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"""base_image.py was written by Ryan Petersburg for use with fiber
characterization on the EXtreme PREcision Spectrograph
"""
from ast import literal_eval
from collections import Iterable
from datetime import datetime
import numpy as np
from .input_output import (save_image_object, save_image, save_data,
... | {
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"""Base implementation classes.
The public-facing ``Events`` serves as the base class for an event interface;
it's public attributes represent different kinds of events. These attributes
are mirrored onto a ``_Dispatch`` class, which serves as a container for
collections of listener functions. These collections ar... | {
"repo_name": "mitsuhiko/sqlalchemy",
"path": "lib/sqlalchemy/event/base.py",
"copies": "2",
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"""Base implementation for sending notifications"""
import logging
from datetime import timedelta
from notifications.notifiers.exceptions import InvalidTriggerFrequencyError
from notifications.models import NotificationBase
from open_discussions.utils import now_in_utc, normalize_to_start_of_day
DELTA_ONE_DAY = timed... | {
"repo_name": "mitodl/open-discussions",
"path": "notifications/notifiers/base.py",
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"""Base implementation of 0MQ authentication."""
# Copyright (C) PyZMQ Developers
# Distributed under the terms of the Modified BSD License.
import logging
import zmq
from zmq.utils import z85
from zmq.utils.strtypes import bytes, unicode, b, u
from zmq.error import _check_version
from .certs import load_certificat... | {
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"""Base implementation of a provider interface."""
import functools
import os
from os.path import expanduser
try:
from configparser import ConfigParser
except ImportError: # Python 2
from ConfigParser import SafeConfigParser as ConfigParser
from cloudbridge.cloud.interfaces import CloudProvider
from cloudbrid... | {
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"""Base implementation of a search source."""
from functools import partial
from ichnaea.api.locate.result import (
Country,
Position,
)
class Source(object):
"""
A source represents data from the same data source or
collection effort, for example a GeoIP database or our own
crowd-sourced da... | {
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"""Base implementation of a search source."""
from functools import partial
from ichnaea.api.locate.result import (
Position,
PositionResultList,
Region,
RegionResultList,
)
class Source(object):
"""
A source represents data from the same data source or
collection effort, for example a G... | {
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"path": "ichnaea/api/locate/source.py",
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"""Base implementation of event loop.
The event loop can be broken up into a multiplexer (the part
responsible for notifying us of I/O events) and the event loop proper,
which wraps a multiplexer with functionality for scheduling callbacks,
immediately or at a given time in the future.
Whenever a public API takes a c... | {
"repo_name": "lunixbochs/actualvim",
"path": "lib/asyncio/base_events.py",
"copies": "1",
"size": "56458",
"license": "mit",
"hash": -4923457522648890000,
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"line_max": 80,
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"autogenerated": false,
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"""Base implementation of event loop.
The event loop can be broken up into a multiplexer (the part
responsible for notifying us of IO events) and the event loop proper,
which wraps a multiplexer with functionality for scheduling callbacks,
immediately or at a given time in the future.
Whenever a public API takes a ca... | {
"repo_name": "jtackaberry/stagehand",
"path": "external/asyncio/base_events.py",
"copies": "1",
"size": "32576",
"license": "mit",
"hash": 3645350123510339600,
"line_mean": 37.2796709753,
"line_max": 79,
"alpha_frac": 0.548870334,
"autogenerated": false,
"ratio": 4.8061375036884035,
"config_te... |
"""Base implementation of event loop.
The event loop can be broken up into a multiplexer (the part
responsible for notifying us of I/O events) and the event loop proper,
which wraps a multiplexer with functionality for scheduling callbacks,
immediately or at a given time in the future.
Whenever a public API takes a c... | {
"repo_name": "haypo/trollius",
"path": "trollius/base_events.py",
"copies": "1",
"size": "48101",
"license": "apache-2.0",
"hash": -1994558892395120400,
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""" Base implementation of FCRepoRequestFactory interface.
"""
from fcrepo.http.interfaces import I_FCRepoRequestFactory
from fcrepo.http.interfaces import I_FCRepoResponse
from fcrepo.http.interfaces import I_FCRepoResponseBody
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
class B_... | {
"repo_name": "vitorio/ocropodium",
"path": "ocradmin/lib/fcrepo/http/base.py",
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"size": "4198",
"license": "apache-2.0",
"hash": -6349475464220187000,
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"""Base implementation of JSON serialization which provides bidirectional serialization from python to json and back to
python. This is interesting because some of the data types in python are not directly supported in JSON so while you
can generate a JSON representation there is no going back.
"""
# stdlib
import dat... | {
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"path": "src/generic_utils/json_tools/serialization.py",
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"""Base implementation of the :mod:`pymap.interfaces.mailbox` interfaces."""
from __future__ import annotations
import random
import time
from collections.abc import Iterable
from typing import Optional, Final
from .interfaces.mailbox import MailboxInterface
from .parsing.specials import Flag, ObjectId
from .parsing... | {
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"path": "pymap/mailbox.py",
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"has_no_keywords": ... |
""" Base implementation of W_Int which is a machine-sized integer
"""
from rpython.rlib.objectmodel import compute_hash
from nolang.error import AppError
from nolang.objects.root import W_Root, NotImplementedOp
from nolang.builtins.spec import TypeSpec, unwrap_spec
class W_IntObject(W_Root):
def __init__(self, ... | {
"repo_name": "fijal/quill",
"path": "nolang/objects/int.py",
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"""Base implementations of the :mod:`pymap.interfaces.message` interfaces."""
from __future__ import annotations
import re
from abc import ABCMeta
from collections.abc import Collection, Iterable, Mapping, Sequence
from datetime import datetime
from typing import Any, Optional, Final
from .bytes import Writeable
fro... | {
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# base imports
from base.middleware import RequestMiddleware
from base.utils import get_our_models
# django imports
from django.db.models.signals import post_save, post_delete
from django.dispatch import receiver
from django.conf import settings
@receiver(post_save)
def audit_log(sender, instance, created, raw, upda... | {
"repo_name": "magnet-cl/django-project-template-py3",
"path": "base/signals.py",
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"""Base installer class."""
from grow.sdk import sdk_utils
class Error(Exception):
"""Base error for installers."""
