code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def _remove_reduction_blocks(self, architecture):
"""Removes any reduction blocks from the architecture."""
result = []
for block in architecture:
if self._is_reduction_block(block):
continue
result.append(block)
return np.array(result) | Removes any reduction blocks from the architecture. | _remove_reduction_blocks | python | google/model_search | model_search/search/constrained_descent.py | https://github.com/google/model_search/blob/master/model_search/search/constrained_descent.py | Apache-2.0 |
def _add_reduction_blocks(self, architecture, every, reduction_type):
"""Adds a reduction block of given type after `every` few blocks."""
result = []
for i, block in enumerate(architecture):
result.append(block)
if (i + 1) % every == 0:
result.append(block_builder.BlockType[reduction_ty... | Adds a reduction block of given type after `every` few blocks. | _add_reduction_blocks | python | google/model_search | model_search/search/constrained_descent.py | https://github.com/google/model_search/blob/master/model_search/search/constrained_descent.py | Apache-2.0 |
def _get_allowed_depth(self, num_completed_trials):
"""Returns the allowed depth not including reductions and flatten blocks."""
if self._phoenix_spec.replicate_cell:
allowed_depth = self._phoenix_spec.maximum_depth
else:
allowed_depth = common.get_allowed_depth(
num_completed_trials,
... | Returns the allowed depth not including reductions and flatten blocks. | _get_allowed_depth | python | google/model_search | model_search/search/constrained_descent.py | https://github.com/google/model_search/blob/master/model_search/search/constrained_descent.py | Apache-2.0 |
def get_suggestion(self, trials, hparams, my_trial_id=None, model_dir=None):
"""See the base class for details."""
del my_trial_id # Unused.
new_block = block_builder.BlockType[common.get_random_block(
self._phoenix_spec.blocks_to_use)]
if self._is_reduction_block(new_block):
raise Value... | See the base class for details. | get_suggestion | python | google/model_search | model_search/search/constrained_descent.py | https://github.com/google/model_search/blob/master/model_search/search/constrained_descent.py | Apache-2.0 |
def __init__(self,
phoenix_spec,
alpha=0.05,
degree=3,
n_mono=5,
min_for_regression=3,
num_random_samples=10000,
seed=None):
"""Initializes the Harmonica instance.
Args:
phoenix_spec: PhoenixSpec prot... | Initializes the Harmonica instance.
Args:
phoenix_spec: PhoenixSpec proto.
alpha: The alpha of lasso solver (please read on lasso solver to
understand this constant. In a nutshell, this control regularization for
lasso. alpha equal zero means regular linear regression - however, try
... | __init__ | python | google/model_search | model_search/search/harmonica.py | https://github.com/google/model_search/blob/master/model_search/search/harmonica.py | Apache-2.0 |
def translate_architecture_to_feature_assignment(self, architecture):
"""Translates the trial architecture to a {-1, 1} assignment."""
x = np.empty(self._num_params)
x.fill(-1)
depth = 0
for block in architecture:
# These are connector blocks (non-trainable) that connect CNN and DNN.
# T... | Translates the trial architecture to a {-1, 1} assignment. | translate_architecture_to_feature_assignment | python | google/model_search | model_search/search/harmonica.py | https://github.com/google/model_search/blob/master/model_search/search/harmonica.py | Apache-2.0 |
def get_good_architecture(self, num_samples, coefficients):
"""Randomly samples architectures, predict loss, and return minimal."""
if self._seed:
np.random.seed(seed=self._seed)
assignments = []
architectures = []
for _ in range(num_samples):
rand_arc = np.random.randint(
len... | Randomly samples architectures, predict loss, and return minimal. | get_good_architecture | python | google/model_search | model_search/search/harmonica.py | https://github.com/google/model_search/blob/master/model_search/search/harmonica.py | Apache-2.0 |
def get_suggestion(self, trials, hparams, my_trial_id=None, model_dir=None):
"""Suggests a new architecture for Phoenix using the harmonica model.
For details please see:
https://arxiv.org/pdf/1706.00764.pdf
Args:
trials: a list of metadata.trial.Trial
hparams: The suggested hparams.
... | Suggests a new architecture for Phoenix using the harmonica model.
For details please see:
https://arxiv.org/pdf/1706.00764.pdf
Args:
trials: a list of metadata.trial.Trial
hparams: The suggested hparams.
my_trial_id: integer - the trial id which is making the call.
model_dir: stri... | get_suggestion | python | google/model_search | model_search/search/harmonica.py | https://github.com/google/model_search/blob/master/model_search/search/harmonica.py | Apache-2.0 |
def _one_nonzero_per_row(matrix):
"""For each row in matrix, randomly zero all but one of the nonzero values."""
# TODO(b/172564129): can it be done without a loop?
out = np.zeros_like(matrix)
for i in range(matrix.shape[0]):
nonzero_indices = np.flatnonzero(matrix[i])
keep = np.random.choice(nonzero_in... | For each row in matrix, randomly zero all but one of the nonzero values. | _one_nonzero_per_row | python | google/model_search | model_search/search/linear_model.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model.py | Apache-2.0 |
def _predict_best_architecture(self, architectures, losses):
"""Fits a linear model for loss = f(architecture) and finds its argmin.
Main computational subroutine for trial data already in feature vector form.
Args:
architectures: (n_trials, depth) integer matrix of architectures.
losses: (n_t... | Fits a linear model for loss = f(architecture) and finds its argmin.
Main computational subroutine for trial data already in feature vector form.
Args:
architectures: (n_trials, depth) integer matrix of architectures.
losses: (n_trials) positive validation error.
Returns:
predicted_loss... | _predict_best_architecture | python | google/model_search | model_search/search/linear_model.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model.py | Apache-2.0 |
def _suggest_by_padding(self, architectures, losses):
"""Pads architectures with EMPTY_BLOCK and call _predict_best_architecture.
Variable-length architectures are padded into fixed dimensionality
at either head or base, as determined by spec.network_alignment.
Args:
architectures: List of itera... | Pads architectures with EMPTY_BLOCK and call _predict_best_architecture.
Variable-length architectures are padded into fixed dimensionality
at either head or base, as determined by spec.network_alignment.
