id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
20,187 | from typing import Union, Type
from .group_entities import type_from_index
from .categories import POINT, SEGMENT, LINE, CURVE, ELEMENT_2D
from .types import SlvsSketch, SlvsCircle, SlvsGenericEntity
EntityRef = Union[SlvsGenericEntity, int]
def _get_type(value: EntityRef) -> Type[SlvsGenericEntity]:
index = value ... | null |
20,188 | from typing import Union, Type
from .group_entities import type_from_index
from .categories import POINT, SEGMENT, LINE, CURVE, ELEMENT_2D
from .types import SlvsSketch, SlvsCircle, SlvsGenericEntity
EntityRef = Union[SlvsGenericEntity, int]
def _get_type(value: EntityRef) -> Type[SlvsGenericEntity]:
index = value ... | null |
20,189 | from typing import Union, Type
from .group_entities import type_from_index
from .categories import POINT, SEGMENT, LINE, CURVE, ELEMENT_2D
from .types import SlvsSketch, SlvsCircle, SlvsGenericEntity
EntityRef = Union[SlvsGenericEntity, int]
def _get_type(value: EntityRef) -> Type[SlvsGenericEntity]:
def is_sketch(ent... | null |
20,190 | from typing import Union, Type
from .group_entities import type_from_index
from .categories import POINT, SEGMENT, LINE, CURVE, ELEMENT_2D
from .types import SlvsSketch, SlvsCircle, SlvsGenericEntity
EntityRef = Union[SlvsGenericEntity, int]
def _get_type(value: EntityRef) -> Type[SlvsGenericEntity]:
index = value ... | null |
20,191 | import logging
import bpy
from bpy.props import IntProperty
from bpy.types import Context
import math
from mathutils import Vector, Matrix
def slvs_entity_pointer(cls, name, **kwargs):
index_prop = name + "_i"
annotations = {}
if hasattr(cls, "__annotations__"):
annotations = cls.__annotations__.co... | null |
20,192 | import logging
import bpy
from bpy.props import IntProperty
from bpy.types import Context
import math
from mathutils import Vector, Matrix
def tag_update(self, context: Context):
self.tag_update() | null |
20,193 | import logging
import bpy
from bpy.props import IntProperty
from bpy.types import Context
import math
from mathutils import Vector, Matrix
def round_v(vec, ndigits=None):
values = []
for v in vec:
values.append(round(v, ndigits=ndigits))
return Vector(values) | null |
20,194 | import logging
import bpy
from bpy.props import IntProperty
from bpy.types import Context
import math
from mathutils import Vector, Matrix
def get_connection_point(seg_1, seg_2):
points = seg_1.connection_points()
for p in seg_2.connection_points():
if p in points:
return p | null |
20,195 | import logging
import bpy
from bpy.props import IntProperty
from bpy.types import Context
import math
from mathutils import Vector, Matrix
def get_bezier_curve_midpoint_positions(
curve_element, segment_count, midpoints, angle, cyclic=False
):
positions = []
if segment_count == 1:
return []
if... | null |
20,196 | import logging
import bpy
from bpy.props import IntProperty
from bpy.types import Context
import math
from mathutils import Vector, Matrix
def create_bezier_curve(
segment_count,
bezier_points,
locations,
center,
base_offset,
invert=False,
cyclic=False,
):
if cyclic:
bezier_poin... | null |
20,197 | import logging
import bpy
from bpy.props import IntProperty
from bpy.types import Context
import math
from mathutils import Vector, Matrix
POINT = (*POINT3D, *POINT2D)
LINE = (SlvsLine3D, SlvsLine2D)
CURVE = (SlvsCircle, SlvsArc)
class SlvsWorkplane(SlvsGenericEntity, PropertyGroup):
"""Representation of a plane ... | null |
20,198 | import logging
import math
from bpy.types import PropertyGroup
from bpy.props import BoolProperty, FloatProperty, EnumProperty
from bpy.utils import register_classes_factory
from mathutils import Vector, Matrix
from mathutils.geometry import distance_point_to_plane, intersect_point_line
from ..solver import Solver
from... | null |
20,199 | import logging
import math
from bpy.types import PropertyGroup
from bpy.props import BoolProperty, FloatProperty, EnumProperty
from bpy.utils import register_classes_factory
from mathutils import Vector, Matrix
from mathutils.geometry import distance_point_to_plane, intersect_point_line
from ..solver import Solver
from... | null |
20,200 | import logging
import math
from bpy.types import PropertyGroup
from bpy.props import BoolProperty, FloatProperty, EnumProperty
from bpy.utils import register_classes_factory
from mathutils import Vector, Matrix
from mathutils.geometry import distance_point_to_plane, intersect_point_line
from ..solver import Solver
from... | null |
20,201 | import logging
