id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
19,978 | import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
def is_npu_available():
"""Returns a bool indicating if NPU is currently available."""
return hasattr(torch, 'npu') and torch.npu.is_available()
The provided code snippet includes necessary dependencies for implementing the `get_d... | Returns an available device, cpu, cuda or npu. |
19,979 | import logging
from mmcv.utils import get_logger
The provided code snippet includes necessary dependencies for implementing the `get_root_logger` function. Write a Python function `def get_root_logger(log_file=None, log_level=logging.INFO)` to solve the following problem:
Get root logger. Args: log_file (str): File pa... | Get root logger. Args: log_file (str): File path of log. Defaults to None. log_level (int): The level of logger. Defaults to logging.INFO. Returns: :obj:`logging.Logger`: The obtained logger |
19,980 | import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
import torch.distributed as dist
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmdet.apis import set_random_seed
from mmtrack import __version__
from mmtrack.apis ... | null |
19,981 | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
The provided code snippet includes necessary dependencies for implementing the `mmtrack2torchserve` function. Write a Python function `def mmtrack2torchserve( config_file: str, checkpoint... | Converts mmtracking model (config + checkpoint) to TorchServe `.mar`. Args: config_file (str): In MMTracking config format. The contents vary for each task repository. checkpoint_file (str): In MMTracking checkpoint format. The contents vary for each task repository. output_folder (str): Folder where `{model_name}.mar`... |
19,982 | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
def parse_args():
parser = ArgumentParser(
description='Convert mmtrack models to TorchServe `.mar` format.')
parser.add_argument('config', type=str, help='config file path')
... | null |
19,983 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='UAV123 dataset to COCO Video format')
parser.add_argument(
'-i',
'--input',
help='root directory... | null |
19,984 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `convert_uav123` function. Write a Python function `def convert_uav123(uav123, ann_dir, save_dir)` to solve the following ... | Convert trackingnet dataset to COCO style. Args: uav123 (dict): The converted COCO style annotations. ann_dir (str): The path of trackingnet test dataset save_dir (str): The path to save `uav123`. |
19,985 | import argparse
import glob
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='GOT10k dataset to COCO Video format')
parser.add_argument(
'-i',
'--input',
help='ro... | null |
19,986 | import argparse
import glob
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `convert_got10k` function. Write a Python function `def convert_got10k(ann_dir, save_dir, split='test')` to so... | Convert got10k dataset to COCO style. Args: ann_dir (str): The path of got10k dataset save_dir (str): The path to save `got10k`. split (str): the split ('train'， 'val' or 'test') of dataset. |
19,987 | import argparse
import glob
import os
import os.path as osp
import time
import numpy as np
def parse_args():
parser = argparse.ArgumentParser(
description='Generate the information of GOT10k dataset')
parser.add_argument(
'-i',
'--input',
help='root directory of GOT10k dataset',... | null |
19,988 | import argparse
import glob
import os
import os.path as osp
import time
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `gen_data_infos` function. Write a Python function `def gen_data_infos(data_root, save_dir, split='train')` to solve the following problem:
Generate ... | Generate dataset information. Args: data_root (str): The path of dataset. save_dir (str): The path to save the information of dataset. split (str): the split ('train' or 'test') of dataset. |
19,989 | import argparse
import glob
import os
import os.path as osp
import time
def parse_args():
parser = argparse.ArgumentParser(
description='Generate the information of TrackingNet dataset')
parser.add_argument(
'-i',
'--input',
help='root directory of TrackingNet dataset',
)
... | null |
19,990 | import argparse
import glob
import os
import os.path as osp
import time
The provided code snippet includes necessary dependencies for implementing the `gen_data_infos` function. Write a Python function `def gen_data_infos(data_root, save_dir, split='train', chunks=['all'])` to solve the following problem:
Generate dat... | Generate dataset information. args: data_root (str): The path of dataset. save_dir (str): The path to save the information of dataset. split (str): the split ('train' or 'test') of dataset. chunks (list): the chunks of train set of TrackingNet. |