Args:
architectures: List of iterables of block_builder.BlockType values (or
integers).... | _suggest_by_padding | python | google/model_search | model_search/search/linear_model.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model.py | Apache-2.0 |
def _pad_architecture(self, arch, maxdepth):
"""Pad with empty blocks according to spec network alignment."""
empties = [block_builder.BlockType.EMPTY_BLOCK.value] * (
maxdepth - len(arch))
align = self._phoenix_spec.linear_model.network_alignment
if align == phoenix_spec_pb2.LinearModelSpec.NET... | Pad with empty blocks according to spec network alignment. | _pad_architecture | python | google/model_search | model_search/search/linear_model.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model.py | Apache-2.0 |
def _get_suggestion(architectures,
blocks_to_use,
losses,
grow=False,
remove_outliers=False,
pass_flatten=False):
"""Testing subroutine to handle boilerplate Trial construction, dirs, etc."""
# TODO(b/172564129): Fi... | Testing subroutine to handle boilerplate Trial construction, dirs, etc. | _get_suggestion | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def test_one_trial(self):
"""Degenerate case: one data point. Just make sure it doesn't explode."""
blocks_to_use = np.arange(1, 4)
architectures = np.array([[1, 2, 1]])
losses = ([1.0])
best = _get_suggestion(architectures, blocks_to_use, losses)
# The degenerate model might end up suggesting s... | Degenerate case: one data point. Just make sure it doesn't explode. | test_one_trial | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def test_two_trials(self):
"""Underdetermined case - should find pattern in subset of dimensions."""
blocks_to_use = np.arange(1, 4)
architectures = np.array([[1, 2, 1], [1, 1, 2]])
losses = ([1.0, 2.0])
best = _get_suggestion(architectures, blocks_to_use, losses)
# The model won't be able to pr... | Underdetermined case - should find pattern in subset of dimensions. | test_two_trials | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def test_three_trials(self):
"""Suggestion should combine trial 1 and 2's improvements over trial 0."""
blocks_to_use = np.arange(1, 4)
architectures = np.array([[1, 1, 1], [1, 2, 1], [1, 1, 2]])
losses = ([2.0, 1.0, 1.0])
best = _get_suggestion(architectures, blocks_to_use, losses)
self.assertE... | Suggestion should combine trial 1 and 2's improvements over trial 0. | test_three_trials | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def test_loss_equals_id(self):
"""Larger-scale overdetermined case with easily predicted model output.
Each block contributes its own id worth of loss,
so tower of all block 1 should be best.
"""
nblocks = 4
blocks_to_use = np.arange(1, nblocks + 1)
depth = 9
ntrials = 10 * nblocks * de... | Larger-scale overdetermined case with easily predicted model output.
Each block contributes its own id worth of loss,
so tower of all block 1 should be best.
| test_loss_equals_id | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def test_randomized(self):
"""Overdetermined case with randomly chosen linear model.
Hard to predict the argmin, but can check its expected performance.
"""
np.random.seed(0)
nblocks = 10
blocks_to_use = np.arange(1, nblocks + 1)
depth = 10
ntrials = 2 * nblocks * depth
architecture... | Overdetermined case with randomly chosen linear model.
Hard to predict the argmin, but can check its expected performance.
| test_randomized | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def test_ids_nonrange(self):
"""Make sure we correctly handle non-contiguous range of blocks."""
np.random.seed(0)
# TODO(b/172564129): This test is not correct.
block_ints = [
b.value
for b in block_builder.BlockType
if b.value not in [126, 127, 128, 129]
]
blocks_to_use... | Make sure we correctly handle non-contiguous range of blocks. | test_ids_nonrange | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def test_flatten_modelfitting(self):
"""Ensure that we correctly deal with the flatten block in fitting.
1) Flatten blocks won't be in spec.blocks_to_use, even though
the trial architectures loaded from filesystem will contain them.
2) The model shouldn't try to place a flatten block, since there i... | Ensure that we correctly deal with the flatten block in fitting.
1) Flatten blocks won't be in spec.blocks_to_use, even though
the trial architectures loaded from filesystem will contain them.
2) The model shouldn't try to place a flatten block, since there is only
one valid position at the conv... | test_flatten_modelfitting | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def test_flatten_output(self, grow):
"""Ensure we output suggestions with a flatten block correctly placed."""
# Make trials s.t. the linear model will output all convolutions.
architectures = [
np.repeat(block_builder.BlockType.EMPTY_BLOCK, 4),
np.repeat(block_builder.BlockType.CONVOLUTION... | Ensure we output suggestions with a flatten block correctly placed. | test_flatten_output | python | google/model_search | model_search/search/linear_model_test.py | https://github.com/google/model_search/blob/master/model_search/search/linear_model_test.py | Apache-2.0 |
def get_suggestion(self, trials, hparams, my_trial_id=None, model_dir=None):
"""Suggests a new architecture for a Phoenix model.
Note that this algorithm performs on top of hparams oracle. Meaning, it will
receive suggested trial hparams, and determine the Phoenix
architecture. This algorithm has the f... | Suggests a new architecture for a Phoenix model.
Note that this algorithm performs on top of hparams oracle. Meaning, it will
receive suggested trial hparams, and determine the Phoenix
architecture. This algorithm has the final say. We implemented a simple
"Identity" algorithm, that passes oracle's sug... | get_suggestion | python | google/model_search | model_search/search/search_algorithm.py | https://github.com/google/model_search/blob/master/model_search/search/search_algorithm.py | Apache-2.0 |
def create_spec(problem_type,
complexity_thresholds=None,
max_depth=None,
min_depth=None,
blocks_to_use=None):
"""Creates a phoenix_spec_pb2.PhoenixSpec with the given options."""
output = phoenix_spec_pb2.PhoenixSpec()
if complexity_thresholds is no... | Creates a phoenix_spec_pb2.PhoenixSpec with the given options. | create_spec | python | google/model_search | model_search/search/test_utils.py | https://github.com/google/model_search/blob/master/model_search/search/test_utils.py | Apache-2.0 |
def is_mutation_or_equal(previous_architecture, new_architecture):
"""Returns whether if new arch is mutation of or equal to previous arch."""
