code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
Description
===========
:Class: `roman.tweakreg.TweakRegStep`
:Alias: tweakreg
Overview
--------
This step uses the coordinates of point-like sources from an input catalog
(i.e. the result from `SourceDetectionStep` saved in the
`meta.tweakreg_catalog` attribute) and compares them with the
coordinates from a Gaia cat... | /romancal-0.12.0.tar.gz/romancal-0.12.0/docs/roman/tweakreg/README.rst | 0.952607 | 0.827619 | README.rst | pypi |
=====
Steps
=====
.. _writing-a-step:
Writing a step
==============
Writing a new step involves writing a class that has a `process`
method to perform work and a `spec` member to define its configuration
parameters. (Optionally, the `spec` member may be defined in a
separate `spec` file).
Inputs and outputs
------... | /romancal-0.12.0.tar.gz/romancal-0.12.0/docs/roman/stpipe/devel_step.rst | 0.804444 | 0.818193 | devel_step.rst | pypi |
.. _stpipe-user-pipelines:
=========
Pipelines
=========
It is important to note that a Pipeline is also a Step, so everything
that applies to a Step in the :ref:`stpipe-user-steps` chapter also
applies to Pipelines.
Configuring a Pipeline
======================
This section describes how to set parameters on the i... | /romancal-0.12.0.tar.gz/romancal-0.12.0/docs/roman/stpipe/user_pipeline.rst | 0.927863 | 0.651277 | user_pipeline.rst | pypi |
=====
Steps
=====
.. _configuring-a-step:
Configuring a Step
==================
This section describes how to instantiate a Step and set configuration
parameters on it.
Steps can be configured by:
- Instantiating the Step directly from Python
- Reading the input from a parameter file
.. _running_a_step_f... | /romancal-0.12.0.tar.gz/romancal-0.12.0/docs/roman/stpipe/user_step.rst | 0.928141 | 0.902653 | user_step.rst | pypi |
Introduction
============
This document is intended to be a core reference guide to the formats, naming convention and
data quality flags used by the reference files for pipeline steps requiring them, and is not
intended to be a detailed description of each of those pipeline steps. It also does not give
details on pip... | /romancal-0.12.0.tar.gz/romancal-0.12.0/docs/roman/references_general/references_general.rst | 0.933484 | 0.780955 | references_general.rst | pypi |
Description
============
This step determines the mean count rate, in units of counts per second, for
each pixel by performing a linear fit to the data in the input file. The fit
is done using the "ordinary least squares" method.
The fit is performed independently for each pixel. There can be up to two
output files ... | /romancal-0.12.0.tar.gz/romancal-0.12.0/docs/roman/ramp_fitting/description.rst | 0.937397 | 0.985936 | description.rst | pypi |
Description
===========
Overview
--------
The ``skymatch`` step can be used to compute sky values in a collection of input
images that contain both sky and source signal. The sky values can be computed
for each image separately or in a way that matches the sky levels amongst the
collection of images so as to minimize ... | /romancal-0.12.0.tar.gz/romancal-0.12.0/docs/roman/skymatch/description.rst | 0.969942 | 0.979255 | description.rst | pypi |
import math
import copy
import time
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
from torchsummary import summary
import torch.backends.cudnn as cudnn
import torch.optim as optim
class conv_block(nn.Module):
def __init__(self, in_channels, out_channels, ... | /romaniya_menim-0.0.34-py3-none-any.whl/romaniya_menim/models/fcn.py | 0.899949 | 0.313709 | fcn.py | pypi |
import math
import copy
import time
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
from torchsummary import summary
import torch.backends.cudnn as cudnn
import torch.optim as optim
class Residual_block(nn.Module):
def __init__(self, in_channels, out_chann... | /romaniya_menim-0.0.34-py3-none-any.whl/romaniya_menim/models/inception.py | 0.921627 | 0.309454 | inception.py | pypi |
import math
import copy
import time
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
from torchsummary import summary
import torch.backends.cudnn as cudnn
import torch.optim as optim
class conv_block(nn.Module):
def __init__(self, in_channels, out_channels, *... | /romaniya_menim-0.0.34-py3-none-any.whl/romaniya_menim/models/cnn.py | 0.898431 | 0.317797 | cnn.py | pypi |
