Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
test_result_gather_different_shapes | () | Test that tensors of varying shape get gathered into a list. | Test that tensors of varying shape get gathered into a list. | def test_result_gather_different_shapes():
""" Test that tensors of varying shape get gathered into a list. """
outputs = [
{
"foo": torch.tensor(1)
},
{
"foo": torch.zeros(2, 3)
},
{
"foo": torch.zeros(1, 2, 3)
},
]
res... | [
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test_result_gather_mixed_types | () | Test that a collection of mixed types gets gathered into a list. | Test that a collection of mixed types gets gathered into a list. | def test_result_gather_mixed_types():
""" Test that a collection of mixed types gets gathered into a list. """
outputs = [
{
"foo": 1.2
},
{
"foo": ["bar", None]
},
{
"foo": torch.tensor(1)
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]
result = Result.gather(ou... | [
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MeanSquaredLogError.update | (self, preds: torch.Tensor, target: torch.Tensor) |
Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
|
Update state with predictions and targets. | def update(self, preds: torch.Tensor, target: torch.Tensor):
"""
Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
sum_squared_log_error, n_obs = _mean_squared_log_error_update(preds, target)
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MeanSquaredLogError.compute | (self) |
Compute mean squared logarithmic error over state.
|
Compute mean squared logarithmic error over state.
| def compute(self):
"""
Compute mean squared logarithmic error over state.
"""
return _mean_squared_log_error_compute(self.sum_squared_log_error, self.total) | [
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parse_dat | (fname: str, debug: bool = False) | Parse a UKSOFT2001 file. | Parse a UKSOFT2001 file. | def parse_dat(fname: str, debug: bool = False) -> dict[str, Any]:
"""Parse a UKSOFT2001 file."""
data = {}
def parse_block(block, field_dict):
for key, (pos, encoding) in field_dict.items():
data[key] = parse_bytes(block, pos, encoding)
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476,
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559,
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do_ecc_align | (
input_img: Image,
template_img: Image,
max_iter: int = 500,
eps: float = 1e-4,
trafo: str = "translation",
mask: Union[np.ndarray, None] = None,
) | Finds warp matrix between two Images using ECC
Takes two images and calculates the warp matrix between both applying the transformation
specified by 'trafo' using the ECC Algorithm. A optional mask can be given to masked areas of
the images that are not taken into account during registration
Parameter... | Finds warp matrix between two Images using ECC | def do_ecc_align(
input_img: Image,
template_img: Image,
max_iter: int = 500,
eps: float = 1e-4,
trafo: str = "translation",
mask: Union[np.ndarray, None] = None,
) -> np.ndarray:
"""Finds warp matrix between two Images using ECC
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LEEMImg.get_field_string | (self, field: str, fmt: Optional[str] = None) | Get a string that contains the value and its unit. Some LEEM-specific fields
have different default formats than the pure DataObject method. | Get a string that contains the value and its unit. Some LEEM-specific fields
have different default formats than the pure DataObject method. | def get_field_string(self, field: str, fmt: Optional[str] = None) -> str:
"""Get a string that contains the value and its unit. Some LEEM-specific fields
have different default formats than the pure DataObject method."""
if fmt is None:
if field == "temperature":
fmt ... | [
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LEEMImg.normalize | (
self,
mcp: Image = None,
dark_counts: Union[int, float, Image] = 100,
inplace: bool = False,
) | Normalization of LEEM Images
Parameters
----------
mcp : Image, optional
[description], by default None
dark_counts : Union[int, float, Image], optional
[description], by default 100
inplace : bool, optional
[description], by default False
... | Normalization of LEEM Images | def normalize(
self,
mcp: Image = None,
dark_counts: Union[int, float, Image] = 100,
inplace: bool = False,
) -> LEEMImg:
"""Normalization of LEEM Images
Parameters
----------
mcp : Image, optional
[description], by default None
da... | [
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LEEMStack._single_construct | (self, source: Any) | Construct a single DataObject. | Construct a single DataObject. | def _single_construct(self, source: Any) -> LEEMImg:
"""Construct a single DataObject."""
