Datasets:
project_name stringlengths 6 104 | file_name stringlengths 4 89 | full_name stringlengths 1 102 | func_name stringlengths 1 85 | docstring stringlengths 13 836 | docstring_tokens listlengths 4 122 | code stringlengths 23 39.7k | code_tokens stringlengths 29 44.6k | url int64 3 986k |
|---|---|---|---|---|---|---|---|---|
arshpreetsingh/quantopian-machinelearning | key_processor.py | KeyPressEvent.app | app | The current `Application` object. | [
"The",
"current",
"`Application`",
"object."
] | def app(self):
return self._app | ['def', 'app(self):', 'return', 'self._app'] | 892,308 |
jeromewang-github/computer_vision | demo_mnn.py | area_of | area_of | Compute the areas of rectangles given two corners. | [
"Compute",
"the",
"areas",
"of",
"rectangles",
"given",
"two",
"corners."
] | def area_of(left_top, right_bottom):
hw = np.clip(right_bottom - left_top, 0.0, None)
return hw[..., 0] * hw[..., 1] | ['def', 'area_of(left_top,', 'right_bottom):', 'hw', '=', 'np.clip(right_bottom', '-', 'left_top,', '0.0,', 'None)', 'return', 'hw[...,', '0]', '*', 'hw[...,', '1]'] | 474,862 |
weimin17/Object-Detection_HelmetDetection | model_hparams.py | create_hparams | create_hparams | Returns hyperparameters, including any flag value overrides. | [
"Returns",
"hyperparameters,",
"including",
"any",
"flag",
"value",
"overrides."
] | def create_hparams(hparams_overrides=None):
hparams = tf.contrib.training.HParams(load_pretrained=True)
if hparams_overrides:
hparams = hparams.parse(hparams_overrides)
return hparams | ['def', 'create_hparams(hparams_overrides=None):', 'hparams', '=', 'tf.contrib.training.HParams(load_pretrained=True)', 'if', 'hparams_overrides:', 'hparams', '=', 'hparams.parse(hparams_overrides)', 'return', 'hparams'] | 751,511 |
ldkong1205/LaserMix | dbsampler.py | DataBaseSampler.filter_by_difficulty | filter_by_difficulty | Filter ground truths by difficulties. | [
"Filter",
"ground",
"truths",
"by",
"difficulties."
] | def filter_by_difficulty(db_infos: dict, removed_difficulty: list) -> dict:
new_db_infos = {}
for (key, dinfos) in db_infos.items():
new_db_infos[key] = [info for info in dinfos if info['difficulty'] not in removed_difficulty]
return new_db_infos | ['def', 'filter_by_difficulty(db_infos:', 'dict,', 'removed_difficulty:', 'list)', '->', 'dict:', 'new_db_infos', '=', '{}', 'for', '(key,', 'dinfos)', 'in', 'db_infos.items():', 'new_db_infos[key]', '=', '[info', 'for', 'info', 'in', 'dinfos', 'if', "info['difficulty']", 'not', 'in', 'removed_difficulty]', 'return', '... | 623,794 |
paulorauber/rl | tensor_specs.py | OneHotDiscreteTensorSpec.to_categorical_spec | to_categorical_spec | Converts the spec to the equivalent categorical spec. | [
"Converts",
"the",
"spec",
"to",
"the",
"equivalent",
"categorical",
"spec."
] | def to_categorical_spec(self) -> DiscreteTensorSpec:
return DiscreteTensorSpec(self.space.n, device=self.device, shape=self.shape[:-1], mask=self.mask) | ['def', 'to_categorical_spec(self)', '->', 'DiscreteTensorSpec:', 'return', 'DiscreteTensorSpec(self.space.n,', 'device=self.device,', 'shape=self.shape[:-1],', 'mask=self.mask)'] | 858,715 |
am-shashank/artificial-intelligence | core.py | _extrema_operation.outer | outer | Return the function applied to the outer product of a and b. | [
"Return",
"the",
"function",
"applied",
"to",
"the",
"outer",
"product",
"of",
"a",
"and",
"b."