def __init__(self, message):
super(Error, self).__init__(message)
self.message = message
class MissingPrerequisiteError(Error):
"""Installer is missing a prerequisite."""... | {
"repo_name": "grow/grow",
"path": "grow/sdk/installers/base_installer.py",
"copies": "1",
"size": "1566",
"license": "mit",
"hash": 1791362426249264600,
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"has_no... |
"""Base installer class."""
from grow.sdk import sdk_utils
class Error(Exception):
"""Base error for installers."""
pass
class MissingPrerequisiteError(Error):
"""Installer is missing a prerequisite."""
def __init__(self, message, install_commands=None):
super(MissingPrerequisiteError, sel... | {
"repo_name": "grow/pygrow",
"path": "grow/sdk/installers/base_installer.py",
"copies": "1",
"size": "1301",
"license": "mit",
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"""Base Integration for Cortex XSOAR - Unit Tests file
Pytest Unit Tests: all funcion names must start with "test_"
More details: https://xsoar.pan.dev/docs/integrations/unit-testing
You must add at least a Unit Test function for every XSOAR command
you are implementing with your integration
"""
import json
import ... | {
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"path": "Packs/Microsoft365Defender/Integrations/Microsoft365Defender/Microsoft365Defender_test.py",
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"""Base interface for metrics to implement.
To create a new metric:
1. Subclass `Metric` or `PercentMetric`
2. Implement `_score_value`, `_compute_value`, and (unless you're using
`PercentMetric`) `_format_value` and define the UNIT (for history plots)
3. Call `metrics.base.Metric.register(YourNewMetric)`
4. Import... | {
"repo_name": "ampproject/amp-github-apps",
"path": "project-metrics/metrics_service/metrics/base.py",
"copies": "1",
"size": "6084",
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'''base is a class that resources inherit from to provide common http
methods'''
from pastry.pastry_client import PastryClient
from pastry.exceptions import HttpError
class Base(object):
'''
Base class for chef resources to inherit from
'''
_base_url = None
@classmethod
def base_url(cls):
... | {
"repo_name": "stephenbm/pastry",
"path": "pastry/resources/base.py",
"copies": "1",
"size": "2592",
"license": "mit",
"hash": -6472688376330814000,
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... |
"""Base Job class to be populated by Scrapers, manipulated by Filters and saved
to csv / etc by Exporter
"""
from copy import deepcopy
from datetime import date, datetime
from typing import Dict, List, Optional
from bs4 import BeautifulSoup
from jobfunnel.resources import (CSV_HEADER, MAX_BLOCK_LIST_DESC_CHARS,
... | {
"repo_name": "PaulMcInnis/JobPy",
"path": "jobfunnel/backend/job.py",
"copies": "1",
"size": "9486",
"license": "mit",
"hash": -5077340875396982000,
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"autogenerated": false,
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""" Base kernel estimators on distributions. """
from ite.cost.x_initialization import InitKernel, InitKnnK, InitBagGram
from ite.cost.x_verification import VerEqualDSubspaces
from ite.shared import estimate_d_temp2
from numpy import mean
# scipy.spatial.distance.cdist is slightly slow; you can obtain some
# speed-up... | {
"repo_name": "gdikov/vae-playground",
"path": "third_party/ite/cost/base_k.py",
"copies": "1",
"size": "7607",
"license": "mit",
"hash": 4140586154001577500,
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"autogenerated": false,
"ratio": 3.747290640394089,
"config_test... |
_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa
model = dict(
teacher_config='configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_co... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py",
"copies": "1",
"size": "1628",
"license": "apache-2.0",
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"ratio": 2.782905982905983,
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"""Base library for the Archiver project."""
##==============================================================#
## DEVELOPED 2014, REVISED 2014, Jeff Rimko. #
##==============================================================#
##==============================================================#
## SECTIO... | {
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"config_test": false,
"has_no_... |
"""Baseline benchmark for custom dot operation"""
import sys
import numpy as np
import torch
from absl import flags
from absl import app
FLAGS = flags.FLAGS
flags.DEFINE_integer("batch_size", 16384, "Batch Size")
PADDING_SIZE = 1
def dot_based_interact_benchmark(num_rows, num_cols, batch_size, num_iterations=50):
... | {
"repo_name": "mlperf/training_results_v0.7",
"path": "NVIDIA/benchmarks/dlrm/implementations/pytorch/scripts/dot_based_interact/baseline.py",
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"""Baseline estimation algorithms."""
import numpy as np
import scipy.linalg as LA
import math
def baseline(y, deg=3, max_it=100, tol=1e-3):
"""Computes the baseline of a given data.
Iteratively performs a polynomial fitting in the data to detect its
baseline. At every iteration, the fitting weights on ... | {
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# Baseline / first draft heavily inspired by
# https://github.com/laurent-dinh/dl_tutorials/blob/master/part_4_rnn/imdb_main.py
import theano
from theano import tensor as T
from dataset import IMDB
from blocks.bricks.lookup import LookupTable
from blocks.initialization import Uniform, Constant
from blocks.bricks.recu... | {
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"path": "IMDB/main.py",
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"autogenerated": false,
"ratio": 3.718137254901961,
"config_test": true,
"has_no_keyword... |
# Baseline / first draft heavily inspired by
# https://github.com/laurent-dinh/dl_tutorials/blob/master/part_4_rnn/imdb_main.py
import theano
from theano import tensor as T
import numpy as np
from dataset import IMDBText, GloveTransformer
from blocks.initialization import Uniform, Constant, IsotropicGaussian, Ndarra... | {
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"path": "IMDB/glove_conv.py",
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"line_max": 143,
"alpha_frac": 0.6214674274,
"autogenerated": false,
"ratio": 3.4494279176201372,
"config_test": true,
"has_n... |
"""Baseline for comparability: linear svm by SAGA. Uses structured
format."""