19,991 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='TrackingNet test dataset to COCO Video format')
parser.add_argument(
'-i',
'--input',
help='root... | null |
19,992 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `convert_trackingnet` function. Write a Python function `def convert_trackingnet(ann_dir, save_dir, split='test')` to solv... | Convert trackingnet dataset to COCO style. Args: ann_dir (str): The path of trackingnet test dataset save_dir (str): The path to save `trackingnet`. split (str): the split ('train' or 'test') of dataset. |
19,993 | import argparse
import glob
import os
import os.path as osp
import re
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='OTB100 dataset to COCO Video format')
parser.add_argument(
'-i',
'--input',
... | null |
19,994 | import argparse
import glob
import os
import os.path as osp
import re
from collections import defaultdict
import mmcv
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `convert_otb100` function. Write a Python function `def convert_otb100(otb, ann_dir, save_dir)` to s... | Convert OTB100 dataset to COCO style. Args: otb (dict): The converted COCO style annotations. ann_dir (str): The path of OTB100 dataset save_dir (str): The path to save `OTB100`. |
19,995 | import argparse
import multiprocessing
import os
import os.path as osp
import re
import socket
from urllib import error, request
from tqdm import tqdm
def download_url(url_savedir_tuple):
url = url_savedir_tuple[0]
saved_dir = url_savedir_tuple[1]
video_zip = osp.basename(url)
if not osp.isdir(saved_di... | null |
19,996 | import argparse
import multiprocessing
import os
import os.path as osp
import re
import socket
from urllib import error, request
from tqdm import tqdm
def parse_url(homepage, href=None):
html = request.urlopen(homepage + 'datasets.html').read().decode('utf-8')
if BeautifulSoup is not None:
soup = Beaut... | null |
19,997 | import argparse
import os.path as osp
from collections import defaultdict
import mmcv
from tao.toolkit.tao import Tao
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='Make annotation files for TAO')
parser.add_argument('-i', '--input', help='path of TAO json file')... | null |
19,998 | import argparse
import os.path as osp
from collections import defaultdict
import mmcv
from tao.toolkit.tao import Tao
from tqdm import tqdm
def get_classes(tao_path, filter_classes=True):
train = mmcv.load(osp.join(tao_path, 'train.json'))
train_classes = list(set([_['category_id'] for _ in train['annotations... | null |
19,999 | import argparse
import os.path as osp
from collections import defaultdict
import mmcv
from tao.toolkit.tao import Tao
from tqdm import tqdm
def convert_tao(file, classes):
tao = Tao(file)
raw = mmcv.load(file)
out = defaultdict(list)
out['tracks'] = raw['tracks'].copy()
out['info'] = raw['info'].c... | null |
20,000 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
import numpy as np
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MOT label and detections to COCO-VID format.')
parser.add_argument('-i', '--input', help='pat... | null |
20,001 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
import numpy as np
from tqdm import tqdm
USELESS = [3, 4, 5, 6, 9, 10, 11]
IGNORES = [2, 7, 8, 12, 13]
def parse_gts(gts, is_mot15):
outputs = defaultdict(list)
for gt in gts:
gt = gt.strip().split(',')
... | null |
20,002 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
import numpy as np
from tqdm import tqdm
def parse_dets(dets):
outputs = defaultdict(list)
for det in dets:
det = det.strip().split(',')
frame_id, ins_id = map(int, det[:2])
assert ins_id == ... | null |
20,003 | import argparse
import json
import os
import os.path as osp
from collections import defaultdict
import mmcv
from PIL import Image
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='CrowdHuman to COCO Video format')
parser.add_argument(
'-i',
'--input'... | null |
20,004 | import argparse
import json
import os
import os.path as osp
from collections import defaultdict
import mmcv
from PIL import Image
from tqdm import tqdm
def load_odgt(filename):
with open(filename, 'r') as f:
lines = f.readlines()
data_infos = [json.loads(line.strip('\n')) for line in lines]
return d... | Convert CrowdHuman dataset in COCO style. Args: ann_dir (str): The path of CrowdHuman dataset. save_dir (str): The path to save annotation files. mode (str): Convert train dataset or validation dataset. Options are 'train', 'val'. Default: 'train'. |
20,005 | import argparse
import os
import os.path as osp
import random
import mmcv
import numpy as np
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MOT dataset into ReID dataset.')