from typing import List
import bpy
from bpy.types import PropertyGroup, Context
from bpy.props import BoolProperty
from gpu_extras.batch import batch_for_shader
import math
from mathutils import Vector, Matrix
from mathutils.geometry import intersect_line_sphere_2d, intersect_sphere_sphere_2d
from bpy.ut... | null |
20,202 | import logging
from typing import Union, Generator
import bpy
from bpy.types import PropertyGroup, Context
from bpy.utils import register_class, unregister_class
from bpy.props import IntProperty, BoolProperty, PointerProperty, IntVectorProperty
from .. import global_data
from ..solver import solve_system
from .utiliti... | null |
20,203 | import logging
from typing import Union, Generator
import bpy
from bpy.types import PropertyGroup, Context
from bpy.utils import register_class, unregister_class
from bpy.props import IntProperty, BoolProperty, PointerProperty, IntVectorProperty
from .. import global_data
from ..solver import solve_system
from .utiliti... | null |
20,204 | import bpy
import logging
from bpy.app.handlers import persistent
def register_handlers():
def _setup_builtin_handlers():
def register():
_setup_builtin_handlers()
register_handlers() | null |
20,205 | import bpy
import logging
from bpy.app.handlers import persistent
def unregister_handlers():
global _builtin_handlers
for handler_name in _builtin_handlers.keys():
msg = "Remove <{}> builtin handlers: ".format(handler_name)
for cb in _builtin_handlers[handler_name]:
handler_list = ge... | null |
20,207 |
def apply_with_stopping_condition(
module, apply_fn, apply_condition=None, stopping_condition=None, **other_args
):
if stopping_condition(module):
return
if apply_condition(module):
apply_fn(module, **other_args)
for child in module.children():
apply_with_stopping_condition(
... | null |
20,210 | from typing import Optional
from transformers import AutoModelForCausalLM, AutoTokenizer
import open_clip
from .flamingo import Flamingo
from .flamingo_lm import FlamingoLMMixin
from .utils import extend_instance
def _infer_decoder_layers_attr_name(model):
for k in __KNOWN_DECODER_LAYERS_ATTR_NAMES:
if k.lo... | Initialize a Flamingo model from a pretrained vision encoder and language encoder. Appends special tokens to the tokenizer and freezes backbones. Args: clip_vision_encoder_path (str): path to pretrained clip model (e.g. "ViT-B-32") clip_vision_encoder_pretrained (str): name of pretraining dataset for clip model (e.g. "... |
20,211 | import argparse
import glob
import os
import random
import numpy as np
import torch
import wandb
from data import get_data
from distributed import init_distributed_device, world_info_from_env
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP... | null |
20,212 | import ast
import json
import logging
import os
import random
import sys
from dataclasses import dataclass
from multiprocessing import Value
import braceexpand
import numpy as np
import webdataset as wds
from PIL import Image
from torch.utils.data import DataLoader, IterableDataset, get_worker_info
from torch.utils.dat... | null |
20,213 | import ast
import json
import logging
import os
import random
import sys
from dataclasses import dataclass
from multiprocessing import Value
import braceexpand
import numpy as np
import webdataset as wds
from PIL import Image
from torch.utils.data import DataLoader, IterableDataset, get_worker_info
from torch.utils.dat... | null |
20,214 | import ast
import json
import logging
import os
import random
import sys
from dataclasses import dataclass
from multiprocessing import Value
import braceexpand
import numpy as np
import webdataset as wds
from PIL import Image
from torch.utils.data import DataLoader, IterableDataset, get_worker_info
from torch.utils.dat... | null |
20,215 | import ast
import json
import logging
import os
import random
import sys
from dataclasses import dataclass
from multiprocessing import Value
import braceexpand
import numpy as np
import webdataset as wds
from PIL import Image
from torch.utils.data import DataLoader, IterableDataset, get_worker_info
from torch.utils.dat... | get dataloader worker seed from pytorch |
20,216 | import functools
import io
import json
import math
import re
import random
import numpy as np
import torch
import torchvision
import webdataset as wds
from PIL import Image
import base64
from scipy.optimize import linear_sum_assignment
from data_utils import *
def get_dataset_fn(dataset_type):
"""
Helper functi... | Interface for getting the webdatasets |
20,218 | import os
import torch