import os
from hashlib import sha1
import warnings
from collections import Counter
import dill
import numpy as np
from sklearn.base import clone
from sklearn.metrics import f1_score
from sklearn.preprocessing import LabelEncoder
from sklea... | {
"repo_name": "vene/marseille",
"path": "experiments/exp_baseline_linear.py",
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# Baseline Model on the Sonar Dataset
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from keras.constraints import maxnorm
from keras.optimizers import SGD
from sklearn.model_selection... | {
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"""baseline
Revision ID: c1b678211e9d
Revises:
Create Date: 2017-04-12 10:21:55.050636
"""
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision = 'c1b678211e9d'
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
op.create_table(
"synon... | {
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"autogenerated": false,
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# baselineTeam.py
# ---------------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein ... | {
"repo_name": "startupjing/Artificial-Intelligence",
"path": "artificial_intelligence/agents/Appetizer.py",
"copies": "1",
"size": "16136",
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"autogenerated": false,
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# BaselineTurbineAnalysis.py
#
# NREL 5MW Wind Turbine Analysis
# Using parameters specified by the NREL report for the 5MW tower, perform
# aerodynamic, structural and cost analysis on the turbine to verify that the
# numbers are reasonable.
#
# Author: Lewis Li (lewisli@stanford.edu)
# Original Date: November 1st 2... | {
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"alpha_frac": 0.7103007636,
"autogenerated": false,
"ratio": 2.654944145635085,
"config_tes... |
# base_linter.py - base class for linters
import os
import os.path
import json
import re
import subprocess
import sublime
# If the linter uses an executable that takes stdin, use this input method.
INPUT_METHOD_STDIN = 1
# If the linter uses an executable that does not take stdin but you wish to use
# a temp file s... | {
"repo_name": "SublimeLinter/SublimeLinter-for-ST2",
"path": "sublimelinter/modules/base_linter.py",
"copies": "9",
"size": "16096",
"license": "mit",
"hash": 8952983130331455000,
"line_mean": 38.2585365854,
"line_max": 156,
"alpha_frac": 0.5985337972,
"autogenerated": false,
"ratio": 4.164553686... |
"""Base loader module for OpenGLContext
"""
import logging
log = logging.getLogger( __name__ )
import urllib
class BaseHandler( object ):
"""Base handler class providing common loading operations
"""
filename_extensions = []
def __call__( self, baseURL, filename, file, *args, **named ):
"""Loa... | {
"repo_name": "alexus37/AugmentedRealityChess",
"path": "pythonAnimations/pyOpenGLChess/engineDirectory/oglc-env/lib/python2.7/site-packages/OpenGLContext/loaders/base.py",
"copies": "2",
"size": "2707",
"license": "mit",
"hash": 7546242551531360000,
"line_mean": 36.5972222222,
"line_max": 97,
"alpha... |
"""Base mapping module for easier specific usage."""
from django.conf import settings
from elasticsearch.exceptions import NotFoundError
from elasticutils.contrib.django import S as _S
from elasticutils.contrib.django import MappingType
from elasticutils.contrib.django import Indexable
from django_esutils import tas... | {
"repo_name": "novafloss/django-esutils",
"path": "django_esutils/mappings.py",
"copies": "1",
"size": "9820",
"license": "mit",
"hash": 3065893885938983400,
"line_mean": 31.091503268,
"line_max": 96,
"alpha_frac": 0.5468431772,
"autogenerated": false,
"ratio": 4.126050420168068,
"config_test":... |
"""Base marshmallow-jsonapi test case module."""
from marshmallow_jsonapi import fields, Schema
from tests.unit import UnitTestCase
def dasherize(text):
"""Replace underscores with hyphens."""
return text.replace('_', '-')
class Person(Schema):
id = fields.Integer()
name = fields.String()
kids_... | {
"repo_name": "caxiam/sqlalchemy-jsonapi-collections",
"path": "tests/marshmallow_jsonapi.py",
"copies": "1",
"size": "1454",
"license": "apache-2.0",
"hash": -5019470019974274000,
"line_mean": 23.2333333333,
"line_max": 72,
"alpha_frac": 0.6568088033,
"autogenerated": false,
"ratio": 3.951086956... |
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py",
"copies": "1",
"size": "2276",
"license": "apache-2.0",
"hash": 5358766446313754000,
"line_mean": 30.1780821918,
"line_max": 77,
"alpha_frac": 0.5065905097,
"autogenerated": false,
"ratio": 3... |
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indice... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py",
"copies": "1",
"size": "2068",
"license": "apache-2.0",
"hash": 5421941811678082000,
"line_mean": 31.3125,
"line_max": 79,
"alpha_frac": 0.5604448743,
"autogenerated": false,... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnet50_caffe_bgr')),
rpn_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py",
"copies": "1",
"size": "2047",
"license": "apache-2.0",
"hash": -7662613366104065000,
"line_mean": 33.1166666667,
"line_max": 78,
"alpha_frac": 0.5539814362,
"autogenerated": false,
"ra... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.5... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py",
"copies": "1",
"size": "1606",
"license": "apache-2.0",
"hash": -5407061246212093000,
"line_mean": 31.7755102041,
"line_max": 77,
"alpha_frac": 0.5628891656,
"autogenerated": false,
... |
_base_ = 'mask_rcnn_r50_fpn_crop640_50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(
type='FPG',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
inter_channels=256,
num_outs=5,
stack_times=9,
paths=['bu'] * 9,
... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py",
"copies": "1",
"size": "1450",
"license": "apache-2.0",
"hash": 3305787184946826000,
"line_mean": 29.2083333333,
"line_max": 64,
"alpha_frac": 0.4565517241,
"autogenerated": false,
"ratio": 3.5452... |
"""Base material for signature backends."""
from django.urls import reverse
class SignatureBackend(object):
"""Encapsulate signature workflow and integration with vendor backend.
Here is a typical workflow:
* :class:`~django_anysign.models.SignatureType` instance is created. It
encapsulates the ba... | {
"repo_name": "novafloss/django-anysign",
"path": "django_anysign/backend.py",
"copies": "1",
"size": "5255",
"license": "bsd-3-clause",
"hash": -4274443762849322000,
"line_mean": 34.0333333333,
"line_max": 79,
"alpha_frac": 0.6660323501,
"autogenerated": false,
"ratio": 4.838858195211786,
"con... |
"""Base migration 2.0.0
Revision ID: 7cc292cfbb0a
Revises: None
Create Date: 2020-02-21 19:33:55.870010
"""
# revision identifiers, used by Alembic.
revision = '7cc292cfbb0a'
down_revision = None
from alembic import op
import sqlalchemy as sa
def upgrade():
op.create_table('pages',
sa.Column('id', sa.Inte... | {
"repo_name": "rmed/akamatsu",
"path": "akamatsu/migrations/versions/7cc292cfbb0a_base_2.0.0.py",
"copies": "1",
"size": "5024",
"license": "mit",
"hash": 6126811211671782000,
"line_mean": 39.192,
"line_max": 98,
"alpha_frac": 0.647093949,
"autogenerated": false,
"ratio": 3.390013495276653,
"co... |
"""Base model class."""
import abc
from datetime import datetime
from backend.database import redis_db
from backend.utils import convert, get_logger
logger = get_logger('models')
class BaseModel(metaclass=abc.ABCMeta):
"""Class that implements the common model methods.