parser.add_argument('-i', '--input', help='path of MOT data')
parser.add_argum... | null |
20,006 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='Convert DanceTrack label and detections to \
COCO-VID format.')
parser.add_argument('-i', '--input', help='path ... | null |
20,007 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
USELESS = [3, 4, 5, 6, 9, 10, 11]
IGNORES = [2, 7, 8, 12, 13]
def parse_gts(gts):
outputs = defaultdict(list)
for gt in gts:
gt = gt.strip().split(',')
frame_id, ins_id = map(in... | null |
20,008 | import argparse
import glob
import os
import os.path as osp
import xml.etree.ElementTree as ET
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='ImageNet DET to COCO Video format')
parser.add_argument(
'-i',
... | null |
20,009 | import argparse
import glob
import os
import os.path as osp
import xml.etree.ElementTree as ET
from collections import defaultdict
import mmcv
from tqdm import tqdm
CLASSES = ('airplane', 'antelope', 'bear', 'bicycle', 'bird', 'bus', 'car',
'cattle', 'dog', 'domestic_cat', 'elephant', 'fox', 'giant_panda',
... | Convert ImageNet DET dataset in COCO style. Args: DET (dict): The converted COCO style annotations. ann_dir (str): The path of ImageNet DET dataset save_dir (str): The path to save `DET`. |
20,010 | import argparse
import os
import os.path as osp
import xml.etree.ElementTree as ET
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='ImageNet VID to COCO Video format')
parser.add_argument(
'-i',
'--inp... | null |
20,011 | import argparse
import os
import os.path as osp
import xml.etree.ElementTree as ET
from collections import defaultdict
import mmcv
from tqdm import tqdm
CLASSES = ('airplane', 'antelope', 'bear', 'bicycle', 'bird', 'bus', 'car',
'cattle', 'dog', 'domestic_cat', 'elephant', 'fox', 'giant_panda',
'h... | Convert ImageNet VID dataset in COCO style. Args: VID (dict): The converted COCO style annotations. ann_dir (str): The path of ImageNet VID dataset. save_dir (str): The path to save `VID`. mode (str): Convert train dataset or validation dataset. Options are 'train', 'val'. Default: 'train'. |
20,012 | import argparse
import copy
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='YouTube-VIS to COCO Video format')
parser.add_argument(
'-i',
'--input',
help='root ... | null |
20,013 | import argparse
import copy
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `convert_vis` function. Write a Python function `def convert_vis(ann_dir, save_dir, dataset_version, mode='tra... | Convert YouTube-VIS dataset in COCO style. Args: ann_dir (str): The path of YouTube-VIS dataset. save_dir (str): The path to save `VIS`. dataset_version (str): The version of dataset. Options are '2019', '2021'. mode (str): Convert train dataset or validation dataset or test dataset. Options are 'train', 'valid', 'test... |
20,014 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='LaSOT test dataset to COCO Video format')
parser.add_argument(
'-i',
'--input',
help='root direc... | null |
20,015 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmcv
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `convert_lasot` function. Write a Python function `def convert_lasot(ann_dir, save_dir, split='test')` to solve the follow... | Convert lasot dataset to COCO style. Args: ann_dir (str): The path of lasot dataset save_dir (str): The path to save `lasot`. split (str): the split ('train' or 'test') of dataset. |
20,016 | import argparse
import glob
import os
import os.path as osp
import time
import numpy as np
def parse_args():
parser = argparse.ArgumentParser(
description='Generate the information of LaSOT dataset')
parser.add_argument(
'-i',
'--input',
help='root directory of LaSOT dataset',
... | null |
20,017 | import argparse
