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AIChallenger/AI_Challenger_2018 | f0e4376152c8fe5a098ed92a973cec96b13e1a24 | Baselines/autonomous_driving_perception208_baseline/detection/utils/config_util.py | python | _update_focal_loss_alpha | (configs, alpha) | Updates the alpha value for a sigmoid focal loss.
The configs dictionary is updated in place, and hence not returned.
Args:
configs: Dictionary of configuration objects. See outputs from
get_configs_from_pipeline_file() or get_configs_from_multiple_files().
alpha: Class weight multiplier for sigmoid loss.
Raises:
TypeError: If the classification loss is not `weighted_sigmoid_focal`. | Updates the alpha value for a sigmoid focal loss. | [
"Updates",
"the",
"alpha",
"value",
"for",
"a",
"sigmoid",
"focal",
"loss",
"."
] | def _update_focal_loss_alpha(configs, alpha):
"""Updates the alpha value for a sigmoid focal loss.
The configs dictionary is updated in place, and hence not returned.
Args:
configs: Dictionary of configuration objects. See outputs from
get_configs_from_pipeline_file() or get_configs_from_multiple_files().
alpha: Class weight multiplier for sigmoid loss.
Raises:
TypeError: If the classification loss is not `weighted_sigmoid_focal`.
"""
classification_loss = _get_classification_loss(configs["model"])
classification_loss_type = classification_loss.WhichOneof(
"classification_loss")
if classification_loss_type != "weighted_sigmoid_focal":
raise TypeError("Classification loss must be `weighted_sigmoid_focal`.")
classification_loss.weighted_sigmoid_focal.alpha = alpha | [
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QCoDeS/Qcodes | 3cda2cef44812e2aa4672781f2423bf5f816f9f9 | qcodes/instrument_drivers/rohde_schwarz/ZNB.py | python | ZNBChannel.__init__ | (
self,
parent: "ZNB",
name: str,
channel: int,
vna_parameter: Optional[str] = None,
existing_trace_to_bind_to: Optional[str] = None,
) | Args:
parent: Instrument that this channel is bound to.
name: Name to use for this channel.
channel: channel on the VNA to use
vna_parameter: Name of parameter on the vna that this should
measure such as S12. If left empty this will fall back to
`name`.
existing_trace_to_bind_to: Name of an existing trace on the VNA.
If supplied try to bind to an existing trace with this name
rather than creating a new trace. | Args:
parent: Instrument that this channel is bound to.
name: Name to use for this channel.
channel: channel on the VNA to use
vna_parameter: Name of parameter on the vna that this should
measure such as S12. If left empty this will fall back to
`name`.
existing_trace_to_bind_to: Name of an existing trace on the VNA.
If supplied try to bind to an existing trace with this name
rather than creating a new trace. | [
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self,
parent: "ZNB",
name: str,
channel: int,
vna_parameter: Optional[str] = None,
existing_trace_to_bind_to: Optional[str] = None,
) -> None:
"""
Args:
parent: Instrument that this channel is bound to.
name: Name to use for this channel.
channel: channel on the VNA to use
vna_parameter: Name of parameter on the vna that this should
measure such as S12. If left empty this will fall back to
`name`.
existing_trace_to_bind_to: Name of an existing trace on the VNA.
If supplied try to bind to an existing trace with this name
rather than creating a new trace.
"""
n = channel
self._instrument_channel = channel
# Additional wait when adjusting instrument timeout to sweep time.
self._additional_wait = 1
if vna_parameter is None:
vna_parameter = name
self._vna_parameter = vna_parameter
super().__init__(parent, name)
if existing_trace_to_bind_to is None:
self._tracename = f"Trc{channel}"
else:
traces = self._parent.ask("CONFigure:TRACe:CATalog?")
if existing_trace_to_bind_to not in traces:
raise RuntimeError(
f"Trying to bind to"
f" {existing_trace_to_bind_to} "
f"which is not in {traces}"
)
self._tracename = existing_trace_to_bind_to
# map hardware channel to measurement
# hardware channels are mapped one to one to QCoDeS channels
# we are not using sub traces within channels.
if existing_trace_to_bind_to is None:
self.write(
f"CALC{self._instrument_channel}:PAR:SDEF"
f" '{self._tracename}', '{self._vna_parameter}'"
)
# Source power is dependent on model, but not well documented.
# Here we assume -60 dBm for ZNB20, the others are set,
# due to lack of knowledge, to -80 dBm as of before the edit.
full_modelname = self._parent.get_idn()["model"]
if full_modelname is not None:
model = full_modelname.split("-")[0]
else:
raise RuntimeError("Could not determine ZNB model")
self._model_min_source_power = {
"ZNB4": -80,
"ZNB8": -80,
"ZNB20": -60,
"ZNB40": -60,
}
if model not in self._model_min_source_power.keys():
raise RuntimeError(f"Unsupported ZNB model: {model}")
self._min_source_power: float
self._min_source_power = self._model_min_source_power[model]
self.add_parameter(
name="vna_parameter",
label="VNA parameter",
get_cmd=f"CALC{self._instrument_channel}:PAR:MEAS? "
f"'{self._tracename}'",
get_parser=self._strip,
)
self.add_parameter(
name="power",
label="Power",
unit="dBm",
get_cmd=f"SOUR{n}:POW?",
set_cmd=f"SOUR{n}:POW {{:.4f}}",
get_parser=float,
vals=vals.Numbers(self._min_source_power, 25),
)
self.add_parameter(
name="bandwidth",
label="Bandwidth",
unit="Hz",
get_cmd=f"SENS{n}:BAND?",
set_cmd=self._set_bandwidth,
get_parser=int,
vals=vals.Enum(
*np.append(10 ** 6,
np.kron([1, 1.5, 2, 3, 5, 7], 10 ** np.arange(6)))
),
docstring="Measurement bandwidth of the IF filter. "
"The inverse of this sets the integration "
"time per point. "
"There is an 'increased bandwidth option' "
"(p. 4 of manual) that does not get taken "
"into account here.",
)
self.add_parameter(
name="avg",
label="Averages",
unit="",
get_cmd=f"SENS{n}:AVER:COUN?",
set_cmd=f"SENS{n}:AVER:COUN {{:.4f}}",
get_parser=int,
vals=vals.Ints(1, 5000),
)
self.add_parameter(
name="start",
get_cmd=f"SENS{n}:FREQ:START?",
set_cmd=self._set_start,
get_parser=float,
vals=vals.Numbers(self._parent._min_freq,
self._parent._max_freq - 10),
)
self.add_parameter(
name="stop",
get_cmd=f"SENS{n}:FREQ:STOP?",
set_cmd=self._set_stop,
get_parser=float,
vals=vals.Numbers(self._parent._min_freq + 1,
self._parent._max_freq),
)
self.add_parameter(
name="center",
get_cmd=f"SENS{n}:FREQ:CENT?",
set_cmd=self._set_center,
get_parser=float,
vals=vals.Numbers(
self._parent._min_freq + 0.5, self._parent._max_freq - 10
),
)
self.add_parameter(
name="span",
get_cmd=f"SENS{n}:FREQ:SPAN?",
set_cmd=self._set_span,
get_parser=float,
vals=vals.Numbers(1,
self._parent._max_freq - self._parent._min_freq),
)
self.add_parameter(
name="npts",
get_cmd=f"SENS{n}:SWE:POIN?",
set_cmd=self._set_npts,
get_parser=int,
)
self.add_parameter(
name="status",
get_cmd=f"CONF:CHAN{n}:MEAS?",
set_cmd=f"CONF:CHAN{n}:MEAS {{}}",
get_parser=int,
)
self.add_parameter(
name="format",
get_cmd=partial(self._get_format, tracename=self._tracename),
set_cmd=self._set_format,
val_mapping={
"dB": "MLOG\n",
"Linear Magnitude": "MLIN\n",
"Phase": "PHAS\n",
"Unwr Phase": "UPH\n",
"Polar": "POL\n",
"Smith": "SMIT\n",
"Inverse Smith": "ISM\n",
"SWR": "SWR\n",
"Real": "REAL\n",
"Imaginary": "IMAG\n",
"Delay": "GDEL\n",
"Complex": "COMP\n",
},
)
self.add_parameter(
name="trace_mag_phase",
start=self.start(),
stop=self.stop(),
npts=self.npts(),
channel=n,
parameter_class=FrequencySweepMagPhase,
)
self.add_parameter(
name="trace",
start=self.start(),
stop=self.stop(),
npts=self.npts(),
channel=n,
parameter_class=FrequencySweep,
)
self.add_parameter(
name="electrical_delay",
label="Electrical delay",
get_cmd=f"SENS{n}:CORR:EDEL2:TIME?",
set_cmd=f"SENS{n}:CORR:EDEL2:TIME {{}}",
get_parser=float,
unit="s",
)
self.add_parameter(
name="sweep_time",
label="Sweep time",
get_cmd=f"SENS{n}:SWE:TIME?",
get_parser=float,
unit="s",
)
self.add_parameter(
name="sweep_type",
get_cmd=f"SENS{n}:SWE:TYPE?",
set_cmd=self._set_sweep_type,
val_mapping={
"Linear": "LIN\n",
"Logarithmic": "LOG\n",
"Power": "POW\n",
"CW_Time": "CW\n",
"CW_Point": "POIN\n",
"Segmented": "SEGM\n",
},
docstring="The sweep_type parameter is used to set "
"the type of measurement sweeps. It "
"allows switching the default linear "
"VNA sweep type to other types. Note that "
"at the moment only the linear and "
"CW_Point modes have supporting "
"measurement parameters.",
)
self.add_parameter(
name="cw_frequency",
get_cmd=f"SENS{n}:FREQ:CW?",
set_cmd=self._set_cw_frequency,
get_parser=float,
vals=vals.Numbers(
self._parent._min_freq + 0.5, self._parent._max_freq - 10
),
docstring="Parameter for setting frequency and "
"querying for it when VNA sweep type is "
"set to CW_Point mode.",
)
self.add_parameter(
"cw_check_sweep_first",
parameter_class=ManualParameter,
initial_value=True,
vals=vals.Bool(),
docstring="Parameter that enables a few commands "
"which are called before each get in "
"continuous wave mode checking whether "
"the vna is setup correctly. Is recommended "
"to be turned, but can be turned off if "
"one wants to minimize overhead in fast "
"measurements. ",
)
self.add_parameter(
name="trace_fixed_frequency",
npts=self.npts(),
bandwidth=self.bandwidth(),
parameter_class=FixedFrequencyTraceIQ,
)
self.add_parameter(
name="point_fixed_frequency", parameter_class=FixedFrequencyPointIQ
)
self.add_parameter(
name="point_fixed_frequency_mag_phase",
parameter_class=FixedFrequencyPointMagPhase,
)
self.add_parameter(
name="averaging_enabled",
initial_value=False,
get_cmd=None,
set_cmd=self._enable_averaging,
vals=vals.Bool(),
val_mapping=create_on_off_val_mapping(on_val="ON", off_val="OFF"),
)
self.add_parameter(
name="auto_sweep_time_enabled",
initial_value=False,
get_cmd=None,
set_cmd=self._enable_auto_sweep_time,
vals=vals.Bool(),
val_mapping=create_on_off_val_mapping(on_val="ON", off_val="OFF"),
docstring="When enabled, the (minimum) sweep time is "
"calculated internally using the other channel settings "
"and zero delay",
)
self.add_function(
"set_electrical_delay_auto", call_cmd=f"SENS{n}:CORR:EDEL:AUTO ONCE"
)
self.add_function(
"autoscale",
call_cmd=f"DISPlay:TRACe1:Y:SCALe:AUTO ONCE, {self._tracename}",
) | [
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... | https://github.com/QCoDeS/Qcodes/blob/3cda2cef44812e2aa4672781f2423bf5f816f9f9/qcodes/instrument_drivers/rohde_schwarz/ZNB.py#L277-L573 | ||
gitpython-developers/gitdb | 2913a6454c9dfc803679dc5f75315e2d821ee977 | gitdb/util.py | python | file_contents_ro | (fd, stream=False, allow_mmap=True) | return contents | :return: read-only contents of the file represented by the file descriptor fd
:param fd: file descriptor opened for reading
:param stream: if False, random access is provided, otherwise the stream interface
is provided.
:param allow_mmap: if True, its allowed to map the contents into memory, which
allows large files to be handled and accessed efficiently. The file-descriptor
will change its position if this is False | :return: read-only contents of the file represented by the file descriptor fd | [
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] | def file_contents_ro(fd, stream=False, allow_mmap=True):
""":return: read-only contents of the file represented by the file descriptor fd
:param fd: file descriptor opened for reading
:param stream: if False, random access is provided, otherwise the stream interface
is provided.