It is the application main interface ... | {
"repo_name": "akita8/scrapper",
"path": "backend/models/base.py",
"copies": "1",
"size": "2967",
"license": "mit",
"hash": 574469000652498000,
"line_mean": 32.3370786517,
"line_max": 80,
"alpha_frac": 0.5931917762,
"autogenerated": false,
"ratio": 4.226495726495727,
"config_test": false,
"ha... |
"""Base model class"""
import model_utils
class Model:
def __init__(self, params_path):
"""Constructor.
Loads model params"""
if params_path != None:
self.params = self.load_params(params_path)
else:
self.params = None
self.input_size = None
... | {
"repo_name": "Lazea/TensorFlow",
"path": "models/model.py",
"copies": "1",
"size": "1112",
"license": "apache-2.0",
"hash": -8483226613648303000,
"line_mean": 27.5128205128,
"line_max": 80,
"alpha_frac": 0.5917266187,
"autogenerated": false,
"ratio": 4.164794007490637,
"config_test": false,
... |
"""Base model for all Resolwe models."""
from versionfield import VersionField
from django.conf import settings
from django.db import IntegrityError, models, transaction
from .fields import ResolweSlugField
VERSION_NUMBER_BITS = (8, 10, 14)
# Maximum number of slug-generation retries.
MAX_SLUG_RETRIES = 10
class ... | {
"repo_name": "jberci/resolwe",
"path": "resolwe/flow/models/base.py",
"copies": "1",
"size": "1975",
"license": "apache-2.0",
"hash": 7623019116879391000,
"line_mean": 29.859375,
"line_max": 93,
"alpha_frac": 0.6162025316,
"autogenerated": false,
"ratio": 4.369469026548672,
"config_test": fals... |
"""`BaseModel`, `Model`, `NotBuiltError`, `Percept`, `SpatialModel`,
`TemporalModel`"""
import sys
from abc import ABCMeta, abstractmethod
from copy import deepcopy
import numpy as np
from ..implants import ProsthesisSystem
from ..stimuli import Stimulus
from ..percepts import Percept
from ..utils import PrettyPrin... | {
"repo_name": "mbeyeler/pulse2percept",
"path": "pulse2percept/models/base.py",
"copies": "1",
"size": "36720",
"license": "bsd-3-clause",
"hash": -2298450902789611000,
"line_mean": 38.7402597403,
"line_max": 79,
"alpha_frac": 0.5774782135,
"autogenerated": false,
"ratio": 4.298255882008662,
"c... |
"""Base Model."""
from collections import MutableSequence
from types import SimpleNamespace
from typing import Any, Callable, Dict, Iterable, Mapping, Optional
from marshmallow import INCLUDE, Schema, fields, post_load
from yarl import URL
from pyrh.exceptions import InvalidOperation
JSON = Dict[str, Any]
MAX_REPR_... | {
"repo_name": "Jamonek/Robinhood",
"path": "pyrh/models/base.py",
"copies": "1",
"size": "5458",
"license": "mit",
"hash": -5284246800066483000,
"line_mean": 28.3440860215,
"line_max": 137,
"alpha_frac": 0.6260534995,
"autogenerated": false,
"ratio": 4.14741641337386,
"config_test": false,
"h... |
"""Base Model."""
import abc
import typing
from pathlib import Path
import dill
import numpy as np
import keras
import keras.backend as K
import pandas as pd
import matchzoo
from matchzoo import DataGenerator
from matchzoo.engine import hyper_spaces
from matchzoo.engine.base_preprocessor import BasePreprocessor
from... | {
"repo_name": "faneshion/MatchZoo",
"path": "matchzoo/engine/base_model.py",
"copies": "1",
"size": "20711",
"license": "apache-2.0",
"hash": 8609216548545224000,
"line_mean": 34.6471600688,
"line_max": 79,
"alpha_frac": 0.5659794312,
"autogenerated": false,
"ratio": 4.267669482794148,
"config_... |
"""base_model.py - Some things done to spare time.
Extra functionality that is used by all models. It extends db.Model with extra
functions.
"""
from app import db
from app.utils import serialize_sqla
from datetime import datetime
import dateutil.parser
class BaseEntity(object):
__table_args__ = {'sqlite_autoin... | {
"repo_name": "JelteF/bottor",
"path": "tracker/app/utils/base_model.py",
"copies": "1",
"size": "4034",
"license": "mit",
"hash": 7387832347309037000,
"line_mean": 30.7637795276,
"line_max": 79,
"alpha_frac": 0.566187407,
"autogenerated": false,
"ratio": 4.375271149674621,
"config_test": false... |
"""Base models for "Content", including the indexing and search features
that we want any piece of content to have."""
import logging
import uuid
import requests
from django.conf import settings
from django.contrib.contenttypes.models import ContentType
from django.core.urlresolvers import NoReverseMatch, reverse
fro... | {
"repo_name": "theonion/django-bulbs",
"path": "bulbs/content/models.py",
"copies": "1",
"size": "23187",
"license": "mit",
"hash": 8838590554091110000,
"line_mean": 33.5044642857,
"line_max": 102,
"alpha_frac": 0.5397420969,
"autogenerated": false,
"ratio": 4.506705539358601,
"config_test": fa... |
"""Base models for "Content", including the indexing and search features
that we want any piece of content to have."""
