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renpy/pygame_sdl2 | c8c732109c38da2453b85270882115a24b71b238 | src/pygame_sdl2/sprite.py | python | Sprite.alive | (self) | return truth(self.__g) | does the sprite belong to any groups
Sprite.alive(): return bool
Returns True when the Sprite belongs to one or more Groups. | does the sprite belong to any groups | [
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"""does the sprite belong to any groups
Sprite.alive(): return bool
Returns True when the Sprite belongs to one or more Groups.
"""
return truth(self.__g) | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/core/virtual_network_client.py | python | VirtualNetworkClient.bulk_add_virtual_circuit_public_prefixes | (self, virtual_circuit_id, bulk_add_virtual_circuit_public_prefixes_details, **kwargs) | Adds one or more customer public IP prefixes to the specified public virtual circuit.
Use this operation (and not :func:`update_virtual_circuit`)
to add prefixes to the virtual circuit. Oracle must verify the customer's ownership
of each prefix before traffic for that prefix will flow across the virtual circuit.
:param str virtual_circuit_id: (required)
The `OCID`__ of the virtual circuit.
__ https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm
:param oci.core.models.BulkAddVirtualCircuitPublicPrefixesDetails bulk_add_virtual_circuit_public_prefixes_details: (required)
Request with publix prefixes to be added to the virtual circuit
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type None
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/core/bulk_add_virtual_circuit_public_prefixes.py.html>`__ to see an example of how to use bulk_add_virtual_circuit_public_prefixes API. | Adds one or more customer public IP prefixes to the specified public virtual circuit.
Use this operation (and not :func:`update_virtual_circuit`)
to add prefixes to the virtual circuit. Oracle must verify the customer's ownership
of each prefix before traffic for that prefix will flow across the virtual circuit. | [
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"""
Adds one or more customer public IP prefixes to the specified public virtual circuit.
Use this operation (and not :func:`update_virtual_circuit`)
to add prefixes to the virtual circuit. Oracle must verify the customer's ownership
of each prefix before traffic for that prefix will flow across the virtual circuit.
:param str virtual_circuit_id: (required)
The `OCID`__ of the virtual circuit.
__ https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm
:param oci.core.models.BulkAddVirtualCircuitPublicPrefixesDetails bulk_add_virtual_circuit_public_prefixes_details: (required)
Request with publix prefixes to be added to the virtual circuit
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
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:return: A :class:`~oci.response.Response` object with data of type None
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/core/bulk_add_virtual_circuit_public_prefixes.py.html>`__ to see an example of how to use bulk_add_virtual_circuit_public_prefixes API.
"""
resource_path = "/virtualCircuits/{virtualCircuitId}/actions/bulkAddPublicPrefixes"
method = "POST"
expected_kwargs = ["retry_strategy"]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"bulk_add_virtual_circuit_public_prefixes got unknown kwargs: {!r}".format(extra_kwargs))
path_params = {
"virtualCircuitId": virtual_circuit_id
}
path_params = {k: v for (k, v) in six.iteritems(path_params) if v is not missing}
for (k, v) in six.iteritems(path_params):
if v is None or (isinstance(v, six.string_types) and len(v.strip()) == 0):
raise ValueError('Parameter {} cannot be None, whitespace or empty string'.format(k))
header_params = {
"accept": "application/json",
"content-type": "application/json"
}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
path_params=path_params,
header_params=header_params,
body=bulk_add_virtual_circuit_public_prefixes_details)
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
path_params=path_params,
header_params=header_params,
body=bulk_add_virtual_circuit_public_prefixes_details) | [
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yuzhoujr/leetcode | 6a2ad1fc11225db18f68bfadd21a7419d2cb52a4 | dp/70.py | python | Solution.climbStairs | (self, n) | return res[-1] | :type n: int
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PaddlePaddle/Research | 2da0bd6c72d60e9df403aff23a7802779561c4a1 | NLP/ACL2020-GraphSum/src/networks/graphsum/graphsum_reader.py | python | GraphSumReader._pad_tgt_batch_data | (self, insts) | return return_list | Pad the instances to the max sequence length in batch, and generate the
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"""
Pad the instances to the max sequence length in batch, and generate the
corresponding position data and attention bias.
"""
return_list = []
# (batch_size, max_tgt_len)
inst_data = np.array([inst + [self.pad_idx] * (self.max_tgt_len - len(inst)) for inst in insts],
dtype="int64")
return_list += [inst_data]
# (batch_size, max_tgt_len)
inst_pos = np.array([list(range(0, len(inst))) + [0] *
(self.max_tgt_len - len(inst)) for inst in insts], dtype="int64")
return_list += [inst_pos]
# This is used to avoid attention on subsequent words.
slf_attn_bias_data = np.ones((len(insts), self.max_tgt_len, self.max_tgt_len), dtype="float32")
slf_attn_bias_data = np.triu(slf_attn_bias_data, 1) * -1e18
return_list += [slf_attn_bias_data]
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/plugins/output/text_file.py | python | text_file.vulnerability | (self, message, new_line=True, severity=severity.MEDIUM) | This method is called from the output object. The output object was
called from a plugin or from the framework. This method should take an
action when a vulnerability is found. | This method is called from the output object. The output object was
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"""
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awslabs/gluon-ts | 066ec3b7f47aa4ee4c061a28f35db7edbad05a98 | src/gluonts/model/gp_forecaster/gaussian_process.py | python | GaussianProcess.__init__ | (
self,
sigma: Tensor,
kernel: Kernel,
prediction_length: Optional[int] = None,
context_length: Optional[int] = None,
num_samples: Optional[int] = None,
float_type: Type = np.float64,
jitter_method: str = "iter",
max_iter_jitter: int = 10,
neg_tol: float = -1e-8,
diag_weight: float = 1e-6,
increase_jitter: int = 10,
sample_noise: bool = True,
F=None,
) | r"""
Parameters
----------
sigma
Noise parameter of shape (batch_size, num_data_points, 1),
where num_data_points is the number of rows in the Cholesky matrix.
kernel
Kernel object.
prediction_length
Prediction length.
context_length
Training length.
num_samples
The number of samples to be drawn.
float_type
Determines whether to use single or double precision.
jitter_method
Iteratively jitter method or use eigenvalue decomposition depending on problem size.
max_iter_jitter
Maximum number of iterations for jitter to iteratively make the matrix positive definite.
neg_tol
Parameter in the jitter methods to eliminate eliminate matrices with diagonal elements smaller than this
when checking if a matrix is positive definite.
diag_weight
Multiple of mean of diagonal entries to initialize the jitter.
increase_jitter
Each iteration multiply by jitter by this amount
sample_noise
Boolean to determine whether to add :math:`\sigma^2I` to the predictive covariance matrix.
F
A module that can either refer to the Symbol API or the NDArray
API in MXNet. | r"""
Parameters
----------
sigma
Noise parameter of shape (batch_size, num_data_points, 1),
where num_data_points is the number of rows in the Cholesky matrix.
kernel
Kernel object.
prediction_length
Prediction length.
context_length
Training length.
num_samples
The number of samples to be drawn.
float_type
Determines whether to use single or double precision.
jitter_method
Iteratively jitter method or use eigenvalue decomposition depending on problem size.
max_iter_jitter
Maximum number of iterations for jitter to iteratively make the matrix positive definite.
neg_tol
Parameter in the jitter methods to eliminate eliminate matrices with diagonal elements smaller than this
when checking if a matrix is positive definite.
diag_weight
Multiple of mean of diagonal entries to initialize the jitter.
increase_jitter
Each iteration multiply by jitter by this amount
sample_noise
Boolean to determine whether to add :math:`\sigma^2I` to the predictive covariance matrix.
F
A module that can either refer to the Symbol API or the NDArray
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self,
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kernel: Kernel,
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context_length: Optional[int] = None,
num_samples: Optional[int] = None,
float_type: Type = np.float64,
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neg_tol: float = -1e-8,
diag_weight: float = 1e-6,
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sample_noise: bool = True,
F=None,
) -> None:
r"""
Parameters
----------
sigma
Noise parameter of shape (batch_size, num_data_points, 1),
where num_data_points is the number of rows in the Cholesky matrix.
kernel
Kernel object.
prediction_length
Prediction length.
context_length
Training length.
num_samples
The number of samples to be drawn.
float_type
Determines whether to use single or double precision.
jitter_method
Iteratively jitter method or use eigenvalue decomposition depending on problem size.
max_iter_jitter
Maximum number of iterations for jitter to iteratively make the matrix positive definite.
neg_tol
Parameter in the jitter methods to eliminate eliminate matrices with diagonal elements smaller than this
when checking if a matrix is positive definite.
diag_weight
Multiple of mean of diagonal entries to initialize the jitter.
increase_jitter
Each iteration multiply by jitter by this amount
sample_noise
Boolean to determine whether to add :math:`\sigma^2I` to the predictive covariance matrix.
F
A module that can either refer to the Symbol API or the NDArray
API in MXNet.
"""
assert (
prediction_length is None or prediction_length > 0
), "The value of `prediction_length` should be > 0"
assert (
context_length is None or context_length > 0
), "The value of `context_length` should be > 0"
assert (
num_samples is None or num_samples > 0
), "The value of `num_samples` should be > 0"
self.sigma = sigma
self.kernel = kernel
self.prediction_length = prediction_length
self.context_length = (
context_length if context_length is not None else prediction_length
)
self.num_samples = num_samples
self.F = F if F else getF(sigma)
self.float_type = float_type
self.jitter_method = jitter_method
self.max_iter_jitter = max_iter_jitter
self.neg_tol = neg_tol
self.diag_weight = diag_weight
self.increase_jitter = increase_jitter
self.sample_noise = sample_noise | [
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bwohlberg/sporco | df67462abcf83af6ab1961bcb0d51b87a66483fa | sporco/admm/cbpdn.py | python | ConvMinL1InL2Ball.eval_objfn | (self) | return (g1v, g0v) | Compute components of regularisation function as well as total
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/path.py | python | Path.read_hexhash | (self, hash_name) | return self._hash(hash_name).hexdigest() | Calculate given hash for this file, returning hexdigest.
List of supported hashes can be obtained from :mod:`hashlib` package.
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""" Calculate given hash for this file, returning hexdigest.
List of supported hashes can be obtained from :mod:`hashlib` package.
This reads the entire file.