] | def outer(self, a, b):
ma = getmask(a)
mb = getmask(b)
if ma is nomask and mb is nomask:
m = nomask
else:
ma = getmaskarray(a)
mb = getmaskarray(b)
m = logical_or.outer(ma, mb)
result = self.f.outer(filled(a), filled(b))
if not isinstance(result, MaskedArray):
... | ['def', 'outer(self,', 'a,', 'b):', 'ma', '=', 'getmask(a)', 'mb', '=', 'getmask(b)', 'if', 'ma', 'is', 'nomask', 'and', 'mb', 'is', 'nomask:', 'm', '=', 'nomask', 'else:', 'ma', '=', 'getmaskarray(a)', 'mb', '=', 'getmaskarray(b)', 'm', '=', 'logical_or.outer(ma,', 'mb)', 'result', '=', 'self.f.outer(filled(a),', 'fil... | 171,725 |
KleinYuan/tf-object-detection | oid_tfrecord_creation.py | tf_example_from_annotations_data_frame | tf_example_from_annotations_data_frame | Populates a TF Example message with image annotations from a data frame. | [
"Populates",
"a",
"TF",
"Example",
"message",
"with",
"image",
"annotations",
"from",
"a",
"data",
"frame."
] | def tf_example_from_annotations_data_frame(annotations_data_frame, label_map, encoded_image):
filtered_data_frame = annotations_data_frame[annotations_data_frame.LabelName.isin(label_map)]
image_id = annotations_data_frame.ImageID.iloc[0]
feature_map = {standard_fields.TfExampleFields.object_bbox_ymin: data... | ['def', 'tf_example_from_annotations_data_frame(annotations_data_frame,', 'label_map,', 'encoded_image):', 'filtered_data_frame', '=', 'annotations_data_frame[annotations_data_frame.LabelName.isin(label_map)]', 'image_id', '=', 'annotations_data_frame.ImageID.iloc[0]', 'feature_map', '=', '{standard_fields.TfExampleFie... | 914,811 |
seltzerfish/guardyn | gtest_help_test.py | GTestHelpTest.testRunsTestsWithoutHelpFlag | testRunsTestsWithoutHelpFlag | Verifies that when no help flag is specified, the tests are run and the help message is not printed. | [
"Verifies",
"that",
"when",
"no",
"help",
"flag",
"is",
"specified,",
"the",
"tests",
"are",
"run",
"and",
"the",
"help",
"message",
"is",
"not",
"printed."
] | def testRunsTestsWithoutHelpFlag(self):
self.TestNonHelpFlag(None) | ['def', 'testRunsTestsWithoutHelpFlag(self):', 'self.TestNonHelpFlag(None)'] | 572,280 |
zackmcnulty/CSE_446-Machine_Learning | figure.py | Figure.get_figheight | get_figheight | Return the figure height as a float. | [
"Return",
"the",
"figure",
"height",
"as",
"a",
"float."
] | def get_figheight(self):
return self.bbox_inches.height | ['def', 'get_figheight(self):', 'return', 'self.bbox_inches.height'] | 194,326 |
Speech-Lab-IITM/CCC-wav2vec-2.0 | model.py | PipelineParallelTransformerModel.max_positions_helper | max_positions_helper | Maximum input length supported by the encoder or decoder. | [
"Maximum",
"input",
"length",
"supported",
"by",
"the",
"encoder",
"or",
"decoder."