import uuid
from django.conf import settings
from django.contrib.auth import get_user_model
from django.contrib.contenttypes.models import ContentType
from django.core.urlresolvers import NoReverseMa... | {
"repo_name": "pombredanne/django-bulbs",
"path": "bulbs/content/models.py",
"copies": "1",
"size": "13330",
"license": "mit",
"hash": -6929639677820611000,
"line_mean": 31.1980676329,
"line_max": 104,
"alpha_frac": 0.6269317329,
"autogenerated": false,
"ratio": 4.219689775245331,
"config_test"... |
"""Base models"""
from django.db import models
from django.core.exceptions import ObjectDoesNotExist
from django.conf import settings
class TeamManager(models.Manager):
def create_team(self, team_id):
team = self.create(team_id=team_id)
return team
class RosterManager(models.Manager):
def c... | {
"repo_name": "blakefinney/FantasyLeague",
"path": "apps/base/models.py",
"copies": "1",
"size": "12981",
"license": "mit",
"hash": -2241237199193584600,
"line_mean": 31.6155778894,
"line_max": 140,
"alpha_frac": 0.5730683306,
"autogenerated": false,
"ratio": 3.679421768707483,
"config_test": f... |
"""Base models"""
from django.db import models
class Job(models.Model):
id = models.AutoField(primary_key=True)
name = models.CharField('job name', max_length=50)
company_id = models.ForeignKey('Company')
url = models.CharField(max_length=100)
description = models.CharField(max_length=200)
stil... | {
"repo_name": "cynngah/uofthacksIV",
"path": "jobradar/apps/base/models.py",
"copies": "1",
"size": "1259",
"license": "mit",
"hash": -1344485793936838700,
"line_mean": 37.1515151515,
"line_max": 78,
"alpha_frac": 0.7426528991,
"autogenerated": false,
"ratio": 3.4493150684931506,
"config_test":... |
"""Base models."""
from django.db import models
from django.utils.text import slugify
from django.core.urlresolvers import reverse
from django.core.validators import RegexValidator
from django.core.urlresolvers import reverse
def alphanumeric_validator():
return RegexValidator(r'^[a-zA-Z0-9-_ ]+$',
'Only... | {
"repo_name": "0x0mar/memex-explorer",
"path": "source/base/models.py",
"copies": "1",
"size": "1316",
"license": "bsd-2-clause",
"hash": 4132131158361306000,
"line_mean": 27,
"line_max": 77,
"alpha_frac": 0.6732522796,
"autogenerated": false,
"ratio": 4.138364779874214,
"config_test": false,
... |
"""Base models."""
import os
import subprocess
import shutil
import json
from django.db import models
from django.utils.text import slugify
from django.core.urlresolvers import reverse
from django.core.validators import RegexValidator
from django.core.urlresolvers import reverse
from django.db.models.signals import p... | {
"repo_name": "memex-explorer/memex-explorer",
"path": "source/base/models.py",
"copies": "2",
"size": "4424",
"license": "bsd-2-clause",
"hash": -5589132799072304000,
"line_mean": 26.8238993711,
"line_max": 95,
"alpha_frac": 0.6552893309,
"autogenerated": false,
"ratio": 3.904677846425419,
"co... |
"""Base models."""
import os
import subprocess
import shutil
from django.db import models
from django.utils.text import slugify
from django.core.urlresolvers import reverse
from django.core.validators import RegexValidator
from django.core.urlresolvers import reverse
from django.db.models.signals import post_save
fr... | {
"repo_name": "firebitsbr/memex-explorer",
"path": "source/base/models.py",
"copies": "2",
"size": "3570",
"license": "bsd-2-clause",
"hash": 4858856916519665000,
"line_mean": 27.56,
"line_max": 95,
"alpha_frac": 0.6635854342,
"autogenerated": false,
"ratio": 3.975501113585746,
"config_test": f... |
"""Base models
Revision ID: 734b944fd3a7
Revises:
Create Date: 2016-08-15 23:43:35.411885
"""
# revision identifiers, used by Alembic.
revision = '734b944fd3a7'
down_revision = None
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
def upgrade():
### commands auto generate... | {
"repo_name": "beslave/auto-collector",
"path": "migrations/versions/734b944fd3a7_base_models.py",
"copies": "1",
"size": "6579",
"license": "mit",
"hash": -5283518127747292000,
"line_mean": 48.4661654135,
"line_max": 169,
"alpha_frac": 0.6715306278,
"autogenerated": false,
"ratio": 3.43192488262... |
"""Base module containing the core components for the colors system."""
import enum
import functools
import inspect
import typing
import colormath
import colormath.color_conversions
import colormath.color_diff
import colormath.color_objects
class ColorMeta(type):
"""
Metaclass for colors that sets up class ... | {
"repo_name": "xlorepdarkhelm/colors",
"path": "colors/base.py",
"copies": "1",
"size": "42592",
"license": "mit",
"hash": -6135173416011191000,
"line_mean": 28.9311314125,
"line_max": 79,
"alpha_frac": 0.5362744177,
"autogenerated": false,
"ratio": 4.141176470588236,
"config_test": false,
"h... |
"""Base module for all units. """
import asyncio
import logging
import math
import uuid
from datetime import datetime
from simple_commander.utils.float_range import float_range
from simple_commander.utils.constants import ACTION_INTERVAL, MAX_ANGLE, MAX_SPEED, STEP_INTERVAL, UNIT_PROPERTIES
from simple_commander.utils... | {
"repo_name": "pzdeb/SimpleCommander",
"path": "src/simple_commander/game/unit.py",
"copies": "1",
"size": "8655",
"license": "mit",
"hash": -220365450819460930,
"line_mean": 44.7936507937,
"line_max": 143,
"alpha_frac": 0.5698440208,
"autogenerated": false,
"ratio": 3.594269102990033,
"config_... |
'''Base module for calling SoX '''
import subprocess
from pathlib import Path
from subprocess import CalledProcessError
from typing import Union, List, Optional, Tuple, Iterable, Any
import numpy as np
from typing_extensions import Literal
from . import NO_SOX
from .log import logger
SOXI_ARGS = ['B', 'b', 'c', 'a'... | {
"repo_name": "rabitt/pysox",
"path": "sox/core.py",
"copies": "1",
"size": "7060",
"license": "bsd-3-clause",
"hash": -5203610106002796000,
"line_mean": 25.8441064639,
"line_max": 81,
"alpha_frac": 0.5814447592,
"autogenerated": false,
"ratio": 3.8327904451682953,
"config_test": false,
"has_... |
"Base Module for modules supporting a variable number in input slots."
from progressivis.table.module import TableModule
from progressivis.table import BaseTable
from progressivis.core.slot import SlotDescriptor
class NAry(TableModule):
"Base class for modules supporting a variable number of input slots."