.. seealso:: :meth:`hashlib.hash.hexdigest`
"""
return self._hash(hash_name).hexdigest() | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_pvc.py | python | OCPVC.delete | (self) | return self._delete(self.kind, self.config.name) | delete the object | delete the object | [
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'''delete the object'''
return self._delete(self.kind, self.config.name) | [
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fake-name/ChromeController | 6c70d855e33e06463516b263bf9e6f34c48e29e8 | ChromeController/Generator/Generated.py | python | ChromeRemoteDebugInterface.DOMStorage_enable | (self) | return subdom_funcs | Function path: DOMStorage.enable
Domain: DOMStorage
Method name: enable
No return value.
Description: Enables storage tracking, storage events will now be delivered to the client. | Function path: DOMStorage.enable
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shiweibsw/Translation-Tools | 2fbbf902364e557fa7017f9a74a8797b7440c077 | venv/Lib/site-packages/pip-9.0.3-py3.6.egg/pip/_vendor/distro.py | python | LinuxDistribution._get_lsb_release_info | (self) | Get the information items from the lsb_release command output.
Returns:
A dictionary containing all information items. | Get the information items from the lsb_release command output. | [
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"""
Get the information items from the lsb_release command output.
Returns:
A dictionary containing all information items.
"""
cmd = 'lsb_release -a'
process = subprocess.Popen(
cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
stdout, stderr = process.communicate()
stdout, stderr = stdout.decode('utf-8'), stderr.decode('utf-8')
code = process.returncode
if code == 0:
content = stdout.splitlines()
return self._parse_lsb_release_content(content)
elif code == 127: # Command not found
return {}
else:
if sys.version_info[:2] >= (3, 5):
raise subprocess.CalledProcessError(code, cmd, stdout, stderr)
elif sys.version_info[:2] >= (2, 7):
raise subprocess.CalledProcessError(code, cmd, stdout)
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rohitgirdhar/AttentionalPoolingAction | 9ab0acd9360fc9763b27073a7da057f996c01c58 | models/slim/nets/inception_v2_tsn.py | python | _reduced_kernel_size_for_small_input | (input_tensor, kernel_size) | return kernel_size_out | Define kernel size which is automatically reduced for small input.
If the shape of the input images is unknown at graph construction time this
function assumes that the input images are is large enough.
Args:
input_tensor: input tensor of size [batch_size, height, width, channels].
kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
Returns:
a tensor with the kernel size.
TODO(jrru): Make this function work with unknown shapes. Theoretically, this
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shape = tf.shape(input_tensor)
return = tf.pack([tf.minimum(shape[1], kernel_size[0]),
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"""Define kernel size which is automatically reduced for small input.
If the shape of the input images is unknown at graph construction time this
function assumes that the input images are is large enough.
Args:
input_tensor: input tensor of size [batch_size, height, width, channels].
kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
Returns:
a tensor with the kernel size.
TODO(jrru): Make this function work with unknown shapes. Theoretically, this
can be done with the code below. Problems are two-fold: (1) If the shape was
known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot
handle tensors that define the kernel size.
shape = tf.shape(input_tensor)
return = tf.pack([tf.minimum(shape[1], kernel_size[0]),
tf.minimum(shape[2], kernel_size[1])])
"""
shape = input_tensor.get_shape().as_list()
if shape[1] is None or shape[2] is None:
kernel_size_out = kernel_size
else:
kernel_size_out = [min(shape[1], kernel_size[0]),
min(shape[2], kernel_size[1])]
return kernel_size_out | [
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marcwebbie/passpie | 421c40a57ad5f55e3f14b323c929a2c41dfb5527 | passpie/cli.py | python | list_database | (db) | Print credential as a table | Print credential as a table | [
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"""Print credential as a table"""
credentials = db.credentials()
if credentials:
table = Table(
db.config['headers'],
table_format=db.config['table_format'],
colors=db.config['colors'],
hidden=db.config['hidden'],
hidden_string=db.config['hidden_string'],
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click.echo(table.render(credentials)) | [
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zachwill/flask-engine | 7c8ad4bfe36382a8c9286d873ec7b785715832a4 | libs/werkzeug/formparser.py | python | default_stream_factory | (total_content_length, filename, content_type,
content_length=None) | return StringIO() | The stream factory that is used per default. | The stream factory that is used per default. | [
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"""The stream factory that is used per default."""
if total_content_length > 1024 * 500:
return TemporaryFile('wb+')
return StringIO() | [
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hvac/hvac | ec048ded30d21c13c21cfa950d148c8bfc1467b0 | hvac/adapters.py | python | Adapter.urljoin | (*args) | return "/".join(map(lambda x: str(x).strip("/"), args)) | Joins given arguments into a url. Trailing and leading slashes are stripped for each argument.
:param args: Multiple parts of a URL to be combined into one string.
:type args: str | unicode
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:param args: Multiple parts of a URL to be combined into one string.
:type args: str | unicode
:return: Full URL combining all provided arguments
:rtype: str | unicode
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/pickle.py | python | Pickler.dump | (self, obj) | Write a pickled representation of obj to the open file. | Write a pickled representation of obj to the open file. | [
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"""Write a pickled representation of obj to the open file."""
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self.save(obj)
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pillone/usntssearch | 24b5e5bc4b6af2589d95121c4d523dc58cb34273 | NZBmegasearch/mechanize/_beautifulsoup.py | python | Tag.__iter__ | (self) | return iter(self.contents) | Iterating over a tag iterates over its contents. | Iterating over a tag iterates over its contents. | [
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kozec/sc-controller | ce92c773b8b26f6404882e9209aff212c4053170 | scc/lib/usb1.py | python | USBTransfer.__init__ | (self, handle, iso_packets, before_submit, after_completion) | You should not instanciate this class directly.
Call "getTransfer" method on an USBDeviceHandle instance to get
instances of this class. | You should not instanciate this class directly.
Call "getTransfer" method on an USBDeviceHandle instance to get
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"""
You should not instanciate this class directly.
Call "getTransfer" method on an USBDeviceHandle instance to get
instances of this class.
"""
if iso_packets < 0:
raise ValueError(
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)
self.__handle = handle
self.__before_submit = before_submit
self.__after_completion = after_completion
self.__num_iso_packets = iso_packets
result = libusb1.libusb_alloc_transfer(iso_packets)
if not result:
# pylint: disable=undefined-variable
raise USBErrorNoMem
# pylint: enable=undefined-variable
self.__transfer = result
self.__ctypesCallbackWrapper = libusb1.libusb_transfer_cb_fn_p(
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pwnieexpress/pwn_plug_sources | 1a23324f5dc2c3de20f9c810269b6a29b2758cad | src/set/src/core/setcore.py | python | meta_database | () | [] | def meta_database():
# DEFINE METASPLOIT PATH
meta_path = file("%s/config/set_config" % (definepath),"r").readlines()
for line in meta_path:
line = line.rstrip()
match = re.search("METASPLOIT_DATABASE=", line)
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line = line.replace("METASPLOIT_DATABASE=","")
msf_database = line.rstrip()
return msf_database | [
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ayoolaolafenwa/PixelLib | ae56003c416a98780141a1170c9d888fe9a31317 | pixellib/torchbackend/instance/data/transforms/augmentation.py | python | _get_aug_input_args | (aug, aug_input) | return args | Get the arguments to be passed to ``aug.get_transform`` from the input ``aug_input``. | Get the arguments to be passed to ``aug.get_transform`` from the input ``aug_input``. | [
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"""
Get the arguments to be passed to ``aug.get_transform`` from the input ``aug_input``.
"""
if aug.input_args is None:
# Decide what attributes are needed automatically
prms = list(inspect.signature(aug.get_transform).parameters.items())
# The default behavior is: if there is one parameter, then its "image"
# (work automatically for majority of use cases, and also avoid BC breaking),
# Otherwise, use the argument names.
if len(prms) == 1:
names = ("image",)
else:
names = []
for name, prm in prms:
if prm.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
raise TypeError(
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"""
)
names.append(name)
aug.input_args = tuple(names)
args = []
for f in aug.input_args:
try:
args.append(getattr(aug_input, f))
except AttributeError as e:
raise AttributeError(
f"{type(aug)}.get_transform needs input attribute '{f}', "
f"but it is not an attribute of {type(aug_input)}!"
) from e
return args | [
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ChenRocks/fast_abs_rl | a3cd65016082ab842be4e42b0b26b7bc046f4ad5 | model/rl.py | python | PtrExtractorRL.forward | (self, attn_mem, n_step) | return outputs | atten_mem: Tensor of size [num_sents, input_dim] | atten_mem: Tensor of size [num_sents, input_dim] | [
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attn_feat = torch.mm(attn_mem, self._attn_wm)
hop_feat = torch.mm(attn_mem, self._hop_wm)
outputs = []
lstm_in = self._init_i.unsqueeze(0)
lstm_states = (self._init_h.unsqueeze(1), self._init_c.unsqueeze(1))
for _ in range(n_step):
h, c = self._lstm_cell(lstm_in, lstm_states)
query = h[:, -1, :]
for _ in range(self._n_hop):
query = PtrExtractorRL.attention(hop_feat, query,
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score = PtrExtractorRL.attention_score(
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if self.training:
prob = F.softmax(score, dim=-1)
out = torch.distributions.Categorical(prob)
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for o in outputs:
score[0, o[0, 0].item()][0] = -1e18
out = score.max(dim=1, keepdim=True)[1]
outputs.append(out)
lstm_in = attn_mem[out[0, 0].item()].unsqueeze(0)
lstm_states = (h, c)
return outputs | [
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pytroll/satpy | 09e51f932048f98cce7919a4ff8bd2ec01e1ae98 | satpy/readers/sar_c_safe.py | python | SAFEXMLCalibration.get_dataset | (self, key, info, chunks=None) | return self.get_calibration(key["name"], chunks=chunks or CHUNK_SIZE) | Load a dataset. | Load a dataset. | [
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stanfordnlp/stanza-old | 920c55d8eaa1e7105971059c66eb448a74c100d6 | stanza/text/vocab.py | python | FrozenVocab.__init__ | (self, vocab) | [] | def __init__(self, vocab):
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rytilahti/python-miio | b6e53dd16fac77915426e7592e2528b78ef65190 | miio/huizuo.py | python | HuizuoLampFan.set_natural_fan_mode | (self) | Set fan mode to 'Natural wind' (only for models with fan) | Set fan mode to 'Natural wind' (only for models with fan) | [
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thanethomson/statik | ea422b8fccd1430f60e3d8b62d9221365ec4e31f | statik/templating.py | python | StatikJinjaTemplate.__init__ | (self, provider, template, **kwargs) | Constructor.