] | def max_positions_helper(self, embedding_layer, max_positions_field='max_source_positions'):
if embedding_layer.embed_positions is None:
return getattr(embedding_layer, max_positions_field)
return min(getattr(embedding_layer, max_positions_field), embedding_layer.embed_positions.max_positions) | ['def', 'max_positions_helper(self,', 'embedding_layer,', "max_positions_field='max_source_positions'):", 'if', 'embedding_layer.embed_positions', 'is', 'None:', 'return', 'getattr(embedding_layer,', 'max_positions_field)', 'return', 'min(getattr(embedding_layer,', 'max_positions_field),', 'embedding_layer.embed_positi... | 103,951 |
ChenhongyiYang/PPAL | transformer.py | DeformableDetrTransformer.get_reference_points | get_reference_points | Get the reference points used in decoder. | [
"Get",
"the",
"reference",
"points",
"used",
"in",
"decoder."
] | def get_reference_points(spatial_shapes, valid_ratios, device):
reference_points_list = []
for (lvl, (H, W)) in enumerate(spatial_shapes):
(ref_y, ref_x) = torch.meshgrid(torch.linspace(0.5, H - 0.5, H, dtype=torch.float32, device=device), torch.linspace(0.5, W - 0.5, W, dtype=torch.float32, device=devi... | ['def', 'get_reference_points(spatial_shapes,', 'valid_ratios,', 'device):', 'reference_points_list', '=', '[]', 'for', '(lvl,', '(H,', 'W))', 'in', 'enumerate(spatial_shapes):', '(ref_y,', 'ref_x)', '=', 'torch.meshgrid(torch.linspace(0.5,', 'H', '-', '0.5,', 'H,', 'dtype=torch.float32,', 'device=device),', 'torch.lin... | 821,829 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | imagenet_main.py | record_parser | record_parser | Parse an ImageNet record from `value`. | [
"Parse",
"an",
"ImageNet",
"record",
"from",
"`value`."
] | def record_parser(value, is_training):
keys_to_features = {'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''), 'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'), 'image/class/label': tf.FixedLenFeature([], dtype=tf.int64, default_value=-1), 'image/class/text': tf.FixedLenFeat... | ['def', 'record_parser(value,', 'is_training):', 'keys_to_features', '=', "{'image/encoded':", 'tf.FixedLenFeature((),', 'tf.string,', "default_value=''),", "'image/format':", 'tf.FixedLenFeature((),', 'tf.string,', "default_value='jpeg'),", "'image/class/label':", 'tf.FixedLenFeature([],', 'dtype=tf.int64,', 'default_... | 13,997 |
scikit-learn/scikit-learn | test_column_transformer.py | test_column_transform_set_output_mixed | test_column_transform_set_output_mixed | Check ColumnTransformer outputs mixed types correctly. | [
"Check",
"ColumnTransformer",
"outputs",
"mixed",
"types",
"correctly."
] | def test_column_transform_set_output_mixed(remainder, fit_transform):
pd = pytest.importorskip('pandas')
df = pd.DataFrame({'pet': pd.Series(['dog', 'cat', 'snake'], dtype='category'), 'color': pd.Series(['green', 'blue', 'red'], dtype='object'), 'age': [1.4, 2.1, 4.4], 'height': [20, 40, 10], 'distance': pd.Se... | ['def', 'test_column_transform_set_output_mixed(remainder,', 'fit_transform):', 'pd', '=', "pytest.importorskip('pandas')", 'df', '=', "pd.DataFrame({'pet':", "pd.Series(['dog',", "'cat',", "'snake'],", "dtype='category'),", "'color':", "pd.Series(['green',", "'blue',", "'red'],", "dtype='object'),", "'age':", '[1.4,',... | 852,897 |
ermongroup/MA-AIRL | tf_util.py | TfInput.make_feed_dict | make_feed_dict | Given data input it to the placeholder(s). | [
"Given",
"data",
"input",
"it",
"to",
"the",
"placeholder(s)."
] | def make_feed_dict(data):
raise NotImplemented() | ['def', 'make_feed_dict(data):', 'raise', 'NotImplemented()'] | 620,093 |
greydanus/pythonic_ocr | environment.py | Template.is_up_to_date | is_up_to_date | If this variable is `False` there is a newer version available. | [
"If",
"this",
"variable",
"is",
"`False`",
"there",
"is",
"a",
"newer",
"version",
"available."