return LEEMImg(source, time_origin=self._time_origin) | [
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LEEMStack.align | (
self,
inplace: bool = False,
mask: Union[bool, np.ndarray, None] = True,
**kwargs,
) |
Image registration of the stack
The images of the stack are registered subsequently by calling Image.find_warp_matrix() for
each image and finally warping them using Image.warp()
Parameters
----------
inplace : bool, default False
If 'True' the stack itself... |
Image registration of the stack | def align(
self,
inplace: bool = False,
mask: Union[bool, np.ndarray, None] = True,
**kwargs,
) -> LEEMStack:
"""
Image registration of the stack
The images of the stack are registered subsequently by calling Image.find_warp_matrix() for
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LEEMStack.normalize | (
self, mcp: Union[Image, str, None] = None, inplace: bool = False, **kwargs
) |
Normalization of images in stack
The images in the stack are normalized by appling Image.normalize(mcp) to each image
Parameters
----------
mcp : Image, str or None
Image or filename of image if str. If 'None' the mcp attribute of the images will be
use... |
Normalization of images in stack | def normalize(
self, mcp: Union[Image, str, None] = None, inplace: bool = False, **kwargs
) -> LEEMStack:
"""
Normalization of images in stack
The images in the stack are normalized by appling Image.normalize(mcp) to each image
Parameters
----------
mcp : Im... | [
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IVCurve.__init__ | (self, *args, **kwargs) |
It is called and behaves exactly like StitchedLine but is called with *xaxis* = "energy" by
default.
See Also
--------
dataobject.StitchedLine
|
It is called and behaves exactly like StitchedLine but is called with *xaxis* = "energy" by
default. | def __init__(self, *args, **kwargs):
"""
It is called and behaves exactly like StitchedLine but is called with *xaxis* = "energy" by
default.
See Also
--------
dataobject.StitchedLine
"""
print(*args)
print(**kwargs)
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Plugin.connect | (
self,
model: Module,
*args: Sequence,
**kwargs: Sequence,
) | Connects the plugin with the accelerator (and thereby with trainer and model).
Will be called by the accelerator.
| Connects the plugin with the accelerator (and thereby with trainer and model).
Will be called by the accelerator.
| def connect(
self,
model: Module,
*args: Sequence,
**kwargs: Sequence,
) -> Optional[Tuple[Module, Sequence, Sequence]]:
"""Connects the plugin with the accelerator (and thereby with trainer and model).
Will be called by the accelerator.
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Plugin.pre_dispatch | (self) | Hook to do something before the training/evaluation/prediction starts. | Hook to do something before the training/evaluation/prediction starts. | def pre_dispatch(self) -> None:
"""Hook to do something before the training/evaluation/prediction starts.""" | [
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Plugin.post_dispatch | (self) | Hook to do something after the training/evaluation/prediction finishes. | Hook to do something after the training/evaluation/prediction finishes. | def post_dispatch(self) -> None:
"""Hook to do something after the training/evaluation/prediction finishes.""" | [
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Plugin.train_step_context | (self) | A contextmanager for the trainstep | A contextmanager for the trainstep | def train_step_context(self) -> Generator:
"""A contextmanager for the trainstep"""
yield | [
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Plugin.val_step_context | (self) | A contextmanager for the validation step | A contextmanager for the validation step | def val_step_context(self) -> Generator:
"""A contextmanager for the validation step"""
yield | [
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Plugin.test_step_context | (self) | A contextmanager for the teststep | A contextmanager for the teststep | def test_step_context(self) -> Generator:
"""A contextmanager for the teststep"""
yield | [
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Plugin.predict_context | (self) | A contextmanager for the predict step | A contextmanager for the predict step | def predict_context(self) -> Generator:
"""A contextmanager for the predict step"""
yield | [
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get_package_name | (filepath) |
Given a path where a package is installed, determine its name.
Parameters
----------
filepath : str
Path to a file. If the determination fails, "numpy" is returned.