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provider: The provider that created this template.
template: The Jinja2 template to wrap.
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munificent/magpie | f5138e3d316ec1a664b5eadba1bcc8573d3faca3 | dep/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings.HasExplicitIdlRules | (self, spec) | return self._HasExplicitRuleForExtension(spec, 'idl') | Determine if there's an explicit rule for idl files. When there isn't we
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pculture/miro | d8e4594441939514dd2ac29812bf37087bb3aea5 | tv/lib/frontends/widgets/cellpack.py | python | Layout.add | (self, x, y, width, height, drawing_function=None,
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ntoll/drogulus | d74b78d0bf0220b91f075dbd3f9a06c2663b474e | drogulus/dht/validators.py | python | validate_string | (val) | return isinstance(val, str) | Returns a boolean to indicate that a field is a string of some sort. | Returns a boolean to indicate that a field is a string of some sort. | [
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dbrgn/RPLCD | e651d9cfc0e24e1ad47fe63cf50d3fec0d751c61 | RPLCD/contextmanagers.py | python | cleared | (lcd) | Context manager to clear display before writing. DEPRECATED. | Context manager to clear display before writing. DEPRECATED. | [
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flennerhag/mlens | 6cbc11354b5f9500a33d9cefb700a1bba9d3199a | mlens/externals/joblib/_parallel_backends.py | python | SequentialBackend.effective_n_jobs | (self, n_jobs) | return 1 | Determine the number of jobs which are going to run in parallel | Determine the number of jobs which are going to run in parallel | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/queue.py | python | _PySimpleQueue.get_nowait | (self) | return self.get(block=False) | Remove and return an item from the queue without blocking.
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'''
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AstroPrint/AstroBox | e7e3b8a7d33ea85fcb6b2696869c0d719ceb8b75 | src/astroprint/externaldrive/mac_dev.py | python | ExternalDriveManager.getRemovableDrives | (self) | return self.getDirContents('%s/*' % self.ROOT_MOUNT_POINT, 'usb') | [] | def getRemovableDrives(self):
return self.getDirContents('%s/*' % self.ROOT_MOUNT_POINT, 'usb') | [
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v8/services/services/recommendation_service/client.py | python | RecommendationServiceClient.ad_group_path | (customer_id: str, ad_group_id: str,) | return "customers/{customer_id}/adGroups/{ad_group_id}".format(
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tensorflow/lingvo | ce10019243d954c3c3ebe739f7589b5eebfdf907 | lingvo/core/program.py | python | SimpleProgramSchedule.Params | (cls) | return p | Params for a SimpleProgramSchedule. | Params for a SimpleProgramSchedule. | [
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p.Define('task_dict', None, 'dataset_name -> task params')
p.Define('task_name', None, 'High level task name')
p.Define('logdir', None, 'Log directory')
p.Define('train_program', None, 'Train program params')
p.Define('train_executions_per_eval', 1, '')
p.Define('eval_programs', [], 'List of eval program params.')
p.Define('num_splits_per_client', None, '')
p.Define('dataset_names', [], 'List of all dataset names.')
p.Define('async_postprocess', True,
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# TODO(blee): Clean these up.
p.Define('ml_perf', hyperparams.Params(), 'MlPerf configuration.')
mlp = p.ml_perf
mlp.Define('submission_metadata', None,
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mlp.Define('benchmark_name', None, 'Benchmark name for compliance log.')
mlp.Define('steps_per_epoch', None, 'Number of training steps per epoch.')
mlp.Define('decoder_metric_name', None,
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mlp.Define('decoder_metric_success_threshold', None,
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mlp.Define('max_steps_to_train', None,
'Maximum number of steps to reach target accuracy')
return p | [
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rst2pdf/rst2pdf | dac0653f8eb894aa5b83cf0877ca3420cdfaf4b2 | rst2pdf/sphinxnodes.py | python | SphinxHandler.__init__ | (self) | This is where the magic happens. Make a copy of the elements
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | crio/datadog_checks/crio/config_models/__init__.py | python | ConfigMixin.shared_config | (self) | return self._config_model_shared | [] | def shared_config(self) -> SharedConfig:
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django/django-localflavor | 5d9c3bdc4a6b5e114da2b7226b9b0bcf32757a66 | localflavor/fr/forms.py | python | FRNationalIdentificationNumber._check_corsica | (self, commune_of_origin, current_year, department_of_origin, year_of_birth) | Departments number 20, 2A and 2B represent Corsica | Departments number 20, 2A and 2B represent Corsica | [
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# For people born before 1976, Corsica number was 20
if current_year < int(year_of_birth) < 76 and department_of_origin != '20':
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# For people born from 1976, Corsica dep number is either 2A or 2B
if (int(year_of_birth) > 75 and department_of_origin not in ['2A', '2B']):
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google/deepvariant | 9cf1c7b0e2342d013180aa153cba3c9331c9aef7 | third_party/nucleus/util/variant_utils.py | python | variant_type | (variant) | Gets the VariantType of variant.
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variant: nucleus.genomics.v1.Variant.
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VariantType indicating the type of this variant.
"""
if is_ref(variant):
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wistbean/fxxkpython | 88e16d79d8dd37236ba6ecd0d0ff11d63143968c | vip/qyxuan/projects/Snake/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/urllib3/contrib/securetransport.py | python | WrappedSocket._set_ciphers | (self) | Sets up the allowed ciphers. By default this matches the set in
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styxit/HTPC-Manager | 490697460b4fa1797106aece27d873bc256b2ff1 | libs/cherrypy/_cptools.py | python | SessionTool.regenerate | (self) | Drop the current session and make a new one (with a new id). | Drop the current session and make a new one (with a new id). | [
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conf = dict([(k, v) for k, v in self._merged_args().items()
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tgalal/python-axolotl | b8d1a2e04bda38575dc5c0c6daf1b545283e31d7 | axolotl/groups/groupsessionbuilder.py | python | GroupSessionBuilder.create | (self, senderKeyName) | :type senderKeyName: SenderKeyName | :type senderKeyName: SenderKeyName | [
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"""
:type senderKeyName: SenderKeyName
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senderKeyRecord.setSenderKeyState(KeyHelper.generateSenderKeyId(),
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state = senderKeyRecord.getSenderKeyState();
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except (InvalidKeyException, InvalidKeyIdException) as e:
raise AssertionError(e) | [
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wrye-bash/wrye-bash | d495c47cfdb44475befa523438a40c4419cb386f | Mopy/bash/bolt.py | python | Path.psize | (self) | Size of file or directory. | Size of file or directory. | [
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"""Size of file or directory."""
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try:
return sum(sum(op_size(join(x, f)) for f in files)
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except ValueError:
return 0
else:
return os.path.getsize(self._s) | [
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janrueth/SiriServerCore | dcc028c1fdddcc362e484b9ad655420ce953c8d2 | biplist/__init__.py | python | PlistWriter.writeObjectReference | (self, obj, output) | Tries to write an object reference, adding it to the references
table. Does not write the actual object bytes or set the reference
position. Returns a tuple of whether the object was a new reference
(True if it was, False if it already was in the reference table)
and the new output. | Tries to write an object reference, adding it to the references
table. Does not write the actual object bytes or set the reference
position. Returns a tuple of whether the object was a new reference
(True if it was, False if it already was in the reference table)
and the new output. | [
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"""Tries to write an object reference, adding it to the references
table. Does not write the actual object bytes or set the reference
position. Returns a tuple of whether the object was a new reference
(True if it was, False if it already was in the reference table)
and the new output.
"""
position = self.positionOfObjectReference(obj)
if position is None:
self.writtenReferences[obj] = len(self.writtenReferences)
output += self.binaryInt(len(self.writtenReferences) - 1, bytes=self.trailer.objectRefSize)
return (True, output)
else:
output += self.binaryInt(position, bytes=self.trailer.objectRefSize)
return (False, output) | [
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bmuller/twistar | 1eb46ff2577473e0a26932ee57473e26203a3db2 | twistar/dbobject.py | python | DBObject.beforeDelete | (self) | Method called before a L{DBObject} is deleted. Classes can overwrite this method.
If False is returned, then the L{DBObject} is not deleted from database.
This method may return a C{Deferred}. | Method called before a L{DBObject} is deleted. Classes can overwrite this method.
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"""
Method called before a L{DBObject} is deleted. Classes can overwrite this method.
If False is returned, then the L{DBObject} is not deleted from database.
This method may return a C{Deferred}.
""" | [
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pyglet/pyglet | 2833c1df902ca81aeeffa786c12e7e87d402434b | pyglet/shapes.py | python | Triangle.x2 | (self) | return self._x2 | Second X coordinate of the shape.
:type: int or float | Second X coordinate of the shape. | [
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"""Second X coordinate of the shape.
:type: int or float
"""
return self._x2 | [
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gxcuizy/Python | 72167d12439a615a8fd4b935eae1fb6516ed4e69 | 从零学Python-掘金活动/day07/juejin_poins.py | python | save_avatar | (object_id, pictures) | 下载用户头像 | 下载用户头像 | [
"下载用户头像"
] | def save_avatar(object_id, pictures):
"""下载用户头像"""
# 拼接图片路径
path = os.path.join('.', object_id)
# 图片名称
img_name = 'avatar.jpg'
img_path = os.path.join(path, img_name)
print('开始下载图片:' + img_path)
with open(img_path, 'wb') as img:
# 下载图片
img_re = requests.get(pictures)
img.write(img_re.content)
print('下载图片完毕！') | [
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playframework/play1 | 0ecac3bc2421ae2dbec27a368bf671eda1c9cba5 | python/Lib/xml/sax/saxutils.py | python | escape | (data, entities={}) | return data | Escape &, <, and > in a string of data.
You can escape other strings of data by passing a dictionary as
the optional entities parameter. The keys and values must all be
strings; each key will be replaced with its corresponding value. | Escape &, <, and > in a string of data. | [
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] | def escape(data, entities={}):
"""Escape &, <, and > in a string of data.
You can escape other strings of data by passing a dictionary as
the optional entities parameter. The keys and values must all be
strings; each key will be replaced with its corresponding value.