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... | null |
20,219 | import os
import torch
try:
import horovod.torch as hvd
except ImportError:
hvd = None
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 worl... | null |
20,220 | import time
from contextlib import suppress
import torch
from tqdm import tqdm
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import (
FullStateDictConfig,
StateDictType,
)
from torch.distributed.fsdp.api import FullOptimStateDictConfig
import os
import wandb
fro... | null |
20,221 | import time
from contextlib import suppress
import torch
from tqdm import tqdm
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import (
FullStateDictConfig,
StateDictType,
)
from torch.distributed.fsdp.api import FullOptimStateDictConfig
import os
import wandb
fro... | null |
20,222 | import time
from contextlib import suppress
import torch
from tqdm import tqdm
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import (
FullStateDictConfig,
StateDictType,
)
from torch.distributed.fsdp.api import FullOptimStateDictConfig
import os
import wandb
fro... | Save training checkpoint with model, optimizer, and lr_scheduler state. |
20,223 | import numpy as np
import torch
import random
import torch.nn as nn
from contextlib import suppress
The provided code snippet includes necessary dependencies for implementing the `get_indices_of_unique` function. Write a Python function `def get_indices_of_unique(x)` to solve the following problem:
Return the indices ... | Return the indices of x that correspond to unique elements. If value v is unique and two indices in x have value v, the first index is returned. |
20,224 | import numpy as np
import torch
import random
import torch.nn as nn
from contextlib import suppress
The provided code snippet includes necessary dependencies for implementing the `unwrap_model` function. Write a Python function `def unwrap_model(model)` to solve the following problem:
Unwrap a model from a DataParalle... | Unwrap a model from a DataParallel or DistributedDataParallel wrapper. |
20,225 | import numpy as np
import torch
import random
import torch.nn as nn
from contextlib import suppress
def get_cast_dtype(precision: str):
cast_dtype = None
if precision == "bf16":
cast_dtype = torch.bfloat16
elif precision == "fp16":
cast_dtype = torch.float16
return cast_dtype | null |
20,226 | import numpy as np
import torch
import random
import torch.nn as nn
from contextlib import suppress
def get_autocast(precision):
if precision == "amp":
return torch.cuda.amp.autocast
elif precision == "amp_bfloat16" or precision == "amp_bf16":
# amp_bfloat16 is more stable than amp float16 for ... | null |
20,227 | import argparse
import importlib
import json
import os
import uuid
import random
from collections import defaultdict
import numpy as np
import torch
from sklearn.metrics import roc_auc_score
import utils
import math
from coco_metric import compute_cider, postprocess_captioning_generation
from eval_datasets import (
... | Evaluate a model on COCO dataset. Args: args (argparse.Namespace): arguments eval_model (BaseEvalModel): model to evaluate seed (int, optional): seed for random number generator. Defaults to 42. max_generation_length (int, optional): maximum length of the generated caption. Defaults to 20. num_beams (int, optional): nu... |
20,228 | import argparse
import importlib
import json
import os
import uuid
import random
from collections import defaultdict
import numpy as np
import torch
from sklearn.metrics import roc_auc_score
import utils
import math
from coco_metric import compute_cider, postprocess_captioning_generation
from eval_datasets import (
... | Evaluate a model on VQA datasets. Currently supports VQA v2.0, OK-VQA, VizWiz and TextVQA. Args: args (argparse.Namespace): arguments eval_model (BaseEvalModel): model to evaluate seed (int, optional): random seed. Defaults to 42. max_generation_length (int, optional): max generation length. Defaults to 5. num_beams (i... |
20,229 | import argparse
import importlib
import json
import os
import uuid
import random
from collections import defaultdict
import numpy as np
import torch
from sklearn.metrics import roc_auc_score
import utils
import math
from coco_metric import compute_cider, postprocess_captioning_generation
from eval_datasets import (
... | Evaluate a model on classification dataset. Args: eval_model (BaseEvalModel): model to evaluate seed (int, optional): random seed. Defaults to 42. num_shots (int, optional): number of shots to use. Defaults to 8. no_kv_caching (bool): whether to disable key-value caching dataset_name (str, optional): dataset name. Defa... |