] | def is_up_to_date(self):
if self._uptodate is None:
return True
return self._uptodate() | ['def', 'is_up_to_date(self):', 'if', 'self._uptodate', 'is', 'None:', 'return', 'True', 'return', 'self._uptodate()'] | 299,248 |
keya-desai/Natural-Language-Processing | parsing_system.py | ParsingSystem.can_apply | can_apply | Determine whether the given transition is legal for this configuration. | [
"Determine",
"whether",
"the",
"given",
"transition",
"is",
"legal",
"for",
"this",
"configuration."
] | def can_apply(self, configuration: Configuration, transition: str) -> bool:
if transition.startswith('L') or transition.startswith('R'):
label = transition[2:-1]
if transition.startswith('L'):
h = configuration.get_stack(0)
else:
h = configuration.get_stack(1)
... | ['def', 'can_apply(self,', 'configuration:', 'Configuration,', 'transition:', 'str)', '->', 'bool:', 'if', "transition.startswith('L')", 'or', "transition.startswith('R'):", 'label', '=', 'transition[2:-1]', 'if', "transition.startswith('L'):", 'h', '=', 'configuration.get_stack(0)', 'else:', 'h', '=', 'configuration.g... | 688,306 |
nosmokingbandit/watcher | servers.py | check_port | check_port | Raise an error if the given port is not free on the given host. | [
"Raise",
"an",
"error",
"if",
"the",
"given",
"port",
"is",
"not",
"free",
"on",
"the",
"given",
"host."
] | def check_port(host, port, timeout=1.0):
if not host:
raise ValueError("Host values of '' or None are not allowed.")
host = client_host(host)
port = int(port)
import socket
try:
info = socket.getaddrinfo(host, port, socket.AF_UNSPEC, socket.SOCK_STREAM)
except socket.gaierror:
... | ['def', 'check_port(host,', 'port,', 'timeout=1.0):', 'if', 'not', 'host:', 'raise', 'ValueError("Host', 'values', 'of', "''", 'or', 'None', 'are', 'not', 'allowed.")', 'host', '=', 'client_host(host)', 'port', '=', 'int(port)', 'import', 'socket', 'try:', 'info', '=', 'socket.getaddrinfo(host,', 'port,', 'socket.AF_UN... | 381,519 |
dawidkopczyk/autoencoder | setup_inception.py | maybe_download_and_extract | maybe_download_and_extract | Download and extract model tar file. | [
"Download",
"and",
"extract",
"model",
"tar",
"file."
] | def maybe_download_and_extract():
dest_directory = FLAGS.model_dir
if not os.path.exists(dest_directory):
os.makedirs(dest_directory)
filename = DATA_URL.split('/')[-1]
filepath = os.path.join(dest_directory, filename)
if not os.path.exists(filepath):
def _progress(count, block_size... | ['def', 'maybe_download_and_extract():', 'dest_directory', '=', 'FLAGS.model_dir', 'if', 'not', 'os.path.exists(dest_directory):', 'os.makedirs(dest_directory)', 'filename', '=', "DATA_URL.split('/')[-1]", 'filepath', '=', 'os.path.join(dest_directory,', 'filename)', 'if', 'not', 'os.path.exists(filepath):', 'def', '_p... | 418,928 |
pedrojrv/nucml | plot.py | xgb_training_w_path | xgb_training_w_path | Plot XGB retraining given the path to the results. | [
"Plot",
"XGB",
"retraining",
"given",
"the",
"path",
"to",
"the",
"results."