if previous_architecture.shape != new_architecture.shape:
return False
mismatch = (
previous_architecture.size -
np.sum(previous_architecture == new_archit... | Returns whether if new arch is mutation of or equal to previous arch. | is_mutation_or_equal | python | google/model_search | model_search/search/test_utils.py | https://github.com/google/model_search/blob/master/model_search/search/test_utils.py | Apache-2.0 |
def str2bool(v):
"""
Converts string to bool type; enables command line
arguments in the format of '--arg1 true --arg2 false'
"""
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
... |
Converts string to bool type; enables command line
arguments in the format of '--arg1 true --arg2 false'
| str2bool | python | Jingkang50/OpenOOD | openood/attacks/misc.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/attacks/misc.py | MIT |
def __init__(
self,
net: nn.Module,
id_name: str,
data_root: str = './data',
config_root: str = './configs',
preprocessor: Callable = None,
batch_size: int = 200,
shuffle: bool = False,
num_workers: int = 4,
) -> None:
"""A unified, eas... | A unified, easy-to-use API for evaluating (most) discriminative OOD
detection methods.
Args:
net (nn.Module):
The base classifier.
id_name (str):
The name of the in-distribution dataset.
data_root (str, optional):
The p... | __init__ | python | Jingkang50/OpenOOD | openood/evaluation_api/attackdataset.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/evaluation_api/attackdataset.py | MIT |
def topk(output, target, ks=(1, )):
"""Returns one boolean vector for each k, whether the target is within the
output's top-k."""
_, pred = output.topk(max(ks), 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
return [correct[:k].max(0)[0] for k in ks] | Returns one boolean vector for each k, whether the target is within the
output's top-k. | topk | python | Jingkang50/OpenOOD | openood/evaluators/mos_evaluator.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/evaluators/mos_evaluator.py | MIT |
def __init__(self, config: Config):
"""OOD Evaluator.
Args:
config (Config): Config file from
"""
super(OODEvaluator, self).__init__(config)
self.id_pred = None
self.id_conf = None
self.id_gt = None | OOD Evaluator.
Args:
config (Config): Config file from
| __init__ | python | Jingkang50/OpenOOD | openood/evaluators/ood_evaluator.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/evaluators/ood_evaluator.py | MIT |
def eval_acc(self,
net: nn.Module,
data_loader: DataLoader,
postprocessor: BasePostprocessor = None,
epoch_idx: int = -1,
fsood: bool = False,
csid_data_loaders: DataLoader = None):
"""Returns the accuracy scor... | Returns the accuracy score of the labels and predictions.
:return: float
| eval_acc | python | Jingkang50/OpenOOD | openood/evaluators/ood_evaluator.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/evaluators/ood_evaluator.py | MIT |
def _get_item_by_idx(self, iterator, idx):
"""Get the idx-th item of the iterator."""
size = len(self)
idx = operator.index(idx)
if not -size <= idx < size:
raise IndexError('index {} is out of range'.format(idx))
idx %= size
return next(islice(iterator, idx, ... | Get the idx-th item of the iterator. | _get_item_by_idx | python | Jingkang50/OpenOOD | openood/networks/arpl_net.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/networks/arpl_net.py | MIT |
def _make_layer(self, block, planes, num_blocks, stride):
'''
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
'''
norm_lay... |
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
| _make_layer | python | Jingkang50/OpenOOD | openood/networks/resnet18_256x256.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/networks/resnet18_256x256.py | MIT |
def __init__(self, backbone, feature_size, num_classes, dof=16):
'''
dof: degree of freedom of variance
'''
super(RTSNet, self).__init__()
self.backbone = backbone
self.feature_size = feature_size
self.num_classes = num_classes
self.dof = dof
self.... |
dof: degree of freedom of variance
| __init__ | python | Jingkang50/OpenOOD | openood/networks/rts_net.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/networks/rts_net.py | MIT |
def get_GMM_stat(model, train_loader, num_clusters_list, feature_type_list,
reduce_dim_list):
""" Compute GMM.
Args:
model (nn.Module): pretrained model to extract features
train_loader (DataLoader): use all training data to perform GMM
num_clusters_list (list): number o... | Compute GMM.
Args:
model (nn.Module): pretrained model to extract features
train_loader (DataLoader): use all training data to perform GMM
num_clusters_list (list): number of clusters for each layer
feature_type_list (list): feature type for each layer
reduce_dim_list (list)... | get_GMM_stat | python | Jingkang50/OpenOOD | openood/postprocessors/gmm_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/gmm_postprocessor.py | MIT |
def compute_GMM_score(model,
data,
feature_mean,
feature_prec,
component_weight,
transform_matrix,
layer_idx,
feature_type_list,
return_pred=Fal... | Compute GMM.
Args:
model (nn.Module): pretrained model to extract features
data (DataLoader): input one training batch
feature_mean (list): a list of torch.cuda.Tensor()
feature_prec (list): a list of torch.cuda.Tensor()
component_weight (list): a list of torch.cuda.Tensor()... | compute_GMM_score | python | Jingkang50/OpenOOD | openood/postprocessors/gmm_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/gmm_postprocessor.py | MIT |
def get_MDS_stat(model, train_loader, num_classes, feature_type_list,
reduce_dim_list):
""" Compute sample mean and precision (inverse of covariance)
return: sample_class_mean: list of class mean
precision: list of precisions
transform_matrix_list: list of transform_matr... | Compute sample mean and precision (inverse of covariance)
return: sample_class_mean: list of class mean
precision: list of precisions
transform_matrix_list: list of transform_matrix
| get_MDS_stat | python | Jingkang50/OpenOOD | openood/postprocessors/mds_ensemble_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/mds_ensemble_postprocessor.py | MIT |
def get_Mahalanobis_scores(model, test_loader, num_classes, sample_mean,
precision, transform_matrix, layer_index,
feature_type_list, magnitude):
'''
Compute the proposed Mahalanobis confidence score on input dataset
return: Mahalanobis score from layer_... |
Compute the proposed Mahalanobis confidence score on input dataset
return: Mahalanobis score from layer_index
| get_Mahalanobis_scores | python | Jingkang50/OpenOOD | openood/postprocessors/mds_ensemble_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/mds_ensemble_postprocessor.py | MIT |
def _cov(X, shrinkage=None, covariance_estimator=None):
"""Estimate covariance matrix (using optional covariance_estimator).