20,230 | import copy
import functools
import warnings
from dataclasses import dataclass
from typing import (
Any,
cast,
Dict,
Iterable,
Iterator,
List,
NamedTuple,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import torch
import torch.distributed as dist
import torch.distributed.fsdp.... | Flattens the full optimizer state dict, still keying by unflattened parameter names. If ``shard_state=True``, then FSDP-managed ``FlatParameter`` 's optimizer states are sharded, and otherwise, they are kept unsharded. If ``use_orig_params`` is True, each rank will have all FSDP-managed parameters but some of these par... |
20,231 | import copy
import functools
import warnings
from dataclasses import dataclass
from typing import (
Any,
cast,
Dict,
Iterable,
Iterator,
List,
NamedTuple,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import torch
import torch.distributed as dist
import torch.distributed.fsdp.... | Processes positive-dimension tensor states in ``flat_optim_state_dict`` by replacing them with metadata. This is done so the processed optimizer state dict can be broadcast from rank 0 to all ranks without copying those tensor states, and thus, this is meant to only be called on rank 0. Args: flat_optim_state_dict (Dic... |
20,232 | import copy
import functools
import warnings
from dataclasses import dataclass
from typing import (
Any,
cast,
Dict,
Iterable,
Iterator,
List,
NamedTuple,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import torch
import torch.distributed as dist
import torch.distributed.fsdp.... | Broadcasts the processed optimizer state dict from rank 0 to all ranks. Args: processed_optim_state_dict (Optional[Dict[str, Any]]): The flattened optimizer state dict with positive-dimension tensor states replaced with metadata if on rank 0; ignored otherwise. Returns: Dict[str, Any]: The processed optimizer state dic... |
20,233 | import copy
import functools
import warnings
from dataclasses import dataclass
from typing import (
Any,
cast,
Dict,
Iterable,
Iterator,
List,
NamedTuple,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import torch
import torch.distributed as dist
import torch.distributed.fsdp.... | Takes ``processed_optim_state_dict``, which has metadata in place of positive-dimension tensor states, and broadcasts those tensor states from rank 0 to all ranks. For tensor states corresponding to FSDP parameters, rank 0 shards the tensor and broadcasts shard-by-shard, and for tensor states corresponding to non-FSDP ... |
20,234 | import copy
import functools
import warnings
from dataclasses import dataclass
from typing import (
Any,
cast,
Dict,
Iterable,
Iterator,
List,
NamedTuple,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import torch
import torch.distributed as dist
import torch.distributed.fsdp.... | Rekeys the optimizer state dict from unflattened parameter names to flattened parameter IDs according to the calling rank's ``optim``, which may be different across ranks. In particular, the unflattened parameter names are represented as :class:`_OptimStateKey` s. |
20,235 | import copy
import functools
import warnings
from dataclasses import dataclass
from typing import (
Any,
cast,
Dict,
Iterable,
Iterator,
List,
NamedTuple,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import torch
import torch.distributed as dist
import torch.distributed.fsdp.... | Consolidates the optimizer state and returns it as a :class:`dict` following the convention of :meth:`torch.optim.Optimizer.state_dict`, i.e. with keys ``"state"`` and ``"param_groups"``. The flattened parameters in ``FSDP`` modules contained in ``model`` are mapped back to their unflattened parameters. Parameter keys ... |
20,236 | import os
import shutil
import subprocess
import winreg
import ctypes
import logging
import tkinter as tk
from tkinter import messagebox
def is_admin():
try:
return ctypes.windll.shell32.IsUserAnAdmin()
except:
return False | null |
20,237 | import os
import shutil
import subprocess
import winreg
import ctypes
import logging
import tkinter as tk
from tkinter import messagebox
def with_restore_point_creation_frequency(minutes, func):
# Define the key path
key_path = r'SOFTWARE\Microsoft\Windows NT\CurrentVersion\SystemRestore'
# Open the key
... | null |
20,238 | import os
import shutil
import subprocess
import winreg
import ctypes
import logging
import tkinter as tk
from tkinter import messagebox
def create_restore_point(name):