] | def xgb_training_w_path(path_to_csv, save=False, saving_path='xgb_training.png'):
training_progress = pd.read_csv(path_to_csv)
plt.figure(figsize=(18, 8))
plt.plot(training_progress.mae_train, label='Train MAE', marker='x', markersize='20')
plt.plot(training_progress.mae_test, label='Validation MAE', ma... | ['def', 'xgb_training_w_path(path_to_csv,', 'save=False,', "saving_path='xgb_training.png'):", 'training_progress', '=', 'pd.read_csv(path_to_csv)', 'plt.figure(figsize=(18,', '8))', 'plt.plot(training_progress.mae_train,', "label='Train", "MAE',", "marker='x',", "markersize='20')", 'plt.plot(training_progress.mae_test... | 249,756 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | check.py | Eq | Eq | Raises an error if |lhs| does not equal |rhs|. | [
"Raises",
"an",
"error",
"if",
"|lhs|",
"does",
"not",
"equal",
"|rhs|."
] | def Eq(lhs, rhs, message='', error=ValueError):
if lhs != rhs:
raise error('Expected (%s) == (%s): %s' % (lhs, rhs, message)) | ['def', 'Eq(lhs,', 'rhs,', "message='',", 'error=ValueError):', 'if', 'lhs', '!=', 'rhs:', 'raise', "error('Expected", '(%s)', '==', '(%s):', "%s'", '%', '(lhs,', 'rhs,', 'message))'] | 28,935 |
yoonc5536/computer_vision | draw.py | choose_color_by_layertype | choose_color_by_layertype | Define colors for nodes based on the layer type. | [
"Define",
"colors",
"for",
"nodes",
"based",
"on",
"the",
"layer",
"type."
] | def choose_color_by_layertype(layertype):
color = '#6495ED'
if layertype == 'Convolution':
color = '#FF5050'
elif layertype == 'Pooling':
color = '#FF9900'
elif layertype == 'InnerProduct':
color = '#CC33FF'
return color | ['def', 'choose_color_by_layertype(layertype):', 'color', '=', "'#6495ED'", 'if', 'layertype', '==', "'Convolution':", 'color', '=', "'#FF5050'", 'elif', 'layertype', '==', "'Pooling':", 'color', '=', "'#FF9900'", 'elif', 'layertype', '==', "'InnerProduct':", 'color', '=', "'#CC33FF'", 'return', 'color'] | 472,559 |
ifwe/digsby | simplemenu.py | SimpleMenuSpine.TriggerItem | TriggerItem | Steps to take when a item is clicked. | [
"Steps",
"to",
"take",
"when",
"a",
"item",
"is",
"clicked."
] | def TriggerItem(self, item):
if item.method is not None:
wx.CallAfter(item.method, item)
elif self.Parent.callback:
wx.CallAfter(self.Parent.callback, item)
else:
menuevent = MenuEvent(wx.wxEVT_COMMAND_MENU_SELECTED, item.id)
self.Parent.AddPendingEvent(menuevent)
self.Pa... | ['def', 'TriggerItem(self,', 'item):', 'if', 'item.method', 'is', 'not', 'None:', 'wx.CallAfter(item.method,', 'item)', 'elif', 'self.Parent.callback:', 'wx.CallAfter(self.Parent.callback,', 'item)', 'else:', 'menuevent', '=', 'MenuEvent(wx.wxEVT_COMMAND_MENU_SELECTED,', 'item.id)', 'self.Parent.AddPendingEvent(menueve... | 185,591 |
ArdaGunay99/Key_Detection_Unsupervised_Learning | triinterpolate.py | _Sparse_Matrix_coo.diag | diag | Returns the (dense) vector of the diagonal elements. | [
"Returns",
"the",
"(dense)",
"vector",
"of",
"the",
"diagonal",
"elements."
] | def diag(self):
in_diag = self.rows == self.cols
diag = np.zeros(min(self.n, self.n), dtype=np.float64)
diag[self.rows[in_diag]] = self.vals[in_diag]
return diag | ['def', 'diag(self):', 'in_diag', '=', 'self.rows', '==', 'self.cols', 'diag', '=', 'np.zeros(min(self.n,', 'self.n),', 'dtype=np.float64)', 'diag[self.rows[in_diag]]', '=', 'self.vals[in_diag]', 'return', 'diag'] | 258,065 |
chainer/chainer | link.py | ChainList.insert | insert | Insert a child link at the given index. | [
"Insert",
"a",
"child",
"link",
"at",
"the",
"given",
"index."