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
shrinkage : {'empirical', 'auto'} or float, default=None
Shrinkage parameter,... | Estimate covariance matrix (using optional covariance_estimator).
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
shrinkage : {'empirical', 'auto'} or float, default=None
Shrinkage parameter, possible values:
- None or 'empirical': no shrinkage... | _cov | python | Jingkang50/OpenOOD | openood/postprocessors/mds_ensemble_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/mds_ensemble_postprocessor.py | MIT |
def _class_means(X, y):
"""Compute class means.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Returns
-------
means : array-like of shape (n_classes, n_featur... | Compute class means.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Returns
-------
means : array-like of shape (n_classes, n_features)
Class means.
| _class_means | python | Jingkang50/OpenOOD | openood/postprocessors/mds_ensemble_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/mds_ensemble_postprocessor.py | MIT |
def _class_cov(X, y, priors, shrinkage=None, covariance_estimator=None):
"""Compute weighted within-class covariance matrix.
The per-class covariance are weighted by the class priors.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shap... | Compute weighted within-class covariance matrix.
The per-class covariance are weighted by the class priors.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
priors : arr... | _class_cov | python | Jingkang50/OpenOOD | openood/postprocessors/mds_ensemble_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/mds_ensemble_postprocessor.py | MIT |
def _solve_eigen(self, X, y, shrinkage):
"""Eigenvalue solver.
The eigenvalue solver computes the optimal solution of the Rayleigh
coefficient (basically the ratio of between class scatter to within
class scatter). This solver supports both classification and
dimensionality reduc... | Eigenvalue solver.
The eigenvalue solver computes the optimal solution of the Rayleigh
coefficient (basically the ratio of between class scatter to within
class scatter). This solver supports both classification and
dimensionality reduction (with optional shrinkage).
Parameters
... | _solve_eigen | python | Jingkang50/OpenOOD | openood/postprocessors/mds_ensemble_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/mds_ensemble_postprocessor.py | MIT |
def compute_channel_distances(mavs, features, eu_weight=0.5):
"""
Input:
mavs (channel, C)
features: (N, channel, C)
Output:
channel_distances: dict of distance distribution from MAV
for each channel.
"""
eucos_dists, eu_dists, cos_dists = [], [], []
for channel, ... |
Input:
mavs (channel, C)
features: (N, channel, C)
Output:
channel_distances: dict of distance distribution from MAV
for each channel.
| compute_channel_distances | python | Jingkang50/OpenOOD | openood/postprocessors/openmax_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/openmax_postprocessor.py | MIT |
def fit_weibull(means, dists, categories, tailsize=20, distance_type='eucos'):
"""
Input:
means (C, channel, C)
dists (N_c, channel, C) * C
Output:
weibull_model : Perform EVT based analysis using tails of distances
and save weibull model parameters for re-adj... |
Input:
means (C, channel, C)
dists (N_c, channel, C) * C
Output:
weibull_model : Perform EVT based analysis using tails of distances
and save weibull model parameters for re-adjusting
softmax scores
| fit_weibull | python | Jingkang50/OpenOOD | openood/postprocessors/openmax_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/openmax_postprocessor.py | MIT |
def openmax(weibull_model,
categories,
input_score,
eu_weight,
alpha=10,
distance_type='eucos'):
"""Re-calibrate scores via OpenMax layer
Output:
openmax probability and softmax probability
"""
nb_classes = len(categories)
ranked_l... | Re-calibrate scores via OpenMax layer
Output:
openmax probability and softmax probability
| openmax | python | Jingkang50/OpenOOD | openood/postprocessors/openmax_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/openmax_postprocessor.py | MIT |
def update_distances(self,
cluster_centers,
only_new=True,
reset_dist=False):
"""Update min distances given cluster centers.
Args:
cluster_centers: indices of cluster centers
only_new: only calculate distance... | Update min distances given cluster centers.
Args:
cluster_centers: indices of cluster centers
only_new: only calculate distance for newly selected points and
update min_distances.
rest_dist: whether to reset min_distances.
| update_distances | python | Jingkang50/OpenOOD | openood/postprocessors/patchcore_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/patchcore_postprocessor.py | MIT |
def select_batch_(self, model, already_selected, N, **kwargs):
"""Diversity promoting active learning method that greedily forms a
batch to minimize the maximum distance to a cluster center among all
unlabeled datapoints.
Args:
model: model with scikit-like API with decision_f... | Diversity promoting active learning method that greedily forms a
batch to minimize the maximum distance to a cluster center among all
unlabeled datapoints.
Args:
model: model with scikit-like API with decision_function implemented
already_selected: index of datapoints alread... | select_batch_ | python | Jingkang50/OpenOOD | openood/postprocessors/patchcore_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/patchcore_postprocessor.py | MIT |
def kernel(feat, feat_t, prob, prob_t, split=2):
"""Kernel function (assume feature is normalized)"""
size = ceil(len(feat_t) / split)
rel_full = []
for i in range(split):
feat_t_ = feat_t[i * size:(i + 1) * size]
prob_t_ = prob_t[i * size:(i + 1) * size]
with torch.no_grad():
... | Kernel function (assume feature is normalized) | kernel | python | Jingkang50/OpenOOD | openood/postprocessors/relation_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/relation_postprocessor.py | MIT |
def get_relation(feat, feat_t, prob, prob_t, pow=1, chunk=50, thres=0.03):
"""Get relation values (top-k and summation)
Args:
feat (torch.Tensor [N,D]): features of the source data
feat_t (torch.Tensor [N',D]): features of the target data
prob (torch.Tensor [N,C]): probabilty vectors of... | Get relation values (top-k and summation)
Args:
feat (torch.Tensor [N,D]): features of the source data
feat_t (torch.Tensor [N',D]): features of the target data
prob (torch.Tensor [N,C]): probabilty vectors of the source data
prob_t (torch.Tensor [N',C]): probabilty vectors of the t... | get_relation | python | Jingkang50/OpenOOD | openood/postprocessors/relation_postprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/postprocessors/relation_postprocessor.py | MIT |
def __call__(self, img):
"""
Args:
img (Tensor): Tensor image of size (C, H, W).