# Define the command
cmd = f'powershell.exe -Command "Checkpoint-Computer -Description \'{name}\' -RestorePointType \'MODIFY_SETTINGS\'"'
... | null |
20,239 | import os
import shutil
import subprocess
import winreg
import ctypes
import logging
import tkinter as tk
from tkinter import messagebox
def uninstall_msix_package(package_full_name):
# Define the PowerShell command
cmd = f'powershell.exe -Command "Get-AppxPackage *{package_full_name}* | Remove-AppxPackage"'
... | null |
20,240 | import os
import shutil
import subprocess
import winreg
import ctypes
import logging
import tkinter as tk
from tkinter import messagebox
def delete_directory(dir_path):
# Check if the directory exists
if os.path.exists(dir_path):
# Delete the directory
shutil.rmtree(dir_path) | null |
20,241 | import os
import shutil
import subprocess
import winreg
import ctypes
import logging
import tkinter as tk
from tkinter import messagebox
def delete_registry_folders():
try:
key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, r"SOFTWARE\Microsoft\Windows\CurrentVersion\Uninstall", 0, winreg.KEY_ALL_ACCESS)
e... | null |
20,242 | import os
import shutil
import subprocess
import winreg
import ctypes
import logging
import tkinter as tk
from tkinter import messagebox
target_string = "MicrosoftCorporationII.WindowsSubsystemForAndroid_8wekyb3d8bbwe"
def delete_folders_and_files(root_path):
target_string = 'MicrosoftCorporationII.WindowsSubsyste... | null |
20,243 | import os
import shutil
import subprocess
import winreg
import ctypes
import logging
import tkinter as tk
from tkinter import messagebox
def delete_shortcuts(target_string, start_menu_dir):
# Walk through the file system starting from the start_menu_dir
for dirpath, dirnames, filenames in os.walk(start_menu_di... | null |
20,244 | import sys
import zipfile
from pathlib import Path
import platform
import os
from typing import Any, OrderedDict
workdir = Path(sys.argv[3]) / "magisk"
def extract_as(zip, name, as_name, dir):
info = zip.getinfo(name)
info.filename = as_name
zip.extract(info, workdir / dir) | null |
20,245 | import html
import logging
import os
import re
import sys
from pathlib import Path
from threading import Thread
from typing import Any, OrderedDict
from xml.dom import minidom
from requests import Session
from packaging import version
release_type = sys.argv[2] if sys.argv[2] != "" else "Retail"
user = ''
session = Ses... | null |
20,246 | from __future__ import annotations
from io import TextIOWrapper
from typing import OrderedDict
from pathlib import Path
import sys
class Prop(OrderedDict):
def __init__(self, file: TextIOWrapper) -> None:
super().__init__()
for i, line in enumerate(file.read().splitlines(False)):
if '=' ... | null |
20,247 | from argparse import Namespace
from pathlib import Path
from typing import Tuple
from exegol.config.ConstantConfig import ConstantConfig
from exegol.config.DataCache import DataCache
from exegol.config.UserConfig import UserConfig
from exegol.manager.UpdateManager import UpdateManager
from exegol.utils.DockerUtils impo... | Hybrid completer for auto-complet. The selector on exec action is hybrid between image and container depending on the mode (tmp or not). This completer will supply the adequate data. |
20,248 | from argparse import Namespace
from pathlib import Path
from typing import Tuple
from exegol.config.ConstantConfig import ConstantConfig
from exegol.config.DataCache import DataCache
from exegol.config.UserConfig import UserConfig
from exegol.manager.UpdateManager import UpdateManager
from exegol.utils.DockerUtils impo... | Completer function for build profile parameter. The completer must be trigger only when an image name have already been chosen. |
20,249 | from argparse import Namespace
from pathlib import Path
from typing import Tuple
from exegol.config.ConstantConfig import ConstantConfig
from exegol.config.DataCache import DataCache
from exegol.config.UserConfig import UserConfig
from exegol.manager.UpdateManager import UpdateManager
from exegol.utils.DockerUtils impo... | null |
20,250 | from argparse import Namespace
from pathlib import Path
from typing import Tuple
from exegol.config.ConstantConfig import ConstantConfig
from exegol.config.DataCache import DataCache
from exegol.config.UserConfig import UserConfig
from exegol.manager.UpdateManager import UpdateManager
from exegol.utils.DockerUtils impo... | No option to auto-complet |
20,251 | import re
from typing import Tuple, Union