in... | {
"repo_name": "jdfekete/progressivis",
"path": "progressivis/table/nary.py",
"copies": "1",
"size": "1565",
"license": "bsd-2-clause",
"hash": 7384814225121404000,
"line_mean": 35.3953488372,
"line_max": 77,
"alpha_frac": 0.6217252396,
"autogenerated": false,
"ratio": 3.5730593607305936,
"confi... |
"""Base module for plugout"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import inspect
import imp
import logging
import os
from pydoc import locate
import re
class PluginManager(object):
"""Loads and expos... | {
"repo_name": "mistercrunch/plugout",
"path": "plugout/core.py",
"copies": "1",
"size": "4368",
"license": "apache-2.0",
"hash": 4449792688843614700,
"line_mean": 32.8604651163,
"line_max": 90,
"alpha_frac": 0.5508241758,
"autogenerated": false,
"ratio": 4.6075949367088604,
"config_test": false... |
'''Base Module for Sensor Collection Management'''
import functools
import collections
import logging
import multiprocessing
import os
import subprocess
logger = logging.getLogger(__name__)
__all__ = ['RA_DELIMITER', 'RA_FIELDS']
intconv = functools.partial(int, base=0)
# Ra Options and Fields
RA_DELIMITER = ','
R... | {
"repo_name": "effluxsystems/pyefflux",
"path": "efflux/helpers/argus.py",
"copies": "1",
"size": "4391",
"license": "mit",
"hash": -4643313438979160000,
"line_mean": 28.0794701987,
"line_max": 76,
"alpha_frac": 0.5167387839,
"autogenerated": false,
"ratio": 3.892730496453901,
"config_test": fa... |
"""``base`` module of ``dataql.parsers``.
It provides the base parser each subclass should inherit from, and the metaclass used
to manage the creation of the grammar using the ones from all parent classes.
"""
# pylint: disable=no-self-use
from abc import ABCMeta
from inspect import isfunction
import re
import sys
... | {
"repo_name": "twidi/py-dataql",
"path": "dataql/parsers/base.py",
"copies": "1",
"size": "20809",
"license": "bsd-2-clause",
"hash": -7875728081134748000,
"line_mean": 31.2620155039,
"line_max": 100,
"alpha_frac": 0.5619203229,
"autogenerated": false,
"ratio": 4.223462553277857,
"config_test":... |
'''Base module to handle the collection and the output of statistical data.'''
import logging
import time
import multiprocessing as mp
import queue
from collections import Counter
log = logging.getLogger(__name__)
current_milli_time = lambda: int(round(time.time() * 1000))
def is_number(val):
'''Function to ch... | {
"repo_name": "vrde/logstats",
"path": "logstats/base.py",
"copies": "1",
"size": "3761",
"license": "mit",
"hash": 5425838153360693000,
"line_mean": 28.3828125,
"line_max": 85,
"alpha_frac": 0.5426748205,
"autogenerated": false,
"ratio": 4.132967032967033,
"config_test": false,
"has_no_keywo... |
"""Base Module to handle UI Plugins
Class: UIPlugin()
"""
from zoo.libs.plugin import plugin
from qt import QtCore
class UIPlugin(plugin.Plugin):
"""Base Plugin for UI, a UI Plugin allows the client to implement their own UI widgets and attach it
to the MainWindow. To Initialize a widget you should overload... | {
"repo_name": "dsparrow27/vortexUI",
"path": "vortex/ui/plugin.py",
"copies": "1",
"size": "1658",
"license": "mit",
"hash": 1299665098695924700,
"line_mean": 27.1016949153,
"line_max": 104,
"alpha_frac": 0.6121833534,
"autogenerated": false,
"ratio": 4.284237726098191,
"config_test": false,
... |
""" Base mutual information estimators. """
from numpy import sum, sqrt, isnan, exp, mean, eye, ones, dot, cumsum, \
hstack, newaxis, maximum, prod, abs, arange, log
from numpy.linalg import norm
from scipy.spatial.distance import pdist, squareform
from scipy.special import factorial
from scipy.linal... | {
"repo_name": "gdikov/vae-playground",
"path": "third_party/ite/cost/base_i.py",
"copies": "1",
"size": "27036",
"license": "mit",
"hash": -5645735774696138000,
"line_mean": 32.5850931677,
"line_max": 79,
"alpha_frac": 0.5350643586,
"autogenerated": false,
"ratio": 3.868364572900272,
"config_te... |
base = [{'name': 'Apple', 'price': 10, 'quantity': 1}] # example one element base
# base = [] # it can be also just empty list at the beginning
def entry():
global base
name = raw_input('Name of the product: ')
quantity = raw_input('Quantity: ')
price = raw_input('Price per unit: ')
is_new_entry... | {
"repo_name": "KamilWo/PythonTest",
"path": "Shelve/simple_products.py",
"copies": "1",
"size": "1272",
"license": "mit",
"hash": -2208772473465385200,
"line_mean": 27.9318181818,
"line_max": 98,
"alpha_frac": 0.570754717,
"autogenerated": false,
"ratio": 3.7744807121661723,
"config_test": fals... |
_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DETR',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/detr/detr_r50_8x2_150e_coco.py",
"copies": "1",
"size": "5858",
"license": "apache-2.0",
"hash": -9132870899364444000,
"line_mean": 38.0533333333,
"line_max": 79,
"alpha_frac": 0.4858313418,
"autogenerated": false,
"ratio": 3.90793862575050... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=d... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/atss/atss_r50_fpn_1x_coco.py",
"copies": "1",
"size": "1925",
"license": "apache-2.0",
"hash": -5611486281767052000,
"line_mean": 30.0483870968,
"line_max": 79,
"alpha_frac": 0.5283116883,
"autogenerated": false,
"ratio": 3.171334431630972,... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='SparseRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py",
"copies": "1",
"size": "3469",
"license": "apache-2.0",
"hash": 1883631208086032100,
"line_mean": 35.5157894737,
"line_max": 79,
"alpha_frac": 0.4966849236,
"autogenerated": false,
"ratio": 3.557... |
_base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py",
"copies": "1",
"size": "3296",
"license": "apache-2.0",
"hash": -472638863512212700,
"line_mean": 35.6222222222,
"line_max": 79,
"alpha_frac": 0.4993932039,
"autogenerated": false,
"ratio": 3.408... |
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_ou... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py",
"copies": "1",
"size": "1462",
"license": "apache-2.0",
"hash": -1768147576058297600,