] | def insert(self, index: int, link: Link) -> None:
if index == len(self._children):
self._children.append(link)
link.name = str(index)
else:
self._children.insert(index, link)
for (i, c) in enumerate(self._children):
c.name = str(i) | ['def', 'insert(self,', 'index:', 'int,', 'link:', 'Link)', '->', 'None:', 'if', 'index', '==', 'len(self._children):', 'self._children.append(link)', 'link.name', '=', 'str(index)', 'else:', 'self._children.insert(index,', 'link)', 'for', '(i,', 'c)', 'in', 'enumerate(self._children):', 'c.name', '=', 'str(i)'] | 477,011 |
thu-ml/tianshou | discrete.py | FullQuantileFunction.forward | forward | Mapping: s -> Q(s, \*). | [
"Mapping:",
"s",
"->",
"Q(s,",
"\\*)."
] | def forward(self, obs: Union[np.ndarray, torch.Tensor], propose_model: FractionProposalNetwork, fractions: Optional[Batch]=None, **kwargs: Any) -> tuple[Any, torch.Tensor]:
(logits, hidden) = self.preprocess(obs, state=kwargs.get('state', None))
if fractions is None:
(taus, tau_hats, entropies) = propos... | ['def', 'forward(self,', 'obs:', 'Union[np.ndarray,', 'torch.Tensor],', 'propose_model:', 'FractionProposalNetwork,', 'fractions:', 'Optional[Batch]=None,', '**kwargs:', 'Any)', '->', 'tuple[Any,', 'torch.Tensor]:', '(logits,', 'hidden)', '=', 'self.preprocess(obs,', "state=kwargs.get('state',", 'None))', 'if', 'fracti... | 355,329 |
kornia/kornia | face_detection.py | FaceDetectorResult.ymin | ymin | The bounding box top-left y-coordinate. | [
"The",
"bounding",
"box",
"top-left",
"y-coordinate."
] | def ymin(self) -> torch.Tensor:
return self._data[..., 1] | ['def', 'ymin(self)', '->', 'torch.Tensor:', 'return', 'self._data[...,', '1]'] | 621,586 |
roboflow/roboflow-computer-vision-utilities | uploadby_split.py | get_image_paths | get_image_paths | Get a list of image file paths from a directory. | [
"Get",
"a",
"list",
"of",
"image",
"file",
"paths",
"from",
"a",
"directory."
] | def get_image_paths(directory: str):
image_extensions = {'.jpeg', '.jpg', '.png'}
image_paths = []
for file in os.listdir(directory):
file_extension = os.path.splitext(file)[1].lower()
if file_extension in image_extensions:
image_paths.append(os.path.join(directory, file))
re... | ['def', 'get_image_paths(directory:', 'str):', 'image_extensions', '=', "{'.jpeg',", "'.jpg',", "'.png'}", 'image_paths', '=', '[]', 'for', 'file', 'in', 'os.listdir(directory):', 'file_extension', '=', 'os.path.splitext(file)[1].lower()', 'if', 'file_extension', 'in', 'image_extensions:', 'image_paths.append(os.path.j... | 825,952 |
zihuitang/medical_AI_platform | warnings.py | formatwarning | formatwarning | Function to format a warning the standard way. | [
"Function",
"to",
"format",
"a",
"warning",
"the",
"standard",
"way."
] | def formatwarning(message, category, filename, lineno, line=None):
msg = WarningMessage(message, category, filename, lineno, None, line)
return _formatwarnmsg_impl(msg) | ['def', 'formatwarning(message,', 'category,', 'filename,', 'lineno,', 'line=None):', 'msg', '=', 'WarningMessage(message,', 'category,', 'filename,', 'lineno,', 'None,', 'line)', 'return', '_formatwarnmsg_impl(msg)'] | 281,792 |
rifqind/Agent-Programs-3KS1 | tests.py | test_none | test_none | Return true if the variable is none. | [
"Return",
"true",
"if",
"the",
"variable",
"is",
"none."