The provided code snippet includes necessary dependencies for implementing the `boolFormatter` function. Write a Python function `def boolFormatter(val: bool) -> str` to solve the following problem:
Generic text formatter for bool value
Here is the function:
def boolFormatte... | Generic text formatter for bool value |
20,252 | import re
from typing import Tuple, Union
The provided code snippet includes necessary dependencies for implementing the `getColor` function. Write a Python function `def getColor(val: Union[bool, int, str]) -> Tuple[str, str]` to solve the following problem:
Generic text color getter for bool value
Here is the funct... | Generic text color getter for bool value |
20,253 | import re
from typing import Tuple, Union
The provided code snippet includes necessary dependencies for implementing the `richLen` function. Write a Python function `def richLen(text: str) -> int` to solve the following problem:
Get real length of a text without Rich colors
Here is the function:
def richLen(text: st... | Get real length of a text without Rich colors |
20,254 | import re
from typing import Tuple, Union
def getArchColor(arch: str) -> str:
if arch.startswith("arm"):
color = "slate_blue3"
elif "amd64" == arch:
color = "medium_orchid3"
else:
color = "yellow3"
return color | null |
20,255 | import rich.prompt
The provided code snippet includes necessary dependencies for implementing the `Confirm` function. Write a Python function `def Confirm(question: str, default: bool) -> bool` to solve the following problem:
Quick function to format rich Confirmation and options on every exegol interaction
Here is t... | Quick function to format rich Confirmation and options on every exegol interaction |
20,256 | from typing import Union, Optional, Dict
from git import RemoteProgress
from git.objects.submodule.base import UpdateProgress
from rich.console import Console
from rich.progress import Progress, ProgressColumn, GetTimeCallable, Task
from exegol.utils.ExeLog import console as exelog_console
from exegol.utils.ExeLog impo... | null |
20,257 |
logger: ExeLog = cast(ExeLog, logging.getLogger("main"))
logger.setLevel(logging.INFO)
def print_exception_banner():
logger.error("It seems that something unexpected happened ...")
logger.error("To draw our attention to the problem and allow us to fix it, you can share your error with us "
"... | null |
20,258 | import logging
import re
import stat
import subprocess
from pathlib import Path, PurePath
from typing import Optional
from exegol.config.EnvInfo import EnvInfo
from exegol.utils.ExeLog import logger
logger: ExeLog = cast(ExeLog, logging.getLogger("main"))
logger.setLevel(logging.INFO)
The provided code snippet includ... | Parse docker volume path to find the corresponding host path. |
20,259 | import logging
import re
import stat
import subprocess
from pathlib import Path, PurePath
from typing import Optional
from exegol.config.EnvInfo import EnvInfo
from exegol.utils.ExeLog import logger
def resolvPath(path: Path) -> str:
"""Resolv a filesystem path depending on the environment.
On WSL, Windows PATH... | Try to resolv a filesystem path from a string. |
20,260 | import logging
import re
import stat
import subprocess
from pathlib import Path, PurePath
from typing import Optional
from exegol.config.EnvInfo import EnvInfo
from exegol.utils.ExeLog import logger
logger: ExeLog = cast(ExeLog, logging.getLogger("main"))
logger.setLevel(logging.INFO)
The provided code snippet includ... | Set the setgid permission bit to every recursive directory |
20,261 | import numpy as np
def convert_to_ndc(origins, directions, ndc_coeffs, near: float = 1.0):
"""Convert a set of rays to NDC coordinates."""
t = (near - origins[Ellipsis, 2]) / directions[Ellipsis, 2]
origins = origins + t[Ellipsis, None] * directions
dx, dy, dz = directions[:, 0], directions[:, 1], direc... | null |
20,262 | import numpy as np
def pad_poses(p: np.ndarray) -> np.ndarray:
"""Pad [..., 3, 4] pose matrices with a homogeneous bottom row [0,0,0,1]."""
bottom = np.broadcast_to([0, 0, 0, 1.0], p[..., :1, :4].shape)
return np.concatenate([p[..., :3, :4], bottom], axis=-2)
def unpad_poses(p: np.ndarray) -> np.ndarray:
... | Transforms poses so principal components lie on XYZ axes. Args: poses: a (N, 3, 4) array containing the cameras' camera to world transforms. Returns: A tuple (poses, transform), with the transformed poses and the applied camera_to_world transforms. |
20,263 | import numpy as np
def focus_point_fn(poses: np.ndarray) -> np.ndarray:
"""Calculate nearest point to all focal axes in poses."""