Examples
--------
>>> np.testing.nosetester.get_package_name('nonsense')
'numpy'
|
Given a path where a package is installed, determine its name. | def get_package_name(filepath):
"""
Given a path where a package is installed, determine its name.
Parameters
----------
filepath : str
Path to a file. If the determination fails, "numpy" is returned.
Examples
--------
>>> np.testing.nosetester.get_package_name('nonsense')
... | [
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run_module_suite | (file_to_run=None, argv=None) |
Run a test module.
Equivalent to calling ``$ nosetests <argv> <file_to_run>`` from
the command line
Parameters
----------
file_to_run : str, optional
Path to test module, or None.
By default, run the module from which this function is called.
argv : list of strings
... |
Run a test module. | def run_module_suite(file_to_run=None, argv=None):
"""
Run a test module.
Equivalent to calling ``$ nosetests <argv> <file_to_run>`` from
the command line
Parameters
----------
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NoseTester._test_argv | (self, label, verbose, extra_argv) | Generate argv for nosetest command
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
see ``test`` docstring
verbose : int, optional
Verbosity value for test outputs, in the range 1-10. Default is 1.
extra_argv : list, opt... | Generate argv for nosetest command | def _test_argv(self, label, verbose, extra_argv):
''' Generate argv for nosetest command
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
see ``test`` docstring
verbose : int, optional
Verbosity value for test outputs, in... | [
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NoseTester._get_custom_doctester | (self) | Return instantiated plugin for doctests
Allows subclassing of this class to override doctester
A return value of None means use the nose builtin doctest plugin
| Return instantiated plugin for doctests | def _get_custom_doctester(self):
""" Return instantiated plugin for doctests
Allows subclassing of this class to override doctester
A return value of None means use the nose builtin doctest plugin
"""
from .noseclasses import NumpyDoctest
return NumpyDoctest() | [
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NoseTester.prepare_test_args | (self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, timer=False) |
Run tests for module using nose.
This method does the heavy lifting for the `test` method. It takes all
the same arguments, for details see `test`.
See Also
--------
test
|
Run tests for module using nose. | def prepare_test_args(self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, timer=False):
"""
Run tests for module using nose.
This method does the heavy lifting for the `test` method. It takes all
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NoseTester.test | (self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, raise_warnings=None,
timer=False) |
Run tests for module using nose.
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
Identifies the tests to run. This can be a string to pass to
the nosetests executable with the '-A' option, or one of several
special ... |
Run tests for module using nose. | def test(self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, raise_warnings=None,
timer=False):
"""
Run tests for module using nose.
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
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NoseTester.bench | (self, label='fast', verbose=1, extra_argv=None) |
Run benchmarks for module using nose.
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
Identifies the benchmarks to run. This can be a string to pass to
the nosetests executable with the '-A' option, or one of several
... |
Run benchmarks for module using nose. | def bench(self, label='fast', verbose=1, extra_argv=None):
"""
Run benchmarks for module using nose.
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
Identifies the benchmarks to run. This can be a string to pass to
the n... | [
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OpenIDCode.get_jwt_config | (self, grant) | Get the JWT configuration for OpenIDCode extension. The JWT
configuration will be used to generate ``id_token``.
{
'key': '',
'alg': '',
'iss': '',
'exp': 3600
}
:param grant: AuthorizationCodeGrant instance
... | Get the JWT configuration for OpenIDCode extension. The JWT
configuration will be used to generate ``id_token``. | def get_jwt_config(self, grant): # pragma: no cover
"""Get the JWT configuration for OpenIDCode extension. The JWT
configuration will be used to generate ``id_token``.
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OptimizerConnector.update_learning_rates | (self, interval: str, monitor_metrics=None) | Update learning rates.
Args:
interval: either 'epoch' or 'step'.
monitor_metrics: dict of possible values to monitor
| Update learning rates. | def update_learning_rates(self, interval: str, monitor_metrics=None):
"""Update learning rates.
