.array([self.note_to_int[x] for x in self.notes])
-
-
-
- def generate_track(self, track_idx=None):
-
- if track_idx is None:
- # Choose a random track
- track_idx = np.random.choice(len(self.track_data))
-
- # Get the note indices corresponding to the beginning and ending of the track
- song_note_idx_first = self.track_data.loc[track_idx]['noteStartIdx']
- song_note_idx_last = self.track_data.loc[track_idx+1]['noteStartIdx']
-
- # Choose a random starting point within the track
- start_idx = np.random.randint(low=song_note_idx_first,
- high=song_note_idx_last)
-
- # Choose a number of initial notes to select from the track, at most 100.
- #num_initial_notes = np.random.choice(min(100, song_note_idx_last - start_idx))
- num_initial_notes = np.random.choice(min(100, song_note_idx_last - start_idx))
-
- # Select the initial notes (tokens)
- start_tokens = [_ for _ in self.ints[start_idx:start_idx+num_initial_notes]]
-
-
- max_tokens = 100
-
-
-
- def sample_from(logits, top_k=10):
- logits, indices = tf.math.top_k(logits, k=top_k, sorted=True)
- indices = np.asarray(indices).astype("int32")
- preds = keras.activations.softmax(tf.expand_dims(logits, 0))[0]
- preds = np.asarray(preds).astype("float32")
- return np.random.choice(indices, p=preds)
-
- num_tokens_generated = 0
- tokens_generated = []
-
- while num_tokens_generated <= max_tokens:
- pad_len = self.sequence_length - len(start_tokens)
- sample_index = len(start_tokens) - 1
- if pad_len < 0:
- x = start_tokens[:self.sequence_length]
- sample_index = self.sequence_length - 1
- elif pad_len > 0:
- x = start_tokens + [0] * pad_len
- else:
- x = start_tokens
- x = np.array([x])
- y, _ = self.model.predict(x)
- sample_token = sample_from(y[0][sample_index])
- tokens_generated.append(sample_token)
- start_tokens.append(sample_token)
- num_tokens_generated = len(tokens_generated)
-
- generated_notes = [self.int_to_note[num] for num in np.concatenate((start_tokens, tokens_generated))]
-
- return track_idx, generated_notes
-
-
-
- def generate_track_batch(self, artist=None):
-
- self.track_indices = np.zeros(self.NUM_TRACKS_TO_GENERATE)
- self.tracks = np.zeros(self.NUM_TRACKS_TO_GENERATE, dtype=object)
-
-
- for i in tqdm(range(self.NUM_TRACKS_TO_GENERATE)):
- if artist is None:
- idx, t = self.generate_track()
- else:
- idx, t = self.generate_track(track_idx=np.random.choice(list(self.track_data[self.track_data.artist==artist].index)))
- self.track_indices[i] = idx
- self.tracks[i] = t
-
-
-
- def save_tracks(self, filepath='_generation.gp5'):
-
- songWriter = SongWriter(initialTempo=self.track_data.loc[self.track_indices[0]]['tempo'])
-
- for idx in range(len(self.tracks)):
- new_track = adjust_to_4_4(self.tracks[idx])
-
- # Get the tempo and tuning (lowest string note) of the song:
- #print( track_data.loc[track_indices[idx]])
- tempo = self.track_data.loc[self.track_indices[idx]]['tempo']
- instrument = self.track_data.loc[self.track_indices[idx]]['instrument']
- name = self.track_data.loc[self.track_indices[idx]]['song']
- lowest_string = self.track_data.loc[self.track_indices[idx]]['tuning']
-
- if not self.as_fingerings:
- # Get all the unique pitch values from the new track
- pitchnames = set.union(*[set([beat[0].split('_')[0] for beat in measure]) for measure in new_track])
- pitchnames.discard('rest') # Ignore rests
- pitchnames.discard('tied') # Ignore tied notes
- pitchnames.discard('dead') # Ignore dead/ghost notes
- lowest_string = min([MIDI[pitch] for pitch in pitchnames]) # Get the lowest MIDI value / pitch
- lowest_string = min(lowest_string, MIDI['E2']) # Don't allow any tunings higher than standard.
-
-
- # Standard tuning
- tuning = {1: MIDI['E4'],
- 2: MIDI['B3'],
- 3: MIDI['G3'],
- 4: MIDI['D3'],
- 5: MIDI['A2'],
- 6: MIDI['E2']}
-
- if lowest_string <= MIDI['B1']:
- # 7-string guitar case
- tuning[7] = MIDI['B1']
- downtune = MIDI['B1'] - lowest_string
- else:
- # downtune the tuning by however much is necessary.
- downtune = MIDI['E2'] - lowest_string
-
- tuning = {k: v - downtune for k, v in tuning.items()} # Adjust to the new tuning
-
- # Write the track to the song writer
- songWriter.decompress_track(new_track, tuning, tempo=tempo, instrument=instrument, name=name, as_fingerings=self.as_fingerings)
-
-
-
- songWriter.write(filepath)
- print('Finished')
-
-
-
-
-
-
-
-
-
-'''
-
-
-def init_generator():
- global NUM_TRACKS_TO_GENERATE, notes, note_to_int, int_to_note, n_vocab, track_data, model, ints
-
- with mgzip.open('data\\notes_data.pickle.gz', 'rb') as filepath:
- notes = pickle.load(filepath)
- note_to_int = pickle.load(filepath)
- int_to_note = pickle.load(filepath)
- n_vocab = pickle.load(filepath)
-
- with mgzip.open('data\\track_data.pickle.gz', 'rb') as filepath:
- track_data = pickle.load(filepath)
-
- #with mgzip.open('output\\generated_songs.pickle.gz', 'rb') as filepath:
- # track_indices = pickle.load(filepath)
- # tracks = pickle.load(filepath)
-
- model = keras.models.load_model('minigpt')
-
- ints = np.array([note_to_int[x] for x in notes])
-
-
-
-
-def generate_track(track_idx=None):
- global track_data, ints, int_to_note
-
- if track_idx is None:
- # Choose a random track
- track_idx = np.random.choice(len(track_data))
-
- # Get the note indices corresponding to the beginning and ending of the track
- song_note_idx_first = track_data.loc[track_idx]['noteStartIdx']
- song_note_idx_last = track_data.loc[track_idx+1]['noteStartIdx']
-
- # Choose a random starting point within the track
- start_idx = np.random.randint(low=song_note_idx_first,
- high=song_note_idx_last)
-
- # Choose a number of initial notes to select from the track, at most 100.