Returns:
Tensor: Image with n_holes of dimension length x length
cut out of it.
"""
h = img.size(1)
w = img.size(2)
mask = np.ones((h, w), np.floa... |
Args:
img (Tensor): Tensor image of size (C, H, W).
Returns:
Tensor: Image with n_holes of dimension length x length
cut out of it.
| __call__ | python | Jingkang50/OpenOOD | openood/preprocessors/cutout_preprocessor.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/preprocessors/cutout_preprocessor.py | MIT |
def get_similarity_matrix(outputs, chunk=2, multi_gpu=False):
"""Compute similarity matrix.
- outputs: (B', d) tensor for B' = B * chunk
- sim_matrix: (B', B') tensor
"""
if multi_gpu:
outputs_gathered = []
for out in outputs.chunk(chunk):
gather_t = [
t... | Compute similarity matrix.
- outputs: (B', d) tensor for B' = B * chunk
- sim_matrix: (B', B') tensor
| get_similarity_matrix | python | Jingkang50/OpenOOD | openood/trainers/csi_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/csi_trainer.py | MIT |
def Supervised_NT_xent(sim_matrix,
labels,
temperature=0.5,
chunk=2,
eps=1e-8,
multi_gpu=False):
"""Compute NT_xent loss.
- sim_matrix: (B', B') tensor for B' = B * chunk (first 2B are pos samples... | Compute NT_xent loss.
- sim_matrix: (B', B') tensor for B' = B * chunk (first 2B are pos samples)
| Supervised_NT_xent | python | Jingkang50/OpenOOD | openood/trainers/csi_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/csi_trainer.py | MIT |
def __init__(self, size=None, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.)):
"""Inception Crop size (tuple): size of forwarding image (C, W, H)
scale (tuple): range of size of the origin size cropped ratio (tuple):
range of aspect ratio of the origin aspect ratio cropped.
"""
sup... | Inception Crop size (tuple): size of forwarding image (C, W, H)
scale (tuple): range of size of the origin size cropped ratio (tuple):
range of aspect ratio of the origin aspect ratio cropped.
| __init__ | python | Jingkang50/OpenOOD | openood/trainers/csi_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/csi_trainer.py | MIT |
def __init__(self):
"""
img_size : (int, int, int)
Height and width must be powers of 2. E.g. (32, 32, 1) or
(64, 128, 3). Last number indicates number of channels, e.g. 1 for
grayscale or 3 for RGB
"""
super(HorizontalFlipLayer, self).__init__()
... |
img_size : (int, int, int)
Height and width must be powers of 2. E.g. (32, 32, 1) or
(64, 128, 3). Last number indicates number of channels, e.g. 1 for
grayscale or 3 for RGB
| __init__ | python | Jingkang50/OpenOOD | openood/trainers/csi_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/csi_trainer.py | MIT |
def init_center_c(train_loader, net, eps=0.1):
"""Initialize hypersphere center c as the mean from an initial forward pass
on the data."""
n_samples = 0
first_iter = True
train_dataiter = iter(train_loader)
net.eval()
with torch.no_grad():
for train_step in tqdm(range(1,
... | Initialize hypersphere center c as the mean from an initial forward pass
on the data. | init_center_c | python | Jingkang50/OpenOOD | openood/trainers/dsvdd_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/dsvdd_trainer.py | MIT |
def prepare_mixup(batch, alpha=1.0, use_cuda=True):
"""Returns mixed inputs, pairs of targets, and lambda."""
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = batch['data'].size()[0]
if use_cuda:
index = torch.randperm(batch_size).cuda()
else:
... | Returns mixed inputs, pairs of targets, and lambda. | prepare_mixup | python | Jingkang50/OpenOOD | openood/trainers/mixup_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/mixup_trainer.py | MIT |
def get_lr(step, dataset_size, base_lr=0.003):
"""Returns learning-rate for `step` or None at the end."""
supports = get_schedule(dataset_size)
# Linear warmup
if step < supports[0]:
return base_lr * step / supports[0]
# End of training
elif step >= supports[-1]:
return None
... | Returns learning-rate for `step` or None at the end. | get_lr | python | Jingkang50/OpenOOD | openood/trainers/mos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/mos_trainer.py | MIT |
def KNN_dis_search_distance(target,
index,
K=50,
num_points=10,
length=2000,
depth=342):
'''
data_point: Queue for searching k-th points
target: the target of the searc... |
data_point: Queue for searching k-th points
target: the target of the search
K
| KNN_dis_search_distance | python | Jingkang50/OpenOOD | openood/trainers/npos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/npos_trainer.py | MIT |
def preprocess_features(npdata, pca=256):
"""Preprocess an array of features.
Args:
npdata (np.array N * ndim): features to preprocess
pca (int): dim of output
Returns:
np.array of dim N * pca: data PCA-reduced, whitened and L2-normalized
"""
_, ndim = npdata.shape
npdata... | Preprocess an array of features.
Args:
npdata (np.array N * ndim): features to preprocess
pca (int): dim of output
Returns:
np.array of dim N * pca: data PCA-reduced, whitened and L2-normalized
| preprocess_features | python | Jingkang50/OpenOOD | openood/trainers/udg_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/udg_trainer.py | MIT |
def run_kmeans(x, nmb_clusters, verbose=False):
"""Runs kmeans on 1 GPU.
Args:
x: data
nmb_clusters (int): number of clusters
Returns:
list: ids of data in each cluster
"""
n_data, d = x.shape
# faiss implementation of k-means
clus = faiss.Clustering(d, nmb_clusters... | Runs kmeans on 1 GPU.
Args:
x: data
nmb_clusters (int): number of clusters
Returns:
list: ids of data in each cluster
| run_kmeans | python | Jingkang50/OpenOOD | openood/trainers/udg_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/udg_trainer.py | MIT |
def cluster(self, data, verbose=True):
"""Performs k-means clustering.
Args:
x_data (np.array N * dim): data to cluster
"""
# PCA-reducing, whitening and L2-normalization
xb = preprocess_features(data, pca=self.pca_dim)
if np.isnan(xb).any():
row_... | Performs k-means clustering.