"""
# must do ampersand first
data = data.replace("&", "&")
data = data.replace(">", ">")
data = data.replace("<", "<")
if entities:
data = __dict_replace(data, entities)
return data | [
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craigmacartney/Wave-U-Net-For-Speech-Enhancement | c8ccbd286cbe73d7539e5703e4407762304e3068 | Models/UnetAudioSeparator.py | python | UnetAudioSeparator.get_padding | (self, shape) | Calculates the required amounts of padding along each axis of the input and output, so that the Unet works and has the given shape as output shape
:param shape: Desired output shape
:return: Input_shape, output_shape, where each is a list [batch_size, time_steps, channels] | Calculates the required amounts of padding along each axis of the input and output, so that the Unet works and has the given shape as output shape
:param shape: Desired output shape
:return: Input_shape, output_shape, where each is a list [batch_size, time_steps, channels] | [
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'''
Calculates the required amounts of padding along each axis of the input and output, so that the Unet works and has the given shape as output shape
:param shape: Desired output shape
:return: Input_shape, output_shape, where each is a list [batch_size, time_steps, channels]
'''
if self.context:
# Check if desired shape is possible as output shape - go from output shape towards lowest-res feature map
rem = float(shape[1]) # Cut off batch size number and channel
#rem = rem + self.filter_size - 1
for i in range(self.num_layers):
rem = rem + self.merge_filter_size - 1
rem = (rem + 1.) / 2.# out = in + in - 1 <=> in = (out+1)/
# Round resulting feature map dimensions up to nearest integer
x = np.asarray(np.ceil(rem),dtype=np.int64)
assert(x >= 2)
# Compute input and output shapes based on lowest-res feature map
output_shape = x
input_shape = x
# Extra conv
input_shape = input_shape + self.filter_size - 1
# Go from centre feature map through up- and downsampling blocks
for i in range(self.num_layers):
output_shape = 2*output_shape - 1 #Upsampling
output_shape = output_shape - self.merge_filter_size + 1 # Conv
input_shape = 2*input_shape - 1 # Decimation
input_shape = input_shape + self.filter_size - 1 # Conv
input_shape = np.concatenate([[shape[0]], [input_shape], [self.num_channels]])
output_shape = np.concatenate([[shape[0]], [output_shape], [self.num_channels]])
return input_shape, output_shape
else:
return [shape[0], shape[1], self.num_channels], [shape[0], shape[1], self.num_channels] | [
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cuthbertLab/music21 | bd30d4663e52955ed922c10fdf541419d8c67671 | music21/musicxml/m21ToXml.py | python | ScoreExporter.setScoreLayouts | (self) | sets `self.scoreLayouts` and `self.firstScoreLayout`
>>> b = corpus.parse('schoenberg/opus19', 2)
>>> SX = musicxml.m21ToXml.ScoreExporter(b)
>>> SX.setScoreLayouts()
>>> SX.scoreLayouts
<music21.stream.Score 0x...>
>>> len(SX.scoreLayouts)
1
>>> SX.firstScoreLayout
<music21.layout.ScoreLayout> | sets `self.scoreLayouts` and `self.firstScoreLayout` | [
"sets",
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'''
sets `self.scoreLayouts` and `self.firstScoreLayout`
>>> b = corpus.parse('schoenberg/opus19', 2)
>>> SX = musicxml.m21ToXml.ScoreExporter(b)
>>> SX.setScoreLayouts()
>>> SX.scoreLayouts
<music21.stream.Score 0x...>
>>> len(SX.scoreLayouts)
1
>>> SX.firstScoreLayout
<music21.layout.ScoreLayout>
'''
s = self.stream
scoreLayouts = s.getElementsByClass('ScoreLayout').stream()
if scoreLayouts:
scoreLayout = scoreLayouts[0]
else:
scoreLayout = None
self.scoreLayouts = scoreLayouts
self.firstScoreLayout = scoreLayout | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.3/django/contrib/admin/options.py | python | ModelAdmin.get_fieldsets | (self, request, obj=None) | return [(None, {'fields': fields})] | Hook for specifying fieldsets for the add form. | Hook for specifying fieldsets for the add form. | [
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] | def get_fieldsets(self, request, obj=None):
"Hook for specifying fieldsets for the add form."
if self.declared_fieldsets:
return self.declared_fieldsets
form = self.get_form(request, obj)
fields = form.base_fields.keys() + list(self.get_readonly_fields(request, obj))
return [(None, {'fields': fields})] | [
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pySTEPS/pysteps | bd9478538249e1d64036a721ceb934085d6e1da9 | pysteps/io/exporters.py | python | export_forecast_dataset | (field, exporter) | Write a forecast array into a file.
If the exporter was initialized with n_ens_members>1, the written dataset
has dimensions (n_ens_members,num_timesteps,shape[0],shape[1]), where shape
refers to the shape of the two-dimensional forecast grids. Otherwise, the
dimensions are (num_timesteps,shape[0],shape[1]). If the exporter was
initialized with incremental!=None, the array is appended to the existing
dataset either along the ensemble member or time axis.
Parameters
----------
exporter: dict
An exporter object created with any initialization method implemented
in :py:mod:`pysteps.io.exporters`.
field: array_like
The array to write. The required shape depends on the choice of the
'incremental' parameter the exporter was initialized with:
+-----------------+---------------------------------------------------+
| incremental | required shape |
+=================+===================================================+
| None | (num_ens_members,num_timesteps,shape[0],shape[1]) |
+-----------------+---------------------------------------------------+
| 'timestep' | (num_ens_members,shape[0],shape[1]) |
+-----------------+---------------------------------------------------+
| 'member' | (num_timesteps,shape[0],shape[1]) |
+-----------------+---------------------------------------------------+
If the exporter was initialized with num_ens_members=1,
the num_ens_members dimension is dropped. | Write a forecast array into a file. | [
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"."
] | def export_forecast_dataset(field, exporter):
"""Write a forecast array into a file.
If the exporter was initialized with n_ens_members>1, the written dataset
has dimensions (n_ens_members,num_timesteps,shape[0],shape[1]), where shape
refers to the shape of the two-dimensional forecast grids. Otherwise, the
dimensions are (num_timesteps,shape[0],shape[1]). If the exporter was
initialized with incremental!=None, the array is appended to the existing
dataset either along the ensemble member or time axis.
Parameters
----------
exporter: dict
An exporter object created with any initialization method implemented
in :py:mod:`pysteps.io.exporters`.
field: array_like
The array to write. The required shape depends on the choice of the
'incremental' parameter the exporter was initialized with:
+-----------------+---------------------------------------------------+
| incremental | required shape |
+=================+===================================================+
| None | (num_ens_members,num_timesteps,shape[0],shape[1]) |
+-----------------+---------------------------------------------------+
| 'timestep' | (num_ens_members,shape[0],shape[1]) |
+-----------------+---------------------------------------------------+
| 'member' | (num_timesteps,shape[0],shape[1]) |
+-----------------+---------------------------------------------------+
If the exporter was initialized with num_ens_members=1,
the num_ens_members dimension is dropped.
"""
if exporter["method"] == "netcdf" and not NETCDF4_IMPORTED:
raise MissingOptionalDependency(
"netCDF4 package is required for netcdf "
"exporters but it is not installed"
)
if exporter["incremental"] is None:
if exporter["num_ens_members"] > 1:
shp = (
exporter["num_ens_members"],
exporter["num_timesteps"],
exporter["shape"][0],
exporter["shape"][1],
)
else:
shp = (
exporter["num_timesteps"],
exporter["shape"][0],
exporter["shape"][1],
)
if field.shape != shp:
raise ValueError(
"field has invalid shape: %s != %s" % (str(field.shape), str(shp))
)
elif exporter["incremental"] == "timestep":
if exporter["num_ens_members"] > 1:
shp = (
exporter["num_ens_members"],
exporter["shape"][0],
exporter["shape"][1],
)
else:
shp = exporter["shape"]
if field.shape != shp:
raise ValueError(
"field has invalid shape: %s != %s" % (str(field.shape), str(shp))
)
elif exporter["incremental"] == "member":
shp = (exporter["num_timesteps"], exporter["shape"][0], exporter["shape"][1])
if field.shape != shp:
raise ValueError(
"field has invalid shape: %s != %s" % (str(field.shape), str(shp))
)
if exporter["method"] == "geotiff":
_export_geotiff(field, exporter)
elif exporter["method"] == "netcdf":
_export_netcdf(field, exporter)
elif exporter["method"] == "kineros":
_export_kineros(field, exporter)
else:
raise ValueError("unknown exporter method %s" % exporter["method"]) | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/docutils/utils/math/math2html.py | python | Position.next | (self) | return self.current() | Advance the position and return the next character. | Advance the position and return the next character. | [
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"Advance the position and return the next character."
self.skipcurrent()
return self.current() | [
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simpeg/simpeg | d93145d768b5512621cdd75566b4a8175fee9ed3 | SimPEG/electromagnetics/utils/analytic_utils.py | python | MagneticLoopVectorPotential | (
srcLoc, obsLoc, component, radius, orientation="Z", mu=mu_0
) | This code has been deprecated after SimPEG 0.11.5. Please use geoana instead. "
.. code::
>> pip install geoana
>> from geoana.electromagnetics.static import MagneticDipoleWholeSpace | This code has been deprecated after SimPEG 0.11.5. Please use geoana instead. " | [
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] | def MagneticLoopVectorPotential(
srcLoc, obsLoc, component, radius, orientation="Z", mu=mu_0
):
"""
This code has been deprecated after SimPEG 0.11.5. Please use geoana instead. "
.. code::
>> pip install geoana
>> from geoana.electromagnetics.static import MagneticDipoleWholeSpace
"""
raise Exception(
"This code has been deprecated after SimPEG 0.11.5. "
"Please use geoana instead. "
"\n >> pip install geoana "
"\n >> from geoana.electromagnetics.static import CircularLoopWholeSpace"
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Wramberg/TerminalView | b0856fa62c1fdd3ad968bf6b8aaa344962b65adf | pyte/screens.py | python | Screen.carriage_return | (self) | Move the cursor to the beginning of the current line. | Move the cursor to the beginning of the current line. | [
"Move",
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"cursor",
"to",
"the",
"beginning",
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"the",
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] | def carriage_return(self):
"""Move the cursor to the beginning of the current line."""
self.cursor.x = 0 | [
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] | https://github.com/Wramberg/TerminalView/blob/b0856fa62c1fdd3ad968bf6b8aaa344962b65adf/pyte/screens.py#L454-L456 | ||
freedombox/FreedomBox | 335a7f92cc08f27981f838a7cddfc67740598e54 | plinth/modules/upgrades/__init__.py | python | can_activate_backports | () | return True | Return whether backports can be activated. | Return whether backports can be activated. | [
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"can",
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"activated",
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] | def can_activate_backports():
"""Return whether backports can be activated."""