import glob
import os
import os.path as osp
import time
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `gen_data_infos` function. Write a Python function `def gen_data_infos(data_root, save_dir, split='train')` to solve the following problem:
Generate ... | Generate dataset information. Args: data_root (str): The path of dataset. save_dir (str): The path to save the information of dataset. split (str): the split ('train' or 'test') of dataset. |
20,018 | import argparse
import glob
import os
import os.path as osp
import time
def parse_args():
parser = argparse.ArgumentParser(
description='Generate the information of VOT dataset')
parser.add_argument(
'-i',
'--input',
help='root directory of VOT dataset',
)
parser.add_arg... | null |
20,019 | import argparse
import glob
import os
import os.path as osp
import time
The provided code snippet includes necessary dependencies for implementing the `gen_data_infos` function. Write a Python function `def gen_data_infos(data_root, save_dir, dataset_type='vot2018')` to solve the following problem:
Generate dataset in... | Generate dataset information. Args: data_root (str): The path of dataset. save_dir (str): The path to save the information of dataset. |
20,020 | import argparse
import os
import os.path as osp
import socket
import zipfile
from urllib import error, request
from tqdm import tqdm
VOT_DATASETS = dict(
vot2018='http://data.votchallenge.net/vot2018/main/description.json',
vot2018_lt= # noqa: E251
'http://data.votchallenge.net/vot2018/longterm/description... | null |
20,021 | import argparse
import os
import os.path as osp
from collections import defaultdict
import cv2
import mmcv
import numpy as np
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='VOT dataset to COCO Video format')
parser.add_argument(
'-i',
'--input',
... | null |
20,022 | import argparse
import os
import os.path as osp
from collections import defaultdict
import cv2
import mmcv
import numpy as np
from tqdm import tqdm
def parse_attribute(video_path, attr_name, img_num):
"""Parse attribute of each video in VOT.
Args:
video_path (str): The path of video.
attr_name (... | Convert vot dataset to COCO style. Args: ann_dir (str): The path of vot dataset save_dir (str): The path to save `vot`. dataset_type (str): The type of vot challenge. |
20,023 | import argparse
import os
import numpy as np
import torch
from mmcv import Config, DictAction, get_logger, print_log
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp1... | null |
20,024 | import argparse
import os
import os.path as osp
import mmcv
import motmetrics as mm
import numpy as np
from mmcv import Config
from mmcv.utils import print_log
from mmtrack.core.utils import imshow_mot_errors
from mmtrack.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(
des... | null |
20,025 | import argparse
import os
import os.path as osp
import mmcv
import motmetrics as mm
import numpy as np
from mmcv import Config
from mmcv.utils import print_log
from mmtrack.core.utils import imshow_mot_errors
from mmtrack.datasets import build_dataset
The provided code snippet includes necessary dependencies for imple... | Evaluate the results of the video. Args: resfiles (dict): A dict containing the directory of the MOT results. dataset (Dataset): MOT dataset of the video to be evaluated. video_name (str): Name of the video to be evaluated. Returns: tuple: (acc, res, gt), acc contains the results of MOT metrics, res is the results of i... |
20,026 | import argparse
import os
import os.path as osp
import mmcv
def parse_args():
parser = argparse.ArgumentParser(
description='Make dummy results for MOT Challenge.')
parser.add_argument('json_file', help='Input JSON file.')
parser.add_argument('out_folder', help='Output folder.')