- #num_initial_notes = np.random.choice(min(100, song_note_idx_last - start_idx))
- num_initial_notes = np.random.choice(min(100, song_note_idx_last - start_idx))
-
- # Select the initial notes (tokens)
- start_tokens = [_ for _ in ints[start_idx:start_idx+num_initial_notes]]
-
-
- max_tokens = 100
-
-
-
- def sample_from(logits, top_k=10):
- logits, indices = tf.math.top_k(logits, k=top_k, sorted=True)
- indices = np.asarray(indices).astype("int32")
- preds = keras.activations.softmax(tf.expand_dims(logits, 0))[0]
- preds = np.asarray(preds).astype("float32")
- return np.random.choice(indices, p=preds)
-
- num_tokens_generated = 0
- tokens_generated = []
-
- while num_tokens_generated <= max_tokens:
- pad_len = maxlen - len(start_tokens)
- sample_index = len(start_tokens) - 1
- if pad_len < 0:
- x = start_tokens[:maxlen]
- sample_index = maxlen - 1
- elif pad_len > 0:
- x = start_tokens + [0] * pad_len
- else:
- x = start_tokens
- x = np.array([x])
- y, _ = model.predict(x)
- sample_token = sample_from(y[0][sample_index])
- tokens_generated.append(sample_token)
- start_tokens.append(sample_token)
- num_tokens_generated = len(tokens_generated)
-
- generated_notes = [int_to_note[num] for num in np.concatenate((start_tokens, tokens_generated))]
-
- return track_idx, generated_notes
-
-
-
-
-def generate_track_batch(artist=None):
- global track_indices, tracks, NUM_TRACKS_TO_GENERATE, track_data
-
- track_indices = np.zeros(NUM_TRACKS_TO_GENERATE)
- tracks = np.zeros(NUM_TRACKS_TO_GENERATE, dtype=object)
-
-
- for i in tqdm(range(NUM_TRACKS_TO_GENERATE)):
- if artist is None:
- idx, t = generate_track()
- else:
- idx, t = generate_track(track_idx=np.random.choice(list(track_data[track_data.artist==artist].index)))
- track_indices[i] = idx
- tracks[i] = t
-
-
-
-
-
-# Generation helper methods:
-def thirty_seconds_to_duration(count):
- if count % 3 == 0:
- # If the note is dotted, do 32 / (i * 2/3), and return isDotted = True.
- return (48//count, True)
- else:
- # If the note is not dotted, to 32 / i, and return isDotted = False.
- return (32//count, False)
-
-
-def quantize_thirty_seconds(value):
-
- # 32nd-note values of each fundamental type of note (not including 64th-notes, of course).
- vals = np.array([32, # whole
- 24, # dotted half
- 16, # half
- 12, # dotted quarter
- 8, # quarter
- 6, # dotted eigth
- 4, # eigth
- 3, # dotted sixteenth
- 2, # sixteenth
- 1]) # thirty-second
-
- list_out = []
-
- for v in vals:
- if v <= value:
- list_out.append(thirty_seconds_to_duration(v))
- value -= v
-
- return np.array(list_out)
-
-
-
-
-def adjust_to_4_4(prediction_output):
-
- #Adjust prediction output to be in 4/4 time.
- #Then, separate the beats into measures.
-
-
- # This will be the prediction output
- new_prediction_output = []
-
-
- time = 0
- for beat in prediction_output:
-
- # Calculate the fraction of a measure encompassed by the current beat / chord.
- beat_time = (1 / beat[1]) * (1 + 0.5 * beat[2])
-
- # Calculate the fraction of a measure taken up by all notes in the measure.
- # Calculate any residual time to see if this measure (in 4/4 time) is longer than 1 measure.
- measure_time = time + beat_time
- leftover_time = (measure_time) % 1
-
- # If the measure count (i.e., the measure integer) has changed and there is significant left-over beat time:
- if (int(measure_time) > int(time)) and (leftover_time > 1/128):
-
- # Calculate the initial 32nd notes encompassed by this beat in the current measure.
- this_measure_thirty_seconds = int(32 * (1 - time % 1))
- # Calculate the remaining 32nd notes encompassed by this beat in the next measure.
- next_measure_thirty_seconds = int(32 * leftover_time)
-
- # Get the Duration object parameters for this measure and the next measure.
- this_measure_durations = quantize_thirty_seconds(this_measure_thirty_seconds)
- next_measure_durations = quantize_thirty_seconds(next_measure_thirty_seconds)
-
-
- #print(f'{{ {32 / beat[1]}')
- for duration_idx, duration in enumerate(this_measure_durations):
- time += (1 / duration[0]) * (1 + 0.5 * duration[1])
-
- #print(time, '\t', time * 32)
-
- chord = beat[0] if duration_idx == 0 else 'tied'
-
- new_prediction_output.append((chord, duration[0], duration[1]))
-
-
- for duration in next_measure_durations:
- time += (1 / duration[0]) * (1 + 0.5 * duration[1])
-
- #print(time, '\t', time * 32)
-
- new_prediction_output.append(('tied', duration[0], duration[1]))
-
-
- continue
-
-
- time += beat_time
- new_prediction_output.append((beat[0], beat[1], beat[2]))
-
- #print(time, '\t', time * 32)
-
-
-
- # Code for debugging
-
- #time = 0
- #time2 = 0
- #idx = 0
-
- #for idx2, beat2 in enumerate(new_prediction_output[:100]):
- # beat = prediction_output[idx]
-
- # if time == time2:
- # print(beat[0], '\t', time, '\t\t', beat2[0], '\t', time2)
-
- # idx += 1
-
- # time += (1 / beat[1]) * (1 + 0.5 * beat[2])
-
- # else:
- # print('\t\t\t\t', beat2[0], '\t', time2)
-
-
-
- # time2 += (1 / beat2[1]) * (1 + 0.5 * beat2[2])
-
-
- # Use the previously calculated cumulative time as the number of measures in the new 4/4 song.
- num_measures = int(np.ceil(time))
-
- song = np.empty(num_measures, dtype=object)
-
- time = 0
- m_idx = 0
-
- timestamps = []
-
- for beat in new_prediction_output:
- #print(time)
- timestamps.append(time)
-
- m_idx = int(time)
-
- if song[m_idx] is None:
-
- song[m_idx] = [beat]
- else:
- song[m_idx].append(beat)
-
-
- time += (1 / beat[1]) * (1 + 0.5 * beat[2])
-
-
- print(f'4/4 adjusted correctly: {set(range(num_measures)).issubset(set(timestamps))}')
-
- return song
-
-
-
-
-
-
-def save_tracks(filepath='_generation.gp5'):
- global track_data, track_indice, tracks
-
- songWriter = SongWriter(initialTempo=track_data.loc[track_indices[0]]['tempo'])
-
- for idx in range(len(tracks)):
- new_track = adjust_to_4_4(tracks[idx])
-
- # Get the tempo and tuning (lowest string note) of the song:
- #print( track_data.loc[track_indices[idx]])
- tempo = track_data.loc[track_indices[idx]]['tempo']
- instrument = track_data.loc[track_indices[idx]]['instrument']
- name = track_data.loc[track_indices[idx]]['song']
- lowest_string = track_data.loc[track_indices[idx]]['tuning']
-
- if not as_fingerings:
- # Get all the unique pitch values from the new track
- pitchnames = set.union(*[set([beat[0].split('_')[0] for beat in measure]) for measure in new_track])
- pitchnames.discard('rest') # Ignore rests
- pitchnames.discard('tied') # Ignore tied notes
- pitchnames.discard('dead') # Ignore dead/ghost notes
- lowest_string = min([MIDI[pitch] for pitch in pitchnames]) # Get the lowest MIDI value / pitch
- lowest_string = min(lowest_string, MIDI['E2']) # Don't allow any tunings higher than standard.