:param allow_mmap: if True, its allowed to map the contents into memory, which
allows large files to be handled and accessed efficiently. The file-descriptor
will change its position if this is False"""
try:
if allow_mmap:
# supports stream and random access
try:
return mmap.mmap(fd, 0, access=mmap.ACCESS_READ)
except EnvironmentError:
# python 2.4 issue, 0 wants to be the actual size
return mmap.mmap(fd, os.fstat(fd).st_size, access=mmap.ACCESS_READ)
# END handle python 2.4
except OSError:
pass
# END exception handling
# read manually
contents = os.read(fd, os.fstat(fd).st_size)
if stream:
return _RandomAccessBytesIO(contents)
return contents | [
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pycurl/pycurl | 23afe62edb66a751604974bef5dad63edd79d3de | python/curl/__init__.py | python | Curl.get_info | (self, *args) | return self.handle.getinfo(*args) | Get information about retrieval. | Get information about retrieval. | [
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] | def get_info(self, *args):
"Get information about retrieval."
return self.handle.getinfo(*args) | [
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CT83/SmoothStream | 7fbac6183008cdaeb99970ccadb344f113e90d50 | camera/Camera.py | python | Camera.__init__ | (self, height=RESOLUTION_H, width=RESOLUTION_W) | [] | def __init__(self, height=RESOLUTION_H, width=RESOLUTION_W):
self.current_frame = None
self.height = height
self.width = width
self.camera = None | [
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... | https://github.com/CT83/SmoothStream/blob/7fbac6183008cdaeb99970ccadb344f113e90d50/camera/Camera.py#L10-L14 | ||||
nasa-jpl-memex/memex-explorer | d2910496238359b3676b4467721017fc82f0b324 | source/apps/crawl_space/viz/stream.py | python | NutchUrlTrails.__init__ | (self, crawl_name, num_urls=DEFAULT_NUM_URLS) | Create a NutchUrlTrails instance for visualizing a running Nutch crawl in real-time using Bokeh
:param name: The name of the crawl (as identified by the queue)
:param num_urls: The number of URLs to display in the visualization
:return: A NutchUrLTrails instance | Create a NutchUrlTrails instance for visualizing a running Nutch crawl in real-time using Bokeh
:param name: The name of the crawl (as identified by the queue)
:param num_urls: The number of URLs to display in the visualization
:return: A NutchUrLTrails instance | [
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"""
Create a NutchUrlTrails instance for visualizing a running Nutch crawl in real-time using Bokeh
:param name: The name of the crawl (as identified by the queue)
:param num_urls: The number of URLs to display in the visualization
:return: A NutchUrLTrails instance
"""
self.crawl_name = crawl_name
self.num_urls = num_urls
self.open_urls = {}
self.closed_urls = {}
self.old_segments = None
self.old_circles = None
self.session = Session()
self.session.use_doc(self.crawl_name)
self.document = Document()
con = Connection()
exchange = Exchange(EXCHANGE_NAME, 'direct', durable=False)
queue = Queue(crawl_name, exchange=exchange, routing_key=crawl_name)
self.queue = con.SimpleQueue(name=queue) | [
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qiueer/zabbix | 31983dedbd59d917ecd71bb6f36b35302673a783 | Mongodb/scripts/qiueer/python/QLog.py | python | Log.get_map_level | (self,level="debug") | [] | def get_map_level(self,level="debug"):
level = str(level).lower()
#print "get_map_level:",level
if level == "debug":
return logging.DEBUG
if level == "info":
return logging.INFO
if level == "warn":
return logging.WARN
if level == "error":
return logging.ERROR
if level == "critical":
return logging.CRITICAL | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | examples/showoci/showoci_service.py | python | ShowOCIService.__load_core_network_single_privateip | (self, virtual_network, ip_id, return_name=True) | [] | def __load_core_network_single_privateip(self, virtual_network, ip_id, return_name=True):
try:
if 'privateip' not in ip_id:
return ""
arr = virtual_network.get_private_ip(
ip_id,
retry_strategy=oci.retry.DEFAULT_RETRY_STRATEGY
).data
if arr:
if return_name:
return str(arr.ip_address) + " - " + str(arr.display_name)
else:
return str(arr.ip_address)
return ""
except oci.exceptions.ServiceError as e:
if self.__check_service_error(e.code):
pass
raise
except Exception as e:
self.__print_error("__get_core_network_privateip", e)
return "" | [
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CGCookie/retopoflow | 3d8b3a47d1d661f99ab0aeb21d31370bf15de35e | retopoflow/rf/rf_target.py | python | RetopoFlow_Target.get_vis_accel | (self, force=False) | return self.accel_vis_accel | [] | def get_vis_accel(self, force=False):
target_version = self.get_target_version(selection=False)
view_version = self.get_view_version()
recompute = self.accel_recompute
recompute |= target_version != self.accel_target_version
recompute |= view_version != self.accel_view_version
recompute |= self.accel_vis_verts is None
recompute |= self.accel_vis_edges is None
recompute |= self.accel_vis_faces is None
recompute |= self.accel_vis_accel is None
recompute |= options['visible bbox factor'] != self._last_visible_bbox_factor
recompute |= options['visible dist offset'] != self._last_visible_dist_offset
recompute |= options['selection occlusion test'] != self._last_selection_occlusion_test
recompute |= options['selection backface test'] != self._last_selection_backface_test
recompute &= not self.accel_defer_recomputing
recompute &= not self._nav and (time.time() - self._nav_time) > 0.25
recompute &= self._draw_count != self._last_draw_count
self.accel_recompute = False
if force or recompute:
# print(f'RECOMPUTE VIS ACCEL {random.random()}')
# print(f' accel recompute: {self.accel_recompute}')
# print(f' target change: {target_version != self.accel_target_version}')
# print(f' view change: {view_version != self.accel_view_version} ({self.accel_view_version.get_hash() if self.accel_view_version else None}, {view_version.get_hash()})')
# print(f' geom change: {self.accel_vis_verts is None} {self.accel_vis_edges is None} {self.accel_vis_faces is None} {self.accel_vis_accel is None}')
# print(f' bbox change: {options["visible bbox factor"] != self._last_visible_bbox_factor}')
# print(f' dist offset change: {options["visible dist offset"] != self._last_visible_dist_offset}')
# print(f' navigating: {not self._nav} {time.time() - self._nav_time > 0.25}')
# print(f' draw change: {self._draw_count != self._last_draw_count}')
self.accel_target_version = target_version
self.accel_view_version = view_version
self.accel_vis_verts = self.visible_verts()
self.accel_vis_edges = self.visible_edges(verts=self.accel_vis_verts)
self.accel_vis_faces = self.visible_faces(verts=self.accel_vis_verts)
self.accel_vis_accel = Accel2D(self.accel_vis_verts, self.accel_vis_edges, self.accel_vis_faces, self.get_point2D)
self._last_visible_bbox_factor = options['visible bbox factor']
self._last_visible_dist_offset = options['visible dist offset']
self._last_selection_occlusion_test = options['selection occlusion test']
self._last_selection_backface_test = options['selection backface test']
self._last_draw_count = self._draw_count
else:
self.accel_vis_verts = { bmv for bmv in self.accel_vis_verts if bmv.is_valid } if self.accel_vis_verts is not None else None
self.accel_vis_edges = { bme for bme in self.accel_vis_edges if bme.is_valid } if self.accel_vis_edges is not None else None
self.accel_vis_faces = { bmf for bmf in self.accel_vis_faces if bmf.is_valid } if self.accel_vis_faces is not None else None
return self.accel_vis_accel | [
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psychopy/psychopy | 01b674094f38d0e0bd51c45a6f66f671d7041696 | psychopy/data/utils.py | python | checkValidFilePath | (filepath, makeValid=True) | return True | Checks whether file path location (e.g. is a valid folder)
This should also check whether we have write-permissions to the folder
but doesn't currently do that!
added in: 1.90.00 | Checks whether file path location (e.g. is a valid folder) | [
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] | def checkValidFilePath(filepath, makeValid=True):
"""Checks whether file path location (e.g. is a valid folder)
This should also check whether we have write-permissions to the folder
but doesn't currently do that!
added in: 1.90.00
"""
folder = os.path.split(os.path.abspath(filepath))[0]
if not os.path.isdir(folder):
os.makedirs(folder) # spit an error if we fail
return True | [
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/pip/pip/_vendor/pyparsing.py | python | Or.streamline | (self) | return self | [] | def streamline(self):
super(Or, self).streamline()
if __compat__.collect_all_And_tokens:
self.saveAsList = any(e.saveAsList for e in self.exprs)
return self | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/polys/galoistools.py | python | gf_trace_map | (a, b, c, n, f, p, K) | return gf_compose_mod(a, V, f, p, K), U | Compute polynomial trace map in ``GF(p)[x]/(f)``.
Given a polynomial ``f`` in ``GF(p)[x]``, polynomials ``a``, ``b``,
``c`` in the quotient ring ``GF(p)[x]/(f)`` such that ``b = c**t
(mod f)`` for some positive power ``t`` of ``p``, and a positive
integer ``n``, returns a mapping::
a -> a**t**n, a + a**t + a**t**2 + ... + a**t**n (mod f)
In factorization context, ``b = x**p mod f`` and ``c = x mod f``.
This way we can efficiently compute trace polynomials in equal
degree factorization routine, much faster than with other methods,
like iterated Frobenius algorithm, for large degrees.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.galoistools import gf_trace_map
>>> gf_trace_map([1, 2], [4, 4], [1, 1], 4, [3, 2, 4], 5, ZZ)
([1, 3], [1, 3])
References
==========
.. [1] [Gathen92]_ | Compute polynomial trace map in ``GF(p)[x]/(f)``. | [
"Compute",
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"trace",
"map",
"in",
"GF",
"(",
"p",
")",
"[",
"x",
"]",
"/",
"(",
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")",
"."
] | def gf_trace_map(a, b, c, n, f, p, K):
"""
Compute polynomial trace map in ``GF(p)[x]/(f)``.
Given a polynomial ``f`` in ``GF(p)[x]``, polynomials ``a``, ``b``,
``c`` in the quotient ring ``GF(p)[x]/(f)`` such that ``b = c**t
(mod f)`` for some positive power ``t`` of ``p``, and a positive
integer ``n``, returns a mapping::
a -> a**t**n, a + a**t + a**t**2 + ... + a**t**n (mod f)
In factorization context, ``b = x**p mod f`` and ``c = x mod f``.
This way we can efficiently compute trace polynomials in equal
degree factorization routine, much faster than with other methods,
like iterated Frobenius algorithm, for large degrees.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.galoistools import gf_trace_map
>>> gf_trace_map([1, 2], [4, 4], [1, 1], 4, [3, 2, 4], 5, ZZ)
([1, 3], [1, 3])
References
==========
.. [1] [Gathen92]_
"""
u = gf_compose_mod(a, b, f, p, K)
v = b
if n & 1:
U = gf_add(a, u, p, K)
V = b
else:
U = a
V = c
n >>= 1
while n:
u = gf_add(u, gf_compose_mod(u, v, f, p, K), p, K)
v = gf_compose_mod(v, v, f, p, K)
if n & 1:
U = gf_add(U, gf_compose_mod(u, V, f, p, K), p, K)
V = gf_compose_mod(v, V, f, p, K)
n >>= 1
return gf_compose_mod(a, V, f, p, K), U | [
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djblets/djblets | 0496e1ec49e43d43d776768c9fc5b6f8af56ec2c | djblets/cache/serials.py | python | generate_ajax_serial | () | Generate a template-based AJAX serial number for requests and ETags.
The serial number can be appended to filenames involving dynamic loads of
URLs in order to make a URL that can be cached forever without fear of
change.
This will crawl the template files (using directories in
:django:setting:`TEMPLATE_DIRS`), figuring out the latest timestamp, and
return that value. | Generate a template-based AJAX serial number for requests and ETags. | [
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"-",
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"serial",
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] | def generate_ajax_serial():
"""Generate a template-based AJAX serial number for requests and ETags.
The serial number can be appended to filenames involving dynamic loads of
URLs in order to make a URL that can be cached forever without fear of
change.
This will crawl the template files (using directories in
:django:setting:`TEMPLATE_DIRS`), figuring out the latest timestamp, and
return that value.
"""
AJAX_SERIAL = getattr(settings, "AJAX_SERIAL", 0)
if not AJAX_SERIAL:
template_dirs = itertools.chain.from_iterable(
template_settings.get('DIRS', [])
for template_settings in getattr(settings, 'TEMPLATES', None)
)
for template_path in template_dirs:
for root, dirs, files in os.walk(template_path):
for name in files:
mtime = int(os.stat(os.path.join(root, name)).st_mtime)
if mtime > AJAX_SERIAL:
AJAX_SERIAL = mtime
setattr(settings, "AJAX_SERIAL", AJAX_SERIAL) | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/utils/zfs.py | python | from_bool_alt | (value) | return from_bool(value) | Convert zfs bool_alt to python bool | Convert zfs bool_alt to python bool | [
"Convert",
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"to",
"python",
"bool"
] | def from_bool_alt(value):
"""
Convert zfs bool_alt to python bool
"""
return from_bool(value) | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | tensorflow_dl_models/research/object_detection/core/post_processing.py | python | batch_multiclass_non_max_suppression | (boxes,
scores,
score_thresh,
iou_thresh,
max_size_per_class,
max_total_size=0,
clip_window=None,
change_coordinate_frame=False,
num_valid_boxes=None,
masks=None,
additional_fields=None,
scope=None,
parallel_iterations=32) | Multi-class version of non maximum suppression that operates on a batch.
This op is similar to `multiclass_non_max_suppression` but operates on a batch
of boxes and scores. See documentation for `multiclass_non_max_suppression`
for details.
Args:
boxes: A [batch_size, num_anchors, q, 4] float32 tensor containing
detections. If `q` is 1 then same boxes are used for all classes
otherwise, if `q` is equal to number of classes, class-specific boxes
are used.
scores: A [batch_size, num_anchors, num_classes] float32 tensor containing
the scores for each of the `num_anchors` detections.
score_thresh: scalar threshold for score (low scoring boxes are removed).
iou_thresh: scalar threshold for IOU (new boxes that have high IOU overlap
with previously selected boxes are removed).
max_size_per_class: maximum number of retained boxes per class.
max_total_size: maximum number of boxes retained over all classes. By
default returns all boxes retained after capping boxes per class.
clip_window: A float32 tensor of the form [y_min, x_min, y_max, x_max]
representing the window to clip boxes to before performing non-max
suppression.
change_coordinate_frame: Whether to normalize coordinates after clipping
relative to clip_window (this can only be set to True if a clip_window
is provided)
num_valid_boxes: (optional) a Tensor of type `int32`. A 1-D tensor of shape
[batch_size] representing the number of valid boxes to be considered
for each image in the batch. This parameter allows for ignoring zero
paddings.
masks: (optional) a [batch_size, num_anchors, q, mask_height, mask_width]
float32 tensor containing box masks. `q` can be either number of classes
or 1 depending on whether a separate mask is predicted per class.
additional_fields: (optional) If not None, a dictionary that maps keys to
tensors whose dimensions are [batch_size, num_anchors, ...].
scope: tf scope name.
parallel_iterations: (optional) number of batch items to process in
parallel.
Returns:
'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor
containing the non-max suppressed boxes.
'nmsed_scores': A [batch_size, max_detections] float32 tensor containing
the scores for the boxes.
'nmsed_classes': A [batch_size, max_detections] float32 tensor
containing the class for boxes.
'nmsed_masks': (optional) a
[batch_size, max_detections, mask_height, mask_width] float32 tensor
containing masks for each selected box. This is set to None if input
`masks` is None.
'nmsed_additional_fields': (optional) a dictionary of
[batch_size, max_detections, ...] float32 tensors corresponding to the
tensors specified in the input `additional_fields`. This is not returned
if input `additional_fields` is None.