args = parser.p... | null |
20,027 | import argparse
import os
from itertools import product
import mmcv
import torch
from dotty_dict import dotty
from mmcv import Config, DictAction, get_logger, print_log
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist... | null |
20,028 | import argparse
import os
from itertools import product
import mmcv
import torch
from dotty_dict import dotty
from mmcv import Config, DictAction, get_logger, print_log
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist... | null |
20,029 | import argparse
import glob
import os.path as osp
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output chec... | null |
20,030 | import argparse
import glob
import os.path as osp
import subprocess
import torch
def process_checkpoint(in_file, out_file):
exp_dir = osp.dirname(in_file)
log_json_path = list(sorted(glob.glob(osp.join(exp_dir,
'*.log.json'))))[-1]
model_time = osp.split(l... | null |
20,031 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def cal_train_time(log_dicts, args):
for i, log_dict in enumerate(log_dicts):
print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
all_times = []... | null |
20,032 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def plot_curve(log_dicts, args):
if args.backend is not None:
plt.switch_backend(args.backend)
sns.set_style(args.style)
# if legend is None, use {filename}_{key}... | null |
20,033 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def add_plot_parser(subparsers):
parser_plt = subparsers.add_parser(
'plot_curve', help='parser for plotting curves')
parser_plt.add_argument(
'json_logs',
... | null |
20,034 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def load_json_logs(json_logs):
# load and convert json_logs to log_dict, key is epoch, value is a sub dict
# keys of sub dict is different metrics, e.g. memory, bbox_mAP
... | null |
20,035 | import argparse
import time
import torch
from mmcv import Config
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
from mmdet.datasets import replace_ImageToTensor
from mmtrack.datasets import build_dataloader, build_dataset
from mmtrack.... | null |
20,036 | import argparse
from mmcv import Config, DictAction
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
parser.add_argument(
'--options', nargs='+', action=DictAction, help='arguments in dict')
args = pa... | null |
20,037 | import os
from setuptools import setup, find_packages
import versioneer
def read_file(fname):
with open(fname, 'r') as f:
return f.read() | null |
20,046 | import logging
import os
import sys
from gooey import Gooey, GooeyParser
from ffsubsync.constants import (
RELEASE_URL,
WEBSITE,
DEV_WEBSITE,
DESCRIPTION,
LONG_DESCRIPTION,
PROJECT_NAME,
PROJECT_LICENSE,
COPYRIGHT_YEAR,
SUBSYNC_RESOURCES_ENV_MAGIC,
)
from ffsubsync.ffsubsync import r... | null |
20,047 | import errno
import os
import re
import subprocess
import sys
HANDLERS = {}
The provided code snippet includes necessary dependencies for implementing the `register_vcs_handler` function. Write a Python function `def register_vcs_handler(vcs, method)` to solve the following problem:
Decorator to mark a method as the h... | Decorator to mark a method as the handler for a particular VCS. |
20,048 | import errno
import os
import re
import subprocess
import sys
The provided code snippet includes necessary dependencies for implementing the `run_command` function. Write a Python function `def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None)` to solve the following pro... | Call the given command(s). |
20,050 | import errno
import os
import re
import subprocess
import sys
def get_keywords():
"""Get the keywords needed to look up the version information."""