-
-
- # Standard tuning
- tuning = {1: MIDI['E4'],
- 2: MIDI['B3'],
- 3: MIDI['G3'],
- 4: MIDI['D3'],
- 5: MIDI['A2'],
- 6: MIDI['E2']}
-
- if lowest_string <= MIDI['B1']:
- # 7-string guitar case
- tuning[7] = MIDI['B1']
- downtune = MIDI['B1'] - lowest_string
- else:
- # downtune the tuning by however much is necessary.
- downtune = MIDI['E2'] - lowest_string
-
- tuning = {k: v - downtune for k, v in tuning.items()} # Adjust to the new tuning
-
- # Write the track to the song writer
- songWriter.decompress_track(new_track, tuning, tempo=tempo, instrument=instrument, name=name, as_fingerings=as_fingerings)
-
-
-
- songWriter.write(filepath)
- print('Finished')
-'''
\ No newline at end of file
diff --git a/spaces/jy46604790/Fake-News-Recognition/Part1.md b/spaces/jy46604790/Fake-News-Recognition/Part1.md
deleted file mode 100644
index 02fb7c00e81e91af8a54eaab51c75b095b785b9f..0000000000000000000000000000000000000000
--- a/spaces/jy46604790/Fake-News-Recognition/Part1.md
+++ /dev/null
@@ -1,28 +0,0 @@
-## Background
-
-With the widespread use of various social apps. Anyone can be a producer, carrier, and disseminator of information. Broadly speaking, any summary, paraphrase, or commentary on new emerging things can be considered news. In this wide range of information, there might be restorations of the original appearance of the events, and there might also be some biased or false information.
-
-## Problem
-
-Most of the time, ordinary people do not have the channels and ability to distinguish the right and wrong information. However, when fake news reaches a certain scale of dissemination, it is very likely to cause huge losses to people or cause misunderstandings. For example, during the pandemic, if there is fake news claiming that a drug that is more harmful to people can protect people from the virus, then those who believe the news may suffer losses. Therefore, we want to provide a way to identify the possibility of fake news to prevent people from being misled by fake news.
-
-Here are two example of fake news:
-
-```
-title:
-Donald Trump Sends Out Embarrassing New Year's Eve Message; This is Disturbing
-
-content:
-Donald Trump just couldn t wish all Americans a Happy New Year and leave it at that. Instead, he had to give a shout out to his enemies, haters and the very dishonest fake news media. The former reality show star had just one job to do and he couldn t do it. As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year, President Angry Pants tweeted. 2018 will be a great year for America! As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year. 2018 will be a great year for America! Donald J. Trump (@realDonaldTrump) December 31, 2017Trump s tweet went down about as welll as you d expect.What kind of president sends a New Year s greeting like this despicable, petty, infantile gibberish? Only Trump! His lack of decency won t even allow him to rise above the gutter long enough to wish the American citizens a happy new year! Bishop Talbert Swan (@TalbertSwan) December 31, 2017no one likes you Calvin (@calvinstowell) December 31, 2017Your impeachment would make 2018 a great year for America, but I ll also accept regaining control of Congress. Miranda Yaver (@mirandayaver) December 31, 2017Do you hear yourself talk? When you have to include that many people that hate you you have to wonder? Why do the they all hate me? Alan Sandoval (@AlanSandoval13) December 31, 2017Who uses the word Haters in a New Years wish?? Marlene (@marlene399) December 31, 2017You can t just say happy new year? Koren pollitt (@Korencarpenter) December 31, 2017Here s Trump s New Year s Eve tweet from 2016.Happy New Year to all, including to my many enemies and those who have fought me and lost so badly they just don t know what to do. Love! Donald J. Trump (@realDonaldTrump) December 31, 2016This is nothing new for Trump. He s been doing this for years.Trump has directed messages to his enemies and haters for New Year s, Easter, Thanksgiving, and the anniversary of 9/11. pic.twitter.com/4FPAe2KypA Daniel Dale (@ddale8) December 31, 2017Trump s holiday tweets are clearly not presidential.How long did he work at Hallmark before becoming President? Steven Goodine (@SGoodine) December 31, 2017He s always been like this . . . the only difference is that in the last few years, his filter has been breaking down. Roy Schulze (@thbthttt) December 31, 2017Who, apart from a teenager uses the term haters? Wendy (@WendyWhistles) December 31, 2017he s a fucking 5 year old Who Knows (@rainyday80) December 31, 2017So, to all the people who voted for this a hole thinking he would change once he got into power, you were wrong! 70-year-old men don t change and now he s a year older.Photo by Andrew Burton/Getty Images.
-```
-
-```
-title:
-Former GOP Rep Throws Support Behind Obamacare After Becoming Unemployed With Pre-Existing Condition
-
-content:
-Former GOP Rep Throws Support Behind Obamacare After Becoming Unemployed With Pre-Existing Condition
-This proves that Republicans never care about the struggles Americans face until they go through it themselves.Donald Trump is continuing to push Republicans to pass his Trumpcare bill to repeal the Affordable Care Act, which would strip healthcare from millions of Americans and end protections that help people with pre-existing conditions get access to affordable health insurance.Republicans have been whining about what is also known as Obamacare for a long time now. They even demonized the landmark healthcare law so much that they ran campaigns on it. That includes David Jolly, who ran an anti-Obamacare campaign in Florida back in 2014 for a House seat.Jolly has since lost his seat in Congress, but he is singing a much different tune about the Affordable Care Act now.You see, Jolly became unemployed after losing his re-election bid. And to top it all off, he has a pre-existing condition that would have made getting insurance much harder had it not been for the protections provided by Obamacare, the very same law that Jolly opposed.During an appearance on MSNBC on Monday night, Jolly revealed that he has a pre-existing condition and went to say that he now supports Obamacare and does not want it to be repealed because doing so would put millions of Americans at serious risk. On January 4th, I was a former member of Congress unemployed with no health insurance, Jolly explained. And a pre-existing condition. And while I ultimately chose a private sector plan, I also knew in 2017 that Obamacare provided an exchange that was a safety net that wasn t there before, Jolly continued. If I had to rely on it, I knew it was there. Jolly went to say that the politics of Obamacare are different today than they were in 2013 when he ran for Congress out of anger towards Obamacare. But he now realizes that Obamacare provides protections and options for people who need a safety net.Here s the video via YouTube. Jolly s relevant remarks are at the 9:30 mark.22 million Americans would lose their healthcare coverage if Republicans pass Trump s healthcare bill. Republicans in Congress, meanwhile, get to keep their taxpayer-funded health insurance. That s not right.The Trumpcare bill is cruel. It destroys healthcare for the poor just so the wealthy can get a major tax cut. It should not only end the political careers of any Republican who votes for it, it should put them behind bars. People will die because of this bill. And when that happens, every Republican who voted for it should be charged with murder.Featured Image: Screenshot
-```
-
-As is shown above, the main feature of fake news is to create a feeling for reader that counter common sense. So this model can be applied to the major social media to avoid damage from fake news as much as possible.