import math
import copy
import time
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
from torchsummary import summary
import torch.backends.cudnn as cudnn
import torch.optim as optim
class conv_block(nn.Module):
def __init__(self, in_channels, out_channels, ... | /romaniya_menim-0.0.34-py3-none-any.whl/romaniya_menim/models/resnet.py | 0.913922 | 0.268249 | resnet.py | pypi |
import math
import copy
import time
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
from torchsummary import summary
import torch.backends.cudnn as cudnn
import torch.optim as optim
class mlp_block(nn.Module):
def __init__(self, in_channels, out_channels, d... | /romaniya_menim-0.0.34-py3-none-any.whl/romaniya_menim/models/mlp.py | 0.887778 | 0.316303 | mlp.py | pypi |
import re
from collections import OrderedDict
from .romanizer import romanizer
has_capitals = False
data = OrderedDict()
# https://en.wikipedia.org/wiki/Aramaic_alphabet
# https://en.wikipedia.org/wiki/Brahmi_script
# letters from 𑀅 to 𑀣
# alef:http://en.wiktionary.org/wiki/
data['a'] = dict(letter=[u'𑀅'], name... | /romanize3-0.1.14.tar.gz/romanize3-0.1.14/romanize/brh.py | 0.518302 | 0.304701 | brh.py | pypi |
import re
from collections import OrderedDict
from .romanizer import romanizer
has_capitals = False
data = OrderedDict()
# https://en.wikipedia.org/wiki/Aramaic_alphabet
# https://en.wikipedia.org/wiki/Phoenician_alphabet
# 1 = 𐤖
# 2 = 𐤚
# 3 = 𐤛
# 10 = 𐤗
# 20 = 𐤘
# 100 = 𐤙
# letters from 𐤀 to 𐤈
# alef:h... | /romanize3-0.1.14.tar.gz/romanize3-0.1.14/romanize/phn.py | 0.511473 | 0.233455 | phn.py | pypi |
import re
from collections import OrderedDict
from .romanizer import romanizer
has_capitals = False
data = OrderedDict()
# https://en.wikipedia.org/wiki/Aramaic_alphabet
# letters from ܐ to ܛ
# alef:http://en.wiktionary.org/wiki/
data['alap'] = dict(letter=[u'ܐ'], name=u'ܐ', segment='vowel', subsegment='', transli... | /romanize3-0.1.14.tar.gz/romanize3-0.1.14/romanize/syc.py | 0.514156 | 0.251832 | syc.py | pypi |
import re
from collections import OrderedDict
from .romanizer import romanizer
has_capitals = False
data = OrderedDict()
# https://en.wikipedia.org/wiki/Aramaic_alphabet
# letters from 𐡀 to 𐡈
# alef:http://en.wiktionary.org/wiki/
data['alap'] = dict(letter=[u'𐡀'], name=u'𐡀', segment='vowel', subsegment='', tra... | /romanize3-0.1.14.tar.gz/romanize3-0.1.14/romanize/arm.py | 0.503662 | 0.300816 | arm.py | pypi |
import re
from collections import OrderedDict
from .romanizer import romanizer
has_capitals = True
data = OrderedDict()
# http://en.wikipedia.org/wiki/Coptic_alphabet
# letters from ⲁ to ⲑ (1 - 9)
# alef:http://en.wiktionary.org/wiki/
data['alpha'] = dict(letter=[u'ⲁ'], name=u'ⲁ', segment='vowel', subsegment='', t... | /romanize3-0.1.14.tar.gz/romanize3-0.1.14/romanize/cop.py | 0.519278 | 0.302056 | cop.py | pypi |
import re
from collections import OrderedDict
from .romanizer import romanizer
has_capitals = False
data = OrderedDict()
# https://en.wikipedia.org/wiki/Aramaic_alphabet
# https://en.wikipedia.org/wiki/Abjad_numerals
# letters from ا to ط
# https://en.wikipedia.org/wiki/%D8%A7
data['alif'] = dict(letter=[u'ا'], na... | /romanize3-0.1.14.tar.gz/romanize3-0.1.14/romanize/ara.py | 0.59561 | 0.401482 | ara.py | pypi |
from itertools import count
__version__ = '0.0.3'
_map = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
class Roman(int):
def __new__(class_, roman):
try:
roman = int(roman)
except ValueError:
roman = str(roman).upper().replace(' ', '')
i... | /rome-0.0.3.tar.gz/rome-0.0.3/rome.py | 0.435421 | 0.246205 | rome.py | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | /romeliagurit%3Fimagurit-0.1.tar.gz/romeliagurit?imagurit-0.1/distributions/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
import md5
from binascii import unhexlify
from docopt import docopt
def get_md5(source):
"""Return the MD5 hash of the file `source`."""
m = md5.new()
while True:
d = source.read(8196)
if not d:
break
m.update(d)
return m.hexdigest()
def hex_to_bstr(d):
"""Re... | /romexpander-0.5.tar.gz/romexpander-0.5/romexpander.py | 0.710628 | 0.374991 | romexpander.py | pypi |
import sympy as sp
from sympy.parsing.sympy_parser import parse_expr
def get_stationary_data(fn, symbol="infer"):
r"""Get stationary point of the fn w.r.t a symbol.