"line_mean": 36.4871794872,
"line_max": 159,
"alpha_frac": 0.6039671683,
"autogenerated": false,
"ratio"... |
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://re... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py",
"copies": "1",
"size": "1369",
"license": "apache-2.0",
"hash": 8944767686329856000,
"line_mean": 35.0263157895,
"line_max": 77,
"alpha_frac": 0.5054784514,
"autogenerated": false,
"ratio": 3.3636... |
_base_ = [
'../_base_/models/fast_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rg... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py",
"copies": "1",
"size": "1944",
"license": "apache-2.0",
"hash": 4659393514406061000,
"line_mean": 36.3846153846,
"line_max": 78,
"alpha_frac": 0.5951646091,
"autogenerated": false,
"ratio": 3.0759493... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_chann... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py",
"copies": "1",
"size": "1724",
"license": "apache-2.0",
"hash": 2284147505367143200,
"line_mean": 36.4782608696,
"line_max": 153,
"alpha_frac": 0.5841067285,
"autogenerated": false,
"ratio": 3... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py",
"copies": "1",
"size": "1072",
"license": "apache-2.0",
"hash": -3253970279435070000,
"line_mean": 32.5,
"line_max": 78,
"alpha_frac": 0.6044776119,
"autogenerated": false,
... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)),
test_cfg=dict(
rcnn=d... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py",
"copies": "1",
"size": "1160",
"license": "apache-2.0",
"hash": 2952953711394906600,
"line_mean": 36.4193548387,
"line_max": 77,
"alpha_frac": 0.5844827586,
"autogenerated": false,
"... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', ... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py",
"copies": "1",
"size": "1486",
"license": "apache-2.0",
"hash": -5348677507592827000,
"line_mean": 35.243902439,
"line_max": 77,
"alpha_frac": 0.5524899058,
"autogen... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
# model settings
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='RetinaNet',
backbone=dict(
type='ResNet',
depth=50,
... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py",
"copies": "1",
"size": "2478",
"license": "apache-2.0",
"hash": 3483695921844629000,
"line_mean": 30.3670886076,
"line_max": 79,
"alpha_frac": 0.6049233253,
"autogenerated": false,
"ratio":... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_chann... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py",
"copies": "1",
"size": "1619",
"license": "apache-2.0",
"hash": -5922719385445301000,
"line_mean": 31.38,
"line_max": 73,
"alpha_frac": 0.5250154416,
"autogenerated": false,
"ratio": 3.19960474308300... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py",
"copies": "1",
"size": "1849",
"license": "apache-2.0",
"hash": -9021665096755140000,
"line_mean": 32.0178571429,
"line_max": 73,
"alpha_frac": 0.5316387236,
"autogenerated": false,
"ratio": 3.24... |
_base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py",
"copies": "1",
"size": "1888",
"license": "apache-2.0",
"hash": 803102750544332700,
"line_mean": 29.9508196721,
"line_max": 77,
"alpha_frac": 0.5699152542,
"autogenerated": false,
"r... |
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe'))
# use caff... | {
"repo_name": "open-mmlab/mmdetection",
"path": "configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py",
"copies": "1",
"size": "1597",
"license": "apache-2.0",
"hash": 6540492683832841000,
"line_mean": 29.1320754717,
"line_max": 77,
"alpha_frac": 0.5760801503,
"autogenerated": false,
... |
'''Base
========
'''
from re import match, compile
from weakref import proxy
from functools import partial
from kivy.event import EventDispatcher
from kivy.properties import (
StringProperty, DictProperty, ObjectProperty, AliasProperty)
from kivy.lang import Builder
from kivy.uix.behaviors.knspace import KNSpace... | {
"repo_name": "matham/moa",
"path": "moa/base.py",
"copies": "1",
"size": "3658",
"license": "mit",
"hash": -4943425983804896000,
"line_mean": 30,
"line_max": 79,
"alpha_frac": 0.6323127392,
"autogenerated": false,
"ratio": 3.691220988900101,
"config_test": false,
"has_no_keywords": false,
... |
# Base node
class SourceElement(object):
'''
A SourceElement is the base class for all elements that occur in a Java
file parsed by plyj.
'''
def __init__(self):
super(SourceElement, self).__init__()
self._fields = []
def __repr__(self):
equals = ("{0}={1!r}".format(k, ... | {
"repo_name": "RealTimeWeb/program-analyzer",
"path": "plyj/model.py",
"copies": "1",
"size": "23359",
"license": "apache-2.0",
"hash": -8955960889848748000,
"line_mean": 28.2719298246,
"line_max": 83,
"alpha_frac": 0.5758380068,
"autogenerated": false,
"ratio": 4.398230088495575,
"config_test"... |
"""BaseNodeVisitor and it's concrete subclasses
"""
import abc
import collections
from py2c.tree import Node, iter_fields
__all__ = ["RecursiveNodeVisitor", "RecursiveNodeTransformer"]
# -----------------------------------------------------------------------------
# Access Path of a node
# -----------------------... | {
"repo_name": "pradyunsg/Py2C",
"path": "py2c/tree/visitors.py",
"copies": "1",
"size": "5199",
"license": "bsd-3-clause",
"hash": -6569909528122783000,
"line_mean": 32.7597402597,
"line_max": 79,
"alpha_frac": 0.5524139258,
"autogenerated": false,
"ratio": 4.544580419580419,
"config_test": fal... |
base_numbers = {
1: "one",
2: "two",
3: "three",
4: "four",
5: "five",
6: "six",
7: "seven",
8: "eight",
9: "nine",
10: "ten",
11: "eleven",
12: "twelve",
13: "thirteen",
14: "fourteen",
15: "fifteen",
16: "sixteen",
17: "seventeen",
18: "eighteen"... | {
"repo_name": "deniscostadsc/playground",
"path": "solutions/project-euler/017/017.py",
"copies": "1",
"size": "1297",
"license": "mit",
"hash": -2228507527659421700,
"line_mean": 22.5818181818,
"line_max": 73,
"alpha_frac": 0.5258288358,
"autogenerated": false,
"ratio": 3.258793969849246,
"con... |
"""Base object class and other classes."""