release, _ = get_current_release()
if release == 'unstable' or (release == 'testing' and not cfg.develop):
return False
return True | [
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fab-jul/imgcomp-cvpr | f03ce0bfa846f7ba1bf9b7ba415b082efe5c192c | code/train.py | python | Distortions.get_ms_ssim | (inp, otp) | [] | def get_ms_ssim(inp, otp):
with tf.name_scope('mean_MS_SSIM'):
return ms_ssim.MultiScaleSSIM(inp, otp, data_format='NCHW', name='MS-SSIM') | [
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PINTO0309/PINTO_model_zoo | 2924acda7a7d541d8712efd7cc4fd1c61ef5bddd | 105_MobileStyleGAN/saved_model_to_tflite.py | python | convert | (saved_model_dir_path,
signature_def,
input_shapes,
model_output_dir_path,
output_no_quant_float32_tflite,
output_weight_quant_tflite,
output_float16_quant_tflite,
output_integer_quant_tflite,
output_full_integer_quant_tflite,
output_integer_quant_type,
string_formulas_for_normalization,
calib_ds_type,
ds_name_for_tfds_for_calibration,
split_name_for_tfds_for_calibration,
download_dest_folder_path_for_the_calib_tfds,
tfds_download_flg,
npy_load_default_path,
load_dest_file_path_for_the_calib_npy,
output_tfjs,
output_tftrt,
output_coreml,
output_edgetpu,
output_onnx,
onnx_opset) | [] | def convert(saved_model_dir_path,
signature_def,
input_shapes,
model_output_dir_path,
output_no_quant_float32_tflite,
output_weight_quant_tflite,
output_float16_quant_tflite,
output_integer_quant_tflite,
output_full_integer_quant_tflite,
output_integer_quant_type,
string_formulas_for_normalization,
calib_ds_type,
ds_name_for_tfds_for_calibration,
split_name_for_tfds_for_calibration,
download_dest_folder_path_for_the_calib_tfds,
tfds_download_flg,
npy_load_default_path,
load_dest_file_path_for_the_calib_npy,
output_tfjs,
output_tftrt,
output_coreml,
output_edgetpu,
output_onnx,
onnx_opset):
print(f'{Color.REVERCE}Start conversion process from saved_model to tflite{Color.RESET}', '=' * 38)
import subprocess
import tensorflow as tf
tf.get_logger().setLevel('INFO')
tf.autograph.set_verbosity(0)
tf.get_logger().setLevel(logging.ERROR)
import tensorflow_datasets as tfds
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph
# Load saved_model and change input shape
# https://github.com/tensorflow/tensorflow/issues/30180#issuecomment-505959220
model = tf.saved_model.load(saved_model_dir_path)
if signature_def:
concrete_func = model.signatures[signature_def]
else:
concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
if input_shapes:
concrete_func_input_tensors = [tensor for tensor in concrete_func.inputs if tensor.dtype != tf.resource and not 'unknown' in tensor.name]
for conc_input, def_input in zip(concrete_func_input_tensors, input_shapes):
print('Before changing the input shape', conc_input)
conc_input.set_shape(def_input)
print('After changing the input shape', conc_input)
else:
concrete_func_input_tensors = [tensor for tensor in concrete_func.inputs if tensor.dtype != tf.resource and not 'unknown' in tensor.name]
for conc_input in concrete_func_input_tensors:
input_shapes.append(conc_input.shape.as_list())
# No Quantization - Input/Output=float32
if output_no_quant_float32_tflite:
try:
print(f'{Color.REVERCE}tflite Float32 convertion started{Color.RESET}', '=' * 51)
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
tflite_model = converter.convert()
with open(f'{model_output_dir_path}/model_float32.tflite', 'wb') as w:
w.write(tflite_model)
print(f'{Color.GREEN}tflite Float32 convertion complete!{Color.RESET} - {model_output_dir_path}/model_float32.tflite')
except Exception as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e)
import traceback
traceback.print_exc()
# Weight Quantization - Input/Output=float32
if output_weight_quant_tflite:
try:
print(f'{Color.REVERCE}Weight Quantization started{Color.RESET}', '=' * 57)
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
tflite_model = converter.convert()
with open(f'{model_output_dir_path}/model_weight_quant.tflite', 'wb') as w:
w.write(tflite_model)
print(f'{Color.GREEN}Weight Quantization complete!{Color.RESET} - {model_output_dir_path}/model_weight_quant.tflite')
except Exception as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e)
import traceback
traceback.print_exc()
# Float16 Quantization - Input/Output=float32
if output_float16_quant_tflite:
try:
print(f'{Color.REVERCE}Float16 Quantization started{Color.RESET}', '=' * 56)
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
tflite_quant_model = converter.convert()
with open(f'{model_output_dir_path}/model_float16_quant.tflite', 'wb') as w:
w.write(tflite_quant_model)
print(f'{Color.GREEN}Float16 Quantization complete!{Color.RESET} - {model_output_dir_path}/model_float16_quant.tflite')
except Exception as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e)
import traceback
traceback.print_exc()
# Downloading datasets for calibration
raw_test_data = None
if output_integer_quant_tflite or output_full_integer_quant_tflite:
if calib_ds_type == 'tfds':
print(f'{Color.REVERCE}TFDS download started{Color.RESET}', '=' * 63)
raw_test_data = tfds.load(name=ds_name_for_tfds_for_calibration,
with_info=False,
split=split_name_for_tfds_for_calibration,
data_dir=download_dest_folder_path_for_the_calib_tfds,
download=tfds_download_flg)
print(f'{Color.GREEN}TFDS download complete!{Color.RESET}')
elif calib_ds_type == 'numpy':
print(f'{Color.REVERCE}numpy dataset load started{Color.RESET}', '=' * 58)
try:
if load_dest_file_path_for_the_calib_npy == npy_load_default_path and not os.path.exists(npy_load_default_path):
os.makedirs(os.path.dirname(npy_load_default_path), exist_ok=True)
import gdown
import subprocess
try:
result = subprocess.check_output(['gdown',
'--id', '1z-K0KZCK3JBH9hXFuBTmIM4jaMPOubGN',
'-O', load_dest_file_path_for_the_calib_npy],
stderr=subprocess.PIPE).decode('utf-8')
except:
result = subprocess.check_output(['sudo', 'gdown',
'--id', '1z-K0KZCK3JBH9hXFuBTmIM4jaMPOubGN',
'-O', load_dest_file_path_for_the_calib_npy],
stderr=subprocess.PIPE).decode('utf-8')
raw_test_data = np.load(load_dest_file_path_for_the_calib_npy)
print(f'{Color.GREEN}numpy dataset load complete!{Color.RESET}')
except subprocess.CalledProcessError as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e.stderr.decode('utf-8'))
import traceback
traceback.print_exc()
else:
pass
def representative_dataset_gen():
if calib_ds_type == 'tfds':
for data in raw_test_data.take(10):
image = data['image'].numpy()
images = []
for shape in input_shapes:
data = tf.image.resize(image, (shape[1], shape[2]))
tmp_image = eval(string_formulas_for_normalization) # Default: (data - [127.5,127.5,127.5]) / [127.5,127.5,127.5]
tmp_image = tmp_image[np.newaxis,:,:,:]
images.append(tmp_image)
yield images
elif calib_ds_type == 'numpy':
# for idx in range(raw_test_data.shape[0]):
# image = raw_test_data[idx]
# images = []
# for shape in input_shapes:
# data = tf.image.resize(image, (shape[1], shape[2]))
# tmp_image = eval(string_formulas_for_normalization) # Default: (data - [127.5,127.5,127.5]) / [127.5,127.5,127.5]
# tmp_image = tmp_image[np.newaxis,:,:,:]
# images.append(tmp_image)
# yield images
for idx in range(10):
data = tf.random.uniform([1,512])
yield [data]
# Integer Quantization
if output_integer_quant_tflite:
try:
print(f'{Color.REVERCE}Integer Quantization started{Color.RESET}', '=' * 56)
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8, tf.lite.OpsSet.SELECT_TF_OPS]
converter.representative_dataset = representative_dataset_gen
tflite_model = converter.convert()
with open(f'{model_output_dir_path}/model_integer_quant.tflite', 'wb') as w:
w.write(tflite_model)
print(f'{Color.GREEN}Integer Quantization complete!{Color.RESET} - {model_output_dir_path}/model_integer_quant.tflite')
except Exception as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e)
import traceback
traceback.print_exc()
# Full Integer Quantization
if output_full_integer_quant_tflite:
try:
print(f'{Color.REVERCE}Full Integer Quantization started{Color.RESET}', '=' * 51)
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8, tf.lite.OpsSet.SELECT_TF_OPS]
inf_type = None
if output_integer_quant_type == 'int8':
inf_type = tf.int8
elif output_integer_quant_type == 'uint8':
inf_type = tf.uint8
else:
inf_type = tf.int8
converter.inference_input_type = inf_type
converter.inference_output_type = inf_type
converter.representative_dataset = representative_dataset_gen
tflite_model = converter.convert()
with open(f'{model_output_dir_path}/model_full_integer_quant.tflite', 'wb') as w:
w.write(tflite_model)
print(f'{Color.GREEN}Full Integer Quantization complete!{Color.RESET} - {model_output_dir_path}/model_full_integer_quant.tflite')
except Exception as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e)
import traceback
traceback.print_exc()
# EdgeTPU convert
if output_edgetpu:
try:
print(f'{Color.REVERCE}EdgeTPU convertion started{Color.RESET}', '=' * 58)
result = subprocess.check_output(['edgetpu_compiler',
'-o', model_output_dir_path,
'-s',
f'{model_output_dir_path}/model_full_integer_quant.tflite'],
stderr=subprocess.PIPE).decode('utf-8')
print(result)
print(f'{Color.GREEN}EdgeTPU convert complete!{Color.RESET} - {model_output_dir_path}/model_full_integer_quant_edgetpu.tflite')
except subprocess.CalledProcessError as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e.stderr.decode('utf-8'))
import traceback
traceback.print_exc()
print("-" * 80)
print('Please install edgetpu_compiler according to the following website.')