'num_detections': A [batch_size] int32 tensor indicating the number of
valid detections per batch item. Only the top num_detections[i] entries in
nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the
entries are zero paddings.
Raises:
ValueError: if `q` in boxes.shape is not 1 or not equal to number of
classes as inferred from scores.shape. | Multi-class version of non maximum suppression that operates on a batch. | [
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"suppression",
"that",
"operates",
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] | def batch_multiclass_non_max_suppression(boxes,
scores,
score_thresh,
iou_thresh,
max_size_per_class,
max_total_size=0,
clip_window=None,
change_coordinate_frame=False,
num_valid_boxes=None,
masks=None,
additional_fields=None,
scope=None,
parallel_iterations=32):
"""Multi-class version of non maximum suppression that operates on a batch.
This op is similar to `multiclass_non_max_suppression` but operates on a batch
of boxes and scores. See documentation for `multiclass_non_max_suppression`
for details.
Args:
boxes: A [batch_size, num_anchors, q, 4] float32 tensor containing
detections. If `q` is 1 then same boxes are used for all classes
otherwise, if `q` is equal to number of classes, class-specific boxes
are used.
scores: A [batch_size, num_anchors, num_classes] float32 tensor containing
the scores for each of the `num_anchors` detections.
score_thresh: scalar threshold for score (low scoring boxes are removed).
iou_thresh: scalar threshold for IOU (new boxes that have high IOU overlap
with previously selected boxes are removed).
max_size_per_class: maximum number of retained boxes per class.
max_total_size: maximum number of boxes retained over all classes. By
default returns all boxes retained after capping boxes per class.
clip_window: A float32 tensor of the form [y_min, x_min, y_max, x_max]
representing the window to clip boxes to before performing non-max
suppression.
change_coordinate_frame: Whether to normalize coordinates after clipping
relative to clip_window (this can only be set to True if a clip_window
is provided)
num_valid_boxes: (optional) a Tensor of type `int32`. A 1-D tensor of shape
[batch_size] representing the number of valid boxes to be considered
for each image in the batch. This parameter allows for ignoring zero
paddings.
masks: (optional) a [batch_size, num_anchors, q, mask_height, mask_width]
float32 tensor containing box masks. `q` can be either number of classes
or 1 depending on whether a separate mask is predicted per class.
additional_fields: (optional) If not None, a dictionary that maps keys to
tensors whose dimensions are [batch_size, num_anchors, ...].
scope: tf scope name.
parallel_iterations: (optional) number of batch items to process in
parallel.
Returns:
'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor
containing the non-max suppressed boxes.
'nmsed_scores': A [batch_size, max_detections] float32 tensor containing
the scores for the boxes.
'nmsed_classes': A [batch_size, max_detections] float32 tensor
containing the class for boxes.
'nmsed_masks': (optional) a
[batch_size, max_detections, mask_height, mask_width] float32 tensor
containing masks for each selected box. This is set to None if input
`masks` is None.
'nmsed_additional_fields': (optional) a dictionary of
[batch_size, max_detections, ...] float32 tensors corresponding to the
tensors specified in the input `additional_fields`. This is not returned
if input `additional_fields` is None.
'num_detections': A [batch_size] int32 tensor indicating the number of
valid detections per batch item. Only the top num_detections[i] entries in
nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the
entries are zero paddings.
Raises:
ValueError: if `q` in boxes.shape is not 1 or not equal to number of
classes as inferred from scores.shape.
"""
q = boxes.shape[2].value
num_classes = scores.shape[2].value
if q != 1 and q != num_classes:
raise ValueError('third dimension of boxes must be either 1 or equal '
'to the third dimension of scores')
original_masks = masks
original_additional_fields = additional_fields
with tf.name_scope(scope, 'BatchMultiClassNonMaxSuppression'):
boxes_shape = boxes.shape
batch_size = boxes_shape[0].value
num_anchors = boxes_shape[1].value
if batch_size is None:
batch_size = tf.shape(boxes)[0]
if num_anchors is None:
num_anchors = tf.shape(boxes)[1]
# If num valid boxes aren't provided, create one and mark all boxes as
# valid.
if num_valid_boxes is None:
num_valid_boxes = tf.ones([batch_size], dtype=tf.int32) * num_anchors
# If masks aren't provided, create dummy masks so we can only have one copy
# of _single_image_nms_fn and discard the dummy masks after map_fn.
if masks is None:
masks_shape = tf.stack([batch_size, num_anchors, 1, 0, 0])
masks = tf.zeros(masks_shape)
if additional_fields is None:
additional_fields = {}
def _single_image_nms_fn(args):
"""Runs NMS on a single image and returns padded output.
Args:
args: A list of tensors consisting of the following:
per_image_boxes - A [num_anchors, q, 4] float32 tensor containing
detections. If `q` is 1 then same boxes are used for all classes
otherwise, if `q` is equal to number of classes, class-specific
boxes are used.
per_image_scores - A [num_anchors, num_classes] float32 tensor
containing the scores for each of the `num_anchors` detections.
per_image_masks - A [num_anchors, q, mask_height, mask_width] float32
tensor containing box masks. `q` can be either number of classes
or 1 depending on whether a separate mask is predicted per class.
per_image_additional_fields - (optional) A variable number of float32
tensors each with size [num_anchors, ...].
per_image_num_valid_boxes - A tensor of type `int32`. A 1-D tensor of
shape [batch_size] representing the number of valid boxes to be
considered for each image in the batch. This parameter allows for
ignoring zero paddings.
Returns:
'nmsed_boxes': A [max_detections, 4] float32 tensor containing the
non-max suppressed boxes.
'nmsed_scores': A [max_detections] float32 tensor containing the scores
for the boxes.
'nmsed_classes': A [max_detections] float32 tensor containing the class
for boxes.
'nmsed_masks': (optional) a [max_detections, mask_height, mask_width]
float32 tensor containing masks for each selected box. This is set to
None if input `masks` is None.
'nmsed_additional_fields': (optional) A variable number of float32
tensors each with size [max_detections, ...] corresponding to the
input `per_image_additional_fields`.
'num_detections': A [batch_size] int32 tensor indicating the number of
valid detections per batch item. Only the top num_detections[i]
entries in nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The
rest of the entries are zero paddings.
"""
per_image_boxes = args[0]
per_image_scores = args[1]
per_image_masks = args[2]
per_image_additional_fields = {
key: value
for key, value in zip(additional_fields, args[3:-1])
}
per_image_num_valid_boxes = args[-1]
per_image_boxes = tf.reshape(
tf.slice(per_image_boxes, 3 * [0],
tf.stack([per_image_num_valid_boxes, -1, -1])), [-1, q, 4])
per_image_scores = tf.reshape(
tf.slice(per_image_scores, [0, 0],
tf.stack([per_image_num_valid_boxes, -1])),
[-1, num_classes])
per_image_masks = tf.reshape(
tf.slice(per_image_masks, 4 * [0],
tf.stack([per_image_num_valid_boxes, -1, -1, -1])),
[-1, q, per_image_masks.shape[2].value,
per_image_masks.shape[3].value])
if per_image_additional_fields is not None:
for key, tensor in per_image_additional_fields.items():
additional_field_shape = tensor.get_shape()
additional_field_dim = len(additional_field_shape)
per_image_additional_fields[key] = tf.reshape(
tf.slice(per_image_additional_fields[key],
additional_field_dim * [0],
tf.stack([per_image_num_valid_boxes] +
(additional_field_dim - 1) * [-1])),
[-1] + [dim.value for dim in additional_field_shape[1:]])
nmsed_boxlist = multiclass_non_max_suppression(
per_image_boxes,
per_image_scores,
score_thresh,
iou_thresh,
max_size_per_class,
max_total_size,
clip_window=clip_window,
change_coordinate_frame=change_coordinate_frame,
masks=per_image_masks,
additional_fields=per_image_additional_fields)
padded_boxlist = box_list_ops.pad_or_clip_box_list(nmsed_boxlist,
max_total_size)
num_detections = nmsed_boxlist.num_boxes()
nmsed_boxes = padded_boxlist.get()
nmsed_scores = padded_boxlist.get_field(fields.BoxListFields.scores)
nmsed_classes = padded_boxlist.get_field(fields.BoxListFields.classes)
nmsed_masks = padded_boxlist.get_field(fields.BoxListFields.masks)
nmsed_additional_fields = [
padded_boxlist.get_field(key) for key in per_image_additional_fields
]
return ([nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks] +
nmsed_additional_fields + [num_detections])
num_additional_fields = 0
if additional_fields is not None:
num_additional_fields = len(additional_fields)
num_nmsed_outputs = 4 + num_additional_fields
batch_outputs = tf.map_fn(
_single_image_nms_fn,
elems=([boxes, scores, masks] + list(additional_fields.values()) +
[num_valid_boxes]),
dtype=(num_nmsed_outputs * [tf.float32] + [tf.int32]),
parallel_iterations=parallel_iterations)
batch_nmsed_boxes = batch_outputs[0]
batch_nmsed_scores = batch_outputs[1]
batch_nmsed_classes = batch_outputs[2]
batch_nmsed_masks = batch_outputs[3]
batch_nmsed_additional_fields = {
key: value
for key, value in zip(additional_fields, batch_outputs[4:-1])
}
batch_num_detections = batch_outputs[-1]
if original_masks is None:
batch_nmsed_masks = None
if original_additional_fields is None:
batch_nmsed_additional_fields = None
return (batch_nmsed_boxes, batch_nmsed_scores, batch_nmsed_classes,
batch_nmsed_masks, batch_nmsed_additional_fields,
batch_num_detections) | [
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perone/Pyevolve | 589b6a9b92ed1fd9ef00987bf4bfe807c4a7b7e0 | examples/pyevolve_ex19_gp.py | python | eval_func | (chromosome) | return score | [] | def eval_func(chromosome):
sz = 20
code_comp = chromosome.getCompiledCode()
square_accum = 0.0
for j in xrange(sz):
a, b = random_lists(5)
target_list = gp_add(gp_mul(a,b),gp_mul(a,b))
ret_list = eval(code_comp)
square_accum += (sum(target_list)-sum(ret_list))**2
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/asyncio/locks.py | python | BoundedSemaphore.__init__ | (self, value=1, *, loop=None) | [] | def __init__(self, value=1, *, loop=None):
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/lib-tk/Tkinter.py | python | Misc.unbind | (self, sequence, funcid=None) | Unbind for this widget for event SEQUENCE the
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"""Unbind for this widget for event SEQUENCE the
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self.tk.call('bind', self._w, sequence, '')
if funcid:
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/zonecfg.py | python | _property | (methode, zone, key, value) | return ret | internal handler for set and clear_property
methode : string
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name of zone
key : string
name of property
value : string
value of property | internal handler for set and clear_property | [
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"""
internal handler for set and clear_property
methode : string
either set, add, or clear
zone : string
name of zone
key : string
name of property
value : string
value of property
"""
ret = {"status": True}
# generate update script
cfg_file = None
if methode not in ["set", "clear"]:
ret["status"] = False
ret["message"] = "unkown methode {}!".format(methode)
else:
cfg_file = salt.utils.files.mkstemp()
with salt.utils.files.fpopen(cfg_file, "w+", mode=0o600) as fp_:
if methode == "set":
if isinstance(value, dict) or isinstance(value, list):
value = _sanitize_value(value)
value = str(value).lower() if isinstance(value, bool) else str(value)
fp_.write("{} {}={}\n".format(methode, key, _sanitize_value(value)))
elif methode == "clear":
fp_.write("{} {}\n".format(methode, key))
# update property
if cfg_file:
_dump_cfg(cfg_file)
res = __salt__["cmd.run_all"](
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zone=zone,
path=cfg_file,
)
)
ret["status"] = res["retcode"] == 0
ret["message"] = res["stdout"] if ret["status"] else res["stderr"]
if ret["message"] == "":
del ret["message"]
else:
ret["message"] = _clean_message(ret["message"])
# cleanup config file
if __salt__["file.file_exists"](cfg_file):
__salt__["file.remove"](cfg_file)
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adobe-type-tools/opentype-svg | 57353dfd094b6ed2225f18482331ff895e003d48 | lib/opentypesvg/utils.py | python | get_output_folder_path | (provided_folder_path, first_font_path) | return os.path.join(os.path.dirname(first_font_path), SVG_FOLDER_NAME) | If the path to the output folder was NOT provided, create
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Returns a valid output folder.
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return os.path.join(os.path.dirname(first_font_path), SVG_FOLDER_NAME) | [
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awesto/django-shop | 13d9a77aff7eede74a5f363c1d540e005d88dbcd | shop/search/mixins.py | python | SearchViewMixin.get_document | (self, language) | [] | def get_document(self, language):
documents = registry.get_documents([ProductModel])
try:
return next(doc for doc in documents if doc._language == language)
except StopIteration:
return next(doc for doc in documents if doc._language is None) | [
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entropy1337/infernal-twin | 10995cd03312e39a48ade0f114ebb0ae3a711bb8 | Modules/build/pillow/build/lib.linux-i686-2.7/PIL/PSDraw.py | python | PSDraw.image | (self, box, im, dpi=None) | Draw a PIL image, centered in the given box. | Draw a PIL image, centered in the given box. | [
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] | def image(self, box, im, dpi=None):
"""Draw a PIL image, centered in the given box."""
# default resolution depends on mode
if not dpi:
if im.mode == "1":
dpi = 200 # fax
else:
dpi = 100 # greyscale
# image size (on paper)
x = float(im.size[0] * 72) / dpi
y = float(im.size[1] * 72) / dpi
# max allowed size
xmax = float(box[2] - box[0])
ymax = float(box[3] - box[1])
if x > xmax:
y = y * xmax / x
x = xmax
if y > ymax:
x = x * ymax / y
y = ymax
dx = (xmax - x) / 2 + box[0]
dy = (ymax - y) / 2 + box[1]
self._fp_write("gsave\n%f %f translate\n" % (dx, dy))
if (x, y) != im.size:
# EpsImagePlugin._save prints the image at (0,0,xsize,ysize)
sx = x / im.size[0]
sy = y / im.size[1]
self._fp_write("%f %f scale\n" % (sx, sy))
EpsImagePlugin._save(im, self.fp, None, 0)
self._fp_write("\ngrestore\n") | [
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hpfeeds/hpfeeds | 539e738d7831ea6a246116acd4f2268ce7c0ef49 | hpfeeds/blocking/reactor.py | python | Reactor.__init__ | (self, protocol_class, connector) | [] | def __init__(self, protocol_class, connector):
self.protocol_class = protocol_class
self.connector = connector
self.closing = False
self.when_connected = threading.Event()
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descarteslabs/descarteslabs-python | ace8a1a89d58b75df1bcaa613a4b3544d7bdc4be | descarteslabs/common/ibis/deserialization/compiler.py | python | all_equal | (left, right, cache=None) | return left == right | Check whether two objects `left` and `right` are equal.