Args:
x_data (np.array N * dim): data to cluster
| cluster | python | Jingkang50/OpenOOD | openood/trainers/udg_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/udg_trainer.py | MIT |
def log_sum_exp(value, num_classes=10, dim=None, keepdim=False):
"""Numerically stable implementation of the operation."""
value.exp().sum(dim, keepdim).log()
# TODO: torch.max(value, dim=None) threw an error at time of writing
weight_energy = torch.nn.Linear(num_classes, 1).cuda()
if dim is not No... | Numerically stable implementation of the operation. | log_sum_exp | python | Jingkang50/OpenOOD | openood/trainers/vos_trainer.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/trainers/vos_trainer.py | MIT |
def get_local_rank() -> int:
"""
Returns:
The rank of the current process
within the local (per-machine) process group.
"""
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
assert (
_LOCAL_PROCESS_GROUP is not None
), 'Local ... |
Returns:
The rank of the current process
within the local (per-machine) process group.
| get_local_rank | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def get_local_size() -> int:
"""
Returns:
The size of the per-machine process group,
i.e. the number of processes per machine.
"""
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) |
Returns:
The size of the per-machine process group,
i.e. the number of processes per machine.
| get_local_size | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def synchronize():
"""Helper function to synchronize (barrier) among all processes when using
distributed training."""
if not dist.is_available():
return
if not dist.is_initialized():
return
world_size = dist.get_world_size()
if world_size == 1:
return
if dist.get_bac... | Helper function to synchronize (barrier) among all processes when using
distributed training. | synchronize | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def _get_global_gloo_group():
"""Return a process group based on gloo backend, containing all the ranks
The result is cached."""
if dist.get_backend() == 'nccl':
return dist.new_group(backend='gloo')
else:
return dist.group.WORLD | Return a process group based on gloo backend, containing all the ranks
The result is cached. | _get_global_gloo_group | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def all_gather(data, group=None):
"""Run all_gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
list[data]: list of ... | Run all_gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
list[data]: list of data gathered from each rank
| all_gather | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def gather(data, dst=0, group=None):
"""Run gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
dst (int): destination rank
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Re... | Run gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
dst (int): destination rank
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
list[data]: on dst, a list of... | gather | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def shared_random_seed():
"""
Returns:
int: a random number that is the same across all workers.
If workers need a shared RNG, they can use this shared seed to
create one.
All workers must call this function, otherwise it will deadlock.
"""
ints = np.random.randint(2**31)
... |
Returns:
int: a random number that is the same across all workers.
If workers need a shared RNG, they can use this shared seed to
create one.
All workers must call this function, otherwise it will deadlock.
| shared_random_seed | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def reduce_dict(input_dict, average=True):
"""Reduce the values in the dictionary from all processes so that process
with rank 0 has the reduced results.
Args:
input_dict (dict): inputs to be reduced.
All the values must be scalar CUDA Tensor.
average (bool): whether to do average o... | Reduce the values in the dictionary from all processes so that process
with rank 0 has the reduced results.
Args:
input_dict (dict): inputs to be reduced.
All the values must be scalar CUDA Tensor.
average (bool): whether to do average or sum
Returns:
a dict with the same k... | reduce_dict | python | Jingkang50/OpenOOD | openood/utils/comm.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/comm.py | MIT |
def setup_config(config_process_order=('merge', 'parse_args', 'parse_refs')):
"""Parsing configuration files and command line augments.
This method reads the command line to
1. extract and stack YAML config files,
2. collect modification in command line arguments,
so that the finalized conf... | Parsing configuration files and command line augments.
This method reads the command line to
1. extract and stack YAML config files,
2. collect modification in command line arguments,
so that the finalized configuration file is generated.
Note:
The default arguments allow the follo... | setup_config | python | Jingkang50/OpenOOD | openood/utils/config.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/config.py | MIT |
def launch(
main_func,
num_gpus_per_machine,
num_machines=1,
machine_rank=0,
dist_url=None,
args=(),
timeout=DEFAULT_TIMEOUT,
):
"""Launch multi-gpu or distributed training. This function must be called
on all machines involved in the training. It will spa... | Launch multi-gpu or distributed training. This function must be called
on all machines involved in the training. It will spawn child processes
(defined by ``num_gpus_per_machine``) on each machine.
Args:
main_func: a function that will be called by `main_func(*args)`
num_gpus_per_machine (i... | launch | python | Jingkang50/OpenOOD | openood/utils/launch.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/launch.py | MIT |
def mkdir_if_missing(dirname):
"""Create dirname if it is missing."""
if not osp.exists(dirname):
try:
os.makedirs(dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise | Create dirname if it is missing. | mkdir_if_missing | python | Jingkang50/OpenOOD | openood/utils/logger.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/logger.py | MIT |
def setup_logger(config):
"""generate exp directory to save configs, logger, checkpoints, etc.
Args:
config: all configs of the experiment
"""
print('------------------ Config --------------------------', flush=True)
print(config, flush=True)
print(u'\u2500' * 70, flush=True)
outpu... | generate exp directory to save configs, logger, checkpoints, etc.
Args:
config: all configs of the experiment
| setup_logger | python | Jingkang50/OpenOOD | openood/utils/logger.py | https://github.com/Jingkang50/OpenOOD/blob/master/openood/utils/logger.py | MIT |
async def maigret(
username: str,
site_dict: Dict[str, MaigretSite],
logger,
query_notify=None,
proxy=None,
tor_proxy=None,
i2p_proxy=None,
timeout=3,
is_parsing_enabled=False,
id_type="username",
debug=False,
forced=False,
max_connections=100,
no_progressbar=Fals... | Main search func
Checks for existence of username on certain sites.
Keyword Arguments:
username -- Username string will be used for search.
site_dict -- Dictionary containing sites data in MaigretSite objects.
query_notify -- Object with base type of QueryNotif... | maigret | python | soxoj/maigret | maigret/checking.py | https://github.com/soxoj/maigret/blob/master/maigret/checking.py | MIT |
def timeout_check(value):
"""Check Timeout Argument.
Checks timeout for validity.
Keyword Arguments:
value -- Time in seconds to wait before timing out request.
Return Value:
Floating point number representing the time (in seconds) that should be
used for the timeout.