print('https://coral.ai/docs/edgetpu/compiler/#system-requirements')
# TensorFlow.js convert
if output_tfjs:
import subprocess
try:
print(f'{Color.REVERCE}TensorFlow.js Float32 convertion started{Color.RESET}', '=' * 44)
result = subprocess.check_output(['tensorflowjs_converter',
'--input_format', 'tf_saved_model',
'--output_format', 'tfjs_graph_model',
'--signature_name', 'serving_default',
'--saved_model_tags', 'serve',
saved_model_dir_path, f'{model_output_dir_path}/tfjs_model_float32'],
stderr=subprocess.PIPE).decode('utf-8')
print(result)
print(f'{Color.GREEN}TensorFlow.js convertion complete!{Color.RESET} - {model_output_dir_path}/tfjs_model_float32')
except subprocess.CalledProcessError as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e.stderr.decode('utf-8'))
import traceback
traceback.print_exc()
try:
print(f'{Color.REVERCE}TensorFlow.js Float16 convertion started{Color.RESET}', '=' * 44)
result = subprocess.check_output(['tensorflowjs_converter',
'--quantize_float16',
'--input_format', 'tf_saved_model',
'--output_format', 'tfjs_graph_model',
'--signature_name', 'serving_default',
'--saved_model_tags', 'serve',
saved_model_dir_path, f'{model_output_dir_path}/tfjs_model_float16'],
stderr=subprocess.PIPE).decode('utf-8')
print(result)
print(f'{Color.GREEN}TensorFlow.js convertion complete!{Color.RESET} - {model_output_dir_path}/tfjs_model_float16')
except subprocess.CalledProcessError as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e.stderr.decode('utf-8'))
import traceback
traceback.print_exc()
# TF-TRT (TensorRT) convert
if output_tftrt:
try:
def input_fn():
input_shapes_tmp = []
for tf_input in input_shapes:
input_shapes_tmp.append(np.zeros(tf_input).astype(np.float32))
yield input_shapes_tmp
print(f'{Color.REVERCE}TF-TRT (TensorRT) Float32 convertion started{Color.RESET}', '=' * 40)
params = tf.experimental.tensorrt.ConversionParams(precision_mode='FP32', maximum_cached_engines=10000)
converter = tf.experimental.tensorrt.Converter(input_saved_model_dir=saved_model_dir_path, conversion_params=params)
converter.convert()
converter.build(input_fn=input_fn)
converter.save(f'{model_output_dir_path}/tensorrt_saved_model_float32')
print(f'{Color.GREEN}TF-TRT (TensorRT) convertion complete!{Color.RESET} - {model_output_dir_path}/tensorrt_saved_model_float32')
print(f'{Color.REVERCE}TF-TRT (TensorRT) Float16 convertion started{Color.RESET}', '=' * 40)
params = tf.experimental.tensorrt.ConversionParams(precision_mode='FP16', maximum_cached_engines=10000)
converter = tf.experimental.tensorrt.Converter(input_saved_model_dir=saved_model_dir_path, conversion_params=params)
converter.convert()
converter.build(input_fn=input_fn)
converter.save(f'{model_output_dir_path}/tensorrt_saved_model_float16')
print(f'{Color.GREEN}TF-TRT (TensorRT) convertion complete!{Color.RESET} - {model_output_dir_path}/tensorrt_saved_model_float16')
except Exception as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e)
import traceback
traceback.print_exc()
print(f'{Color.RED}The binary versions of TensorFlow and TensorRT may not be compatible. Please check the version compatibility of each package.{Color.RESET}')
# CoreML convert
if output_coreml:
try:
import coremltools as ct
print(f'{Color.REVERCE}CoreML convertion started{Color.RESET}', '=' * 59)
mlmodel = ct.convert(saved_model_dir_path, source='tensorflow')
mlmodel.save(f'{model_output_dir_path}/model_coreml_float32.mlmodel')
print(f'{Color.GREEN}CoreML convertion complete!{Color.RESET} - {model_output_dir_path}/model_coreml_float32.mlmodel')
except Exception as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e)
import traceback
traceback.print_exc()
# ONNX convert
if output_onnx:
import subprocess
try:
print(f'{Color.REVERCE}ONNX convertion started{Color.RESET}', '=' * 61)
result = subprocess.check_output(['python3',
'-m', 'tf2onnx.convert',
'--saved-model', model_output_dir_path,
'--opset', str(onnx_opset),
'--output', f'{model_output_dir_path}/model_float32.onnx'],
stderr=subprocess.PIPE).decode('utf-8')
print(result)
print(f'{Color.GREEN}ONNX convertion complete!{Color.RESET} - {model_output_dir_path}/model_float32.onnx')
except subprocess.CalledProcessError as e:
print(f'{Color.RED}ERROR:{Color.RESET}', e.stderr.decode('utf-8'))
import traceback
traceback.print_exc() | [
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p/redis-dump-load | d5affcb9c140e55da738de4f18b360282fb0f9e0 | redisdl.py | python | load_streaming | (fp, host='localhost', port=6379, password=None, db=0,
empty=False, unix_socket_path=None, encoding='utf-8', use_expireat=False,
streaming_backend=None,
) | [] | def load_streaming(fp, host='localhost', port=6379, password=None, db=0,
empty=False, unix_socket_path=None, encoding='utf-8', use_expireat=False,
streaming_backend=None,
):
loader = create_loader(fp, streaming_backend)
r = client(host=host, port=port, password=password, db=db,
unix_socket_path=unix_socket_path, encoding=encoding)
counter = 0
for key, item in loader():
# Create pipeline:
if not counter:
p = r.pipeline(transaction=False)
type = item['type']
value = item['value']
ttl = item.get('ttl')
expireat = item.get('expireat')
_writer(r, p, key, type, value, ttl, expireat, use_expireat=use_expireat)
# Increase counter until 10 000...
counter = (counter + 1) % 10000
# ... then execute:
if not counter:
p.execute()
if counter:
# Finally, execute again:
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dustin/py-github | 1b2f55e7b73ede3b16062b2a1195fb47153bae42 | github/github.py | python | RepositoryEndpoint.watchers | (self, user, repo) | return self._parsed('repos/show/%s/%s/watchers' % (user, repo)) | Find all of the watchers of one of your repositories. | Find all of the watchers of one of your repositories. | [
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wwqgtxx/wwqLyParse | 33136508e52821babd9294fdecffbdf02d73a6fc | wwqLyParse/lib/flask_lib/jinja2/runtime.py | python | Context.get_all | (self) | return dict(self.parent, **self.vars) | Return the complete context as dict including the exported
variables. For optimizations reasons this might not return an
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"""Return the complete context as dict including the exported
variables. For optimizations reasons this might not return an
actual copy so be careful with using it.
"""
if not self.vars:
return self.parent
if not self.parent:
return self.vars
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PaloAltoNetworks/pan-os-python | 30f6cd9e29d0e3c2549d46c722f6dcb507acd437 | panos/userid.py | python | UserId.logout | (self, user, ip) | Logout a single user
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user (str): a username
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Removes a mapping of a user to an IP address
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Args:
user (str): a username
ip (str): an ip address
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openstack/nova | b49b7663e1c3073917d5844b81d38db8e86d05c4 | nova/virt/libvirt/migration.py | python | update_downtime | (guest, instance,
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downtime_steps, elapsed) | return thisstep[1] | Update max downtime if needed
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"""Update max downtime if needed
:param guest: a nova.virt.libvirt.guest.Guest to set downtime for
:param instance: a nova.objects.Instance
:param olddowntime: current set downtime, or None
:param downtime_steps: list of downtime steps
:param elapsed: total time of migration in secs
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the downtime value to set when the marker is hit.
The guest object will be used to change the current
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Any errors hit when updating downtime will be ignored
:returns: the new downtime value
"""
LOG.debug("Current %(dt)s elapsed %(elapsed)d steps %(steps)s",
{"dt": olddowntime, "elapsed": elapsed,
"steps": downtime_steps}, instance=instance)
thisstep = None
for step in downtime_steps:
if elapsed > step[0]:
thisstep = step
if thisstep is None:
LOG.debug("No current step", instance=instance)
return olddowntime
if thisstep[1] == olddowntime:
LOG.debug("Downtime does not need to change",
instance=instance)
return olddowntime
LOG.info("Increasing downtime to %(downtime)d ms "
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{"downtime": thisstep[1],
"waittime": thisstep[0]},
instance=instance)
try:
guest.migrate_configure_max_downtime(thisstep[1])
except libvirt.libvirtError as e:
LOG.warning("Unable to increase max downtime to %(time)d ms: %(e)s",
{"time": thisstep[1], "e": e}, instance=instance)
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Ultimaker/Cura | a1622c77ea7259ecb956acd6de07b7d34b7ac52b | plugins/UM3NetworkPrinting/src/Network/LocalClusterOutputDeviceManager.py | python | LocalClusterOutputDeviceManager._connectToOutputDevice | (self, device: UltimakerNetworkedPrinterOutputDevice, machine: GlobalStack) | Add a device to the current active machine. | Add a device to the current active machine. | [
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# Make sure users know that we no longer support legacy devices.
if Version(device.firmwareVersion) < self.MIN_SUPPORTED_CLUSTER_VERSION:
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return
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if not device.isConnected():
device.connect()
output_device_manager = CuraApplication.getInstance().getOutputDeviceManager()
if device.key not in output_device_manager.getOutputDeviceIds():
output_device_manager.addOutputDevice(device) | [
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nlloyd/SubliminalCollaborator | 5c619e17ddbe8acb9eea8996ec038169ddcd50a1 | libs/twisted/internet/interfaces.py | python | IResolver.lookupMailGroup | (name, timeout = 10) | Lookup the MG records associated with C{name}. | Lookup the MG records associated with C{name}. | [
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naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/Bastion.py | python | BastionClass.__init__ | (self, get, name) | Constructor.