] | def test_none(value):
return value is None | ['def', 'test_none(value):', 'return', 'value', 'is', 'None'] | 42,374 |
Summer0410/Natural-Language-Processing | parsing_system.py | ParsingSystem.make_transitions | make_transitions | Generate all possible transitions which this parsing system can take for any given configuration. | [
"Generate",
"all",
"possible",
"transitions",
"which",
"this",
"parsing",
"system",
"can",
"take",
"for",
"any",
"given",
"configuration."
] | def make_transitions(self) -> None:
for label in self.labels:
self.transitions.append('L(' + label + ')')
for label in self.labels:
self.transitions.append('R(' + label + ')')
self.transitions.append('S') | ['def', 'make_transitions(self)', '->', 'None:', 'for', 'label', 'in', 'self.labels:', "self.transitions.append('L('", '+', 'label', '+', "')')", 'for', 'label', 'in', 'self.labels:', "self.transitions.append('R('", '+', 'label', '+', "')')", "self.transitions.append('S')"] | 688,589 |
Tommy-Ngx/Multi_TimeGAN | mygru_cell.py | MyGRUCell4.call | call | Gated recurrent unit (GRU) with nunits cells. | [
"Gated",
"recurrent",
"unit",
"(GRU)",
"with",
"nunits",
"cells."
] | def call(self, inputs, state):
totalLength = inputs.get_shape().as_list()[1]
inputs_ = inputs[:, 0:totalLength - self._num_units]
rth = inputs[:, totalLength - self._num_units:]
inputs = inputs_
state = math_ops.multiply(rth, state)
if self._gate_linear is None:
bias_ones = self._bias_in... | ['def', 'call(self,', 'inputs,', 'state):', 'totalLength', '=', 'inputs.get_shape().as_list()[1]', 'inputs_', '=', 'inputs[:,', '0:totalLength', '-', 'self._num_units]', 'rth', '=', 'inputs[:,', 'totalLength', '-', 'self._num_units:]', 'inputs', '=', 'inputs_', 'state', '=', 'math_ops.multiply(rth,', 'state)', 'if', 's... | 644,517 |
ryu-ed/SpaceInvaders_Ros | utils.py | find_try_except_wrapper_node | find_try_except_wrapper_node | Return the ExceptHandler or the TryExcept node in which the node is. | [
"Return",
"the",
"ExceptHandler",
"or",
"the",
"TryExcept",
"node",
"in",
"which",
"the",
"node",
"is."
] | def find_try_except_wrapper_node(node: astroid.node_classes.NodeNG) -> Optional[Union[astroid.ExceptHandler, astroid.TryExcept]]:
current = node
ignores = (astroid.ExceptHandler, astroid.TryExcept)
while current and (not isinstance(current.parent, ignores)):
current = current.parent
if current a... | ['def', 'find_try_except_wrapper_node(node:', 'astroid.node_classes.NodeNG)', '->', 'Optional[Union[astroid.ExceptHandler,', 'astroid.TryExcept]]:', 'current', '=', 'node', 'ignores', '=', '(astroid.ExceptHandler,', 'astroid.TryExcept)', 'while', 'current', 'and', '(not', 'isinstance(current.parent,', 'ignores)):', 'cu... | 370,019 |
tensorflow/data-validation | stats_impl.py | generate_statistics_in_memory | generate_statistics_in_memory | Generates statistics for an in-memory list of examples. | [
"Generates",
"statistics",
"for",
"an",
"in-memory",
"list",
"of",
"examples."