Args:
interval: either 'epoch' or 'step'.
monitor_metrics: dict of possible values to monitor
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if not self.trainer.lr_schedulers:
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test_mesh_loss | () | test mesh loss. | test mesh loss. | def test_mesh_loss():
"""test mesh loss."""
loss_cfg = dict(
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joints_2d_loss_weight=1,
joints_3d_loss_weight=1,
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test_gan_loss | () | test gan loss. | test gan loss. | def test_gan_loss():
"""test gan loss."""
with pytest.raises(NotImplementedError):
loss_cfg = dict(
type='GANLoss',
gan_type='test',
real_label_val=1.0,
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XORCipher.__init__ | (self, key=0) |
simple constructor that receives a key or uses
default key = 0
|
simple constructor that receives a key or uses
default key = 0
| def __init__(self, key=0):
"""
simple constructor that receives a key or uses
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XORCipher.encrypt | (self, content, key) |
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output: encrypted string 'content' as a list of chars
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XORCipher.decrypt | (self, content, key) |
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XORCipher.encrypt_string | (self, content, key=0) |
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XORCipher.decrypt_string | (self, content, key=0) |
input: 'content' of type string and 'key' of type int
output: decrypted string 'content'
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input: 'content' of type string and 'key' of type int
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XORCipher.encrypt_file | (self, file, key=0) |
input: filename (str) and a key (int)
output: returns true if encrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
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input: filename (str) and a key (int)
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XORCipher.decrypt_file | (self, file, key) |
input: filename (str) and a key (int)
output: returns true if decrypt process was
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if key not passed the method uses the key by the constructor.
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AcceleratorConnector.check_interactive_compatibility | (self) |
Raises a `MisconfigurationException` if the accelerator and/or plugin
is not compatible with an interactive environment
|
Raises a `MisconfigurationException` if the accelerator and/or plugin
is not compatible with an interactive environment
| def check_interactive_compatibility(self):
"""
Raises a `MisconfigurationException` if the accelerator and/or plugin
is not compatible with an interactive environment
"""
from pytorch_lightning.utilities import _IS_INTERACTIVE
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AcceleratorConnector.check_horovod | (self) | Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod. | Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod. | def check_horovod(self):
"""Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod."""
if not _HOROVOD_AVAILABLE:
raise MisconfigurationException(
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AcceleratorConnector.has_horovodrun | () | Returns True if running with `horovodrun` using Gloo or OpenMPI. | Returns True if running with `horovodrun` using Gloo or OpenMPI. | def has_horovodrun() -> bool:
"""Returns True if running with `horovodrun` using Gloo or OpenMPI."""
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buildmodule | (m, um) |
Return
|
Return
| def buildmodule(m, um):
"""
Return
"""
global f2py_version, options
outmess('\tBuilding module "%s"...\n' % (m['name']))
ret = {}
mod_rules = defmod_rules[:]
vrd = capi_maps.modsign2map(m)
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setup_ddp | (rank, world_size) | Setup ddp enviroment | Setup ddp enviroment | def setup_ddp(rank, world_size):
""" Setup ddp enviroment """
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "8088"
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_patch_comet_atexit | (monkeypatch) | Prevent comet logger from trying to print at exit, since pytest's stdout/stderr redirection breaks it. | Prevent comet logger from trying to print at exit, since pytest's stdout/stderr redirection breaks it. | def _patch_comet_atexit(monkeypatch):
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test_comet_logger_online | (comet) | Test comet online with mocks. | Test comet online with mocks. | def test_comet_logger_online(comet):
"""Test comet online with mocks."""