Arguments:
get - a function that gets the attribute value (by name)
name - a human-readable name for the original object
(suggestion: use repr(object)) | Constructor. | [
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get - a function that gets the attribute value (by name)
name - a human-readable name for the original object
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self._get_ = get
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mediacloud/backend | d36b489e4fbe6e44950916a04d9543a1d6cd5df0 | apps/topics-map/src/python/topics_map/map.py | python | draw_labels | (graph: nx.Graph) | Draw labels, sizing by cohorts. | Draw labels, sizing by cohorts. | [
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] | def draw_labels(graph: nx.Graph) -> None:
"""Draw labels, sizing by cohorts."""
positions = nx.get_node_attributes(graph, 'position')
cohort_size = 35
num_cohorts = math.ceil(len(positions) / cohort_size)
num_cohorts = min(30, num_cohorts)
for i in range(num_cohorts):
labels = get_labels_by_attribute(
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iteration=i,
num_labels=cohort_size,
)
weight = 'bold' if i == 0 else 'normal'
alpha = 1.0 if i == 0 else 0.5
font_size = 8 if i == 0 else 2
nx.draw_networkx_labels(
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pos=positions,
labels=labels,
font_size=font_size,
font_weight=weight,
alpha=alpha
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ni/nidaqmx-python | 62fc6b48cbbb330fe1bcc9aedadc86610a1269b6 | nidaqmx/system/system.py | python | System.tasks | (self) | return PersistedTaskCollection() | nidaqmx.system._collections.PersistedTaskCollection: Indicates
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pycontribs/confluence | e21dc44f61229ca767f4183134ba5887d071babe | confluence/confluence.py | python | Confluence.getPage | (self, page, space) | return page | Returns a page object as a dictionary.
:param page: The page name
:type page: ``str``
:param space: The space name
:type space: ``str``
:return: dictionary. result['content'] contains the body of the page. | Returns a page object as a dictionary. | [
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:type space: ``str``
:return: dictionary. result['content'] contains the body of the page.
"""
if self._token2:
page = self._server.confluence2.getPage(self._token2, space, page)
else:
page = self._server.confluence1.getPage(self._token, space, page)
return page | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/nlp/v20190408/models.py | python | EntityRelationObject.__init__ | (self) | r"""
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注意：此字段可能返回 null，表示取不到有效值。
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注意：此字段可能返回 null，表示取不到有效值。
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注意：此字段可能返回 null，表示取不到有效值。
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注意：此字段可能返回 null，表示取不到有效值。
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ilastik/ilastik | 6acd2c554bc517e9c8ddad3623a7aaa2e6970c28 | lazyflow/utility/pipeline.py | python | Pipeline.close | (self) | Cleanup all operators in this pipeline in the LIFO order. | Cleanup all operators in this pipeline in the LIFO order. | [
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IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/repackage/setuptools/pkg_resources/_vendor/pyparsing.py | python | ParserElement.setDebugActions | ( self, startAction, successAction, exceptionAction ) | return self | Enable display of debugging messages while doing pattern matching. | Enable display of debugging messages while doing pattern matching. | [
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Enable display of debugging messages while doing pattern matching.
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self.debugActions = (startAction or _defaultStartDebugAction,
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/sms/api.py | python | load_and_call | (sms_handler_names, phone_number, text, sms) | return handled | [] | def load_and_call(sms_handler_names, phone_number, text, sms):
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handler = to_function(sms_handler_name)
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ros/ros | 93d8da32091b8b43702eab5d3202f4511dfeb7dc | core/roslib/src/roslib/manifestlib.py | python | Depend.__init__ | (self, package) | Create new depend instance.
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MultiChain/multichain-explorer | 9e850fa79d0759b7348647ccf73a31d387c945a5 | Mce/DataStore.py | python | DataStore.get_sent | (store, chain_id, pubkey_hash, block_height = None) | return store.get_sent_and_last_block_id(
chain_id, pubkey_hash, block_height)[0] | [] | def get_sent(store, chain_id, pubkey_hash, block_height = None):
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chain_id, pubkey_hash, block_height)[0] | [
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zhirongw/lemniscate.pytorch | f7cfe298357cb2b169cd59eb540aca24bed1f9b8 | models/resnet.py | python | Bottleneck.__init__ | (self, inplanes, planes, stride=1, downsample=None) | [] | def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride | [
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dropbox/stone | b7b64320631b3a4d2f10681dca64e0718ebe68ee | stone/ir/data_types.py | python | Struct._filter_fields | (self, filter_function) | return fields | Utility to iterate through all fields (super types first) of a type.
:param filter: A function that takes in a Field object. If it returns
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_vendor/pyparsing.py | python | Forward.__lshift__ | ( self, other ) | return self | [] | def __lshift__( self, other ):
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tonyfischetti/sake | 818f1b1ad97a0d7bcf2c9e0082affb2865b25f26 | sakelib/build.py | python | merge_from_store_and_in_mems | (from_store, in_mem_shas, dont_update_shas_of) | return in_mem_shas | If we don't merge the shas from the sha store and if we build a
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ronreiter/interactive-tutorials | d026d1ae58941863d60eb30a8a94a8650d2bd4bf | suds/xsd/sxbase.py | python | NodeFinder.find | (self, node, list) | return self | Traverse the tree looking for matches.
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_openshift_3.2/library/oc_env.py | python | DeploymentConfig.__init__ | (self, content=None) | Constructor for OpenshiftOC | Constructor for OpenshiftOC | [
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tribe29/checkmk | 6260f2512e159e311f426e16b84b19d0b8e9ad0c | cmk/base/plugins/agent_based/netscaler_sslcertificates.py | python | parse_netscaler_sslcertificates | (string_table: List[StringTable]) | return {certname: int(daysleft) for certname, daysleft in string_table[0]} | >>> parse_netscaler_sslcertificates([[['cert1', '3'], ['cert2', '100']]])
{'cert1': 3, 'cert2': 100} | >>> parse_netscaler_sslcertificates([[['cert1', '3'], ['cert2', '100']]])
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AlexYangLi/ABSA_Keras | 8de8f6a3d8861c68e3f552a4b77bf60d75ee05f6 | models.py | python | SentimentModel.atae_lstm | (self) | return Model([input_text, input_aspect], final_output) | [] | def atae_lstm(self):
input_text = Input(shape=(self.max_len,))
input_aspect = Input(shape=(1,), )
if self.use_elmo:
elmo_embedding = ELMoEmbedding(output_mode=self.config.elmo_output_mode, idx2word=self.config.idx2token,
mask_zero=True, hub_url=self.config.elmo_hub_url,
elmo_trainable=self.config.elmo_trainable)
if self.config.use_elmo_alone:
text_embed = SpatialDropout1D(0.2)(elmo_embedding(input_text))
else:
word_embedding = Embedding(input_dim=self.text_embeddings.shape[0],
output_dim=self.config.word_embed_dim,
weights=[self.text_embeddings], trainable=self.config.word_embed_trainable,
mask_zero=True)
text_embed = SpatialDropout1D(0.2)(concatenate([word_embedding(input_text), elmo_embedding(input_text)]))
else:
word_embedding = Embedding(input_dim=self.text_embeddings.shape[0], output_dim=self.config.word_embed_dim,
weights=[self.text_embeddings], trainable=self.config.word_embed_trainable,
mask_zero=True)
text_embed = SpatialDropout1D(0.2)(word_embedding(input_text))
if self.config.aspect_embed_type == 'random':
asp_embedding = Embedding(input_dim=self.n_aspect, output_dim=self.config.aspect_embed_dim)
else:
asp_embedding = Embedding(input_dim=self.aspect_embeddings.shape[0],
output_dim=self.config.aspect_embed_dim,
trainable=self.config.aspect_embed_trainable)
aspect_embed = asp_embedding(input_aspect)
aspect_embed = Flatten()(aspect_embed) # reshape to 2d
repeat_aspect = RepeatVector(self.max_len)(aspect_embed) # repeat aspect for every word in sequence
input_concat = concatenate([text_embed, repeat_aspect], axis=-1)
hidden_vecs, state_h, _ = LSTM(self.config.lstm_units, return_sequences=True, return_state=True)(input_concat)
concat = concatenate([hidden_vecs, repeat_aspect], axis=-1)
# apply attention mechanism
attend_weight = Attention()(concat)
attend_weight_expand = Lambda(lambda x: K.expand_dims(x))(attend_weight)
attend_hidden = multiply([hidden_vecs, attend_weight_expand])
attend_hidden = Lambda(lambda x: K.sum(x, axis=1))(attend_hidden)
attend_hidden_dense = Dense(self.config.lstm_units)(attend_hidden)
last_hidden_dense = Dense(self.config.lstm_units)(state_h)
final_output = Activation('tanh')(add([attend_hidden_dense, last_hidden_dense]))
return Model([input_text, input_aspect], final_output) | [
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v8/services/services/asset_service/transports/grpc.py | python | AssetServiceGrpcTransport.get_asset | (
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if "get_asset" not in self._stubs:
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sqlmapproject/sqlmap | 3b07b70864624dff4c29dcaa8a61c78e7f9189f7 | thirdparty/bottle/bottle.py | python | BaseRequest.is_xhr | (self) | return requested_with.lower() == 'xmlhttprequest' | True if the request was triggered by a XMLHttpRequest. This only
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | chap19/monitor/monitor/build/lib.linux-x86_64-2.7/monitor/openstack/common/rpc/impl_qpid.py | python | DirectConsumer.__init__ | (self, conf, session, msg_id, callback) | Init a 'direct' queue.
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hubblestack/hubble | 763142474edcecdec5fd25591dc29c3536e8f969 | hubblestack/utils/json.py | python | loads | (s, **kwargs) | .. versionadded:: 2018.3.0
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json_module = kwargs.pop("_json_module", json)
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/trusthub/v1/trust_products/__init__.py | python | TrustProductsContext.trust_products_entity_assignments | (self) | return self._trust_products_entity_assignments | Access the trust_products_entity_assignments
:returns: twilio.rest.trusthub.v1.trust_products.trust_products_entity_assignments.TrustProductsEntityAssignmentsList
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fooof-tools/fooof | 14d6196e0b60c7e6da95b5cf858b20adcc5fc0ac | fooof/plts/style.py | python | apply_axis_style | (ax, style_args=AXIS_STYLE_ARGS, **kwargs) | Apply axis plot style.
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ax : matplotlib.Axes
Figure axes to apply style to.
style_args : list of str
A list of arguments to be sub-selected from `kwargs` and applied as axis styling.
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ax : matplotlib.Axes
Figure axes to apply style to.
style_args : list of str
A list of arguments to be sub-selected from `kwargs` and applied as axis styling.
**kwargs
Keyword arguments that define plot style to apply.