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a l... | Get version information or return default if unable to do so. |
20,051 | import logging
import math
logger = logging.getLogger(__name__)
invphi = (math.sqrt(5) - 1) / 2
invphi2 = (3 - math.sqrt(5)) / 2
The provided code snippet includes necessary dependencies for implementing the `gss` function. Write a Python function `def gss(f, a, b, tol=1e-4)` to solve the following problem:
Golden-se... | Golden-section search. Given a function f with a single local minimum in the interval [a,b], gss returns a subset interval [c,d] that contains the minimum with d-c <= tol. Example: >>> f = lambda x: (x-2)**2 >>> a = 1 >>> b = 5 >>> tol = 1e-5 >>> (c,d) = gss(f, a, b, tol) >>> print(c, d) 1.9999959837979107 2.0000050911... |
20,052 | from collections import defaultdict
from itertools import islice
from typing import Any, Callable, Optional
from typing_extensions import Protocol
class Pipeline:
def __init__(self, steps, verbose=False):
self.steps = steps
self.verbose = verbose
self._validate_steps()
def _validate_step... | Construct a Pipeline from the given estimators. This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically. Parameters ---------- *steps : list of estimators. verbose : bool, default=Fa... |
20,053 | from collections import defaultdict
from itertools import islice
from typing import Any, Callable, Optional
from typing_extensions import Protocol
def _transform_one(transformer, X, y, weight, **fit_params):
res = transformer.transform(X)
# if we have a weight for this transformer, multiply output
if weigh... | null |
20,054 | from collections import defaultdict
from itertools import islice
from typing import Any, Callable, Optional
from typing_extensions import Protocol
The provided code snippet includes necessary dependencies for implementing the `_fit_transform_one` function. Write a Python function `def _fit_transform_one(transformer, X... | Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned with the fitted transformer. If ``weight`` is not ``None``, the result will be multiplied by ``weight``. |
20,055 | from datetime import timedelta
import logging
from typing import Any, cast, List, Optional
import pysubs2
from ffsubsync.sklearn_shim import TransformerMixin
import srt
from ffsubsync.constants import (
DEFAULT_ENCODING,
DEFAULT_MAX_SUBTITLE_SECONDS,
DEFAULT_START_SECONDS,
)
from ffsubsync.file_utils import... | null |
20,056 | import logging
import os
import platform
import subprocess
from ffsubsync.constants import SUBSYNC_RESOURCES_ENV_MAGIC
def subprocess_args(include_stdout=True):
# The following is true only on Windows.
if hasattr(subprocess, "STARTUPINFO"):
# On Windows, subprocess calls will pop up a command window by... | null |
20,057 | import argparse
from datetime import datetime
import logging
import os
import shutil
import subprocess
import sys
from typing import cast, Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
from ffsubsync.aligners import FFTAligner, MaxScoreAligner
from ffsubsync.constants import (
DEFAULT_APPLY_O... | null |
20,058 | import argparse
from datetime import datetime
import logging
import os
import shutil
import subprocess
import sys
from typing import cast, Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
from ffsubsync.aligners import FFTAligner, MaxScoreAligner
from ffsubsync.constants import (
DEFAULT_APPLY_O... | null |
20,059 | import os
from contextlib import contextmanager
import logging
import io
import subprocess
import sys
from datetime import timedelta
from typing import cast, Callable, Dict, List, Optional, Union
import ffmpeg
import numpy as np
import tqdm
from ffsubsync.constants import (
DEFAULT_ENCODING,
DEFAULT_MAX_SUBTITL... | null |
20,060 | import os
from contextlib import contextmanager
import logging
import io
import subprocess
import sys
from datetime import timedelta
from typing import cast, Callable, Dict, List, Optional, Union
import ffmpeg
import numpy as np
import tqdm
from ffsubsync.constants import (
DEFAULT_ENCODING,
DEFAULT_MAX_SUBTITL... | null |
20,061 | import os
from contextlib import contextmanager
import logging
import io
import subprocess
import sys
from datetime import timedelta
from typing import cast, Callable, Dict, List, Optional, Union