directions, origins = poses[:, :3, 2:3], poses[:, :3, 3:4]
m = np.eye(3) - directions * np.transpose(directions, [0, 2, 1])
mt_m = np.transpose(m, [0, 2, 1]) @ m
focus_pt... | Generate an elliptical render path based on the given poses. |
20,264 | import json
import os
import imageio
import numpy as np
import torch
def pose_spherical(theta, phi, radius):
c2w = trans_t(radius)
c2w = rot_phi(phi / 180.0 * np.pi) @ c2w
c2w = rot_theta(theta / 180.0 * np.pi) @ c2w
c2w = (
torch.tensor([[-1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])... | null |
20,265 | import json
import os
import gdown
import imageio
import numpy as np
import torch
def pose_spherical(theta, phi, radius):
c2w = trans_t(radius)
c2w = rot_phi(phi / 180.0 * np.pi) @ c2w
c2w = rot_theta(theta / 180.0 * np.pi) @ c2w
c2w = (
torch.tensor([[-1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [... | null |
20,266 | import glob
import os
from typing import *
import imageio
import numpy as np
def find_files(dir, exts):
if os.path.isdir(dir):
files_grabbed = []
for ext in exts:
files_grabbed.extend(glob.glob(os.path.join(dir, ext)))
if len(files_grabbed) > 0:
files_grabbed = sorted... | null |
20,267 | import os
from subprocess import check_output
import imageio
import numpy as np
import src.data.pose_utils as pose_utils
def ptstocam(pts, c2w):
tt = np.matmul(c2w[:3, :3].T, (pts - c2w[:3, 3])[..., np.newaxis])[..., 0]
return tt | null |
20,268 | import os
from subprocess import check_output
import imageio
import numpy as np
import src.data.pose_utils as pose_utils
def normalize(x):
return x / np.linalg.norm(x)
def viewmatrix(z, up, pos):
vec2 = normalize(z)
vec1_avg = up
vec0 = normalize(np.cross(vec1_avg, vec2))
vec1 = normalize(np.cross(v... | null |
20,269 | import os
from subprocess import check_output
import imageio
import numpy as np
import src.data.pose_utils as pose_utils
def poses_avg(poses):
hwf = poses[0, :3, -1:]
center = poses[:, :3, 3].mean(0)
vec2 = normalize(poses[:, :3, 2].sum(0))
up = poses[:, :3, 1].sum(0)
c2w = np.concatenate([viewmatri... | null |
20,270 | import os
from subprocess import check_output
import imageio
import numpy as np
import src.data.pose_utils as pose_utils
def normalize(x):
return x / np.linalg.norm(x)
def spherify_poses(poses, bds):
p34_to_44 = lambda p: np.concatenate(
[p, np.tile(np.reshape(np.eye(4)[-1, :], [1, 1, 4]), [p.shape[0]... | null |
20,271 | import os
from subprocess import check_output
import imageio
import numpy as np
import src.data.pose_utils as pose_utils
def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True):
poses_arr = np.load(os.path.join(basedir, "poses_bounds.npy"))
poses = poses_arr[:, :-2].reshape([-1, 3, 5]).tra... | null |
20,272 | import json
import os
import gdown
import imageio
import numpy as np
import torch
def pose_spherical(theta, phi, radius):
def load_shiny_blender_data(
datadir: str,
scene_name: str,
train_skip: int,
val_skip: int,
test_skip: int,
cam_scale_factor: float,
white_bkgd: bool,
):
basedir = o... | null |
20,273 | import os
from subprocess import check_output
import imageio
import numpy as np
def ptstocam(pts, c2w):
tt = np.matmul(c2w[:3, :3].T, (pts - c2w[:3, 3])[..., np.newaxis])[..., 0]
return tt | null |
20,274 | import os
from subprocess import check_output
import imageio
import numpy as np
def normalize(x):
def viewmatrix(z, up, pos):
def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
render_poses = []
rads = np.array(list(rads) + [1.0])
hwf = c2w[:, 4:5]
for theta in np.linspace(0.0, 2.0 ... | null |
20,275 | import os
from subprocess import check_output
import imageio
import numpy as np
def poses_avg(poses):
hwf = poses[0, :3, -1:]
center = poses[:, :3, 3].mean(0)
vec2 = normalize(poses[:, :3, 2].sum(0))
up = poses[:, :3, 1].sum(0)
c2w = np.concatenate([viewmatrix(vec2, up, center), hwf], 1)
return ... | null |
20,276 | import os
from subprocess import check_output
import imageio
import numpy as np
def normalize(x):
return x / np.linalg.norm(x)
def spherify_poses(poses, bds):
p34_to_44 = lambda p: np.concatenate(
[p, np.tile(np.reshape(np.eye(4)[-1, :], [1, 1, 4]), [p.shape[0], 1, 1])], 1
)
rays_d = poses[:,... | null |
20,277 | import os