... | Check Timeout Argument.
Checks timeout for validity.
Keyword Arguments:
value -- Time in seconds to wait before timing out request.
Return Value:
Floating point number representing the time (in seconds) that should be
used for the timeout.
NOTE: Will raise an exception ... | timeout_check | python | soxoj/maigret | maigret/checking.py | https://github.com/soxoj/maigret/blob/master/maigret/checking.py | MIT |
def notify_about_errors(
search_results: QueryResultWrapper, query_notify, show_statistics=False
) -> List[Tuple]:
"""
Prepare error notifications in search results, text + symbol,
to be displayed by notify object.
Example:
[
("Too many errors of type "timeout" (50.0%)", "!")
("... |
Prepare error notifications in search results, text + symbol,
to be displayed by notify object.
Example:
[
("Too many errors of type "timeout" (50.0%)", "!")
("Verbose error statistics:", "-")
]
| notify_about_errors | python | soxoj/maigret | maigret/errors.py | https://github.com/soxoj/maigret/blob/master/maigret/errors.py | MIT |
async def increment_progress(self, count):
"""Update progress by calling the provided progress function."""
if self.progress:
if asyncio.iscoroutinefunction(self.progress):
await self.progress(count)
else:
self.progress(count)
await... | Update progress by calling the provided progress function. | increment_progress | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
async def worker(self):
"""Consume tasks from the queue and process them."""
while True:
try:
f, args, kwargs = self.queue.get_nowait()
except asyncio.QueueEmpty:
return
query_future = f(*args, **kwargs)
query_task = create... | Consume tasks from the queue and process them. | worker | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
async def _run(self, queries: Iterable[QueryDraft]):
"""Main runner function to execute tasks with progress tracking."""
self.results: List[Any] = []
queries_list = list(queries)
min_workers = min(len(queries_list), self.workers_count)
workers = [create_task_func()(self.worker())... | Main runner function to execute tasks with progress tracking. | _run | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
async def worker(self):
"""Process tasks from the queue and put results into the results queue."""
while True:
task = await self.queue.get()
if task is self._stop_signal:
self.queue.task_done()
break
try:
f, args, kwarg... | Process tasks from the queue and put results into the results queue. | worker | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
async def run(self, queries: Iterable[Callable[..., Any]]):
"""Run workers to process queries in parallel."""
start_time = time.time()
# Add tasks to the queue
for t in queries:
await self.queue.put(t)
# Create workers
workers = [
asyncio.create_... | Run workers to process queries in parallel. | run | python | soxoj/maigret | maigret/executors.py | https://github.com/soxoj/maigret/blob/master/maigret/executors.py | MIT |
def __init__(
self,
result=None,
verbose=False,
print_found_only=False,
skip_check_errors=False,
color=True,
):
"""Create Query Notify Print Object.
Contains information about a specific method of notifying the results
of a query.
Key... | Create Query Notify Print Object.
Contains information about a specific method of notifying the results
of a query.
Keyword Arguments:
self -- This object.
result -- Object of type QueryResult() containing
resu... | __init__ | python | soxoj/maigret | maigret/notify.py | https://github.com/soxoj/maigret/blob/master/maigret/notify.py | MIT |
def start(self, message, id_type):
"""Notify Start.
Will print the title to the standard output.
Keyword Arguments:
self -- This object.
message -- String containing username that the series
of queries are about... | Notify Start.
Will print the title to the standard output.
Keyword Arguments:
self -- This object.
message -- String containing username that the series
of queries are about.
Return Value:
Nothing.
... | start | python | soxoj/maigret | maigret/notify.py | https://github.com/soxoj/maigret/blob/master/maigret/notify.py | MIT |
def update(self, result, is_similar=False):
"""Notify Update.
Will print the query result to the standard output.
Keyword Arguments:
self -- This object.
result -- Object of type QueryResult() containing
result... | Notify Update.
Will print the query result to the standard output.
Keyword Arguments:
self -- This object.
result -- Object of type QueryResult() containing
results for this query.
Return Value:
Nothin... | update | python | soxoj/maigret | maigret/notify.py | https://github.com/soxoj/maigret/blob/master/maigret/notify.py | MIT |
def __init__(
self,
username,
site_name,
site_url_user,
status,
ids_data=None,
query_time=None,
context=None,
error=None,
tags=[],
):
"""
Keyword Arguments:
self -- This object.
username... |
Keyword Arguments:
self -- This object.
username -- String indicating username that query result
was about.
site_name -- String which identifies site.
site_url_user -- String containing URL f... | __init__ | python | soxoj/maigret | maigret/result.py | https://github.com/soxoj/maigret/blob/master/maigret/result.py | MIT |
def __str__(self):
"""Convert Object To String.
Keyword Arguments:
self -- This object.
Return Value:
Nicely formatted string to get information about this object.
"""
status = str(self.status)
if self.context is not None:
#... | Convert Object To String.
Keyword Arguments:
self -- This object.
Return Value:
Nicely formatted string to get information about this object.
| __str__ | python | soxoj/maigret | maigret/result.py | https://github.com/soxoj/maigret/blob/master/maigret/result.py | MIT |
def extract_id_from_url(self, url: str) -> Optional[Tuple[str, str]]:
"""
Extracts username from url.
It's outdated, detects only a format of https://example.com/{username}
"""
if not self.url_regexp:
return None
match_groups = self.url_regexp.match(url)
... |
Extracts username from url.
It's outdated, detects only a format of https://example.com/{username}
| extract_id_from_url | python | soxoj/maigret | maigret/sites.py | https://github.com/soxoj/maigret/blob/master/maigret/sites.py | MIT |
def ranked_sites_dict(
self,
reverse=False,
top=sys.maxsize,
tags=[],
names=[],
disabled=True,
id_type="username",
):
"""
Ranking and filtering of the sites list
Args:
reverse (bool, optional): Reverse the sorting order. De... |
Ranking and filtering of the sites list
Args:
reverse (bool, optional): Reverse the sorting order. Defaults to False.
top (int, optional): Maximum number of sites to return. Defaults to sys.maxsize.
tags (list, optional): List of tags to filter sites by. Defaults to... | ranked_sites_dict | python | soxoj/maigret | maigret/sites.py | https://github.com/soxoj/maigret/blob/master/maigret/sites.py | MIT |
def _format_top_items(
self, title, items_dict, limit, is_markdown, valid_items=None
):
"""Helper method to format top items lists"""
output = f"Top {limit} {title}:\n"
for item, count in sorted(items_dict.items(), key=lambda x: x[1], reverse=True)[
:limit
]:
... | Helper method to format top items lists | _format_top_items | python | soxoj/maigret | maigret/sites.py | https://github.com/soxoj/maigret/blob/master/maigret/sites.py | MIT |
def attribute(self, attribute_name, db=None, default=None): # type: (str, CanMatrix, typing.Any) -> typing.Any
"""Get Board unit attribute by its name.