If symbol is infer, fn should only contain one symbol."""
if isinstance(fn, str):
fn = parse_expr(fn)
if symbol != "infer" and i... | /graph/optimize.py | 0.794544 | 0.722894 | optimize.py | pypi |
# Developing match up function v2
- Move to a 1D model output
```
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import rompy
from rompy import utils ## Should we import utils in __init__.py?
from shapely.geometry import MultiPoint,Point
%matplotlib inline
xr.set_options(... | /rompy-0.1.0.tar.gz/rompy-0.1.0/notebooks/rompy-dev_matchup_code v2.ipynb | 0.400515 | 0.828141 | rompy-dev_matchup_code v2.ipynb | pypi |
# Developing match up function v2
- Should work on altimetry & waveriders!
```
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import rompy
from rompy import utils ## Should we import utils in __init__.py?
from shapely.geometry import MultiPoint,Point
%matplotlib inline
xr... | /rompy-0.1.0.tar.gz/rompy-0.1.0/notebooks/rompy-dev_matchup_code v2-Alt.ipynb | 0.417271 | 0.665854 | rompy-dev_matchup_code v2-Alt.ipynb | pypi |
# Developing match up function
- Focus on wave rider buoys
```
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import rompy
from rompy import utils ## Should we import utils in __init__.py?
from shapely.geometry import MultiPoint,Point
%matplotlib inline
xr.set_options(dis... | /rompy-0.1.0.tar.gz/rompy-0.1.0/notebooks/rompy-dev_matchup_code.ipynb | 0.461259 | 0.826991 | rompy-dev_matchup_code.ipynb | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | /romullo_distribution-1.0-py3-none-any.whl/romullo_distribution/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
from ciphers.caeser_cipher import CaeserCipher
from ciphers.vigenere_cipher import VigenereCipher
import argparse
VERSION = "1.0.4"
def caeser_cipher(args): # pragma: no cover
caeser = CaeserCipher(rotation=args.rotation)
if args.action == "encrypt":
print(caeser.encrypt(plain_text=args.input))
... | /ron_cipher-1.0.4.tar.gz/ron_cipher-1.0.4/ciphers/__init__.py | 0.481454 | 0.213131 | __init__.py | pypi |
# Stochastic Short Rates
This brief section illustrates the use of stochastic short rate models for simulation and (risk-neutral) discounting. The class used is called `stochastic_short_rate`.
## The Modelling
First, the market environment. As a stochastic short rate model the `square_root_diffusion` class is (cu... | /ron-0.0.1.tar.gz/ron-0.0.1/12_dx_stochastic_short_rates.ipynb | 0.526343 | 0.986967 | 12_dx_stochastic_short_rates.ipynb | pypi |
import importlib
import logging
import re
import tempfile
import time
from collections import defaultdict
from types import TracebackType
from typing import Optional, List, Union, Mapping, Dict, Set, Any, Type
import unicodedata
from ronds_sdk import error
from ronds_sdk.options.pipeline_options import PipelineOption... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/pipeline.py | 0.762513 | 0.214075 | pipeline.py | pypi |
import importlib
from typing import Optional
from ronds_sdk.options.pipeline_options import PipelineOptions
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ronds_sdk.pipeline import Pipeline
from ronds_sdk.transforms.ptransform import PTransform
from ronds_sdk.dataframe import pvalue
class Pipel... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/runners/runner.py | 0.791821 | 0.209631 | runner.py | pypi |
import logging
from pyspark.sql import SparkSession
from ronds_sdk.pipeline import PipelineVisitor
from ronds_sdk.dataframe import pvalue
from ronds_sdk.runners.runner import PipelineRunner, PipelineResult
from ronds_sdk.runners.visitors.spark_runner_visitor import SparkRunnerVisitor
from ronds_sdk.options.pipeline_o... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/runners/spark_runner.py | 0.621771 | 0.184345 | spark_runner.py | pypi |
from typing import TYPE_CHECKING, Union, Callable, List
from ronds_sdk.dataframe import pvalue
from ronds_sdk.tools.utils import RuleParser
from ronds_sdk.transforms.ptransform import PTransform
if TYPE_CHECKING:
from ronds_sdk.options.pipeline_options import KafkaOptions
__all__ = [
'RulesCassandraScan',
... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/transforms/ronds.py | 0.69285 | 0.397295 | ronds.py | pypi |
from typing import TypeVar, Generic, Sequence, Optional, Callable, Union
from ronds_sdk import error
from ronds_sdk.dataframe import pvalue
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ronds_sdk.pipeline import Pipeline, AppliedPTransform
from ronds_sdk.tools.utils import WrapperFunc
InputT = Typ... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/transforms/ptransform.py | 0.856857 | 0.298402 | ptransform.py | pypi |
import logging
import time
from ronds_sdk import error
from ronds_sdk.transforms.ptransform import PTransform, ForeachBatchTransform
from ronds_sdk.dataframe import pvalue
from ronds_sdk.runners.spark_runner import SparkRunner
from pyspark.sql import DataFrame
from typing import TYPE_CHECKING
if TYPE_CHECKING:
fr... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/transforms/spark/transforms.py | 0.646014 | 0.307293 | transforms.py | pypi |
from collections import deque
from typing import Union
from ronds_sdk.tools.constants import JsonKey
# noinspection SpellCheckingInspection
class RuleData(object):
def __init__(self,
device_id, # type: str
rule_ids, # type: list[str]
nodes=None, # type: lis... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/transforms/pandas/rule_merge_data.py | 0.669421 | 0.32314 | rule_merge_data.py | pypi |
import importlib.machinery
import json
import logging
import sys
import time
from os.path import dirname, abspath
from typing import TYPE_CHECKING, Callable
from pathlib import Path
import pandas as pd
from pyspark.sql import DataFrame, SparkSession
from ronds_sdk import error, logger_config
from ronds_sdk.dataframe ... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/transforms/pandas/transforms.py | 0.431704 | 0.20351 | transforms.py | pypi |
from typing import TypeVar, Generic, Optional, Union
from ronds_sdk.transforms.core import Windowing
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ronds_sdk.pipeline import Pipeline, PipelineVisitor
from ronds_sdk.pipeline import AppliedPTransform
T = TypeVar('T')
class PValue(object):
"""
... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/dataframe/pvalue.py | 0.87213 | 0.208078 | pvalue.py | pypi |
# pytype: skip-file
from functools import wraps
from typing import Set
from ronds_sdk import error
__all__ = [
'ValueProvider',
'StaticValueProvider',
'RuntimeValueProvider',
'NestedValueProvider',
'check_accessible',
]
class ValueProvider(object):
"""Base class that all other ValueProvider... | /rondsspark-0.0.4.23.tar.gz/rondsspark-0.0.4.23/ronds_sdk/options/value_provider.py | 0.86587 | 0.248153 | value_provider.py | pypi |
# Rong - A console color utility for python console app
#### Developed by [Md. Almas Ali][1]
***Version 0.0.1***
[](LICENSE)
## Installation
It is very easy to... | /rong-0.0.1.tar.gz/rong-0.0.1/README.md | 0.906366 | 0.822546 | README.md | pypi |
Rōnin
=====
A straightforward but powerful build system based on [Ninja](https://ninja-build.org/) and
[Python](https://www.python.org/), suitable for projects both big and small.
Rōnin comes in [frustration-free packaging](https://en.wikipedia.org/wiki/Wrap_rage). Let's build
all the things!
Features
--------
Curr... | /ronin-1.1.1.tar.gz/ronin-1.1.1/README.md | 0.769427 | 0.767385 | README.md | pypi |
import re
import numpy as np
def to_isoformat(tm):
"""
Returns an ISO 8601 string from a time object (of different types).
:param tm: Time object
:return: (str) ISO 8601 time string
"""
if type(tm) == np.datetime64:
return str(tm).split(".")[0]
else:
return tm.isoformat()... | /roocs_utils-0.6.4.tar.gz/roocs_utils-0.6.4/roocs_utils/utils/time_utils.py | 0.861567 | 0.611295 | time_utils.py | pypi |
import os
from roocs_utils import CONFIG
from roocs_utils.exceptions import InvalidProject
class FileMapper:
"""
Class to represent a set of files that exist in the same directory as one object.
Args:
file_list: the list of files to represent. If dirpath not providedm these should be full file p... | /roocs_utils-0.6.4.tar.gz/roocs_utils-0.6.4/roocs_utils/utils/file_utils.py | 0.469034 | 0.178902 | file_utils.py | pypi |
import datetime
from roocs_utils.exceptions import InvalidParameterValue
from roocs_utils.parameter.base_parameter import _BaseIntervalOrSeriesParameter
from roocs_utils.parameter.param_utils import parse_datetime
from roocs_utils.parameter.param_utils import time_interval
class TimeParameter(_BaseIntervalOrSeriesPa... | /roocs_utils-0.6.4.tar.gz/roocs_utils-0.6.4/roocs_utils/parameter/time_parameter.py | 0.677687 | 0.564939 | time_parameter.py | pypi |
import calendar
from collections.abc import Sequence
from roocs_utils.exceptions import InvalidParameterValue
from roocs_utils.utils.file_utils import FileMapper
from roocs_utils.utils.time_utils import str_to_AnyCalendarDateTime
# Global variables that are generally useful
month_map = {name.lower(): num for num, na... | /roocs_utils-0.6.4.tar.gz/roocs_utils-0.6.4/roocs_utils/parameter/param_utils.py | 0.79542 | 0.475849 | param_utils.py | pypi |
from roocs_utils.exceptions import InvalidParameterValue
from roocs_utils.parameter.base_parameter import _BaseParameter
from roocs_utils.parameter.param_utils import string_to_dict
from roocs_utils.parameter.param_utils import time_components
class TimeComponentsParameter(_BaseParameter):
"""
Class for time ... | /roocs_utils-0.6.4.tar.gz/roocs_utils-0.6.4/roocs_utils/parameter/time_components_parameter.py | 0.769297 | 0.444505 | time_components_parameter.py | pypi |
from roocs_utils.exceptions import InvalidParameterValue
from roocs_utils.parameter.param_utils import interval
from roocs_utils.parameter.param_utils import series
class _BaseParameter:
"""
Base class for parameters used in operations (e.g. subset, average etc.)