\ No newline at end of file
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/samples/README.md b/spaces/keithhon/Real-Time-Voice-Cloning/samples/README.md
deleted file mode 100644
index 1a392d86e42f72e83954619f563f4881da327236..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/samples/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
-The audio files in this folder are provided for toolbox testing and
-benchmarking purposes. These are the same reference utterances
-used by the SV2TTS authors to generate the audio samples located at:
-https://google.github.io/tacotron/publications/speaker_adaptation/index.html
-
-The `p240_00000.mp3` and `p260_00000.mp3` files are compressed
-versions of audios from the VCTK corpus available at:
-https://datashare.is.ed.ac.uk/handle/10283/3443
-VCTK.txt contains the copyright notices and licensing information.
-
-The `1320_00000.mp3`, `3575_00000.mp3`, `6829_00000.mp3`
-and `8230_00000.mp3` files are compressed versions of audios
-from the LibriSpeech dataset available at: https://openslr.org/12
-For these files, the following notice applies:
-```
-LibriSpeech (c) 2014 by Vassil Panayotov
-
-LibriSpeech ASR corpus is licensed under a
-Creative Commons Attribution 4.0 International License.
-
-See - Full Credits for this example go to [Sayak Paul](https://twitter.com/RisingSayak).
- Model card can be found [here](https://huggingface.co/brendenc/VQ-VAE).
- Demo by [Brenden Connors](https://www.linkedin.com/in/brenden-connors-6a0512195)""") - - with gr.Row(): - with gr.Column(): - with gr.Row(): - radio = gr.Radio(choices=['Reconstruction','Discrete Latent Representation']) - with gr.Row(): - button = gr.Button('Run') - with gr.Column(): - out = gr.Plot() - - button.click(plot_sample, radio, out) - -demo.launch() \ No newline at end of file diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/audio2pose_models/audio2pose.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/audio2pose_models/audio2pose.py deleted file mode 100644 index 2b8cd1427038460a7679260a424d2f01d2bcf2c5..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/audio2pose_models/audio2pose.py +++ /dev/null @@ -1,94 +0,0 @@ -import torch -from torch import nn -from src.audio2pose_models.cvae import CVAE -from src.audio2pose_models.discriminator import PoseSequenceDiscriminator -from src.audio2pose_models.audio_encoder import AudioEncoder - -class Audio2Pose(nn.Module): - def __init__(self, cfg, wav2lip_checkpoint, device='cuda'): - super().__init__() - self.cfg = cfg - self.seq_len = cfg.MODEL.CVAE.SEQ_LEN - self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE - self.device = device - - self.audio_encoder = AudioEncoder(wav2lip_checkpoint, device) - self.audio_encoder.eval() - for param in self.audio_encoder.parameters(): - param.requires_grad = False - - self.netG = CVAE(cfg) - self.netD_motion = PoseSequenceDiscriminator(cfg) - - - def forward(self, x): - - batch = {} - coeff_gt = x['gt'].cuda().squeeze(0) #bs frame_len+1 73 - batch['pose_motion_gt'] = coeff_gt[:, 1:, 64:70] - coeff_gt[:, :1, 64:70] #bs frame_len 6 - batch['ref'] = coeff_gt[:, 0, 64:70] #bs 6 - batch['class'] = x['class'].squeeze(0).cuda() # bs - indiv_mels= x['indiv_mels'].cuda().squeeze(0) # bs seq_len+1 80 16 - - # forward - audio_emb_list = [] - audio_emb = self.audio_encoder(indiv_mels[:, 1:, :, :].unsqueeze(2)) #bs seq_len 512 - batch['audio_emb'] = audio_emb - batch = self.netG(batch) - - pose_motion_pred = batch['pose_motion_pred'] # bs frame_len 6 - pose_gt = coeff_gt[:, 1:, 64:70].clone() # bs frame_len 6 - pose_pred = coeff_gt[:, :1, 64:70] + pose_motion_pred # bs frame_len 6 - - batch['pose_pred'] = pose_pred - batch['pose_gt'] = pose_gt - - return batch - - def test(self, x): - - batch = {} - ref = x['ref'] #bs 1 70 - batch['ref'] = x['ref'][:,0,-6:] - batch['class'] = x['class'] - bs = ref.shape[0] - - indiv_mels= x['indiv_mels'] # bs T 1 80 16 - indiv_mels_use = indiv_mels[:, 1:] # we regard the ref as the first frame - num_frames = x['num_frames'] - num_frames = int(num_frames) - 1 - - # - div = num_frames//self.seq_len - re = num_frames%self.seq_len - audio_emb_list = [] - pose_motion_pred_list = [torch.zeros(batch['ref'].unsqueeze(1).shape, dtype=batch['ref'].dtype, - device=batch['ref'].device)] - - for i in range(div): - z = torch.randn(bs, self.latent_dim).to(ref.device) - batch['z'] = z - audio_emb = self.audio_encoder(indiv_mels_use[:, i*self.seq_len:(i+1)*self.seq_len,:,:,:]) #bs seq_len 512 - batch['audio_emb'] = audio_emb - batch = self.netG.test(batch) - pose_motion_pred_list.append(batch['pose_motion_pred']) #list of bs seq_len 6 - - if re != 0: - z = torch.randn(bs, self.latent_dim).to(ref.device) - batch['z'] = z - audio_emb = self.audio_encoder(indiv_mels_use[:, -1*self.seq_len:,:,:,:]) #bs seq_len 512 - if audio_emb.shape[1] != self.seq_len: - pad_dim = self.seq_len-audio_emb.shape[1] - pad_audio_emb = audio_emb[:, :1].repeat(1, pad_dim, 1) - audio_emb = torch.cat([pad_audio_emb, audio_emb], 1) - batch['audio_emb'] = audio_emb - batch = self.netG.test(batch) - pose_motion_pred_list.append(batch['pose_motion_pred'][:,-1*re:,:]) - - pose_motion_pred = torch.cat(pose_motion_pred_list, dim = 1) - batch['pose_motion_pred'] = pose_motion_pred - - pose_pred = ref[:, :1, -6:] + pose_motion_pred # bs T 6 - - batch['pose_pred'] = pose_pred - return batch diff --git a/spaces/kevinwang676/VoiceChangers/src/face3d/options/test_options.py b/spaces/kevinwang676/VoiceChangers/src/face3d/options/test_options.py deleted file mode 100644 index 4ff3ad142779850d1d5a1640bc00f70d34d4a862..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/VoiceChangers/src/face3d/options/test_options.py +++ /dev/null @@ -1,21 +0,0 @@ -"""This script contains the test options for Deep3DFaceRecon_pytorch -""" - -from .base_options import BaseOptions - - -class TestOptions(BaseOptions): - """This class includes test options. - - It also includes shared options defined in BaseOptions. - """ - - def initialize(self, parser): - parser = BaseOptions.initialize(self, parser) # define shared options - parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') - parser.add_argument('--dataset_mode', type=str, default=None, help='chooses how datasets are loaded. [None | flist]') - parser.add_argument('--img_folder', type=str, default='examples', help='folder for test images.') - - # Dropout and Batchnorm has different behavior during training and test. - self.isTrain = False - return parser diff --git a/spaces/kevinwang676/VoiceChangers/src/facerender/sync_batchnorm/batchnorm.py b/spaces/kevinwang676/VoiceChangers/src/facerender/sync_batchnorm/batchnorm.py deleted file mode 100644 index 5f4e763f0366dffa10320116413f8c7181a8aeb1..