Parameters
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left : Union[object, Expr, Node]
right : Union[object, Expr, Node]
cache : Optional[Dict[Tuple[Node, Node], bool]]
A dictionary indicating whether two Nodes are equal | Check whether two objects `left` and `right` are equal.
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----------
left : Union[object, Expr, Node]
right : Union[object, Expr, Node]
cache : Optional[Dict[Tuple[Node, Node], bool]]
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"""Check whether two objects `left` and `right` are equal.
Parameters
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left : Union[object, Expr, Node]
right : Union[object, Expr, Node]
cache : Optional[Dict[Tuple[Node, Node], bool]]
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"""
if cache is None:
cache = {}
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asciidisco/plugin.video.netflix | ceb2638a9676f5839250dadfd079b9e4e4bdd759 | resources/lib/Navigation.py | python | Navigation.open_settings | (self, url) | return Addon(url).openSettings() | Opens a foreign settings dialog | Opens a foreign settings dialog | [
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] | def open_settings(self, url):
"""Opens a foreign settings dialog"""
url = 'inputstream.adaptive' if url == 'is' else url
from xbmcaddon import Addon
return Addon(url).openSettings() | [
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fortharris/Pcode | 147962d160a834c219e12cb456abc130826468e4 | Extensions/Projects/ProjectManager/ProjectView/ProjectView.py | python | SearchThread.run | (self) | [] | def run(self):
resultsDict = {}
for root, dirs, files in os.walk(self.projectDir):
for i in files:
if not self.filterDisabled:
if not i.endswith('.py') or i.endswith('.pyw'):
continue
if i.startswith(self.searchName):
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resultsDict[root].append(i)
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resultsDict[root] = [i]
self.foundList.emit(resultsDict) | [
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pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_internal/req/req_uninstall.py | python | StashedUninstallPathSet.rollback | (self) | Undoes the uninstall by moving stashed files back. | Undoes the uninstall by moving stashed files back. | [
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"""Undoes the uninstall by moving stashed files back."""
for p in self._moves:
logger.info("Moving to %s\n from %s", *p)
for new_path, path in self._moves:
try:
logger.debug("Replacing %s from %s", new_path, path)
if os.path.isfile(new_path) or os.path.islink(new_path):
os.unlink(new_path)
elif os.path.isdir(new_path):
rmtree(new_path)
renames(path, new_path)
except OSError as ex:
logger.error("Failed to restore %s", new_path)
logger.debug("Exception: %s", ex)
self.commit() | [
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NVIDIA/NeMo | 5b0c0b4dec12d87d3cd960846de4105309ce938e | nemo/collections/asr/modules/rnnt.py | python | RNNTJoint.joint | (self, f: torch.Tensor, g: torch.Tensor) | return res | Compute the joint step of the network.
Here,
B = Batch size
T = Acoustic model timesteps
U = Target sequence length
H1, H2 = Hidden dimensions of the Encoder / Decoder respectively
H = Hidden dimension of the Joint hidden step.
V = Vocabulary size of the Decoder (excluding the RNNT blank token).
NOTE:
The implementation of this model is slightly modified from the original paper.
The original paper proposes the following steps :
(enc, dec) -> Expand + Concat + Sum [B, T, U, H1+H2] -> Forward through joint hidden [B, T, U, H] -- *1
*1 -> Forward through joint final [B, T, U, V + 1].
We instead split the joint hidden into joint_hidden_enc and joint_hidden_dec and act as follows:
enc -> Forward through joint_hidden_enc -> Expand [B, T, 1, H] -- *1
dec -> Forward through joint_hidden_dec -> Expand [B, 1, U, H] -- *2
(*1, *2) -> Sum [B, T, U, H] -> Forward through joint final [B, T, U, V + 1].
Args:
f: Output of the Encoder model. A torch.Tensor of shape [B, T, H1]
g: Output of the Decoder model. A torch.Tensor of shape [B, U, H2]
Returns:
Logits / log softmaxed tensor of shape (B, T, U, V + 1). | Compute the joint step of the network. | [
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] | def joint(self, f: torch.Tensor, g: torch.Tensor) -> torch.Tensor:
"""
Compute the joint step of the network.
Here,
B = Batch size
T = Acoustic model timesteps
U = Target sequence length
H1, H2 = Hidden dimensions of the Encoder / Decoder respectively
H = Hidden dimension of the Joint hidden step.
V = Vocabulary size of the Decoder (excluding the RNNT blank token).
NOTE:
The implementation of this model is slightly modified from the original paper.
The original paper proposes the following steps :
(enc, dec) -> Expand + Concat + Sum [B, T, U, H1+H2] -> Forward through joint hidden [B, T, U, H] -- *1
*1 -> Forward through joint final [B, T, U, V + 1].
We instead split the joint hidden into joint_hidden_enc and joint_hidden_dec and act as follows:
enc -> Forward through joint_hidden_enc -> Expand [B, T, 1, H] -- *1
dec -> Forward through joint_hidden_dec -> Expand [B, 1, U, H] -- *2
(*1, *2) -> Sum [B, T, U, H] -> Forward through joint final [B, T, U, V + 1].
Args:
f: Output of the Encoder model. A torch.Tensor of shape [B, T, H1]
g: Output of the Decoder model. A torch.Tensor of shape [B, U, H2]
Returns:
Logits / log softmaxed tensor of shape (B, T, U, V + 1).
"""
# f = [B, T, H1]
f = self.enc(f)
f.unsqueeze_(dim=2) # (B, T, 1, H)
# g = [B, U, H2]
g = self.pred(g)
g.unsqueeze_(dim=1) # (B, 1, U, H)
inp = f + g # [B, T, U, H]
del f, g
res = self.joint_net(inp) # [B, T, U, V + 1]
del inp
if self.preserve_memory:
torch.cuda.empty_cache()
# If log_softmax is automatic
if self.log_softmax is None:
if not res.is_cuda: # Use log softmax only if on CPU
res = res.log_softmax(dim=-1)
else:
if self.log_softmax:
res = res.log_softmax(dim=-1)
return res | [
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zenodo/zenodo | 3c45e52a742ad5a0a7788a67b02fbbc15ab4d8d5 | zenodo/modules/records/serializers/schemas/legacyjson.py | python | FileSchemaV1.dump_checksum | (self, obj) | return hashval | Dump checksum. | Dump checksum. | [
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Pymol-Scripts/Pymol-script-repo | bcd7bb7812dc6db1595953dfa4471fa15fb68c77 | modules/pdb2pqr/contrib/ZSI-2.1-a1/ZSI/TC.py | python | TypeCode.checktype | (self, elt, ps) | See if the type of the "elt" element is what we're looking for.
Return the element's type.
Parameters:
elt -- the DOM element being parsed
ps -- the ParsedSoap object. | See if the type of the "elt" element is what we're looking for.
Return the element's type.
Parameters:
elt -- the DOM element being parsed
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ps -- the ParsedSoap object.
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typeName = _find_type(elt)
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/openshift_facts/library/openshift_facts.py | python | set_builddefaults_facts | (facts) | return facts | Set build defaults including setting proxy values from http_proxy, https_proxy,
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1. http_proxy, https_proxy, no_proxy
2. builddefaults_*
3. builddefaults_git_*
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"""
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common = facts['common']
# Copy values from common to builddefaults
if 'http_proxy' not in builddefaults and 'http_proxy' in common:
builddefaults['http_proxy'] = common['http_proxy']
if 'https_proxy' not in builddefaults and 'https_proxy' in common:
builddefaults['https_proxy'] = common['https_proxy']
if 'no_proxy' not in builddefaults and 'no_proxy' in common:
builddefaults['no_proxy'] = common['no_proxy']
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if 'git_http_proxy' not in builddefaults and 'http_proxy' in builddefaults:
builddefaults['git_http_proxy'] = builddefaults['http_proxy']
if 'git_https_proxy' not in builddefaults and 'https_proxy' in builddefaults:
builddefaults['git_https_proxy'] = builddefaults['https_proxy']
if 'git_no_proxy' not in builddefaults and 'no_proxy' in builddefaults:
builddefaults['git_no_proxy'] = builddefaults['no_proxy']
# If we're actually defining a builddefaults config then create admission_plugin_config
# then merge builddefaults[config] structure into admission_plugin_config
# 'config' is the 'openshift_builddefaults_json' inventory variable
if 'config' in builddefaults:
if 'admission_plugin_config' not in facts['master']:
# Scaffold out the full expected datastructure
facts['master']['admission_plugin_config'] = {'BuildDefaults': {'configuration': {'env': {}}}}
facts['master']['admission_plugin_config'].update(builddefaults['config'])
if 'env' in facts['master']['admission_plugin_config']['BuildDefaults']['configuration']:
delete_empty_keys(facts['master']['admission_plugin_config']['BuildDefaults']['configuration']['env'])
return facts | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/config_entries.py | python | ConfigEntriesFlowManager.__init__ | (
self,
hass: HomeAssistant,
config_entries: ConfigEntries,
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self,
hass: HomeAssistant,
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hass_config: ConfigType,
) -> None:
"""Initialize the config entry flow manager."""
super().__init__(hass)
self.config_entries = config_entries
self._hass_config = hass_config | [
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rkcosmos/deepcut | deb40082efa39493d91070b7c6f9767225e6c5ce | deepcut/train.py | python | generate_best_dataset | (best_path, output_path='cleaned_data', create_val=False) | Generate CSV file for training and testing data
Input
=====
best_path: str, path to BEST folder which contains unzipped subfolder
'article', 'encyclopedia', 'news', 'novel'
cleaned_data: str, path to output folder, the cleaned data will be saved
in the given folder name where training set will be stored in `train` folder
and testing set will be stored on `test` folder
create_val: boolean, True or False, if True, divide training set into training set and
validation set in `val` folder | Generate CSV file for training and testing data | [
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"""
Generate CSV file for training and testing data
Input
=====
best_path: str, path to BEST folder which contains unzipped subfolder
'article', 'encyclopedia', 'news', 'novel'
cleaned_data: str, path to output folder, the cleaned data will be saved
in the given folder name where training set will be stored in `train` folder
and testing set will be stored on `test` folder
create_val: boolean, True or False, if True, divide training set into training set and
validation set in `val` folder
"""
if not os.path.isdir(output_path):
os.mkdir(output_path)
if not os.path.isdir(os.path.join(output_path, 'train')):
os.makedirs(os.path.join(output_path, 'train'))
if not os.path.isdir(os.path.join(output_path, 'test')):
os.makedirs(os.path.join(output_path, 'test'))
if not os.path.isdir(os.path.join(output_path, 'val')) and create_val:
os.makedirs(os.path.join(output_path, 'val'))
for article_type in article_types:
files = glob(os.path.join(best_path, article_type, '*.txt'))
files_train, files_test = train_test_split(files, random_state=0, test_size=0.1)
if create_val:
files_train, files_val = train_test_split(files_train, random_state=0, test_size=0.1)
val_words = generate_words(files_val)
val_df = create_char_dataframe(val_words)
val_df.to_csv(os.path.join(output_path, 'val', 'df_best_{}_val.csv'.format(article_type)), index=False)
train_words = generate_words(files_train)
test_words = generate_words(files_test)
train_df = create_char_dataframe(train_words)
test_df = create_char_dataframe(test_words)
train_df.to_csv(os.path.join(output_path, 'train', 'df_best_{}_train.csv'.format(article_type)), index=False)
test_df.to_csv(os.path.join(output_path, 'test', 'df_best_{}_test.csv'.format(article_type)), index=False)
print("Save {} to CSV file".format(article_type)) | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/turtle.py | python | TurtleScreen.window_height | (self) | return self._window_size()[1] | Return the height of the turtle window.
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cbanack/comic-vine-scraper | 8a7071796c61a9483079ad0e9ade56fcb7596bcd | src/py/gui/forms/finishform.py | python | FinishForm.__build_gui | (self, scraped_n, skipped_n) | Constructs and initializes the gui for this form.
'scraped_n' -> the number of books that were scraped (reported to user)
'skipped_n' -> the number of books that were skipped (reported to user) | Constructs and initializes the gui for this form.
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'skipped_n' -> the number of books that were skipped (reported to user)
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self.Controls.Add(scrape_label)
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self.Controls.Add(ok)
# 3. --- define the keyboard focus tab traversal ordering
ok.TabIndex = 0 | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/PyAMF-0.6.1/pyamf/amf0.py | python | Encoder.writeString | (self, u) | Write a unicode to the data stream. | Write a unicode to the data stream. | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | example/ctp/futures/ApiStruct.py | python | QrySuperUserFunction.__init__ | (self, UserID='') | [] | def __init__(self, UserID=''):
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/messaging/smsbackends/apposit/models.py | python | SQLAppositBackend.is_ethiopia_number | (self, msg) | return phone.startswith(ETHIOPIA_COUNTRY_CODE) | [] | def is_ethiopia_number(self, msg):
phone = strip_plus(msg.phone_number)
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_vendor/pkg_resources/__init__.py | python | yield_lines | (strs) | Yield non-empty/non-comment lines of a string or sequence | Yield non-empty/non-comment lines of a string or sequence | [
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if isinstance(strs, six.string_types):
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s = s.strip()
# skip blank lines/comments
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EmuKit/emukit | cdcb0d070d7f1c5585260266160722b636786859 | emukit/bayesian_optimization/loops/bayesian_optimization_loop.py | python | BayesianOptimizationLoop.__init__ | (self, space: ParameterSpace, model: IModel, acquisition: Acquisition = None, update_interval: int = 1,
batch_size: int = 1, acquisition_optimizer: AcquisitionOptimizerBase = None) | Emukit class that implement a loop for building modular Bayesian optimization
:param space: Input space where the optimization is carried out.