"""
allowed_input_types = None
d... | /roocs_utils-0.6.4.tar.gz/roocs_utils-0.6.4/roocs_utils/parameter/base_parameter.py | 0.881933 | 0.331742 | base_parameter.py | pypi |
from roocs_utils.parameter.base_parameter import _BaseIntervalOrSeriesParameter
from roocs_utils.parameter.param_utils import to_float
from roocs_utils.exceptions import InvalidParameterValue
class LevelParameter(_BaseIntervalOrSeriesParameter):
"""
Class for level parameter used in subsetting operation.
... | /roocs_utils-0.6.4.tar.gz/roocs_utils-0.6.4/roocs_utils/parameter/level_parameter.py | 0.790732 | 0.568955 | level_parameter.py | pypi |
import logging
from typing import List, Optional, Tuple
from .config import CfgNode as CN
from .defaults import _C
__all__ = ["upgrade_config", "downgrade_config"]
def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN:
"""
Upgrade a config from its current version to a newer version.
Args... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/config/compat.py | 0.846895 | 0.197367 | compat.py | pypi |
import functools
import inspect
import logging
from fvcore.common.config import CfgNode as _CfgNode
from detectron2.utils.file_io import PathManager
class CfgNode(_CfgNode):
"""
The same as `fvcore.common.config.CfgNode`, but different in:
1. Use unsafe yaml loading by default.
Note that this ma... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/config/config.py | 0.777131 | 0.214825 | config.py | pypi |
import copy
import io
import logging
import numpy as np
from typing import List
import onnx
import torch
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python.onnx.backend import Caffe2Backend
from tabulate import tabulate
from termcolor import colored
from torch.onnx import OperatorExp... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/export/caffe2_export.py | 0.894703 | 0.335174 | caffe2_export.py | pypi |
import os
import torch
from detectron2.utils.file_io import PathManager
from .torchscript_patch import freeze_training_mode, patch_instances
__all__ = ["scripting_with_instances", "dump_torchscript_IR"]
def scripting_with_instances(model, fields):
"""
Run :func:`torch.jit.script` on a model that uses the ... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/export/torchscript.py | 0.824285 | 0.529446 | torchscript.py | pypi |
import copy
import logging
import os
import torch
from caffe2.proto import caffe2_pb2
from torch import nn
from detectron2.config import CfgNode
from detectron2.utils.file_io import PathManager
from .caffe2_inference import ProtobufDetectionModel
from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/export/api.py | 0.855127 | 0.417925 | api.py | pypi |
import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Ca... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/export/caffe2_patch.py | 0.849691 | 0.251625 | caffe2_patch.py | pypi |
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ROIAlign
from detectron2.utils.memory import retry_if_cuda_oom
from .boxes import Boxes
def polygon_area(x,... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/structures/masks.py | 0.941439 | 0.650503 | masks.py | pypi |
import itertools
from typing import Any, Dict, List, Tuple, Union
import torch
class Instances:
"""
This class represents a list of instances in an image.
It stores the attributes of instances (e.g., boxes, masks, labels, scores) as "fields".
All fields must have the same ``__len__`` which is the numb... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/structures/instances.py | 0.902262 | 0.67662 | instances.py | pypi |
from __future__ import division
from typing import Any, List, Tuple
import torch
from torch import device
from torch.nn import functional as F
from detectron2.layers.wrappers import move_device_like, shapes_to_tensor
class ImageList(object):
"""
Structure that holds a list of images (of possibly
varying ... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/structures/image_list.py | 0.924432 | 0.686091 | image_list.py | pypi |
import numpy as np
from typing import Any, List, Tuple, Union
import torch
from torch.nn import functional as F
class Keypoints:
"""
Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property
containing the x,y location and visibility flag of each keypoint. This tensor has shape
... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/structures/keypoints.py | 0.938674 | 0.802942 | keypoints.py | pypi |
import logging
from detectron2.utils.file_io import PathHandler, PathManager
class ModelCatalog(object):
"""
Store mappings from names to third-party models.