from subprocess import check_output
import imageio
import numpy as np
def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True):
def similarity_from_cameras(c2w):
def transform_pose_llff(poses):
def load_refnerf_real_data(
datadir: str,
scene_name: str,
factor: int,
cam_sc... | null |
20,278 | import os
from subprocess import check_output
from typing import *
import imageio
import numpy as np
def ptstocam(pts, c2w):
tt = np.matmul(c2w[:3, :3].T, (pts - c2w[:3, 3])[..., np.newaxis])[..., 0]
return tt | null |
20,279 | import os
from subprocess import check_output
from typing import *
import imageio
import numpy as np
def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True):
poses_arr = np.load(os.path.join(basedir, "poses_bounds.npy"))
poses = poses_arr[:, :-2].reshape([-1, 3, 5]).transpose([1, 2, 0])
... | null |
20,280 | import glob
import os
from typing import *
import imageio
import numpy as np
def find_files(dir, exts):
if os.path.isdir(dir):
files_grabbed = []
for ext in exts:
files_grabbed.extend(glob.glob(os.path.join(dir, ext)))
if len(files_grabbed) > 0:
files_grabbed = sorted... | null |
20,281 | import numpy as np
import scipy.signal
import torch
import torch.nn as nn
from src.model.dvgo.__global__ import *
from src.model.dvgo.masked_adam import MaskedAdam
class MaskedAdam(torch.optim.Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.99), eps=1e-8):
if not 0.0 <= lr:
raise... | null |
20,282 | import numpy as np
import scipy.signal
import torch
import torch.nn as nn
from src.model.dvgo.__global__ import *
from src.model.dvgo.masked_adam import MaskedAdam
def load_checkpoint(model, optimizer, ckpt_path, no_reload_optimizer):
ckpt = torch.load(ckpt_path)
start = ckpt["global_step"]
model.load_stat... | null |
20,283 | import numpy as np
import scipy.signal
import torch
import torch.nn as nn
from src.model.dvgo.__global__ import *
from src.model.dvgo.masked_adam import MaskedAdam
def load_model(model_class, ckpt_path):
ckpt = torch.load(ckpt_path)
model = model_class(**ckpt["model_kwargs"])
model.load_state_dict(ckpt["mo... | null |
20,284 | import numpy as np
import scipy.signal
import torch
import torch.nn as nn
from src.model.dvgo.__global__ import *
from src.model.dvgo.masked_adam import MaskedAdam
def rgb_ssim(
img0,
img1,
max_val,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03,
return_map=False,
):
# Modified ... | null |
20,285 | import numpy as np
import scipy.signal
import torch
import torch.nn as nn
from src.model.dvgo.__global__ import *
from src.model.dvgo.masked_adam import MaskedAdam
__LPIPS__ = {}
def init_lpips(net_name, device):
assert net_name in ["alex", "vgg"]
import lpips
print(f"init_lpips: lpips_{net_name}")
retu... | null |
20,286 | import functools
import os
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.model.dvgo.__global__ import *
class DenseGrid(nn.Module):
def __init__(self, channels, world_size, xyz_min, xyz_max, **kwargs):
super(DenseGrid, self).__init__()
sel... | null |
20,287 | import functools
import os
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.model.dvgo.__global__ import *
def compute_tensorf_feat(
xy_plane, xz_plane, yz_plane, x_vec, y_vec, z_vec, f_vec, ind_norm
):
# Interp feature (feat shape: [n_pts, n_comp])
... | null |
20,288 | import functools
import os
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.model.dvgo.__global__ import *
def compute_tensorf_val(xy_plane, xz_plane, yz_plane, x_vec, y_vec, z_vec, ind_norm):
# Interp feature (feat shape: [n_pts, n_comp])
xy_feat = (
... | null |
20,289 | import functools
import os
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import segment_coo
import src.model.dvgo.grid as grid
from src.model.dvgo.__global__ import *
from src.model.dvgo.dvgo import Alphas2Weights, Raw2Alpha
def create_full_step_id... | null |
20,290 | import os
from torch.utils.cpp_extension import load
root_dir = __file__.split(os.path.relpath(__file__))[0]
render_utils_cuda = None
total_variation_cuda = None
ub360_utils_cuda = None
adam_upd_cuda = None
sources = ["lib/dvgo/cuda/adam_upd.cpp", "lib/dvgo/cuda/adam_upd_kernel.cu"]
def init():
global render_utils... | null |
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