:param model: The model that approximates the underlying function
:param acquisition: The acquisition function that will be used to collect new points (default, EI). If batch
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:param update_interval: Number of iterations between optimization of model hyper-parameters. Defaults to 1.
:param batch_size: How many points to evaluate in one iteration of the optimization loop. Defaults to 1.
:param acquisition_optimizer: Optimizer selecting next evaluation points
by maximizing acquisition.
Gradient based optimizer is used if None.
Defaults to None. | Emukit class that implement a loop for building modular Bayesian optimization | [
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batch_size: int = 1, acquisition_optimizer: AcquisitionOptimizerBase = None):
"""
Emukit class that implement a loop for building modular Bayesian optimization
:param space: Input space where the optimization is carried out.
:param model: The model that approximates the underlying function
:param acquisition: The acquisition function that will be used to collect new points (default, EI). If batch
size is greater than one, this acquisition must output positive values only.
:param update_interval: Number of iterations between optimization of model hyper-parameters. Defaults to 1.
:param batch_size: How many points to evaluate in one iteration of the optimization loop. Defaults to 1.
:param acquisition_optimizer: Optimizer selecting next evaluation points
by maximizing acquisition.
Gradient based optimizer is used if None.
Defaults to None.
"""
self.model = model
if acquisition is None:
acquisition = ExpectedImprovement(model)
model_updaters = FixedIntervalUpdater(model, update_interval)
if acquisition_optimizer is None:
acquisition_optimizer = GradientAcquisitionOptimizer(space)
if batch_size == 1:
_log.info("Batch size is 1, using SequentialPointCalculator")
candidate_point_calculator = SequentialPointCalculator(acquisition, acquisition_optimizer)
else:
_log.info("Batch size is " + str(batch_size) + ", using LocalPenalizationPointCalculator")
log_acquisition = LogAcquisition(acquisition)
candidate_point_calculator = LocalPenalizationPointCalculator(log_acquisition, acquisition_optimizer, model,
space, batch_size)
loop_state = create_loop_state(model.X, model.Y)
super().__init__(candidate_point_calculator, model_updaters, loop_state) | [
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kamalgill/flask-appengine-template | 11760f83faccbb0d0afe416fc58e67ecfb4643c2 | src/lib/click/core.py | python | Parameter.value_from_envvar | (self, ctx) | return rv | [] | def value_from_envvar(self, ctx):
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nucleic/enaml | 65c2a2a2d765e88f2e1103046680571894bb41ed | enaml/workbench/ui/ui_plugin.py | python | UIPlugin._refresh_actions | (self) | Refresh the actions for the workbench window. | Refresh the actions for the workbench window. | [
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extensions = point.extensions
if not extensions:
self._action_extensions.clear()
self._model.menus = []
return
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action_items = []
new_extensions = {}
old_extensions = self._action_extensions
for extension in extensions:
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m_items, a_items = old_extensions[extension]
else:
m_items, a_items = self._create_action_items(extension)
new_extensions[extension] = (m_items, a_items)
menu_items.extend(m_items)
action_items.extend(a_items)
menus = create_menus(workbench, menu_items, action_items)
self._action_extensions = new_extensions
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EmilyAlsentzer/clinicalBERT | a9d91698929b7189311bba364ccdd0360e847276 | lm_pretraining/create_pretraining_data.py | python | create_training_instances | (input_files, tokenizer, max_seq_length,
dupe_factor, short_seq_prob, masked_lm_prob,
max_predictions_per_seq, rng) | return instances | Create `TrainingInstance`s from raw text. | Create `TrainingInstance`s from raw text. | [
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max_predictions_per_seq, rng):
"""Create `TrainingInstance`s from raw text."""
all_documents = [[]]
# Input file format:
# (1) One sentence per line. These should ideally be actual sentences, not
# entire paragraphs or arbitrary spans of text. (Because we use the
# sentence boundaries for the "next sentence prediction" task).
# (2) Blank lines between documents. Document boundaries are needed so
# that the "next sentence prediction" task doesn't span between documents.
for input_file in input_files:
with tf.gfile.GFile(input_file, "r") as reader:
while True:
line = tokenization.convert_to_unicode(reader.readline())
if not line:
break
line = line.strip()
# Empty lines are used as document delimiters
if not line:
all_documents.append([])
tokens = tokenizer.tokenize(line)
if tokens:
all_documents[-1].append(tokens)
# Remove empty documents
all_documents = [x for x in all_documents if x]
rng.shuffle(all_documents)
vocab_words = list(tokenizer.vocab.keys())
instances = []
for _ in range(dupe_factor):
for document_index in range(len(all_documents)):
instances.extend(
create_instances_from_document(
all_documents, document_index, max_seq_length, short_seq_prob,
masked_lm_prob, max_predictions_per_seq, vocab_words, rng))
rng.shuffle(instances)
return instances | [
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mlrun/mlrun | 4c120719d64327a34b7ee1ab08fb5e01b258b00a | mlrun/k8s_utils.py | python | format_labels | (labels) | Convert a dictionary of labels into a comma separated string | Convert a dictionary of labels into a comma separated string | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/compressor/cache.py | python | socket_cachekey | (key) | return 'django_compressor.%s.%s' % (socket.gethostname(), force_text(key)) | [] | def socket_cachekey(key):
return 'django_compressor.%s.%s' % (socket.gethostname(), force_text(key)) | [
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google/rekall | 55d1925f2df9759a989b35271b4fa48fc54a1c86 | tools/layout_expert/layout_expert/c_ast/c_ast.py | python | CTypeReference.__init__ | (self, name, **kw) | Initializes a CTypeReference object with type name.
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name: a string representing the name of the referenced type. | Initializes a CTypeReference object with type name. | [
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super(CTypeReference, self).__init__(**kw)
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OpenEndedGroup/Field | 4f7c8edfb01bb0ccc927b78d3c500f018a4ae37c | Contents/lib/python/string.py | python | swapcase | (s) | return s.swapcase() | swapcase(s) -> string
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/states/postgres_language.py | python | __virtual__ | () | return True | Only load if the postgres module is present | Only load if the postgres module is present | [
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"""
Only load if the postgres module is present
"""
if "postgres.language_create" not in __salt__:
return (
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naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/mailbox.py | python | Maildir.add | (self, message) | return uniq | Add message and return assigned key. | Add message and return assigned key. | [
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"""Add message and return assigned key."""
tmp_file = self._create_tmp()
try:
self._dump_message(message, tmp_file)
except BaseException:
tmp_file.close()
os.remove(tmp_file.name)
raise
_sync_close(tmp_file)
if isinstance(message, MaildirMessage):
subdir = message.get_subdir()
suffix = self.colon + message.get_info()
if suffix == self.colon:
suffix = ''
else:
subdir = 'new'
suffix = ''
uniq = os.path.basename(tmp_file.name).split(self.colon)[0]
dest = os.path.join(self._path, subdir, uniq + suffix)
if isinstance(message, MaildirMessage):
os.utime(tmp_file.name,
(os.path.getatime(tmp_file.name), message.get_date()))
# No file modification should be done after the file is moved to its
# final position in order to prevent race conditions with changes
# from other programs
try:
if hasattr(os, 'link'):
os.link(tmp_file.name, dest)
os.remove(tmp_file.name)
else:
os.rename(tmp_file.name, dest)
except OSError, e:
os.remove(tmp_file.name)
if e.errno == errno.EEXIST:
raise ExternalClashError('Name clash with existing message: %s'
% dest)
else:
raise
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | python/dgl/_deprecate/graph.py | python | DGLGraph.pop_n_repr | (self, key) | return self._node_frame.pop(key) | Get and remove the specified node repr.
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key : str
The attribute name.
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Tensor
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Parameters
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key : str
The attribute name.
Returns
-------
Tensor
The popped representation
"""
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samuelclay/NewsBlur | 2c45209df01a1566ea105e04d499367f32ac9ad2 | apps/static/views.py | python | android | (request) | return render(request, 'static/android.xhtml') | [] | def android(request):
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glutanimate/review-heatmap | c758478125b60a81c66c87c35b12b7968ec0a348 | src/review_heatmap/libaddon/gui/basic/interface.py | python | CommonWidgetInterface._getComboCurrentValue | (self, combo_widget) | return (text, data) | Get current (item_text, item_data) tuple | Get current (item_text, item_data) tuple | [
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BillBillBillBill/Tickeys-linux | 2df31b8665004c58a5d4ab05277f245267d96364 | tickeys/kivy_32/kivy/core/image/__init__.py | python | Image.height | (self) | return self._size[1] | Image height | Image height | [
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unknown-horizons/unknown-horizons | 7397fb333006d26c3d9fe796c7bd9cb8c3b43a49 | horizons/network/packets/__init__.py | python | packet.__init__ | (self) | ctor | ctor | [
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Pymol-Scripts/Pymol-script-repo | bcd7bb7812dc6db1595953dfa4471fa15fb68c77 | modules/ADT/AutoDockTools/cluster_ad.py | python | Cluster_AD.set_reference | (self, reference=None) | reference should be an index into the data array.
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array, reference could be a separate instance of data. | reference should be an index into the data array.
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gbeced/pyalgotrade | ad2bcc6b25c06c66eee4a8d522ce844504d8ec62 | pyalgotrade/talibext/indicator.py | python | TRIX | (ds, count, timeperiod=-2**31) | return call_talib_with_ds(ds, count, talib.TRIX, timeperiod) | 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA | 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA | [
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coleifer/walrus | 2583ac8ba81d3f6aa43fccbe28c0c13b99a1fa9d | walrus/graph.py | python | Graph.store | (self, s, p, o) | Store a subject-predicate-object triple in the database. | Store a subject-predicate-object triple in the database. | [
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... | https://github.com/coleifer/walrus/blob/2583ac8ba81d3f6aa43fccbe28c0c13b99a1fa9d/walrus/graph.py#L82-L88 | ||
ipython/ipykernel | 0ab288fe42f3155c7c7d9257c9be8cf093d175e0 | ipykernel/iostream.py | python | IOPubThread._really_send | (self, msg, *args, **kwargs) | The callback that actually sends messages | The callback that actually sends messages | [
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"""The callback that actually sends messages"""
mp_mode = self._check_mp_mode()
if mp_mode != CHILD:
# we are master, do a regular send
self.socket.send_multipart(msg, *args, **kwargs)
else:
# we are a child, pipe to master
# new context/socket for every pipe-out
# since forks don't teardown politely, use ctx.term to ensure send has completed
ctx, pipe_out = self._setup_pipe_out()
pipe_out.send_multipart([self._pipe_uuid] + msg, *args, **kwargs)
pipe_out.close()
ctx.term() | [
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kozistr/Awesome-GANs | b4b9a3b8c3fd1d32c864dc5655d80c0650aebee1 | awesome_gans/modules.py | python | conv1d | (x, f=64, k=3, s=1, pad='SAME', reuse=None, is_train=True, name='conv1d') | return tf.layers.conv1d(
inputs=x,
filters=f,
kernel_size=k,
strides=s,
kernel_initializer=w_init,
kernel_regularizer=w_reg,
bias_initializer=b_init,
padding=pad,
reuse=reuse,
name=name,
) | :param x: input
:param f: filters
:param k: kernel size
:param s: strides
:param pad: padding
:param reuse: reusable
:param is_train: trainable
:param name: scope name
:return: net | :param x: input
:param f: filters
:param k: kernel size
:param s: strides
:param pad: padding
:param reuse: reusable
:param is_train: trainable
:param name: scope name
:return: net | [
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"""
:param x: input
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:param k: kernel size
:param s: strides
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:return: net
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strides=s,
kernel_initializer=w_init,
kernel_regularizer=w_reg,
bias_initializer=b_init,
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sknetwork-team/scikit-network | 6e74a2338f53f1e54a25e7e19ab9fcf56371c275 | sknetwork/linkpred/postprocessing.py | python | is_edge | (adjacency: sparse.csr_matrix, query: Union[int, Iterable, Tuple]) | Given a query, return whether each edge is actually in the adjacency.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
query : int, Iterable or Tuple
* If int i, queries (i, j) for all j.
* If Iterable of integers, return queries (i, j) for i in query, for all j.
* If tuple (i, j), queries (i, j).
* If list of tuples or array of shape (n_queries, 2), queries (i, j) in for each line in query.
Returns
-------
y_true : Union[bool, np.ndarray]
For each element in the query, returns ``True`` if the edge exists in the adjacency and ``False`` otherwise.
Examples
--------
>>> from sknetwork.data import house
>>> adjacency = house()
>>> is_edge(adjacency, 0)
array([False, True, False, False, True])
>>> is_edge(adjacency, [0, 1])
array([[False, True, False, False, True],
[ True, False, True, False, True]])
>>> is_edge(adjacency, (0, 1))
True
>>> is_edge(adjacency, [(0, 1), (0, 2)])
array([ True, False]) | Given a query, return whether each edge is actually in the adjacency. | [
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"a",
"query",
"return",
"whether",
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] | def is_edge(adjacency: sparse.csr_matrix, query: Union[int, Iterable, Tuple]) -> Union[bool, np.ndarray]:
"""Given a query, return whether each edge is actually in the adjacency.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
query : int, Iterable or Tuple
* If int i, queries (i, j) for all j.
* If Iterable of integers, return queries (i, j) for i in query, for all j.
* If tuple (i, j), queries (i, j).
* If list of tuples or array of shape (n_queries, 2), queries (i, j) in for each line in query.
Returns
-------
y_true : Union[bool, np.ndarray]
For each element in the query, returns ``True`` if the edge exists in the adjacency and ``False`` otherwise.