# Test api_key given
with patch('pytorch_lightning.loggers.comet.CometExperiment') as comet_experiment:
logger = CometLogger(api_key='key', workspace='dummy-test', project_name='general')
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test_comet_logger_no_api_key_given | (comet) | Test that CometLogger fails to initialize if both api key and save_dir are missing. | Test that CometLogger fails to initialize if both api key and save_dir are missing. | def test_comet_logger_no_api_key_given(comet):
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test_comet_logger_experiment_name | (comet) | Test that Comet Logger experiment name works correctly. | Test that Comet Logger experiment name works correctly. | def test_comet_logger_experiment_name(comet):
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api_key = "key"
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test_comet_logger_manual_experiment_key | (comet) | Test that Comet Logger respects manually set COMET_EXPERIMENT_KEY. | Test that Comet Logger respects manually set COMET_EXPERIMENT_KEY. | def test_comet_logger_manual_experiment_key(comet):
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api_key = "key"
experiment_key = "96346da91469407a85641afe5766b554"
instantation_environ = {}
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test_comet_logger_dirs_creation | (comet, comet_experiment, tmpdir, monkeypatch) | Test that the logger creates the folders and files in the right place. | Test that the logger creates the folders and files in the right place. | def test_comet_logger_dirs_creation(comet, comet_experiment, tmpdir, monkeypatch):
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test_comet_name_default | (comet) | Test that CometLogger.name don't create an Experiment and returns a default value. | Test that CometLogger.name don't create an Experiment and returns a default value. | def test_comet_name_default(comet):
""" Test that CometLogger.name don't create an Experiment and returns a default value. """
api_key = "key"
with patch('pytorch_lightning.loggers.comet.CometExperiment'):
logger = CometLogger(api_key=api_key)
assert logger._experiment is None
asse... | [
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test_comet_name_project_name | (comet) | Test that CometLogger.name does not create an Experiment and returns project name if passed. | Test that CometLogger.name does not create an Experiment and returns project name if passed. | def test_comet_name_project_name(comet):
""" Test that CometLogger.name does not create an Experiment and returns project name if passed. """
api_key = "key"
project_name = "My Project Name"
with patch('pytorch_lightning.loggers.comet.CometExperiment'):
logger = CometLogger(api_key=api_key, pr... | [
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test_comet_version_without_experiment | (comet) | Test that CometLogger.version does not create an Experiment. | Test that CometLogger.version does not create an Experiment. | def test_comet_version_without_experiment(comet):
""" Test that CometLogger.version does not create an Experiment. """
api_key = "key"
experiment_name = "My Name"
comet.generate_guid.return_value = "1234"
with patch('pytorch_lightning.loggers.comet.CometExperiment'):
logger = CometLogger(a... | [
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test_comet_epoch_logging | (comet, comet_experiment, tmpdir, monkeypatch) | Test that CometLogger removes the epoch key from the metrics dict and passes it as argument. | Test that CometLogger removes the epoch key from the metrics dict and passes it as argument. | def test_comet_epoch_logging(comet, comet_experiment, tmpdir, monkeypatch):
""" Test that CometLogger removes the epoch key from the metrics dict and passes it as argument. """
_patch_comet_atexit(monkeypatch)
logger = CometLogger(project_name="test", save_dir=tmpdir)
logger.log_metrics({"test": 1, "epo... | [
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223,
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create_app | (debug=False) | Create an application. | Create an application. | def create_app(debug=False):
"""Create an application."""
app = Flask(__name__)
CORS(app)
socketio.cors_allowed_origins = "*"
socketio.init_app(app)
# these imports required to be after socketio initialization
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on_powerpc | () | True if we are running on a Power PC platform. | True if we are running on a Power PC platform. | def on_powerpc():
""" True if we are running on a Power PC platform."""