import ffmpeg
import numpy as np
import tqdm
from ffsubsync.constants import (
DEFAULT_ENCODING,
DEFAULT_MAX_SUBTITL... | null |
20,062 | import os
from contextlib import contextmanager
import logging
import io
import subprocess
import sys
from datetime import timedelta
from typing import cast, Callable, Dict, List, Optional, Union
import ffmpeg
import numpy as np
import tqdm
from ffsubsync.constants import (
DEFAULT_ENCODING,
DEFAULT_MAX_SUBTITL... | null |
20,063 | import bpy
from .declarations import Macros, Operators, WorkSpaceTools
from .stateful_operator.utilities.keymap import tool_invoke_kmi
addon_keymaps = []
class Operators(str, Enum):
AddAngle = "view3d.slvs_add_angle"
AddArc2D = "view3d.slvs_add_arc2d"
AddCircle2D = "view3d.slvs_add_circle2d"
AddCoincid... | null |
20,064 | import bpy
from .declarations import Macros, Operators, WorkSpaceTools
from .stateful_operator.utilities.keymap import tool_invoke_kmi
addon_keymaps = []
def unregister():
wm = bpy.context.window_manager
kc = wm.keyconfigs.addon
if kc:
for km, kmi in addon_keymaps:
km.keymap_items.remov... | null |
20,065 | import logging
import bpy
import gpu
from bpy.types import Context, Operator
from bpy.utils import register_class, unregister_class
from . import global_data
from .utilities.preferences import use_experimental
from .declarations import Operators
def draw_selection_buffer(context: Context):
def ensure_selection_texture... | null |
20,066 | import logging
import bpy
import gpu
from bpy.types import Context, Operator
from bpy.utils import register_class, unregister_class
from . import global_data
from .utilities.preferences import use_experimental
from .declarations import Operators
def update_elements(context: Context, force: bool = False):
"""
TO... | null |
20,067 | import logging
import bpy
import gpu
from bpy.types import Context, Operator
from bpy.utils import register_class, unregister_class
from . import global_data
from .utilities.preferences import use_experimental
from .declarations import Operators
class View3D_OT_slvs_register_draw_cb(Operator):
bl_idname = Operators... | null |
20,068 | import logging
import bpy
import gpu
from bpy.types import Context, Operator
from bpy.utils import register_class, unregister_class
from . import global_data
from .utilities.preferences import use_experimental
from .declarations import Operators
class View3D_OT_slvs_register_draw_cb(Operator):
bl_idname = Operators... | null |
20,069 | from pathlib import Path
from functools import cache
import gpu
import bpy
import bpy.utils.previews
from gpu_extras.batch import batch_for_shader
from bpy.app import background
from .declarations import Operators
from .shaders import Shaders
def get_folder_path():
return Path(__file__).parent / "resources" / "icon... | null |
20,070 | from pathlib import Path
from functools import cache
import gpu
import bpy
import bpy.utils.previews
from gpu_extras.batch import batch_for_shader
from bpy.app import background
from .declarations import Operators
from .shaders import Shaders
icons = {}
def unload_preview_icons():
global preview_icons
if not pr... | null |
20,071 | from pathlib import Path
from functools import cache
import gpu
import bpy
import bpy.utils.previews
from gpu_extras.batch import batch_for_shader
from bpy.app import background
from .declarations import Operators
from .shaders import Shaders
preview_icons = None
def get_constraint_icon(operator: str):
if not prev... | null |
20,072 | from pathlib import Path
from functools import cache
import gpu
import bpy
import bpy.utils.previews
from gpu_extras.batch import batch_for_shader
from bpy.app import background
from .declarations import Operators
from .shaders import Shaders
icons = {}
def _get_shader():
return Shaders.uniform_color_image_2d()
def... | null |
20,073 | from bpy.types import Context, UILayout
from .. import declarations
from . import VIEW3D_PT_sketcher_base
from .. import declarations
def sketch_selector(
context: Context,
layout: UILayout,
):
row = layout.row(align=True)
row.scale_y = 1.8
active_sketch = context.scene.sketcher.active_sketch
... | null |
20,074 | from bpy.types import Context, UILayout
from .. import declarations
from .. import types
from . import VIEW3D_PT_sketcher_base
from .. import declarations