Examples
--------
>>> from sknetwork.data import house
>>> adjacency = house()
>>> is_edge(adjacency, 0)
array([False, True, False, False, True])
>>> is_edge(adjacency, [0, 1])
array([[False, True, False, False, True],
[ True, False, True, False, True]])
>>> is_edge(adjacency, (0, 1))
True
>>> is_edge(adjacency, [(0, 1), (0, 2)])
array([ True, False])
"""
if np.issubdtype(type(query), np.integer):
return adjacency[query].toarray().astype(bool).ravel()
if isinstance(query, Tuple):
source, target = query
neighbors = adjacency.indices[adjacency.indptr[source]:adjacency.indptr[source + 1]]
return bool(np.isin(target, neighbors, assume_unique=True))
if isinstance(query, list):
query = np.array(query)
if isinstance(query, np.ndarray):
if query.ndim == 1:
return adjacency[query].toarray().astype(bool)
elif query.ndim == 2 and query.shape[1] == 2:
y_true = []
for edge in query:
y_true.append(is_edge(adjacency, (edge[0], edge[1])))
return np.array(y_true)
else:
raise ValueError("Query not understood.")
else:
raise ValueError("Query not understood.") | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/setuptools/__init__.py | python | Command.__init__ | (self, dist, **kw) | Construct the command for dist, updating
vars(self) with any keyword parameters. | Construct the command for dist, updating
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"""
Construct the command for dist, updating
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"""
_Command.__init__(self, dist)
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nextstrain/ncov | 71e7d593e5c97b67ad657bca41fb8e61b50c2803 | workflow/lib/persistent_dict.py | python | ItemDirManager.error_clean_up | (self) | [] | def error_clean_up(self):
if self.delete_on_error:
self.reset() | [
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/core/leoGlobals.py | python | scanForAtLanguage | (c: Cmdr, p: Pos) | return c.target_language | Scan position p and p's ancestors looking only for @language and @ignore directives.
Returns the language found, or c.target_language. | Scan position p and p's ancestors looking only for @language and @ignore directives. | [
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] | def scanForAtLanguage(c: Cmdr, p: Pos):
"""Scan position p and p's ancestors looking only for @language and @ignore directives.
Returns the language found, or c.target_language."""
# Unlike the code in x.scanAllDirectives, this code ignores @comment directives.
if c and p:
for p in p.self_and_parents(copy=False):
d = g.get_directives_dict(p)
if 'language' in d:
z = d["language"]
language, delim1, delim2, delim3 = g.set_language(z, 0)
return language
return c.target_language | [
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rlgraph/rlgraph | 428fc136a9a075f29a397495b4226a491a287be2 | rlgraph/components/layers/nn/residual_layer.py | python | ResidualLayer.__init__ | (self, residual_unit, repeats=2, scope="residual-layer", **kwargs) | Args:
residual_unit (NeuralNetwork):
repeats (int): The number of times that the residual unit should be repeated before applying the addition
with the original input and the activation function. | Args:
residual_unit (NeuralNetwork): | [
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] | def __init__(self, residual_unit, repeats=2, scope="residual-layer", **kwargs):
"""
Args:
residual_unit (NeuralNetwork):
repeats (int): The number of times that the residual unit should be repeated before applying the addition
with the original input and the activation function.
"""
super(ResidualLayer, self).__init__(scope=scope, **kwargs)
self.residual_unit = residual_unit
self.repeats = repeats
# Copy the repeat_units n times and add them to this Component.
self.residual_units = [self.residual_unit] + [
self.residual_unit.copy(scope=self.residual_unit.scope+"-rep"+str(i)) for i in range(repeats - 1)
]
self.add_components(*self.residual_units) | [
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chengzhengxin/groupsoftmax-simpledet | 3f63a00998c57fee25241cf43a2e8600893ea462 | config/cascade_r50v2_c5_red_1x.py | python | get_config | (is_train) | return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \
ModelParam, OptimizeParam, TestParam, \
transform, data_name, label_name, metric_list | [] | def get_config(is_train):
class General:
log_frequency = 10
name = __name__.rsplit("/")[-1].rsplit(".")[-1]
batch_image = 2 if is_train else 1
fp16 = False
class KvstoreParam:
kvstore = "local"
batch_image = General.batch_image
gpus = [0, 1, 2, 3, 4, 5, 6, 7]
fp16 = General.fp16
class NormalizeParam:
# normalizer = normalizer_factory(type="syncbn", ndev=len(KvstoreParam.gpus))
normalizer = normalizer_factory(type="fixbn")
class BackboneParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
class NeckParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
class reduce:
channel = 1024
class RpnParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
batch_image = General.batch_image
class anchor_generate:
scale = (2, 4, 8, 16, 32)
ratio = (0.5, 1.0, 2.0)
stride = 16
image_anchor = 256
class head:
conv_channel = 512
mean = (0, 0, 0, 0)
std = (1, 1, 1, 1)
class proposal:
pre_nms_top_n = 12000 if is_train else 6000
post_nms_top_n = 2000 if is_train else 1000
nms_thr = 0.7
min_bbox_side = 0
class subsample_proposal:
proposal_wo_gt = False
image_roi = 256
fg_fraction = 0.25
fg_thr = 0.5
bg_thr_hi = 0.5
bg_thr_lo = 0.0
class bbox_target:
num_reg_class = 2
class_agnostic = True
weight = (1.0, 1.0, 1.0, 1.0)
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.1, 0.1, 0.2, 0.2)
class BboxParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
num_class = 1 + 80
image_roi = 256
batch_image = General.batch_image
stage = "1st"
loss_weight = 1.0
class regress_target:
class_agnostic = True
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.1, 0.1, 0.2, 0.2)
class subsample_proposal:
proposal_wo_gt = False
image_roi = 256
fg_fraction = 0.25
fg_thr = 0.6
bg_thr_hi = 0.6
bg_thr_lo = 0.0
class bbox_target:
num_reg_class = 2
class_agnostic = True
weight = (1.0, 1.0, 1.0, 1.0)
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.05, 0.05, 0.1, 0.1)
class BboxParam2nd:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
num_class = 1 + 80
image_roi = 256
batch_image = General.batch_image
stage = "2nd"
loss_weight = 0.5
class regress_target:
class_agnostic = True
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.05, 0.05, 0.1, 0.1)
class subsample_proposal:
proposal_wo_gt = False
image_roi = 256
fg_fraction = 0.25
fg_thr = 0.7
bg_thr_hi = 0.7
bg_thr_lo = 0.0
class bbox_target:
num_reg_class = 2
class_agnostic = True
weight = (1.0, 1.0, 1.0, 1.0)
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.033, 0.033, 0.067, 0.067)
class BboxParam3rd:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
num_class = 1 + 80
image_roi = 256
batch_image = General.batch_image
stage = "3rd"
loss_weight = 0.25
class regress_target:
class_agnostic = True
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.033, 0.033, 0.067, 0.067)
class subsample_proposal:
proposal_wo_gt = None
image_roi = None
fg_fraction = None
fg_thr = None
bg_thr_hi = None
bg_thr_lo = None
class bbox_target:
num_reg_class = None
class_agnostic = None
weight = None
mean = None
std = None
class RoiParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
out_size = 7
stride = 16
class DatasetParam:
if is_train:
image_set = ("coco_train2014", "coco_valminusminival2014")
else:
image_set = ("coco_minival2014", )
backbone = Backbone(BackboneParam)
neck = Neck(NeckParam)
rpn_head = RpnHead(RpnParam)
roi_extractor = RoiExtractor(RoiParam)
bbox_head = BboxHead(BboxParam)
bbox_head_2nd = BboxHead(BboxParam2nd)
bbox_head_3rd = BboxHead(BboxParam3rd)
detector = Detector()
if is_train:
train_sym = detector.get_train_symbol(
backbone,
neck,
rpn_head,
roi_extractor,
bbox_head,
bbox_head_2nd,
bbox_head_3rd
)
rpn_test_sym = None
test_sym = None
else:
train_sym = None
rpn_test_sym = detector.get_rpn_test_symbol(backbone, neck, rpn_head)
test_sym = detector.get_test_symbol(
backbone,
neck,
rpn_head,
roi_extractor,
bbox_head,
bbox_head_2nd,
bbox_head_3rd
)
class ModelParam:
train_symbol = train_sym
test_symbol = test_sym
rpn_test_symbol = rpn_test_sym
from_scratch = False
random = True
memonger = False
memonger_until = "stage3_unit21_plus"
class pretrain:
prefix = "pretrain_model/resnet-50"
epoch = 0
fixed_param = ["conv0", "stage1", "gamma", "beta"]
class OptimizeParam:
class optimizer:
type = "sgd"
lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image
momentum = 0.9
wd = 0.0001
clip_gradient = None
class schedule:
begin_epoch = 0
end_epoch = 6
lr_iter = [60000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image),
80000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)]
class warmup:
type = "gradual"
lr = 0.0
iter = 3000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)
class TestParam:
min_det_score = 0.05
max_det_per_image = 100
process_roidb = lambda x: x
process_output = lambda x, y: x
class model:
prefix = "experiments/{}/checkpoint".format(General.name)
epoch = OptimizeParam.schedule.end_epoch
class nms:
type = "nms"
thr = 0.5
class coco:
annotation = "data/coco/annotations/instances_minival2014.json"
# data processing
class ResizeParam:
short = 800
long = 1200 if is_train else 2000
class PadParam:
short = 800
long = 1200
max_num_gt = 100
class AnchorTarget2DParam:
class generate:
short = 800 // 16
long = 1200 // 16
stride = 16
scales = (2, 4, 8, 16, 32)
aspects = (0.5, 1.0, 2.0)
class assign:
allowed_border = 0
pos_thr = 0.7
neg_thr = 0.3
min_pos_thr = 0.0
class sample:
image_anchor = 256
pos_fraction = 0.5
class RenameParam:
mapping = dict(image="data")
from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \
ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \
RenameRecord, AnchorTarget2D
if is_train:
transform = [
ReadRoiRecord(None),
Resize2DImageBbox(ResizeParam),
Flip2DImageBbox(),
Pad2DImageBbox(PadParam),
ConvertImageFromHwcToChw(),
AnchorTarget2D(AnchorTarget2DParam),
RenameRecord(RenameParam.mapping)
]
data_name = ["data", "im_info", "gt_bbox"]
label_name = ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"]
else:
transform = [
ReadRoiRecord(None),
Resize2DImageBbox(ResizeParam),
ConvertImageFromHwcToChw(),
RenameRecord(RenameParam.mapping)
]
data_name = ["data", "im_info", "im_id", "rec_id"]
label_name = []
import core.detection_metric as metric
rpn_acc_metric = metric.AccWithIgnore(
"RpnAcc",
["rpn_cls_loss_output"],
["rpn_cls_label"]
)
rpn_l1_metric = metric.L1(
"RpnL1",
["rpn_reg_loss_output"],
["rpn_cls_label"]
)
# for bbox, the label is generated in network so it is an output
# stage1 metric
box_acc_metric_1st = metric.AccWithIgnore(
"RcnnAcc_1st",
["bbox_cls_loss_1st_output", "bbox_label_blockgrad_1st_output"],
[]
)
box_l1_metric_1st = metric.L1(
"RcnnL1_1st",
["bbox_reg_loss_1st_output", "bbox_label_blockgrad_1st_output"],
[]
)
# stage2 metric
box_acc_metric_2nd = metric.AccWithIgnore(
"RcnnAcc_2nd",
["bbox_cls_loss_2nd_output", "bbox_label_blockgrad_2nd_output"],
[]
)
box_l1_metric_2nd = metric.L1(
"RcnnL1_2nd",
["bbox_reg_loss_2nd_output", "bbox_label_blockgrad_2nd_output"],
[]
)
# stage3 metric
box_acc_metric_3rd = metric.AccWithIgnore(
"RcnnAcc_3rd",
["bbox_cls_loss_3rd_output", "bbox_label_blockgrad_3rd_output"],
[]
)
box_l1_metric_3rd = metric.L1(
"RcnnL1_3rd",
["bbox_reg_loss_3rd_output", "bbox_label_blockgrad_3rd_output"],
[]
)
metric_list = [
rpn_acc_metric,
rpn_l1_metric,
box_acc_metric_1st,
box_l1_metric_1st,
box_acc_metric_2nd,
box_l1_metric_2nd,
box_acc_metric_3rd,
box_l1_metric_3rd
]
return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \
ModelParam, OptimizeParam, TestParam, \
transform, data_name, label_name, metric_list | [
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perfectblue/ctf-writeups | 38c81bbce254885da6b485dd1dd76798120bc98c | 2019/ctfzone-quals-2019/ocb2/ocb/aes.py | python | AES._expandKey | (self, key) | return expandedKey | Rijndael's key expansion
expands an 128,192,256 key into an 176,208,240 bytes key
expandedKey is a pointer to an char array of large enough size
key is a pointer to a non-expanded key | Rijndael's key expansion
expands an 128,192,256 key into an 176,208,240 bytes key | [
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"176",
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"240",
"bytes",
"key"
] | def _expandKey(self, key):
"""
Rijndael's key expansion
expands an 128,192,256 key into an 176,208,240 bytes key
expandedKey is a pointer to an char array of large enough size
key is a pointer to a non-expanded key
"""
size = self.keyBitSize // 8
# current expanded keySize, in bytes
currentSize = 0
rconIteration = 1
# temporary 4-byte variable
t = bytearray([0, 0, 0, 0])
assert self.expandedKeySize
expandedKey = bytearray(self.expandedKeySize)
# set the 16,24,32 bytes of the expanded key to the input key
for j in range(size):
expandedKey[j] = key[j]
currentSize += size
while currentSize < self.expandedKeySize:
# assign the previous 4 bytes to the temporary value t
for k in range(4): t[k] = expandedKey[(currentSize - 4) + k]
#
# every 16,24,32 bytes we apply the core schedule to t
# and increment rconIteration afterwards
#
if currentSize % size == 0:
t = self._core(t, rconIteration)
rconIteration += 1;
# For 256-bit keys, we add an extra sbox to the calculation
if size == (256 // 8) and ((currentSize % size) == 16):
for l in range(4): t[l] = self._getSBoxValue(t[l])