"""
S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron"
# MSRA models have STRIDE_IN_1X1=True. False otherwise.
# NOT... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/checkpoint/catalog.py | 0.600071 | 0.286731 | catalog.py | pypi |
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion to names in C2 weights.
It only deals with typical backbone models.
Args:
original_keys (list[str]):
... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/checkpoint/c2_model_loading.py | 0.885415 | 0.549399 | c2_model_loading.py | pypi |
import argparse
import logging
import os
import sys
import weakref
from collections import OrderedDict
from typing import Optional
import torch
from fvcore.nn.precise_bn import get_bn_modules
from omegaconf import OmegaConf
from torch.nn.parallel import DistributedDataParallel
import detectron2.data.transforms as T
fr... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/engine/defaults.py | 0.778439 | 0.187021 | defaults.py | pypi |
import datetime
import itertools
import logging
import math
import operator
import os
import tempfile
import time
import warnings
from collections import Counter
import torch
from fvcore.common.checkpoint import Checkpointer
from fvcore.common.checkpoint import PeriodicCheckpointer as _PeriodicCheckpointer
from fvcore... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/engine/hooks.py | 0.757705 | 0.225843 | hooks.py | pypi |
import logging
import math
from bisect import bisect_right
from typing import List
import torch
from fvcore.common.param_scheduler import (
CompositeParamScheduler,
ConstantParamScheduler,
LinearParamScheduler,
ParamScheduler,
)
logger = logging.getLogger(__name__)
class WarmupParamScheduler(Composit... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/solver/lr_scheduler.py | 0.924611 | 0.550064 | lr_scheduler.py | pypi |
import torch
from detectron2.layers import nonzero_tuple
__all__ = ["subsample_labels"]
def subsample_labels(
labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int
):
"""
Return `num_samples` (or fewer, if not enough found)
random samples from `labels` which is a mixture of... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/sampling.py | 0.935744 | 0.770551 | sampling.py | pypi |
import itertools
import logging
import numpy as np
from collections import OrderedDict
from collections.abc import Mapping
from typing import Dict, List, Optional, Tuple, Union
import torch
from omegaconf import DictConfig, OmegaConf
from torch import Tensor, nn
from detectron2.layers import ShapeSpec
from detectron2.... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/mmdet_wrapper.py | 0.928149 | 0.40539 | mmdet_wrapper.py | pypi |
import torch
from torch.nn import functional as F
from detectron2.structures import Instances, ROIMasks
# perhaps should rename to "resize_instance"
def detector_postprocess(
results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5
):
"""
Resize the output instances.
The... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/postprocessing.py | 0.859266 | 0.747363 | postprocessing.py | pypi |
import collections
import math
from typing import List
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, move_device_like
from detectron2.structures import Boxes, RotatedBoxes
from detectron2.utils.registry import Registry
ANCHOR_GENERATOR_REGISTRY =... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/anchor_generator.py | 0.943971 | 0.433981 | anchor_generator.py | pypi |
import math
from typing import List, Tuple, Union
import torch
from fvcore.nn import giou_loss, smooth_l1_loss
from torch.nn import functional as F
from detectron2.layers import cat, ciou_loss, diou_loss
from detectron2.structures import Boxes
# Value for clamping large dw and dh predictions. The heuristic is that we... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/box_regression.py | 0.9605 | 0.644281 | box_regression.py | pypi |
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from .backbone import Backbone
from .build import BACKBONE_REGISTRY
from .resnet import build_resnet_backbone
__all__ = ["build_resnet_fp... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/backbone/fpn.py | 0.931009 | 0.435902 | fpn.py | pypi |
import numpy as np
from torch import nn
from detectron2.layers import CNNBlockBase, ShapeSpec, get_norm
from .backbone import Backbone
__all__ = [
"AnyNet",
"RegNet",
"ResStem",
"SimpleStem",
"VanillaBlock",
"ResBasicBlock",
"ResBottleneckBlock",
]
def conv2d(w_in, w_out, k, *, stride=1... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/backbone/regnet.py | 0.983447 | 0.608478 | regnet.py | pypi |
import numpy as np
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import (
CNNBlockBase,
Conv2d,
DeformConv,
ModulatedDeformConv,
ShapeSpec,
get_norm,
)
from .backbone import Backbone
from .build import BACKB... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/backbone/resnet.py | 0.877405 | 0.386619 | resnet.py | pypi |
import logging
import numpy as np
from typing import Dict, List, Optional, Tuple
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.data.detection_utils import convert_image_to_rgb
from detectron2.layers import move_device_like
from detectron2.structures import ImageList, Inst... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/meta_arch/rcnn.py | 0.947974 | 0.418816 | rcnn.py | pypi |
import numpy as np
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from detectron2.data.detection_utils import convert_image_to_rgb
from detectron2.layers import move_device_like
from detectron2.modeling import Backbone