The provided code snippet includes necessary dependencies for implementing the `draw_constraint_listitem` function. Write a Python function `def draw_constraint_li... | Creates a single row inside the ``layout`` describing the ``constraint``. |
20,075 | from bpy.types import Menu
from ..declarations import Operators, Menus
from typing import Iterable
def _get_value_icon(collection: Iterable, property: str, default: bool) -> bool:
values = [getattr(item, property) for item in collection]
if all(values):
return False, "CHECKBOX_HLT"
if not any(value... | null |
20,076 | import gpu
from bpy.types import Gizmo, GizmoGroup
from .. import global_data
from ..declarations import Gizmos, GizmoGroups
from ..draw_handler import ensure_selection_texture
from ..utilities.index import rgb_to_index
from .utilities import context_mode_check
def _spiral(N, M):
x,y = 0,0
dx, dy = 0, -1
... | Returns a list of coordinates to check starting from given position spiraling out |
20,077 | import math
import blf
import gpu
from bpy.types import Gizmo, GizmoGroup
from mathutils import Vector, Matrix
from .. import icon_manager, units
from ..declarations import Gizmos, GizmoGroups, Operators
from ..utilities.preferences import get_prefs
from ..utilities.view import get_2d_coords
from .base import Constrain... | null |
20,078 | import math
from enum import Enum, auto
from mathutils import Matrix
from ..model.types import GenericConstraint
from ..utilities.constants import QUARTER_TURN
from ..utilities.preferences import get_prefs
def get_constraint_color_type(constraint: GenericConstraint):
def get_color(color_type: Color, highlit: bool):
de... | null |
20,079 | import math
from enum import Enum, auto
from mathutils import Matrix
from ..model.types import GenericConstraint
from ..utilities.constants import QUARTER_TURN
from ..utilities.preferences import get_prefs
QUARTER_TURN = tau / 4
def draw_arrow_shape(target, shoulder, width, is_3d=False):
v = shoulder - target
... | null |
20,080 | import math
from enum import Enum, auto
from mathutils import Matrix
from ..model.types import GenericConstraint
from ..utilities.constants import QUARTER_TURN
from ..utilities.preferences import get_prefs
def get_prefs():
return bpy.context.preferences.addons[get_name()].preferences
def get_arrow_size(dist, scal... | null |
20,081 | import math
from enum import Enum, auto
from mathutils import Matrix
from ..model.types import GenericConstraint
from ..utilities.constants import QUARTER_TURN
from ..utilities.preferences import get_prefs
def get_prefs():
return bpy.context.preferences.addons[get_name()].preferences
def get_overshoot(scale, dir)... | null |
20,082 | import math
from enum import Enum, auto
from mathutils import Matrix
from ..model.types import GenericConstraint
from ..utilities.constants import QUARTER_TURN
from ..utilities.preferences import get_prefs
def context_mode_check(context, widget_group):
tools = context.workspace.tools
mode = context.mode
fo... | null |
20,083 | import bpy
import logging
logger = logging.getLogger(__name__)
def update_pointers(scene, index_old, index_new):
"""Replaces all references to an entity index with its new index"""
logger.debug("Update references {} -> {}".format(index_old, index_new))
# NOTE: this should go through all entity pointers and... | Updates type index of entities keeping local index as is |
20,084 | import logging
from typing import List
from bpy.types import Scene
from ..model.types import SlvsGenericEntity
The provided code snippet includes necessary dependencies for implementing the `point_entity_mapping` function. Write a Python function `def point_entity_mapping(scene)` to solve the following problem:
Get a ... | Get a entities per point mapping |
20,085 | import logging
from typing import List
from bpy.types import Scene
from ..model.types import SlvsGenericEntity
def shares_point(seg_1, seg_2):
points = seg_1.connection_points()
for p in seg_2.connection_points():
if p in points:
return True
return False | null |
20,086 | from mathutils import Vector
from math import sin, cos
from .constants import FULL_TURN
def pol2cart(radius: float, angle: float) -> Vector:
x = radius * cos(angle)
y = radius * sin(angle)
return Vector((x, y)) | null |
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