#
# We XOR t with the four-byte block 16,24,32 bytes before the new expanded key.
# This becomes the next four bytes in the expanded key.
#
for m in range(4):
expandedKey[currentSize] = expandedKey[currentSize - size] ^ t[m]
currentSize += 1
return expandedKey | [
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XX-net/XX-Net | a9898cfcf0084195fb7e69b6bc834e59aecdf14f | python3.8.2/Lib/pkg_resources/__init__.py | python | IResourceProvider.get_resource_filename | (manager, resource_name) | Return a true filesystem path for `resource_name`
`manager` must be an ``IResourceManager`` | Return a true filesystem path for `resource_name` | [
"Return",
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] | def get_resource_filename(manager, resource_name):
"""Return a true filesystem path for `resource_name`
`manager` must be an ``IResourceManager``""" | [
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ceph/calamari | 5b8fa1424280984f434f22fd4db69e26de1c12f6 | rlyeh/rlyeh/manager/request_collection.py | python | RequestCollection._on_rados_completion | (self, minion_id, request, result) | Handle JID completion from a ceph.rados_commands operation | Handle JID completion from a ceph.rados_commands operation | [
"Handle",
"JID",
"completion",
"from",
"a",
"ceph",
".",
"rados_commands",
"operation"
] | def _on_rados_completion(self, minion_id, request, result):
"""
Handle JID completion from a ceph.rados_commands operation
"""
if request.state != UserRequest.SUBMITTED:
# Unexpected, ignore.
log.error("Received completion for request %s/%s in state %s" % (
request.id, request.jid, request.state
))
return
if result['error']:
# This indicates a failure within ceph.rados_commands which was caught
# by our code, like one of our Ceph commands returned an error code.
# NB in future there may be UserRequest subclasses which want to receive
# and handle these errors themselves, so this branch would be refactored
# to allow that.
log.error("Request %s experienced an error: %s" % (request.id, result['error_status']))
request.jid = None
request.set_error(result['error_status'])
request.complete()
return
try:
with self._update_index(request):
old_jid = request.jid
request.complete_jid(result)
assert request.jid != old_jid
# After a jid completes, requests may start waiting for cluster
# map updates, we ask ClusterMonitor to hurry up and get them on
# behalf of the request.
if request.awaiting_versions:
assert request.fsid
cluster_monitor = self._manager.clusters[request.fsid]
for sync_type, version in request.awaiting_versions.items():
if version is not None:
log.debug("Notifying cluster of awaited version %s/%s" % (sync_type.str, version))
cluster_monitor.on_version(minion_id, sync_type, version)
# The request may be waiting for an epoch that we already have, if so
# give it to the request right away
for sync_type, want_version in request.awaiting_versions.items():
sync_object = cluster_monitor.get_sync_object(sync_type)
if want_version and sync_type.cmp(sync_object.version, want_version) >= 0:
log.info("Awaited %s %s is immediately available" % (sync_type, want_version))
request.on_map(sync_type, sync_object)
except Exception as e:
# Ensure that a misbehaving piece of code in a UserRequest subclass
# results in a terminated job, not a zombie job
log.exception("Calling complete_jid for %s/%s" % (request.id, request.jid))
request.jid = None
request.set_error("Internal error %s" % e)
request.complete() | [
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homebysix/recipe-robot | fc51b3134b6db7cd86641785d75a0b994ae88154 | scripts/recipe_robot_lib/curler.py | python | check_url | (url, headers=None) | return url, head, None | Test a URL's headers, and switch to HTTPS if available.
Args:
url (str): The URL to check for HTTPS and get response headers from.
headers (dict, optional): Header key/values to be added to HTTP request.
Defaults to None.
Returns:
tuple: Tuple (string, dict, None) consisting of the checked URL
(which may be an HTTPS equivalent to the provided HTTP URL), a
dictionary of the header response from the URL, and a placeholder
None value. | Test a URL's headers, and switch to HTTPS if available. | [
"Test",
"a",
"URL",
"s",
"headers",
"and",
"switch",
"to",
"HTTPS",
"if",
"available",
"."
] | def check_url(url, headers=None):
"""Test a URL's headers, and switch to HTTPS if available.
Args:
url (str): The URL to check for HTTPS and get response headers from.
headers (dict, optional): Header key/values to be added to HTTP request.
Defaults to None.
Returns:
tuple: Tuple (string, dict, None) consisting of the checked URL
(which may be an HTTPS equivalent to the provided HTTP URL), a
dictionary of the header response from the URL, and a placeholder
None value.
"""
# Switch to HTTPS if possible.
if url.startswith("http:"):
robo_print("Checking for HTTPS URL...", LogLevel.VERBOSE, 4)
head, retcode = get_headers("https" + url[4:], headers=headers)
if retcode == 0 and int(head.get("http_result_code")) < 400:
url = "https" + url[4:]
robo_print("Found HTTPS URL: %s" % url, LogLevel.VERBOSE, 4)
return url, head, None
else:
robo_print("No usable HTTPS URL found.", LogLevel.VERBOSE, 4)
# Get URL headers.
head, retcode = get_headers(url, headers=headers)
http_result = int(head.get("http_result_code"))
if retcode == 0 and http_result < 400:
return url, head, None
# Try to mitigate errors.
if http_result == 403:
ua_safari = (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
"AppleWebKit/605.1.15 (KHTML, like Gecko) "
"Version/14.0.1 Safari/605.1.15"
)
ua_chrome = (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/87.0.4280.88 Safari/537.36"
)
# Devmate URLs require a user-agent and don't need a HEAD check first.
if "updates.devmate.com" in url:
return url, head, ua_safari
# Skip domains for which 403 is a known false positive on HEAD check.
if any((x in url for x in KNOWN_403_ON_HEAD)):
return url, head, None
# Try again with alternate user-agents.
for ua in (ua_safari, ua_chrome):
headers["user-agent"] = ua
head, retcode = get_headers(url, headers=headers)
if int(head.get("http_result_code")) < 400:
robo_print(
"Using browser user-agent.", LogLevel.VERBOSE, 4,
)
return url, head, headers["user-agent"]
return url, head, None | [
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tensorflow/datasets | 2e496976d7d45550508395fb2f35cf958c8a3414 | tensorflow_datasets/core/features/audio_feature.py | python | Audio.repr_html | (self, ex: np.ndarray) | return (f'<audio controls src="data:audio/ogg;base64,{audio_str}" '
' controlsList="nodownload" />') | Audio are displayed in the player. | Audio are displayed in the player. | [
"Audio",
"are",
"displayed",
"in",
"the",
"player",
"."
] | def repr_html(self, ex: np.ndarray) -> str:
"""Audio are displayed in the player."""
if self.sample_rate:
rate = self.sample_rate
else:
# We should display an error message once to warn the user the sample
# rate was auto-infered. Requirements:
# * Should appear only once (even though repr_html is called once per
# examples)
# * Ideally should appear on Colab (while `logging.warning` is hidden
# by default)
rate = 16000
audio_str = utils.get_base64(lambda buff: _save_wav(buff, ex, rate))
return (f'<audio controls src="data:audio/ogg;base64,{audio_str}" '
' controlsList="nodownload" />') | [
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wang502/slack-sql | f6531e8bad65c09be779469c3bebdc89a28eccbf | PyGreSQL-5.0/pgdb.py | python | Cursor.callproc | (self, procname, parameters=None) | return parameters | Call a stored database procedure with the given name.
The sequence of parameters must contain one entry for each input
argument that the procedure expects. The result of the call is the
same as this input sequence; replacement of output and input/output
parameters in the return value is currently not supported.
The procedure may also provide a result set as output. These can be
requested through the standard fetch methods of the cursor. | Call a stored database procedure with the given name. | [
"Call",
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"stored",
"database",
"procedure",
"with",
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"given",
"name",
"."
] | def callproc(self, procname, parameters=None):
"""Call a stored database procedure with the given name.
The sequence of parameters must contain one entry for each input
argument that the procedure expects. The result of the call is the
same as this input sequence; replacement of output and input/output
parameters in the return value is currently not supported.
The procedure may also provide a result set as output. These can be
requested through the standard fetch methods of the cursor.
"""
n = parameters and len(parameters) or 0
query = 'select * from "%s"(%s)' % (procname, ','.join(n * ['%s']))
self.execute(query, parameters)
return parameters | [
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/setuptools/setuptools/_vendor/packaging/version.py | python | LegacyVersion.release | (self) | return None | [] | def release(self):
return None | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/sqlalchemy/sql/compiler.py | python | SQLCompiler.__init__ | (self, dialect, statement, column_keys=None,
inline=False, **kwargs) | Construct a new :class:`.SQLCompiler` object.
:param dialect: :class:`.Dialect` to be used
:param statement: :class:`.ClauseElement` to be compiled
:param column_keys: a list of column names to be compiled into an
INSERT or UPDATE statement.
:param inline: whether to generate INSERT statements as "inline", e.g.
not formatted to return any generated defaults
:param kwargs: additional keyword arguments to be consumed by the
superclass. | Construct a new :class:`.SQLCompiler` object. | [
"Construct",
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":",
"class",
":",
".",
"SQLCompiler",
"object",
"."
] | def __init__(self, dialect, statement, column_keys=None,
inline=False, **kwargs):
"""Construct a new :class:`.SQLCompiler` object.
:param dialect: :class:`.Dialect` to be used
:param statement: :class:`.ClauseElement` to be compiled
:param column_keys: a list of column names to be compiled into an
INSERT or UPDATE statement.
:param inline: whether to generate INSERT statements as "inline", e.g.
not formatted to return any generated defaults
:param kwargs: additional keyword arguments to be consumed by the
superclass.
"""
self.column_keys = column_keys
# compile INSERT/UPDATE defaults/sequences inlined (no pre-
# execute)
self.inline = inline or getattr(statement, 'inline', False)
# a dictionary of bind parameter keys to BindParameter
# instances.
self.binds = {}
# a dictionary of BindParameter instances to "compiled" names
# that are actually present in the generated SQL
self.bind_names = util.column_dict()
# stack which keeps track of nested SELECT statements
self.stack = []
# relates label names in the final SQL to a tuple of local
# column/label name, ColumnElement object (if any) and
# TypeEngine. ResultProxy uses this for type processing and
# column targeting
self._result_columns = []
# true if the paramstyle is positional
self.positional = dialect.positional
if self.positional:
self.positiontup = []
self._numeric_binds = dialect.paramstyle == "numeric"
self.bindtemplate = BIND_TEMPLATES[dialect.paramstyle]
self.ctes = None
self.label_length = dialect.label_length \
or dialect.max_identifier_length
# a map which tracks "anonymous" identifiers that are created on
# the fly here
self.anon_map = util.PopulateDict(self._process_anon)
# a map which tracks "truncated" names based on
# dialect.label_length or dialect.max_identifier_length
self.truncated_names = {}
Compiled.__init__(self, dialect, statement, **kwargs)
if (
self.isinsert or self.isupdate or self.isdelete
) and statement._returning:
self.returning = statement._returning
if self.positional and self._numeric_binds:
self._apply_numbered_params() | [
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wucng/TensorExpand | 4ea58f64f5c5082b278229b799c9f679536510b7 | TensorExpand/Object detection/YOLO/tensorflow-yolo/yolo/net/yolo_net.py | python | YoloNet.inference | (self, images) | return predicts | Build the yolo model
Args:
images: 4-D tensor [batch_size, image_height, image_width, channels]
Returns:
predicts: 4-D tensor [batch_size, cell_size, cell_size, num_classes + 5 * boxes_per_cell] | Build the yolo model | [
"Build",
"the",
"yolo",
"model"
] | def inference(self, images):
"""Build the yolo model
Args:
images: 4-D tensor [batch_size, image_height, image_width, channels]
Returns:
predicts: 4-D tensor [batch_size, cell_size, cell_size, num_classes + 5 * boxes_per_cell]
"""
conv_num = 1
temp_conv = self.conv2d('conv' + str(conv_num), images, [7, 7, 3, 64], stride=2)
conv_num += 1
temp_pool = self.max_pool(temp_conv, [2, 2], 2)
temp_conv = self.conv2d('conv' + str(conv_num), temp_pool, [3, 3, 64, 192], stride=1)
conv_num += 1
temp_pool = self.max_pool(temp_conv, [2, 2], 2)
temp_conv = self.conv2d('conv' + str(conv_num), temp_pool, [1, 1, 192, 128], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 128, 256], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [1, 1, 256, 256], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 256, 512], stride=1)
conv_num += 1
temp_conv = self.max_pool(temp_conv, [2, 2], 2)
for i in range(4):
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [1, 1, 512, 256], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 256, 512], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [1, 1, 512, 512], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 512, 1024], stride=1)
conv_num += 1
temp_conv = self.max_pool(temp_conv, [2, 2], 2)
for i in range(2):
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [1, 1, 1024, 512], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 512, 1024], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 1024, 1024], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 1024, 1024], stride=2)
conv_num += 1
#
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 1024, 1024], stride=1)
conv_num += 1
temp_conv = self.conv2d('conv' + str(conv_num), temp_conv, [3, 3, 1024, 1024], stride=1)
conv_num += 1
#Fully connected layer
local1 = self.local('local1', temp_conv, 49 * 1024, 4096)
local1 = tf.nn.dropout(local1, keep_prob=0.5)
local2 = self.local('local2', local1, 4096, self.cell_size * self.cell_size * ( self.num_classes + 5 * self.boxes_per_cell), leaky=False)
local2 = tf.reshape(local2, [tf.shape(local2)[0], self.cell_size, self.cell_size, self.num_classes + 5 * self.boxes_per_cell])
predicts = local2
return predicts | [
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arsaboo/homeassistant-config | 53c998986fbe84d793a0b174757154ab30e676e4 | custom_components/aarlo/pyaarlo/device.py | python | ArloChildDevice.charger_type | (self) | return self._load(CHARGER_KEY, "None") | Returns how the device is recharging. | Returns how the device is recharging. | [
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"""Returns how the device is recharging."""
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/celery/celery/result.py | python | ResultSet.completed_count | (self) | return sum(imap(int, (result.successful() for result in self.results))) | Task completion count.
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/imaplib.py | python | IMAP4.rename | (self, oldmailbox, newmailbox) | return self._simple_command('RENAME', oldmailbox, newmailbox) | Rename old mailbox name to new.
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sauce-archives/isign | 26ad90f8a7930b42d090d0565e8626eb229fc73c | isign/archive.py | python | AppArchive.__init__ | (self, path) | [] | def __init__(self, path):
self.path = path
self.relative_bundle_dir = '.'
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/api/policy_v1beta1_api.py | python | PolicyV1beta1Api.delete_namespaced_pod_disruption_budget | (self, name, namespace, **kwargs) | return self.delete_namespaced_pod_disruption_budget_with_http_info(name, namespace, **kwargs) | delete_namespaced_pod_disruption_budget # noqa: E501
delete a PodDisruptionBudget # noqa: E501
This method makes a synchronous HTTP request by default. To make an
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>>> thread = api.delete_namespaced_pod_disruption_budget(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str name: name of the PodDisruptionBudget (required)
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed
:param int grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately.