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/VoiceChangers/src/facerender/sync_batchnorm/batchnorm.py +++ /dev/null @@ -1,315 +0,0 @@ -# -*- coding: utf-8 -*- -# File : batchnorm.py -# Author : Jiayuan Mao -# Email : maojiayuan@gmail.com -# Date : 27/01/2018 -# -# This file is part of Synchronized-BatchNorm-PyTorch. -# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch -# Distributed under MIT License. - -import collections - -import torch -import torch.nn.functional as F - -from torch.nn.modules.batchnorm import _BatchNorm -from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast - -from .comm import SyncMaster - -__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d'] - - -def _sum_ft(tensor): - """sum over the first and last dimention""" - return tensor.sum(dim=0).sum(dim=-1) - - -def _unsqueeze_ft(tensor): - """add new dementions at the front and the tail""" - return tensor.unsqueeze(0).unsqueeze(-1) - - -_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) -_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) - - -class _SynchronizedBatchNorm(_BatchNorm): - def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): - super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) - - self._sync_master = SyncMaster(self._data_parallel_master) - - self._is_parallel = False - self._parallel_id = None - self._slave_pipe = None - - def forward(self, input): - # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. - if not (self._is_parallel and self.training): - return F.batch_norm( - input, self.running_mean, self.running_var, self.weight, self.bias, - self.training, self.momentum, self.eps) - - # Resize the input to (B, C, -1). - input_shape = input.size() - input = input.view(input.size(0), self.num_features, -1) - - # Compute the sum and square-sum. - sum_size = input.size(0) * input.size(2) - input_sum = _sum_ft(input) - input_ssum = _sum_ft(input ** 2) - - # Reduce-and-broadcast the statistics. - if self._parallel_id == 0: - mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) - else: - mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) - - # Compute the output. - if self.affine: - # MJY:: Fuse the multiplication for speed. - output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) - else: - output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) - - # Reshape it. - return output.view(input_shape) - - def __data_parallel_replicate__(self, ctx, copy_id): - self._is_parallel = True - self._parallel_id = copy_id - - # parallel_id == 0 means master device. - if self._parallel_id == 0: - ctx.sync_master = self._sync_master - else: - self._slave_pipe = ctx.sync_master.register_slave(copy_id) - - def _data_parallel_master(self, intermediates): - """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" - - # Always using same "device order" makes the ReduceAdd operation faster. - # Thanks to:: Tete Xiao (http://tetexiao.com/) - intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) - - to_reduce = [i[1][:2] for i in intermediates] - to_reduce = [j for i in to_reduce for j in i] # flatten - target_gpus = [i[1].sum.get_device() for i in intermediates] - - sum_size = sum([i[1].sum_size for i in intermediates]) - sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) - mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) - - broadcasted = Broadcast.apply(target_gpus, mean, inv_std) - - outputs = [] - for i, rec in enumerate(intermediates): - outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) - - return outputs - - def _compute_mean_std(self, sum_, ssum, size): - """Compute the mean and standard-deviation with sum and square-sum. This method - also maintains the moving average on the master device.""" - assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' - mean = sum_ / size - sumvar = ssum - sum_ * mean - unbias_var = sumvar / (size - 1) - bias_var = sumvar / size - - self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data - self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data - - return mean, bias_var.clamp(self.eps) ** -0.5 - - -class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): - r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a - mini-batch. - - .. math:: - - y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta - - This module differs from the built-in PyTorch BatchNorm1d as the mean and - standard-deviation are reduced across all devices during training. - - For example, when one uses `nn.DataParallel` to wrap the network during - training, PyTorch's implementation normalize the tensor on each device using - the statistics only on that device, which accelerated the computation and - is also easy to implement, but the statistics might be inaccurate. - Instead, in this synchronized version, the statistics will be computed - over all training samples distributed on multiple devices. - - Note that, for one-GPU or CPU-only case, this module behaves exactly same - as the built-in PyTorch implementation. - - The mean and standard-deviation are calculated per-dimension over - the mini-batches and gamma and beta are learnable parameter vectors - of size C (where C is the input size). - - During training, this layer keeps a running estimate of its computed mean - and variance. The running sum is kept with a default momentum of 0.1. - - During evaluation, this running mean/variance is used for normalization. - - Because the BatchNorm is done over the `C` dimension, computing statistics - on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm - - Args: - num_features: num_features from an expected input of size - `batch_size x num_features [x width]` - eps: a value added to the denominator for numerical stability. - Default: 1e-5 - momentum: the value used for the running_mean and running_var - computation. Default: 0.1 - affine: a boolean value that when set to ``True``, gives the layer learnable - affine parameters. Default: ``True`` - - Shape: - - Input: :math:`(N, C)` or :math:`(N, C, L)` - - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) - - Examples: - >>> # With Learnable Parameters - >>> m = SynchronizedBatchNorm1d(100) - >>> # Without Learnable Parameters - >>> m = SynchronizedBatchNorm1d(100, affine=False) - >>> input = torch.autograd.Variable(torch.randn(20, 100)) - >>> output = m(input) - """ - - def _check_input_dim(self, input): - if input.dim() != 2 and input.dim() != 3: - raise ValueError('expected 2D or 3D input (got {}D input)' - .format(input.dim())) - super(SynchronizedBatchNorm1d, self)._check_input_dim(input) - - -class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): - r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch - of 3d inputs - - .. math:: - - y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta - - This module differs from the built-in PyTorch BatchNorm2d as the mean and - standard-deviation are reduced across all devices during training. - - For example, when one uses `nn.DataParallel` to wrap the network during - training, PyTorch's implementation normalize the tensor on each device using - the statistics only on that device, which accelerated the computation and - is also easy to implement, but the statistics might be inaccurate. - Instead, in this synchronized version, the statistics will be computed - over all training samples distributed on multiple devices. - - Note that, for one-GPU or CPU-only case, this module behaves exactly same - as the built-in PyTorch implementation. - - The mean and standard-deviation are calculated per-dimension over - the mini-batches and gamma and beta are learnable parameter vectors - of size C (where C is the input size). - - During training, this layer keeps a running estimate of its computed mean - and variance. The running sum is kept with a default momentum of 0.1. - - During evaluation, this running mean/variance is used for normalization. - - Because the BatchNorm is done over the `C` dimension, computing statistics - on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm - - Args: - num_features: num_features from an expected input of - size batch_size x num_features x height x width - eps: a value added to the denominator for numerical stability. - Default: 1e-5 - momentum: the value used for the running_mean and running_var - computation. Default: 0.1 - affine: a boolean value that when set to ``True``, gives the layer learnable - affine parameters. Default: ``True`` - - Shape: - - Input: :math:`(N, C, H, W)` - - Output: :math:`(N, C, H, W)` (same shape as input) - - Examples: - >>> # With Learnable Parameters - >>> m = SynchronizedBatchNorm2d(100) - >>> # Without Learnable Parameters - >>> m = SynchronizedBatchNorm2d(100, affine=False) - >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) - >>> output = m(input) - """ - - def _check_input_dim(self, input): - if input.dim() != 4: - raise ValueError('expected 4D input (got {}D input)' - .format(input.dim())) - super(SynchronizedBatchNorm2d, self)._check_input_dim(input) - - -class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): - r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch - of 4d inputs - - .. math:: - - y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta - - This module differs from the built-in PyTorch BatchNorm3d as the mean and - standard-deviation are reduced across all devices during training. - - For example, when one uses `nn.DataParallel` to wrap the network during - training, PyTorch's implementation normalize the tensor on each device using - the statistics only on that device, which accelerated the computation and - is also easy to implement, but the statistics might be inaccurate. - Instead, in this synchronized version, the statistics will be computed - over all training samples distributed on multiple devices. - - Note that, for one-GPU or CPU-only case, this module behaves exactly same - as the built-in PyTorch implementation. - - The mean and standard-deviation are calculated per-dimension over - the mini-batches and gamma and beta are learnable parameter vectors - of size C (where C is the input size). - - During training, this layer keeps a running estimate of its computed mean - and variance. The running sum is kept with a default momentum of 0.1. - - During evaluation, this running mean/variance is used for normalization. - - Because the BatchNorm is done over the `C` dimension, computing statistics - on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm - or Spatio-temporal BatchNorm - - Args: - num_features: num_features from an expected input of - size batch_size x num_features x depth x height x width - eps: a value added to the denominator for numerical stability. - Default: 1e-5 - momentum: the value used for the running_mean and running_var - computation. Default: 0.1 - affine: a boolean value that when set to ``True``, gives the layer learnable - affine parameters. Default: ``True`` - - Shape: - - Input: :math:`(N, C, D, H, W)` - - Output: :math:`(N, C, D, H, W)` (same shape as input) - - Examples: - >>> # With Learnable Parameters - >>> m = SynchronizedBatchNorm3d(100) - >>> # Without Learnable Parameters - >>> m = SynchronizedBatchNorm3d(100, affine=False) - >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) - >>> output = m(input) - """ - - def _check_input_dim(self, input): - if input.dim() != 5: - raise ValueError('expected 5D input (got {}D input)' - .format(input.dim())) - super(SynchronizedBatchNorm3d, self)._check_input_dim(input) diff --git a/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/models/mdl_utils.py b/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/models/mdl_utils.py deleted file mode 100644 index 9becb4041a751a7c8bcd3e2c8261961e84772d32..0000000000000000000000000000000000000000 --- a/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/models/mdl_utils.py +++ /dev/null @@ -1,256 +0,0 @@ -import pandas as pd -import pickle -import lib.utils as libPaths - -m_blnTraceOn = False - -#--- load, merge data from file -m_kstrDataPath = libPaths.pth_data -m_kstrModelPath = libPaths.pth_model -m_kstrBinModelPath = libPaths.pth_binModels - -#m_kstrScalerPath_claims = m_kstrBinModelPath + 'stdClaims_scaler_colab.pkl' #--- does not work for scaling claims data; from v1.0.2; using 1.1.1 -#m_kstrScalerPath_claims2 = m_kstrBinModelPath + 'std_scaler_unsuperv_colab.pkl' #--- does not work; expects 32 features -#m_kstrScalerPath_claims = m_kstrBinModelPath + 'stdClaims_scaler_colab_v1.2.1.pkl' -m_kstrScalerPath_claims111 = m_kstrBinModelPath + 'claims_stdScaler_v1.1.1_27cols.pkl' -m_kstrScalerPath_claims121 = m_kstrBinModelPath + 'claims_stdScaler_v1.2.1_27cols.pkl' -m_kstrScalerPath_claims_py3816_sk111hp = m_kstrBinModelPath + 'claims_stdScl_py3816_sk111hp_27cols.pkl' -m_kstrScalerPath_claims = m_kstrScalerPath_claims_py3816_sk111hp - -m_kstrScalerPath_providers111 = m_kstrBinModelPath + 'prov_stdScaler_v1.1.1_32cols.pkl' -m_kstrScalerPath_providers121 = m_kstrBinModelPath + 'prov_stdScaler_v1.2.1_32cols.pkl' -m_kstrScalerPath_prov_py3816_sk111 = m_kstrBinModelPath + 'prov_stdScl_py3816_sk111_32cols.pkl' -m_kstrScalerPath_prov_py3816_sk111hp = m_kstrBinModelPath + 'prov_stdScl_py3816_sk111hp_32cols.pkl' -m_kstrScalerPath_prov = m_kstrScalerPath_prov_py3816_sk111hp - -m_kstrScalerPath_providers_superv = m_kstrBinModelPath + 'gbc_scaler.pkl' -m_kstrScalerPath_providers_train = m_kstrBinModelPath + "stdProvider_scaler.pkl" - - - -def doProviders_stdScaler(pdfFeatEng, blnIsTrain=False, hasGroupByProviderCols=True): - print("INFO (claims.do_stdScaler): blnIsTrain, ", blnIsTrain) - - #--- Note: prediction runs on X_val - ''' - #--- WARN: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. - # In a future version, numeric_only will default to False. Either specify - # numeric_only or select only columns which should be valid for the function. - ''' - - #--- WARN: this code groups all data by provider; any predictions will also be by provider - pdfGroupBy = pdfFeatEng - if (hasGroupByProviderCols): - pdfGroupBy = pdfFeatEng.groupby(['Provider'], as_index=False).agg('sum') - - X = pdfGroupBy - - try: - X = X.drop(columns=['Provider'], axis=1) #--- cannot scale; text - except KeyError: - #--- likely column not found; invalid fxn call - print("ERROR (mdlUtils.doProviders_stdScaler): Provider col not found") - - try: - X = X.drop(columns=['PotentialFraud'], axis=1) - except KeyError: - #--- likely column not found; invalid fxn call - if (blnIsTrain): print("ERROR (mdlUtils.doProviders_stdScaler): Potential Fraud col not found") - - - #--- apply std scaler - #--- WARN: scaling is also grouped by provider - if (m_blnTraceOn): print("INFO (mdlUtils.doProviders_stdScaler) cols: ", X.columns) #--- 32cols - X_std = fitProviders_txfStdScaler(X, blnIsTrain) - return X_std - - - -def doClaims_stdScaler(pdfFeatEng, blnIsTrain=False): - print("INFO (mdlUtils.doClaims_stdScaler): blnIsTrain, ", blnIsTrain) - - #--- Note: prediction runs on X_val - ''' - #--- WARN: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. - # In a future version, numeric_only will default to False. Either specify - # numeric_only or select only columns which should be valid for the function. - ''' - - #--- WARN: this code groups all data by provider; any predictions will also be by provider - X = pdfFeatEng - - try: - X = X.drop(columns=['Provider'], axis=1) #--- cannot scale; text - except KeyError: - #--- likely column not found; invalid fxn call - print("ERROR (mdlUtils.do_stdScaler): Provider col not found") - - try: - X = X.drop(columns=['PotentialFraud'], axis=1) - except KeyError: - #--- likely column not found; invalid fxn call - if (blnIsTrain): print("ERROR (mdlUtils.do_stdScaler): Potential Fraud col not found") - - - #--- apply std scaler - #--- WARN: scaling is also grouped by provider - #print("INFO (mdlUtils.doClaims_stdScaler) cols: ", X.columns) - X_std = fitClaims_txfStdScaler(X, blnIsTrain) - return X_std - - - -def doProviders_stdScaler_toPdf(npaScaled): - #--- NOTE: the list of cols came from doProvider_stdScaler; print(X.columns) - aryCols = ['InscClaimAmtReimbursed', 'DeductibleAmtPaid', 'AdmittedDays', - 'NoOfMonths_PartACov', 'NoOfMonths_PartBCov', 'ChronicCond_Alzheimer', - 'ChronicCond_Heartfailure', 'ChronicCond_KidneyDisease', - 'ChronicCond_Cancer', 'ChronicCond_ObstrPulmonary', - 'ChronicCond_Depression', 'ChronicCond_Diabetes', - 'ChronicCond_IschemicHeart', 'ChronicCond_Osteoporasis', - 'ChronicCond_rheumatoidarthritis', 'ChronicCond_stroke', - 'IPAnnualReimbursementAmt', 'IPAnnualDeductibleAmt', - 'OPAnnualReimbursementAmt', 'OPAnnualDeductibleAmt', 'Age', 'DeadOrNot', - 'Gender_2', 'Race_2', 'Race_3', 'Race_5', - 'ClaimReimbursement_ProviderAvg', - 'ClaimReimbursement_AttendingPhysician', - 'ClaimReimbursement_OperatingPhysician', - 'DeductibleAmtPaid_ProviderAvg', 'DeductibleAmtPaid_AttendingPhysician', - 'DeductibleAmtPaid_OperatingPhysician'] - - #npaScaled = do_stdScaler(pdfFeatEng) - pdfScaled = pd.DataFrame(npaScaled, columns=aryCols) - return pdfScaled - - - -def doClaims_stdScaler_toPdf(npaScaled): - #--- NOTE: the list of cols came from doClaims_stdScaler; print(X.columns) - aryCols = ['InscClaimAmtReimbursed', 'DeductibleAmtPaid', 'AdmittedDays', - 'RenalDiseaseIndicator', 'NoOfMonths_PartACov', 'NoOfMonths_PartBCov', 'ChronicCond_Alzheimer', - 'ChronicCond_Heartfailure', 'ChronicCond_KidneyDisease', - 'ChronicCond_Cancer', 'ChronicCond_ObstrPulmonary', - 'ChronicCond_Depression', 'ChronicCond_Diabetes', - 'ChronicCond_IschemicHeart', 'ChronicCond_Osteoporasis', - 'ChronicCond_rheumatoidarthritis', 'ChronicCond_stroke', - 'IPAnnualReimbursementAmt', 'IPAnnualDeductibleAmt', - 'OPAnnualReimbursementAmt', 'OPAnnualDeductibleAmt', 'Age', 'DeadOrNot', - 'Gender_2', 'Race_2', 'Race_3', 'Race_5'] - - #npaScaled = do_stdScaler(pdfFeatEng) - pdfScaled = pd.DataFrame(npaScaled, columns=aryCols) - return pdfScaled - - - - -def fitClaims_stdScaler(pdfData, blnIsTrain=False): - #--- apply scaler - #--- WARN: scaling is not grouped by provider - from sklearn.preprocessing import StandardScaler - - #--- note: this is a numpy.ndarray - #--- we need to fit the scaler, and then save as a pkl file - #strScalerPath = m_kstrScalerPath_claims - strScalerPath = m_kstrScalerPath_claims -# strScalerPath = m_kstrBinModelPath + "stdClaims_scaler_colab.pkl" - if (m_blnTraceOn): print("INFO (lib.model.fitClaims_stdScalar): ", strScalerPath) - if (blnIsTrain): - scaler = StandardScaler() - sclFit = scaler.fit(pdfData) - #--- if we train locally; write out to gbc_scalar.pkl - #--- we do not want to overwrite the colab version used for test - strScalerPath = m_kstrBinModelPath + "stdClaims_scaler.pkl" - if (m_blnTraceOn): print("INFO (lib.model.fit_stdScalar) Using local pkl for Train: ", strScalerPath) - with open(strScalerPath, 'wb') as filPkl: - pickle.dump(sclFit, filPkl) - else: - #--- we need to load the pkl file - import sklearn - if (m_blnTraceOn): print("INFO (lib.model.fit_stdScalar) Using colab pkl for Test: ", strScalerPath) - with open(strScalerPath, 'rb') as filPkl: - sclFit = pickle.load(filPkl) - if (m_blnTraceOn): print("TRACE (libModel.fitClaims_stdScalar) sclFit.type: ", type(sclFit)) - - #--- testing - scaler = StandardScaler() - if (m_blnTraceOn): print("TRACE (libModel.fitClaims_stdScalar) StdScaler.version: ", scaler.