return platform.processor() == 'powerpc' or \
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TestRoundingFunctions.test_object_direct | (self) | test direct implementation of these magic methods | test direct implementation of these magic methods | def test_object_direct(self):
""" test direct implementation of these magic methods """
class C:
def __floor__(self):
return 1
def __ceil__(self):
return 2
def __trunc__(self):
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TestRoundingFunctions.test_object_indirect | (self) | test implementations via __float__ | test implementations via __float__ | def test_object_indirect(self):
""" test implementations via __float__ """
class C:
def __float__(self):
return -2.5
arr = np.array([C(), C()])
assert_equal(np.floor(arr), [-3, -3])
assert_equal(np.ceil(arr), [-2, -2])
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2542,
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test_invalid_weights_summmary | () | Test that invalid value for weights_summary raises an error. | Test that invalid value for weights_summary raises an error. | def test_invalid_weights_summmary():
""" Test that invalid value for weights_summary raises an error. """
with pytest.raises(MisconfigurationException, match='`mode` can be None, .* got temp'):
UnorderedModel().summarize(mode='temp')
with pytest.raises(MisconfigurationException, match='`weights_sum... | [
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90,
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96,
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test_empty_model_summary_shapes | (mode) | Test that the summary works for models that have no submodules. | Test that the summary works for models that have no submodules. | def test_empty_model_summary_shapes(mode):
""" Test that the summary works for models that have no submodules. """
model = EmptyModule()
summary = model.summarize(mode=mode)
assert summary.in_sizes == []
assert summary.out_sizes == []
assert summary.param_nums == [] | [
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test_linear_model_summary_shapes | (device, mode) | Test that the model summary correctly computes the input- and output shapes. | Test that the model summary correctly computes the input- and output shapes. | def test_linear_model_summary_shapes(device, mode):
""" Test that the model summary correctly computes the input- and output shapes. """
model = UnorderedModel().to(device)
model.train()
summary = model.summarize(mode=mode)
assert summary.in_sizes == [
[2, 10], # layer 2
[2, 7], # ... | [
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test_mixed_dtype_model_summary | () | Test that the model summary works with models that have mixed input- and parameter dtypes. | Test that the model summary works with models that have mixed input- and parameter dtypes. | def test_mixed_dtype_model_summary():
""" Test that the model summary works with models that have mixed input- and parameter dtypes. """
model = MixedDtypeModel()
summary = model.summarize()
assert summary.in_sizes == [
[2, 3], # embed
[2, 3, 20], # reduce
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assert summary.out_... | [
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test_hooks_removed_after_summarize | (mode) | Test that all hooks were properly removed after summary, even ones that were not run. | Test that all hooks were properly removed after summary, even ones that were not run. | def test_hooks_removed_after_summarize(mode):
""" Test that all hooks were properly removed after summary, even ones that were not run. """
model = UnorderedModel()
summary = ModelSummary(model, mode=mode)
# hooks should be removed
for _, layer in summary.summarize().items():
handle = layer.... | [
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test_rnn_summary_shapes | (mode) | Test that the model summary works for RNNs. | Test that the model summary works for RNNs. | def test_rnn_summary_shapes(mode):
""" Test that the model summary works for RNNs. """
model = ParityModuleRNN()
b = 3
t = 5
i = model.rnn.input_size
h = model.rnn.hidden_size
o = model.linear_out.out_features
model.example_input_array = torch.zeros(b, t, 10)
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test_summary_parameter_count | (mode) | Test that the summary counts the number of parameters in every submodule. | Test that the summary counts the number of parameters in every submodule. | def test_summary_parameter_count(mode):
""" Test that the summary counts the number of parameters in every submodule. """
model = UnorderedModel()
summary = model.summarize(mode=mode)
assert summary.param_nums == [
model.layer2.weight.numel() + model.layer2.bias.numel(),
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test_summary_layer_types | (mode) | Test that the summary displays the layer names correctly. | Test that the summary displays the layer names correctly. | def test_summary_layer_types(mode):
""" Test that the summary displays the layer names correctly. """
model = UnorderedModel()
summary = model.summarize(mode=mode)
assert summary.layer_types == [
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test_example_input_array_types | (example_input, expected_size, mode) | Test the types of example inputs supported for display in the summary. | Test the types of example inputs supported for display in the summary. | def test_example_input_array_types(example_input, expected_size, mode):