:param bool orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both.
:param str propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground.
:param V1DeleteOptions body:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1Status
If the method is called asynchronously,
returns the request thread. | delete_namespaced_pod_disruption_budget # noqa: E501 | [
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"""delete_namespaced_pod_disruption_budget # noqa: E501
delete a PodDisruptionBudget # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_namespaced_pod_disruption_budget(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str name: name of the PodDisruptionBudget (required)
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed
:param int grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately.
:param bool orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both.
:param str propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground.
:param V1DeleteOptions body:
:param _preload_content: if False, the urllib3.HTTPResponse object will
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:param _request_timeout: timeout setting for this request. If one
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:return: V1Status
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"""
kwargs['_return_http_data_only'] = True
return self.delete_namespaced_pod_disruption_budget_with_http_info(name, namespace, **kwargs) | [
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AstusRush/AMaDiA | e2ad87318d9dd30bc24428e05c29cb32a29c83aa | External_Libraries/python_control_master/control/frdata.py | python | FrequencyResponseData.__add__ | (self, other) | return FRD(self.fresp + other.fresp, other.omega) | Add two LTI objects (parallel connection). | Add two LTI objects (parallel connection). | [
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"""Add two LTI objects (parallel connection)."""
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# verify that the frequencies match
if len(other.omega) != len(self.omega) or \
(other.omega != self.omega).any():
warn("Frequency points do not match; expect "
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# Convert the second argument to a frequency response function.
# or re-base the frd to the current omega (if needed)
other = _convertToFRD(other, omega=self.omega)
# Check that the input-output sizes are consistent.
if self.inputs != other.inputs:
raise ValueError("The first summand has %i input(s), but the \
second has %i." % (self.inputs, other.inputs))
if self.outputs != other.outputs:
raise ValueError("The first summand has %i output(s), but the \
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return FRD(self.fresp + other.fresp, other.omega) | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/requirementslib/models/utils.py | python | extras_to_string | (extras) | return "[{0}]".format(",".join(sorted(set(extras)))) | Turn a list of extras into a string.
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sqlalchemy/sqlalchemy | eb716884a4abcabae84a6aaba105568e925b7d27 | lib/sqlalchemy/sql/type_api.py | python | TypeDecorator.type_engine | (self, dialect) | Return a dialect-specific :class:`.TypeEngine` instance
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/27/Lib/imaplib.py | python | IMAP4.response | (self, code) | return self._untagged_response(code, [None], code.upper()) | Return data for response 'code' if received, or None.
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merrychap/shellen | c0c5f8325dd3f0decf03e55d57c4714c797a2dea | shellen/shell.py | python | Shellen.__create_handlers | (self) | [] | def __create_handlers(self):
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arizvisa/ida-minsc | 8627a60f047b5e55d3efeecde332039cd1a16eea | base/database.py | python | address.prevref | (cls, **count) | return cls.prevref(ui.current.address(), count.pop('count', 1)) | Return the previous address from the current one that has anything referencing it. | Return the previous address from the current one that has anything referencing it. | [
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liangliangyy/DjangoBlog | 51d3cb9a29964904b6d59da3b771bb2454fd16ee | djangoblog/whoosh_cn_backend.py | python | WhooshSearchBackend.calculate_page | (self, start_offset=0, end_offset=None) | return page_num, page_length | [] | def calculate_page(self, start_offset=0, end_offset=None):
# Prevent against Whoosh throwing an error. Requires an end_offset
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end_offset = 1
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start_offset = 0
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if page_length and page_length > 0:
page_num = int(start_offset / page_length)
# Increment because Whoosh uses 1-based page numbers.
page_num += 1
return page_num, page_length | [
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SeldonIO/alibi-detect | b5ec53cfadcd8e3463d400259f2ea1b752ed1812 | alibi_detect/cd/lsdd.py | python | LSDDDrift.predict | (self, x: Union[np.ndarray, list], return_p_val: bool = True, return_distance: bool = True) | return self._detector.predict(x, return_p_val, return_distance) | Predict whether a batch of data has drifted from the reference data.
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Batch of instances.
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Whether to return the p-value of the permutation test.
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Whether to return the LSDD metric between the new batch and reference data.
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Dictionary containing 'meta' and 'data' dictionaries.
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Predict whether a batch of data has drifted from the reference data.
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Batch of instances.
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IntelAI/models | 1d7a53ccfad3e6f0e7378c9e3c8840895d63df8c | models/recommendation/pytorch/dlrm/training/bfloat16/mlperf_logger.py | python | mlperf_submission_log | (benchmark) | Logs information needed for MLPerf submission | Logs information needed for MLPerf submission | [
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"""
Logs information needed for MLPerf submission
"""
config_logger(benchmark)
log_event(
key=constants.SUBMISSION_BENCHMARK,
value=benchmark,
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log_event(
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log_event(
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log_event(
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value='onprem')
log_event(
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log_event(
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log_event(
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log_event(
key=constants.SUBMISSION_POC_EMAIL,
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"."... | https://github.com/IntelAI/models/blob/1d7a53ccfad3e6f0e7378c9e3c8840895d63df8c/models/recommendation/pytorch/dlrm/training/bfloat16/mlperf_logger.py#L93-L131 | ||
rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/cherrypy/_cpserver.py | python | Server._set_bind_addr | (self, value) | [] | def _set_bind_addr(self, value):
if value is None:
self.socket_file = None
self.socket_host = None
self.socket_port = None
elif isinstance(value, basestring):
self.socket_file = value
self.socket_host = None
self.socket_port = None
else:
try:
self.socket_host, self.socket_port = value
self.socket_file = None
except ValueError:
raise ValueError("bind_addr must be a (host, port) tuple "
"(for TCP sockets) or a string (for Unix "
"domain sockets), not %r" % value) | [
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Erotemic/ubelt | 221d5f6262d5c8e78638e1a38e3adcc9cc9a15e9 | ubelt/timerit.py | python | Timerit.print | (self, verbose=1) | Prints human readable report using the print function
Args:
verbose (int): verbosity level
SeeAlso:
:func:`Timerit.report`
Example:
>>> import math
>>> Timerit(num=10).call(math.factorial, 50).print(verbose=1)
Timed best=...s, mean=...s
>>> Timerit(num=10).call(math.factorial, 50).print(verbose=2)
Timed for: 10 loops, best of 3
time per loop: best=...s, mean=...s
>>> Timerit(num=10).call(math.factorial, 50).print(verbose=3)
Timed for: 10 loops, best of 3
body took: ...
time per loop: best=...s, mean=...s | Prints human readable report using the print function | [
"Prints",
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] | def print(self, verbose=1):
"""
Prints human readable report using the print function
Args:
verbose (int): verbosity level
SeeAlso:
:func:`Timerit.report`
Example:
>>> import math
>>> Timerit(num=10).call(math.factorial, 50).print(verbose=1)
Timed best=...s, mean=...s
>>> Timerit(num=10).call(math.factorial, 50).print(verbose=2)
Timed for: 10 loops, best of 3
time per loop: best=...s, mean=...s
>>> Timerit(num=10).call(math.factorial, 50).print(verbose=3)
Timed for: 10 loops, best of 3
body took: ...
time per loop: best=...s, mean=...s
"""
print(self.report(verbose=verbose)) | [
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intel/virtual-storage-manager | 00706ab9701acbd0d5e04b19cc80c6b66a2973b8 | source/vsm-dashboard/vsm_dashboard/api/vsm.py | python | start_server | (request, servers=None) | return vsmclient(request).servers.start(servers) | Start servers.
servers = [{'id': 1}, {'id': 2}] | Start servers.
servers = [{'id': 1}, {'id': 2}] | [
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"""Start servers.
servers = [{'id': 1}, {'id': 2}]
"""
return vsmclient(request).servers.start(servers) | [
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wger-project/wger | 3a17a2cf133d242d1f8c357faa53cf675a7b3223 | wger/exercises/models/image.py | python | ExerciseImage.delete | (self, *args, **kwargs) | Reset all cached infos | Reset all cached infos | [
"Reset",
"all",
"cached",
"infos"
] | def delete(self, *args, **kwargs):
"""
Reset all cached infos
"""
super(ExerciseImage, self).delete(*args, **kwargs)
for language in Language.objects.all():
delete_template_fragment_cache('muscle-overview', language.id)
delete_template_fragment_cache('exercise-overview', language.id)
delete_template_fragment_cache('exercise-overview-mobile', language.id)
delete_template_fragment_cache('equipment-overview', language.id)
# Make sure there is always a main image
if not ExerciseImage.objects.accepted().filter(
exercise_base=self.exercise_base, is_main=True
).count() and ExerciseImage.objects.accepted().filter(exercise_base=self.exercise_base
).filter(is_main=False).count():
image = ExerciseImage.objects.accepted() \
.filter(exercise_base=self.exercise_base, is_main=False)[0]
image.is_main = True
image.save() | [
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/apps/photos/utils/EXIF.py | python | s2n_motorola | (str) | return x | [] | def s2n_motorola(str):
x = 0
for c in str:
x = (x << 8) | ord(c)
return x | [
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huawei-noah/vega | d9f13deede7f2b584e4b1d32ffdb833856129989 | vega/tools/inference.py | python | _get_model | (args) | return model | Get model. | Get model. | [
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"model",
"."
] | def _get_model(args):
"""Get model."""
from vega.model_zoo import ModelZoo
model = ModelZoo.get_model(args.model_desc, args.model)
if vega.is_torch_backend():
if args.device == "GPU":
model = model.cuda()
model.eval()
return model | [
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Tools/pybench/CommandLine.py | python | Application.check_files | (self,filelist) | return None | Apply some user defined checks on the files given in filelist.
This may modify filelist in place. A typical application
is checking that at least n files are given.
If this method returns anything other than None, the
process is terminated with the return value as exit code. | Apply some user defined checks on the files given in filelist. | [
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"."
] | def check_files(self,filelist):
""" Apply some user defined checks on the files given in filelist.
This may modify filelist in place. A typical application
is checking that at least n files are given.
If this method returns anything other than None, the
process is terminated with the return value as exit code.
"""
return None | [
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] | https://github.com/larryhastings/gilectomy/blob/4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a/Tools/pybench/CommandLine.py#L482-L493 | |
enzienaudio/hvcc | 30e47328958d600c54889e2a254c3f17f2b2fd06 | generators/ir2c/SignalBiquad.py | python | SignalBiquad.get_C_def | (clazz, obj_type, obj_id) | [] | def get_C_def(clazz, obj_type, obj_id):
if obj_type == "__biquad_k~f":
return ["SignalBiquad_k sBiquad_k_{0};".format(obj_id)]
elif obj_type == "__biquad~f":
return ["SignalBiquad sBiquad_s_{0};".format(obj_id)]
else:
raise Exception() | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/fabmetheus_utilities/geometry/geometry_utilities/evaluate_enumerables/string_attribute.py | python | _getAccessibleAttribute | (attributeName, stringObject) | return None | Get the accessible attribute. | Get the accessible attribute. | [
"Get",
"the",
"accessible",
"attribute",
"."
] | def _getAccessibleAttribute(attributeName, stringObject):
'Get the accessible attribute.'
if attributeName in globalNativeFunctionSet:
return getattr(stringObject, attributeName, None)
if attributeName in globalAccessibleAttributeSet:
stringAttribute = StringAttribute(stringObject)
return getattr(stringAttribute, attributeName, None)
return None | [
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CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idaapi.py | python | boolvec_t.insert | (self, *args) | return _idaapi.boolvec_t_insert(self, *args) | insert(self, it, x) -> qvector< bool >::iterator | insert(self, it, x) -> qvector< bool >::iterator | [
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"::",
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] | def insert(self, *args):
"""
insert(self, it, x) -> qvector< bool >::iterator
"""
return _idaapi.boolvec_t_insert(self, *args) | [
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JacquesLucke/animation_nodes | b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1 | animation_nodes/tree_info/forest_data.py | python | ForestData.findLinksSkippingReroutes | (self) | [] | def findLinksSkippingReroutes(self):
rerouteNodes = self.rerouteNodes
nonRerouteNodes = filter(lambda n: n not in rerouteNodes, self.nodes)
socketsByNode = self.socketsByNode
linkedSockets = self.linkedSockets
iterLinkedSockets = self.iterLinkedSockets
chainIterable = chain.from_iterable
for node in nonRerouteNodes:
for socket in chainIterable(socketsByNode[node]):
linkedSockets[socket] = tuple(iterLinkedSockets(socket, set())) | [
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cgarrard/osgeopy-code | bc85f4ec7a630b53502ee491e400057b67cdab22 | Chapter13/listing13_3.py | python | plot_layer | (filename, symbol, layer_index=0, **kwargs) | Plots an OGR layer using the given symbol. | Plots an OGR layer using the given symbol. | [
"Plots",
"an",
"OGR",
"layer",
"using",
"the",
"given",
"symbol",
"."
] | def plot_layer(filename, symbol, layer_index=0, **kwargs):
"""Plots an OGR layer using the given symbol."""
ds = ogr.Open(filename)
for row in ds.GetLayer(layer_index):
geom = row.geometry()
geom_type = geom.GetGeometryType()
# Polygons
if geom_type == ogr.wkbPolygon:
plot_polygon(geom, symbol, **kwargs)
# Multipolygons
elif geom_type == ogr.wkbMultiPolygon:
for i in range(geom.GetGeometryCount()):
subgeom = geom.GetGeometryRef(i)
plot_polygon(subgeom, symbol, **kwargs)
# Lines
elif geom_type == ogr.wkbLineString:
plot_line(geom, symbol, **kwargs)
# Multilines
elif geom_type == ogr.wkbMultiLineString:
for i in range(geom.GetGeometryCount()):
subgeom = geom.GetGeometryRef(i)
plot_line(subgeom, symbol, **kwargs)
# Points
elif geom_type == ogr.wkbPoint:
plot_point(geom, symbol, **kwargs)
# Multipoints
elif geom_type == ogr.wkbMultiPoint:
for i in range(geom.GetGeometryCount()):
subgeom = geom.GetGeometryRef(i)
plot_point(subgeom, symbol, **kwargs) | [
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