__getstate__()['_sklearn_version']) - if (m_blnTraceOn): print("TRACE (libModel.fitClaims_stdScalar) sclFit.version: " , sclFit.__getstate__()['_sklearn_version']) - if (m_blnTraceOn): print("TRACE (libModel.fitClaims_stdScalar) sklearn.version: " , sklearn.__version__) - return sclFit - - - -def fitProviders_stdScaler(pdfData, blnIsTrain=False): - #--- apply scaler - #--- WARN: scaling is also grouped by provider - from sklearn.preprocessing import StandardScaler - - #--- note: this is a numpy.ndarray - #--- we need to fit the scaler, and then save as a pkl file - #strScalerPath = m_kstrScalerPath_providers - #strScalerPath = m_kstrScalerPath_providers_train - strScalerPath = m_kstrScalerPath_prov - print("INFO (libModel.fitProviders_stdScalar): ", strScalerPath) - if (blnIsTrain): - scaler = StandardScaler() - sclFit = scaler.fit(pdfData) - #--- if we train locally; write out to gbc_scalar.pkl - #--- we do not want to overwrite the colab version used for test - strScalerPath = m_kstrScalerPath_providers_train #--- works for provider training - if (m_blnTraceOn): print("TRACE (libModel.fitProviders_stdScalar) Using local pkl for Train: ", strScalerPath) - with open(strScalerPath, 'wb') as filPkl: - pickle.dump(sclFit, filPkl) - else: - #--- we need to load the pkl file - if (m_blnTraceOn): print("TRACE (libModel.fitProviders_stdScalar) Using colab pkl for Test: ", strScalerPath) - with open(strScalerPath, 'rb') as filPkl: - sclFit = pickle.load(filPkl) - if (m_blnTraceOn): print("TRACE (libModel.fitProviders_stdScalar) sclFit.type: ", type(sclFit)) - return sclFit - - - -def fitProviders_stdScalerSuperv(pdfData, blnIsTrain=False): - #--- apply scaler - #--- WARN: scaling is also grouped by provider - from sklearn.preprocessing import StandardScaler - - #--- note: this is a numpy.ndarray - #--- we need to fit the scaler, and then save as a pkl file - strScalerPath = m_kstrScalerPath_prov - if (m_blnTraceOn): print("TRACE (libModel.fitProviders_stdScalar): ", strScalerPath) - if (blnIsTrain): - scaler = StandardScaler() - sclFit = scaler.fit(pdfData) - #--- if we train locally; write out to gbc_scalar.pkl - #--- we do not want to overwrite the colab version used for test - strScalerPath = m_kstrBinModelPath + "stdProvider_scaler.pkl" - if (m_blnTraceOn): print("TRACE (libModel.fitProviders_stdScalar) Using local pkl for Train: ", strScalerPath) - with open(strScalerPath, 'wb') as filPkl: - pickle.dump(sclFit, filPkl) - else: - #--- we need to load the pkl file - if (m_blnTraceOn): print("TRACE (libModel.fitProviders_stdScalar) Using colab pkl for Test: ", strScalerPath) - with open(strScalerPath, 'rb') as filPkl: - sclFit = pickle.load(filPkl) - if (m_blnTraceOn): print("TRACE (libModel.fitProviders_stdScalar) sclFit.type: ", type(sclFit)) - return sclFit - - - -def fitProviders_txfStdScaler(pdfData, blnIsTrain=False): - from sklearn.preprocessing import StandardScaler - sclFit = fitProviders_stdScaler(pdfData, blnIsTrain) - X_std = sclFit.transform(pdfData) - return X_std - - - -def fitClaims_txfStdScaler(pdfData, blnIsTrain=False): - from sklearn.preprocessing import StandardScaler - sclFit = fitClaims_stdScaler(pdfData, blnIsTrain) - - - X_std = sclFit.transform(pdfData) - return X_std \ No newline at end of file diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/logger/tensorboard.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/logger/tensorboard.py deleted file mode 100644 index 4dd5011dc08def6c09eef86d3ce5b124c9fc5372..0000000000000000000000000000000000000000 --- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/logger/tensorboard.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os.path as osp - -from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version -from ...dist_utils import master_only -from ..hook import HOOKS -from .base import LoggerHook - - -@HOOKS.register_module() -class TensorboardLoggerHook(LoggerHook): - - def __init__(self, - log_dir=None, - interval=10, - ignore_last=True, - reset_flag=False, - by_epoch=True): - super(TensorboardLoggerHook, self).__init__(interval, ignore_last, - reset_flag, by_epoch) - self.log_dir = log_dir - - @master_only - def before_run(self, runner): - super(TensorboardLoggerHook, self).before_run(runner) - if (TORCH_VERSION == 'parrots' - or digit_version(TORCH_VERSION) < digit_version('1.1')): - try: - from tensorboardX import SummaryWriter - except ImportError: - raise ImportError('Please install tensorboardX to use ' - 'TensorboardLoggerHook.') - else: - try: - from torch.utils.tensorboard import SummaryWriter - except ImportError: - raise ImportError( - 'Please run "pip install future tensorboard" to install ' - 'the dependencies to use torch.utils.tensorboard ' - '(applicable to PyTorch 1.1 or higher)') - - if self.log_dir is None: - self.log_dir = osp.join(runner.work_dir, 'tf_logs') - self.writer = SummaryWriter(self.log_dir) - - @master_only - def log(self, runner): - tags = self.get_loggable_tags(runner, allow_text=True) - for tag, val in tags.items(): - if isinstance(val, str): - self.writer.add_text(tag, val, self.get_iter(runner)) - else: - self.writer.add_scalar(tag, val, self.get_iter(runner)) - - @master_only - def after_run(self, runner): - self.writer.close() diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/roberta/preprocess_GLUE_tasks.sh b/spaces/koajoel/PolyFormer/fairseq/examples/roberta/preprocess_GLUE_tasks.sh deleted file mode 100644 index 7f215a3b53e1c4a7b1f0320102915a49d84a5015..0000000000000000000000000000000000000000 --- a/spaces/koajoel/PolyFormer/fairseq/examples/roberta/preprocess_GLUE_tasks.sh +++ /dev/null @@ -1,185 +0,0 @@ -#!/bin/bash -# 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. - - -# raw glue data as downloaded by glue download script (https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) -if [[ $# -ne 2 ]]; then - echo "Run as following:" - echo "./examples/roberta/preprocess_GLUE_tasks.sh