""" Test the types of example inputs supported for display in the summary. """
class DummyModule(nn.Module):
def forward(self, *args, **kwargs):
return None
class DummyLightningModule(LightningModule):
de... | [
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test_model_size | (mode) | Test model size is calculated correctly. | Test model size is calculated correctly. | def test_model_size(mode):
""" Test model size is calculated correctly. """
model = PreCalculatedModel()
summary = model.summarize(mode=mode)
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test_empty_model_size | (mode) | Test empty model size is zero. | Test empty model size is zero. | def test_empty_model_size(mode):
""" Test empty model size is zero. """
model = EmptyModule()
summary = model.summarize(mode=mode)
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test_model_size_precision | (monkeypatch, tmpdir, precision) | Test model size for half and full precision. | Test model size for half and full precision. | def test_model_size_precision(monkeypatch, tmpdir, precision):
""" Test model size for half and full precision. """
model = PreCalculatedModel(precision)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
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TestHalf.test_half_conversions | (self) | Checks that all 16-bit values survive conversion
to/from 32-bit and 64-bit float | Checks that all 16-bit values survive conversion
to/from 32-bit and 64-bit float | def test_half_conversions(self):
"""Checks that all 16-bit values survive conversion
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TestHalf.test_half_values | (self) | Confirms a small number of known half values | Confirms a small number of known half values | def test_half_values(self):
"""Confirms a small number of known half values"""
a = np.array([1.0, -1.0,
2.0, -2.0,
0.0999755859375, 0.333251953125, # 1/10, 1/3
65504, -65504, # Maximum magnitude
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TestHalf.test_half_rounding | (self) | Checks that rounding when converting to half is correct | Checks that rounding when converting to half is correct | def test_half_rounding(self):
"""Checks that rounding when converting to half is correct"""
a = np.array([2.0**-25 + 2.0**-35, # Rounds to minimum subnormal
2.0**-25, # Underflows to zero (nearest even mode)
2.0**-26, # Underflows to zero
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TestHalf.test_half_correctness | (self) | Take every finite float16, and check the casting functions with
a manual conversion. | Take every finite float16, and check the casting functions with
a manual conversion. | def test_half_correctness(self):
"""Take every finite float16, and check the casting functions with
a manual conversion."""
# Create an array of all finite float16s
a_bits = self.finite_f16.view(dtype=uint16)
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TestHalf.test_half_ordering | (self) | Make sure comparisons are working right | Make sure comparisons are working right | def test_half_ordering(self):
"""Make sure comparisons are working right"""
# All non-NaN float16 values in reverse order
a = self.nonan_f16[::-1].copy()
# 32-bit float copy
b = np.array(a, dtype=float32)
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TestHalf.test_half_funcs | (self) | Test the various ArrFuncs | Test the various ArrFuncs | def test_half_funcs(self):
"""Test the various ArrFuncs"""
# fill
assert_equal(np.arange(10, dtype=float16),
np.arange(10, dtype=float32))
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TestHalf.test_spacing_nextafter | (self) | Test np.spacing and np.nextafter | Test np.spacing and np.nextafter | def test_spacing_nextafter(self):
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TestHalf.test_half_ufuncs | (self) | Test the various ufuncs | Test the various ufuncs | def test_half_ufuncs(self):
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TestHalf.test_half_coercion | (self) | Test that half gets coerced properly with the other types | Test that half gets coerced properly with the other types | def test_half_coercion(self):
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TestHalf.test_half_array_interface | (self) | Test that half is compatible with __array_interface__ | Test that half is compatible with __array_interface__ | def test_half_array_interface(self):
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ConfluenceRestClient.get_content | (self, start=0, limit=100, type="page", expand="space") |
Returns all content. This only includes pages that the user has permission to view.
:param start: The starting index of the returned boards. Base index: 0.
:param limit: The maximum number of boards to return per page. Default: 50.
:param type: the content type to return. Default value:... |
Returns all content. This only includes pages that the user has permission to view.
:param start: The starting index of the returned boards. Base index: 0.
:param limit: The maximum number of boards to return per page. Default: 50.
:param type: the content type to return. Default value:... | def get_content(self, start=0, limit=100, type="page", expand="space"):
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Returns all content. This only includes pages that the user has permission to view.