"""
# Apply any provided axis style arguments
plot_kwargs = {key : val for key, val in kwargs.items() if key in style_args}
ax.set(**plot_kwargs) | [
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jgyates/genmon | 2cb2ed2945f55cd8c259b09ccfa9a51e23f1341e | genmonlib/generac_HPanel.py | python | GPanelReg.hexsort | (self, e) | [] | def hexsort(self, e):
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1012598167/flask_mongodb_game | 60c7e0351586656ec38f851592886338e50b4110 | python_flask/venv/Lib/site-packages/pymongo/mongo_client.py | python | MongoClient.min_pool_size | (self) | return self.__options.pool_options.min_pool_size | The minimum required number of concurrent connections that the pool
will maintain to each connected server. Default is 0. | The minimum required number of concurrent connections that the pool
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GoogleCloudPlatform/PerfKitBenchmarker | 6e3412d7d5e414b8ca30ed5eaf970cef1d919a67 | perfkitbenchmarker/linux_benchmarks/aws_dynamodb_ycsb_benchmark.py | python | Run | (benchmark_spec) | return samples | Run YCSB on the target vm.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects. | Run YCSB on the target vm. | [
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"""Run YCSB on the target vm.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
"""
vms = benchmark_spec.vms
run_kwargs = {
'dynamodb.awsCredentialsFile': GetRemoteVMCredentialsFullPath(vms[0]),
'dynamodb.primaryKey': FLAGS.aws_dynamodb_primarykey,
'dynamodb.endpoint': benchmark_spec.dynamodb_instance.GetEndPoint(),
'table': 'pkb-{0}'.format(FLAGS.run_uri),
}
if FLAGS.aws_dynamodb_use_sort:
run_kwargs.update({'dynamodb.primaryKeyType': 'HASH_AND_RANGE',
'aws_dynamodb_connectMax': FLAGS.aws_dynamodb_connectMax,
'dynamodb.hashKeyName': FLAGS.aws_dynamodb_primarykey,
'dynamodb.primaryKey': FLAGS.aws_dynamodb_sortkey})
if FLAGS.aws_dynamodb_ycsb_consistentReads:
run_kwargs.update({'dynamodb.consistentReads': 'true'})
load_kwargs = run_kwargs.copy()
if FLAGS['ycsb_preload_threads'].present:
load_kwargs['threads'] = FLAGS.ycsb_preload_threads
# More WCU results in a faster load stage.
benchmark_spec.dynamodb_instance.SetThroughput(wcu=_INITIAL_WRITES.value)
samples = list(benchmark_spec.executor.Load(vms, load_kwargs=load_kwargs))
# Reset the WCU to the initial level.
benchmark_spec.dynamodb_instance.SetThroughput()
samples += list(benchmark_spec.executor.Run(vms, run_kwargs=run_kwargs))
benchmark_metadata = {
'ycsb_client_vms': len(vms),
}
for sample in samples:
sample.metadata.update(
benchmark_spec.dynamodb_instance.GetResourceMetadata())
sample.metadata.update(benchmark_metadata)
return samples | [
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hatRiot/zarp | 2e772350a01c2aeed3f4da9685cd0cc5d6b3ecad | src/lib/scapy/utils.py | python | do_graph | (graph,prog=None,format=None,target=None,type=None,string=None,options=None) | do_graph(graph, prog=conf.prog.dot, format="svg",
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format: output type (svg, ps, gif, jpg, etc.), passed to dot's "-T" option
target: filename or redirect. Defaults pipe to Imagemagick's display program
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if format is None:
if WINDOWS:
format = "png" # use common format to make sure a viewer is installed
else:
format = "svg"
if string:
return graph
if type is not None:
format=type
if prog is None:
prog = conf.prog.dot
start_viewer=False
if target is None:
if WINDOWS:
tempfile = os.tempnam("", "scapy") + "." + format
target = "> %s" % tempfile
start_viewer = True
else:
target = "| %s" % conf.prog.display
if format is not None:
format = "-T %s" % format
w,r = os.popen2("%s %s %s %s" % (prog,options or "", format or "", target))
w.write(graph)
w.close()
if start_viewer:
# Workaround for file not found error: We wait until tempfile is written.
waiting_start = time.time()
while not os.path.exists(tempfile):
time.sleep(0.1)
if time.time() - waiting_start > 3:
warning("Temporary file '%s' could not be written. Graphic will not be displayed." % tempfile)
break
else:
if conf.prog.display == conf.prog._default:
os.startfile(tempfile)
else:
subprocess.Popen([conf.prog.display, tempfile]) | [
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tensorflow/tensorboard | 61d11d99ef034c30ba20b6a7840c8eededb9031c | tensorboard/data/grpc_provider.py | python | make_stub | (channel) | return data_provider_pb2_grpc.TensorBoardDataProviderStub(channel) | Wraps a gRPC channel with a service stub. | Wraps a gRPC channel with a service stub. | [
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iiau-tracker/SPLT | a196e603798e9be969d9d985c087c11cad1cda43 | lib/object_detection/core/losses.py | python | HardExampleMiner._subsample_selection_to_desired_neg_pos_ratio | (self,
indices,
match,
max_negatives_per_positive,
min_negatives_per_image=0) | return (tf.reshape(tf.gather(indices, subsampled_selection_indices), [-1]),
num_positives, num_negatives) | Subsample a collection of selected indices to a desired neg:pos ratio.
This function takes a subset of M indices (indexing into a large anchor
collection of N anchors where M<N) which are labeled as positive/negative
via a Match object (matched indices are positive, unmatched indices
are negative). It returns a subset of the provided indices retaining all
positives as well as up to the first K negatives, where:
K=floor(num_negative_per_positive * num_positives).
For example, if indices=[2, 4, 5, 7, 9, 10] (indexing into 12 anchors),
with positives=[2, 5] and negatives=[4, 7, 9, 10] and
num_negatives_per_positive=1, then the returned subset of indices
is [2, 4, 5, 7].
Args:
indices: An integer tensor of shape [M] representing a collection
of selected anchor indices
match: A matcher.Match object encoding the match between anchors and
groundtruth boxes for a given image, with rows of the Match objects
corresponding to groundtruth boxes and columns corresponding to anchors.
max_negatives_per_positive: (float) maximum number of negatives for
each positive anchor.
min_negatives_per_image: minimum number of negative anchors for a given
image. Allow sampling negatives in image without any positive anchors.
Returns:
selected_indices: An integer tensor of shape [M'] representing a
collection of selected anchor indices with M' <= M.
num_positives: An integer tensor representing the number of positive
examples in selected set of indices.
num_negatives: An integer tensor representing the number of negative
examples in selected set of indices. | Subsample a collection of selected indices to a desired neg:pos ratio. | [
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indices,
match,
max_negatives_per_positive,
min_negatives_per_image=0):
"""Subsample a collection of selected indices to a desired neg:pos ratio.
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indices: An integer tensor of shape [M] representing a collection
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max_negatives_per_positive: (float) maximum number of negatives for
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min_negatives_per_image: minimum number of negative anchors for a given
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Returns:
selected_indices: An integer tensor of shape [M'] representing a
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num_positives: An integer tensor representing the number of positive
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num_negatives: An integer tensor representing the number of negative
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"""
positives_indicator = tf.gather(match.matched_column_indicator(), indices)
negatives_indicator = tf.gather(match.unmatched_column_indicator(), indices)
num_positives = tf.reduce_sum(tf.to_int32(positives_indicator))
max_negatives = tf.maximum(min_negatives_per_image,
tf.to_int32(max_negatives_per_positive *
tf.to_float(num_positives)))
topk_negatives_indicator = tf.less_equal(
tf.cumsum(tf.to_int32(negatives_indicator)), max_negatives)
subsampled_selection_indices = tf.where(
tf.logical_or(positives_indicator, topk_negatives_indicator))
num_negatives = tf.size(subsampled_selection_indices) - num_positives
return (tf.reshape(tf.gather(indices, subsampled_selection_indices), [-1]),
num_positives, num_negatives) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.5/django/core/management/commands/inspectdb.py | python | Command.get_meta | (self, table_name) | return [" class Meta:",
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"""
return [" class Meta:",
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | datadog_checks_dev/datadog_checks/dev/tooling/utils.py | python | check_root | () | return False | Check if root has already been set. | Check if root has already been set. | [
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"""Check if root has already been set."""
existing_root = get_root()
if existing_root:
return True
root = os.getenv('DDEV_ROOT', '')
if root and os.path.isdir(root):
set_root(root)
return True
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ahkab/ahkab | 1e8939194b689909b8184ce7eba478b485ff9e3a | ahkab/results.py | python | ac_solution.get | (self, name, default=None) | return data | Get a solution by variable name. | Get a solution by variable name. | [
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openembedded/bitbake | 98407efc8c670abd71d3fa88ec3776ee9b5c38f3 | lib/pyinotify.py | python | Notifier.read_events | (self) | Read events from device, build _RawEvents, and enqueue them. | Read events from device, build _RawEvents, and enqueue them. | [
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"""
buf_ = array.array('i', [0])
# get event queue size
if fcntl.ioctl(self._fd, termios.FIONREAD, buf_, 1) == -1:
return
queue_size = buf_[0]
if queue_size < self._threshold:
log.debug('(fd: %d) %d bytes available to read but threshold is '
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self._threshold)
return
try:
# Read content from file
r = os.read(self._fd, queue_size)
except Exception as msg:
raise NotifierError(msg)
log.debug('Event queue size: %d', queue_size)
rsum = 0 # counter
while rsum < queue_size:
s_size = 16
# Retrieve wd, mask, cookie and fname_len
wd, mask, cookie, fname_len = struct.unpack('iIII',
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# Retrieve name
bname, = struct.unpack('%ds' % fname_len,
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# FIXME: should we explictly call sys.getdefaultencoding() here ??
uname = bname.decode()
rawevent = _RawEvent(wd, mask, cookie, uname)
if self._coalesce:
# Only enqueue new (unique) events.
raweventstr = str(rawevent)
if raweventstr not in self._eventset:
self._eventset.add(raweventstr)
self._eventq.append(rawevent)
else:
self._eventq.append(rawevent)
rsum += s_size + fname_len | [
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cirosantilli/linux-kernel-module-cheat | 97773a4e3cde9604c4ecec7f25fb60fe21058b29 | lkmc/import_path.py | python | import_path_main | (basename) | return import_path_relative_root(basename).Main() | Import an object of the Main class of a given file.
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Import an object of the Main class of a given file.
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deanishe/alfred-stackexchange | b2047b76165900d55f0c7d18fd7c40131bee94ed | src/workflow/workflow3.py | python | Variables.__init__ | (self, arg=None, **variables) | Create a new `Variables` object. | Create a new `Variables` object. | [
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