from re import fullmatch, search
from datetime import datetime, date, time
class OdataObjectBase(object):
odata = ""
valid_odata_properties = {}
valid_properties = {}
@classmethod
def get_property_odata_name(cls, name):
if name in cls.valid_prope... | {
"repo_name": "elexpander/odataPyModel",
"path": "input/odata_object_base.py",
"copies": "1",
"size": "6557",
"license": "mit",
"hash": 6473507821076856000,
"line_mean": 37.5705882353,
"line_max": 109,
"alpha_frac": 0.5127344822,
"autogenerated": false,
"ratio": 4.197823303457106,
"config_test"... |
"""Base object corresponding to CouchDB entries."""
import inspect
from couchdb.mapping import Document
from Hub.api import couch
from sys import modules
from Hub.v1.Common.db_helpers import get_couch_db
from Hub.v1.Common.helpers import bool_or_string
class HomityObject(Document):
"""Base class for Homity objec... | {
"repo_name": "openhomity/homity-hub",
"path": "Hub/v1/Common/base.py",
"copies": "1",
"size": "5103",
"license": "apache-2.0",
"hash": 422960698086702000,
"line_mean": 32.1363636364,
"line_max": 80,
"alpha_frac": 0.5222418185,
"autogenerated": false,
"ratio": 4.27745180217938,
"config_test": f... |
"""Base objects for measurement and plate objects."""
import inspect
import os
import decorator
import pylab as pl
import six
from numpy import nan, unravel_index
from pandas import DataFrame as DF
from FlowCytometryTools.core import graph
from FlowCytometryTools.core.common_doc import doc_replacer
from FlowCytometry... | {
"repo_name": "eyurtsev/FlowCytometryTools",
"path": "FlowCytometryTools/core/bases.py",
"copies": "1",
"size": "39393",
"license": "mit",
"hash": 6528442181344464000,
"line_mean": 34.4891891892,
"line_max": 116,
"alpha_frac": 0.5546924581,
"autogenerated": false,
"ratio": 4.322725776363437,
"c... |
"""Base objects to be exported for use in Controllers"""
from paste.registry import StackedObjectProxy
from pylons.config import config
from pylons.legacy import h, jsonify, Controller, Response
__all__ = ['c', 'g', 'cache', 'request', 'response', 'session', 'jsonify',
'Controller', 'Response']
def __figu... | {
"repo_name": "santisiri/popego",
"path": "envs/ALPHA-POPEGO/lib/python2.5/site-packages/Pylons-0.9.6.1-py2.5.egg/pylons/__init__.py",
"copies": "1",
"size": "1179",
"license": "bsd-3-clause",
"hash": -7723793162076348000,
"line_mean": 32.6857142857,
"line_max": 86,
"alpha_frac": 0.6692111959,
"aut... |
"""Base objects to be exported for use in Controllers"""
from paste.registry import StackedObjectProxy
from pylons.config import config
from pylons.legacy import h, jsonify, Response
__all__ = ['app_globals', 'c', 'cache', 'config', 'g', 'request', 'response',
'session', 'tmpl_context', 'url']
def __figu... | {
"repo_name": "solos/pylons",
"path": "pylons/__init__.py",
"copies": "1",
"size": "1272",
"license": "bsd-3-clause",
"hash": 6076002369574614000,
"line_mean": 33.3783783784,
"line_max": 77,
"alpha_frac": 0.6721698113,
"autogenerated": false,
"ratio": 3.7411764705882353,
"config_test": false,
... |
"""Base objects to be exported for use in Controllers"""
from paste.registry import StackedObjectProxy
from pylons.configuration import config
__all__ = ['app_globals', 'cache', 'config', 'request', 'response',
'session', 'tmpl_context', 'url']
def __figure_version():
try:
from pkg_resources i... | {
"repo_name": "obeattie/pylons",
"path": "pylons/__init__.py",
"copies": "1",
"size": "1178",
"license": "bsd-3-clause",
"hash": 502936438773251460,
"line_mean": 33.6470588235,
"line_max": 72,
"alpha_frac": 0.6672325976,
"autogenerated": false,
"ratio": 3.887788778877888,
"config_test": false,
... |
"""Base objects to be exported for use in Controllers"""
# Import pkg_resources first so namespace handling is properly done so the
# paste imports work
import pkg_resources
from paste.registry import StackedObjectProxy
from pylons.configuration import config
from pylons.controllers.util import Request
from pylons.con... | {
"repo_name": "grepme/CMPUT410Lab01",
"path": "virt_env/virt1/lib/python2.7/site-packages/Pylons-1.0.1-py2.7.egg/pylons/__init__.py",
"copies": "4",
"size": "1449",
"license": "apache-2.0",
"hash": -222014580887042460,
"line_mean": 34.3414634146,
"line_max": 74,
"alpha_frac": 0.690821256,
"autogene... |
# BaseObject usage example.
from ocempgui.object import BaseObject
from ocempgui.events import EventManager
# Callbacks, which should be invoked for the object.
def ping_callback (obj, additional_data):
print "The object is: %s" % obj.name
print "Passed data is: %s" % additional_data
def pong_callback ():
... | {
"repo_name": "prim/ocempgui",
"path": "doc/examples/baseobject.py",
"copies": "1",
"size": "1631",
"license": "bsd-2-clause",
"hash": -537510359921986750,
"line_mean": 32.2857142857,
"line_max": 67,
"alpha_frac": 0.6750459841,
"autogenerated": false,
"ratio": 3.6900452488687785,
"config_test":... |
"""Base of all build rules."""
from pathlib import Path
from garage import scripts
from foreman import define_parameter, rule, to_path
(define_parameter.path_typed('root')
.with_doc('Path to the root directory of this repository.')
.with_default(Path(__file__).parent.parent.parent.parent))
(define_parameter.pa... | {
"repo_name": "clchiou/garage",
"path": "shipyard/rules/base/build.py",
"copies": "1",
"size": "3761",
"license": "mit",
"hash": 6358119070599622000,
"line_mean": 28.3828125,
"line_max": 78,
"alpha_frac": 0.6360010635,
"autogenerated": false,
"ratio": 3.6443798449612403,
"config_test": false,
... |
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