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ConfluenceRestClient.get_content_search | (self, start=0, limit=100, cql=None, expand="space") |
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:param limit: The maximum number of boards to return per page. Default: 50.
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ConfluenceRestClient.search | (self, cql, cqlcontext=None, expand=None, start=0, limit=500) |
Fetch a list of content using the Confluence Query Language (CQL).
:param cql: a cql query string to use to locate content
:param cqlcontext: the context to execute a cql search in, this is the json serialized form of SearchContext
:param expand: a comma separated list of properties to ... |
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:param cqlcontext: the context to execute a cql search in, this is the json serialized form of SearchContext
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ConfluenceRpcClient.create_user | (self, username=None, password=None) |
Creates user. Uses XML-RPC protocol
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:param username: Username to create
:param password: Password for user
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:param username: Username to create
:param password: Password for user
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EvalModelTemplate.__build_model | (self) |
Simple model for testing
:return:
|
Simple model for testing
:return:
| def __build_model(self):
"""
Simple model for testing
:return:
"""
self.c_d1 = nn.Linear(in_features=self.in_features, out_features=self.hidden_dim)
self.c_d1_bn = nn.BatchNorm1d(self.hidden_dim)
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main | () | Visualize the demo images.
Require the json_file containing boxes.
| Visualize the demo images. | def main():
"""Visualize the demo images.
Require the json_file containing boxes.
"""
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BaseProfiler.__init__ | (self, output_streams: Optional[Union[list, tuple]] = None) |
Args:
output_streams: callable
|
Args:
output_streams: callable
| def __init__(self, output_streams: Optional[Union[list, tuple]] = None):
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BaseProfiler.start | (self, action_name: str) | Defines how to start recording an action. | Defines how to start recording an action. | def start(self, action_name: str) -> None:
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BaseProfiler.profile | (self, action_name: str) |
Yields a context manager to encapsulate the scope of a profiled action.
Example::
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# load training data code
The profiler will start once you've entered the context and will automatically
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BaseProfiler.describe | (self) | Logs a profile report after the conclusion of the training run. | Logs a profile report after the conclusion of the training run. | def describe(self) -> None:
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BaseProfiler.summary | (self) | Create profiler summary in text format. | Create profiler summary in text format. | def summary(self) -> str:
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SimpleProfiler.__init__ | (self, output_filename: Optional[str] = None, extended=True) |
Args:
output_filename: optionally save profile results to file instead of printing
to std out when training is finished.
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Args:
output_filename: optionally save profile results to file instead of printing
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SimpleProfiler.describe | (self) | Logs a profile report after the conclusion of the training run. | Logs a profile report after the conclusion of the training run. | def describe(self):
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SimpleProfiler.__del__ | (self) | Close profiler's stream. | Close profiler's stream. | def __del__(self):
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AdvancedProfiler.__init__ | (self, output_filename: Optional[str] = None, line_count_restriction: float = 1.0) |
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AdvancedProfiler.describe | (self) | Logs a profile report after the conclusion of the training run. | Logs a profile report after the conclusion of the training run. | def describe(self):
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AdvancedProfiler.__del__ | (self) | Close profiler's stream. | Close profiler's stream. | def __del__(self):
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PyTorchProfiler.__init__ | (
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Args:
output_filename: optionally save profile results to file instead of printing
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This profiler uses PyTorch's Autograd Profiler and lets you inspect the cost of
different operators inside your model - both on the CPU and GPU | def __init__(
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PyTorchProfiler.describe | (self) | Logs a profile report after the conclusion of the training run. | Logs a profile report after the conclusion of the training run. | def describe(self):
"""Logs a profile report after the conclusion of the training run."""
super().describe()
if self.output_file:
self.output_file.flush() | [
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PyTorchProfiler.__del__ | (self) | Close profiler's stream. | Close profiler's stream. | def __del__(self):
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Checker.diff | (self) | Flag to determine whether the checker should print diffs to the console | Flag to determine whether the checker should print diffs to the console | def diff(self) -> bool:
"""Flag to determine whether the checker should print diffs to the console"""
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