from detectron2.structures import Boxes, ImageList, Instan... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/meta_arch/dense_detector.py | 0.956856 | 0.630813 | dense_detector.py | pypi |
import numpy as np
from typing import Callable, Dict, Optional, Tuple, Union
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.structures... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/meta_arch/semantic_seg.py | 0.94474 | 0.461017 | semantic_seg.py | pypi |
import logging
import math
from typing import List, Tuple
import torch
from fvcore.nn import sigmoid_focal_loss_jit
from torch import Tensor, nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import CycleBatchNormList, ShapeSpec, batched_nms, cat, get_norm
from d... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/meta_arch/retinanet.py | 0.930387 | 0.324704 | retinanet.py | pypi |
import logging
from typing import Dict, List
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.structures import ImageList
from ..postprocessing import detector_postprocess, sem_seg_postprocess
from .build import META_ARCH_REGISTRY
from .rcnn import GeneralizedRCNN
from .se... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/meta_arch/panoptic_fpn.py | 0.946163 | 0.257975 | panoptic_fpn.py | pypi |
import logging
import math
from typing import List, Tuple, Union
import torch
from detectron2.layers import batched_nms, cat, move_device_like
from detectron2.structures import Boxes, Instances
logger = logging.getLogger(__name__)
def _is_tracing():
# (fixed in TORCH_VERSION >= 1.9)
if torch.jit.is_scriptin... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/proposal_generator/proposal_utils.py | 0.902076 | 0.469155 | proposal_utils.py | pypi |
import itertools
import logging
from typing import Dict, List
import torch
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, batched_nms_rotated, cat
from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated
from detectron2.utils.memory import retry_if_cuda_oom
... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/proposal_generator/rrpn.py | 0.89204 | 0.351826 | rrpn.py | pypi |
import inspect
import logging
import numpy as np
from typing import Dict, List, Optional, Tuple
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, nonzero_tuple
from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
from detectron2... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/roi_heads/roi_heads.py | 0.907487 | 0.541954 | roi_heads.py | pypi |
from typing import List
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm
from detectron2.layers.wrappers import move_device_like
f... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/roi_heads/mask_head.py | 0.959345 | 0.578329 | mask_head.py | pypi |
from typing import List
import torch
from torch import nn
from torch.autograd.function import Function
from detectron2.config import configurable
from detectron2.layers import ShapeSpec
from detectron2.structures import Boxes, Instances, pairwise_iou
from detectron2.utils.events import get_event_storage
from ..box_re... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/roi_heads/cascade_rcnn.py | 0.947986 | 0.480235 | cascade_rcnn.py | pypi |
import logging
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, batched_nms_rotated
from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated
from detectron2.utils.events import get_event_storage
from ..box_regression import Box2... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/roi_heads/rotated_fast_rcnn.py | 0.926012 | 0.584212 | rotated_fast_rcnn.py | pypi |
import numpy as np
from typing import List
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.utils.registry import Registry
__all__ = ["FastRCNNConvFCHead", "build_box_head"... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/roi_heads/box_head.py | 0.934671 | 0.421016 | box_head.py | pypi |
from typing import List
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ConvTranspose2d, cat, interpolate
from detectron2.structures import Instances, heatmaps_to_keypoints
from detectron2.utils.events import get_ev... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/modeling/roi_heads/keypoint_head.py | 0.965892 | 0.535706 | keypoint_head.py | pypi |
import copy
import logging
import numpy as np
import time
from pycocotools.cocoeval import COCOeval
from detectron2 import _C
logger = logging.getLogger(__name__)
class COCOeval_opt(COCOeval):
"""
This is a slightly modified version of the original COCO API, where the functions evaluateImg()
and accumul... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/evaluation/fast_eval_api.py | 0.661158 | 0.413181 | fast_eval_api.py | pypi |
import datetime
import logging
import time
from collections import OrderedDict, abc
from contextlib import ExitStack, contextmanager
from typing import List, Union
import torch
from torch import nn
from detectron2.utils.comm import get_world_size, is_main_process
from detectron2.utils.logger import log_every_n_seconds... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/evaluation/evaluator.py | 0.931851 | 0.600569 | evaluator.py | pypi |
import glob
import logging
import numpy as np
import os
import tempfile
from collections import OrderedDict
import torch
from PIL import Image
from detectron2.data import MetadataCatalog
from detectron2.utils import comm
from detectron2.utils.file_io import PathManager
from .evaluator import DatasetEvaluator
class ... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/evaluation/cityscapes_evaluation.py | 0.530236 | 0.248409 | cityscapes_evaluation.py | pypi |
import itertools
import json
import numpy as np
import os
import torch