:param str attribute_name: attribute name.
:param CanMatrix db: Optional database parameter to get global default attribute value.
:pa... | Get Board unit attribute by its name.
:param str attribute_name: attribute name.
:param CanMatrix db: Optional database parameter to get global default attribute value.
:param default: Default value if attribute doesn't exist.
:return: Return the attribute value if found, else `default`... | attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_attribute(self, attribute, value): # type (attribute: str, value: typing.Any) -> None
"""
Add the Attribute to current ECU. If the attribute already exists, update the value.
:param str attribute: Attribute name
:param any value: Attribute value
"""
try:
... |
Add the Attribute to current ECU. If the attribute already exists, update the value.
:param str attribute: Attribute name
:param any value: Attribute value
| add_attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def attribute(self, attributeName, db=None, default=None):
# type: (str, CanMatrix, typing.Any) -> typing.Any
"""Get any Signal attribute by its name.
:param str attributeName: attribute name, can be mandatory (ex: start_bit, size) or optional (customer) attribute.
:param CanMatrix db: ... | Get any Signal attribute by its name.
:param str attributeName: attribute name, can be mandatory (ex: start_bit, size) or optional (customer) attribute.
:param CanMatrix db: Optional database parameter to get global default attribute value.
:param default: Default value if attribute doesn't exi... | attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_attribute(self, attribute, value):
"""
Add user defined attribute to the Signal. Update the value if the attribute already exists.
:param str attribute: attribute name
:param value: attribute value
"""
try:
self.attributes[attribute] = str(value)
... |
Add user defined attribute to the Signal. Update the value if the attribute already exists.
:param str attribute: attribute name
:param value: attribute value
| add_attribute | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def add_values(self, value, valueName):
"""
Add named Value Description to the Signal.
:param int or str value: signal value (0xFF)
:param str valueName: Human readable value description ("Init")
"""
if isinstance(value, defaultFloatFactory):
self.values[valu... |
Add named Value Description to the Signal.
:param int or str value: signal value (0xFF)
:param str valueName: Human readable value description ("Init")
| add_values | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def set_startbit(self, start_bit, bitNumbering=None, startLittle=None):
"""
Set start_bit.
bitNumbering is 1 for LSB0/LSBFirst, 0 for MSB0/MSBFirst.
If bit numbering is consistent with byte order (little=LSB0, big=MSB0)
(KCD, SYM), start bit unmodified.
Otherwise reverse... |
Set start_bit.
bitNumbering is 1 for LSB0/LSBFirst, 0 for MSB0/MSBFirst.
If bit numbering is consistent with byte order (little=LSB0, big=MSB0)
(KCD, SYM), start bit unmodified.
Otherwise reverse bit numbering. For DBC, ArXML (OSEK),
both little endian and big endian us... | set_startbit | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def get_startbit(self, bit_numbering=None, start_little=None):
"""Get signal start bit. Handle byte and bit order."""
startBitInternal = self.start_bit
# convert from big endian start bit at
# start bit(msbit) to end bit(lsbit)
if start_little is True and self.is_little_endian is... | Get signal start bit. Handle byte and bit order. | get_startbit | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def calculate_raw_range(self):
"""Compute raw signal range based on Signal bit width and whether the Signal is signed or not.
:return: Signal range, i.e. (0, 15) for unsigned 4 bit Signal or (-8, 7) for signed one.
:rtype: tuple
"""
factory = (
self.float_factory
... | Compute raw signal range based on Signal bit width and whether the Signal is signed or not.
:return: Signal range, i.e. (0, 15) for unsigned 4 bit Signal or (-8, 7) for signed one.
:rtype: tuple
| calculate_raw_range | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def set_min(self, min=None):
# type: (canmatrix.types.OptionalPhysicalValue) -> canmatrix.types.OptionalPhysicalValue
"""Set minimal physical Signal value.
:param min: minimal physical value. If None and enabled (`calc_min_for_none`), compute using `calc_min`
"""
self.min = min
... | Set minimal physical Signal value.
:param min: minimal physical value. If None and enabled (`calc_min_for_none`), compute using `calc_min`
| set_min | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def calc_min(self): # type: () -> canmatrix.types.PhysicalValue
"""Compute minimal physical Signal value based on offset and factor and `calculate_raw_range`."""
rawMin = self.calculate_raw_range()[0]
return self.offset + (self.float_factory(rawMin) * self.factor) | Compute minimal physical Signal value based on offset and factor and `calculate_raw_range`. | calc_min | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def set_max(self, max=None):
# type: (canmatrix.types.OptionalPhysicalValue) -> canmatrix.types.OptionalPhysicalValue
"""Set maximal signal value.
:param max: minimal physical value. If None and enabled (`calc_max_for_none`), compute using `calc_max`
"""
self.max = max
... | Set maximal signal value.
:param max: minimal physical value. If None and enabled (`calc_max_for_none`), compute using `calc_max`
| set_max | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
def calc_max(self): # type: () -> canmatrix.types.PhysicalValue
"""Compute maximal physical Signal value based on offset, factor and `calculate_raw_range`."""
rawMax = self.calculate_raw_range()[1]
return self.offset + (self.float_factory(rawMax) * self.factor) | Compute maximal physical Signal value based on offset, factor and `calculate_raw_range`. | calc_max | python | ebroecker/canmatrix | src/canmatrix/canmatrix.py | https://github.com/ebroecker/canmatrix/blob/master/src/canmatrix/canmatrix.py | BSD-2-Clause |
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