from pycocotools.cocoeval import COCOeval, maskUtils
from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
from detectron2.utils.file_io import PathManager
from .coco_evaluation import COCOEvaluator
class RotatedCOCOeval(... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/evaluation/rotated_coco_evaluation.py | 0.565539 | 0.441974 | rotated_coco_evaluation.py | pypi |
import copy
import itertools
import json
import logging
import os
import pickle
from collections import OrderedDict
import torch
import detectron2.utils.comm as comm
from detectron2.config import CfgNode
from detectron2.data import MetadataCatalog
from detectron2.structures import Boxes, BoxMode, pairwise_iou
from det... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/evaluation/lvis_evaluation.py | 0.746509 | 0.296349 | lvis_evaluation.py | pypi |
import itertools
import json
import logging
import numpy as np
import os
from collections import OrderedDict
from typing import Optional, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.utils.comm import all_gathe... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/evaluation/sem_seg_evaluation.py | 0.811863 | 0.333286 | sem_seg_evaluation.py | pypi |
import contextlib
import io
import itertools
import json
import logging
import numpy as np
import os
import tempfile
from collections import OrderedDict
from typing import Optional
from PIL import Image
from tabulate import tabulate
from detectron2.data import MetadataCatalog
from detectron2.utils import comm
from det... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/evaluation/panoptic_evaluation.py | 0.726426 | 0.206634 | panoptic_evaluation.py | pypi |
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
class _ROIAlignRotated(Function):
@staticmethod
def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
ctx.save_for_... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/layers/roi_align_rotated.py | 0.940463 | 0.514156 | roi_align_rotated.py | pypi |
import math
import torch
def diou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Distance Intersection over Union Loss (Zhaohui Zheng et. al)
https://arxiv.org/abs/1911.08287
Args:
boxes1, boxes2 (Tensor): bo... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/layers/losses.py | 0.721253 | 0.849535 | losses.py | pypi |
from torch import nn
from torchvision.ops import roi_align
# NOTE: torchvision's RoIAlign has a different default aligned=False
class ROIAlign(nn.Module):
def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
"""
Args:
output_size (tuple): h, w
spati... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/layers/roi_align.py | 0.956937 | 0.746093 | roi_align.py | pypi |
from copy import deepcopy
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from .batch_norm import get_norm
from .blocks import DepthwiseSeparableConv2d
from .wrappers import Conv2d
class ASPP(nn.Module):
"""
Atrous Spatial Pyramid Pooling (A... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/layers/aspp.py | 0.949599 | 0.566498 | aspp.py | pypi |
from typing import List, Optional
import torch
from torch.nn import functional as F
def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor:
"""
Turn a list of integer scalars or integer Tensor scalars into a vector,
in a way that's both traceable and scriptable.
In ... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/layers/wrappers.py | 0.96796 | 0.776496 | wrappers.py | pypi |
import fvcore.nn.weight_init as weight_init
from torch import nn
from .batch_norm import FrozenBatchNorm2d, get_norm
from .wrappers import Conv2d
"""
CNN building blocks.
"""
class CNNBlockBase(nn.Module):
"""
A CNN block is assumed to have input channels, output channels and a stride.
The input and o... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/layers/blocks.py | 0.962276 | 0.501648 | blocks.py | pypi |
import copy
import numpy as np
from typing import Dict
import torch
from scipy.optimize import linear_sum_assignment
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from ..config.config import CfgNode as CfgNode_
from .base_tracker import BaseTracker
class BaseHungarian... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/tracking/hungarian_tracker.py | 0.842021 | 0.367242 | hungarian_tracker.py | pypi |
from detectron2.config import configurable
from detectron2.utils.registry import Registry
from ..config.config import CfgNode as CfgNode_
from ..structures import Instances
TRACKER_HEADS_REGISTRY = Registry("TRACKER_HEADS")
TRACKER_HEADS_REGISTRY.__doc__ = """
Registry for tracking classes.
"""
class BaseTracker(ob... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/tracking/base_tracker.py | 0.72086 | 0.23118 | base_tracker.py | pypi |
import copy
import numpy as np
from typing import List
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detectron2.structures.boxes import pairwise_iou
from ..config.config import CfgNode as CfgNode_
from .base_tracker import TRACKER_HEADS_REGISTRY, BaseT... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/tracking/bbox_iou_tracker.py | 0.843911 | 0.318141 | bbox_iou_tracker.py | pypi |
import numpy as np
from typing import List
from detectron2.config import CfgNode as CfgNode_
from detectron2.config import configurable
from detectron2.structures import Instances
from detectron2.structures.boxes import pairwise_iou
from detectron2.tracking.utils import LARGE_COST_VALUE, create_prediction_pairs
from... | /roof_mask_Yv8-0.5.9-py3-none-any.whl/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py | 0.926408 | 0.440409 | vanilla_hungarian_bbox_iou_tracker.py | pypi |
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