] | def generate_statistics_in_memory(record_batch: pa.RecordBatch, options: stats_options.StatsOptions=stats_options.StatsOptions()) -> statistics_pb2.DatasetFeatureStatisticsList:
stats_generators = cast(List[stats_generator.CombinerStatsGenerator], get_generators(options, in_memory=True))
partial_stats = generat... | ['def', 'generate_statistics_in_memory(record_batch:', 'pa.RecordBatch,', 'options:', 'stats_options.StatsOptions=stats_options.StatsOptions())', '->', 'statistics_pb2.DatasetFeatureStatisticsList:', 'stats_generators', '=', 'cast(List[stats_generator.CombinerStatsGenerator],', 'get_generators(options,', 'in_memory=Tru... | 497,452 |
scikit-learn/scikit-learn | test_tree.py | test_missing_values_best_splitter_to_left | test_missing_values_best_splitter_to_left | Missing values spanning only one class at fit-time must make missing values at predict-time be classified has belonging to this class. | [
"Missing",
"values",
"spanning",
"only",
"one",
"class",
"at",
"fit-time",
"must",
"make",
"missing",
"values",
"at",
"predict-time",
"be",
"classified",
"has",
"belonging",
"to",
"this",
"class."
] | def test_missing_values_best_splitter_to_left(criterion):
X = np.array([[np.nan] * 4 + [0, 1, 2, 3, 4, 5]]).T
y = np.array([0] * 4 + [1] * 6)
dtc = DecisionTreeClassifier(random_state=42, max_depth=2, criterion=criterion)
dtc.fit(X, y)
X_test = np.array([[np.nan, 5, np.nan]]).T
y_pred = dtc.pred... | ['def', 'test_missing_values_best_splitter_to_left(criterion):', 'X', '=', 'np.array([[np.nan]', '*', '4', '+', '[0,', '1,', '2,', '3,', '4,', '5]]).T', 'y', '=', 'np.array([0]', '*', '4', '+', '[1]', '*', '6)', 'dtc', '=', 'DecisionTreeClassifier(random_state=42,', 'max_depth=2,', 'criterion=criterion)', 'dtc.fit(X,',... | 854,228 |
rudranil723/mini-main | autopep8.py | FixPEP8.fix_w391 | fix_w391 | Remove trailing blank lines. | [
"Remove",
"trailing",
"blank",
"lines."
] | def fix_w391(self, _):
blank_count = 0
for line in reversed(self.source):
line = line.rstrip()
if line:
break
else:
blank_count += 1
original_length = len(self.source)
self.source = self.source[:original_length - blank_count]
return range(1, 1 + origin... | ['def', 'fix_w391(self,', '_):', 'blank_count', '=', '0', 'for', 'line', 'in', 'reversed(self.source):', 'line', '=', 'line.rstrip()', 'if', 'line:', 'break', 'else:', 'blank_count', '+=', '1', 'original_length', '=', 'len(self.source)', 'self.source', '=', 'self.source[:original_length', '-', 'blank_count]', 'return',... | 313,928 |
PacktPublishing/Hands-On-Artificial--for-Banking | test_base.py | sparse_test_class | sparse_test_class | Construct a base class, optionally converting some of the tests in the suite to check that the feature is not implemented. | [
"Construct",
"a",
"base",
"class,",
"optionally",
"converting",
"some",
"of",
"the",
"tests",
"in",
"the",
"suite",
"to",
"check",
"that",
"the",
"feature",
"is",
"not",
"implemented."
] | def sparse_test_class(getset=True, slicing=True, slicing_assign=True, fancy_indexing=True, fancy_assign=True, fancy_multidim_indexing=True, fancy_multidim_assign=True, minmax=True, nnz_axis=True):
bases = (_TestCommon, _possibly_unimplemented(_TestGetSet, getset), _TestSolve, _TestInplaceArithmetic, _TestArithmetic... | ['def', 'sparse_test_class(getset=True,', 'slicing=True,', 'slicing_assign=True,', 'fancy_indexing=True,', 'fancy_assign=True,', 'fancy_multidim_indexing=True,', 'fancy_multidim_assign=True,', 'minmax=True,', 'nnz_axis=True):', 'bases', '=', '(_TestCommon,', '_possibly_unimplemented(_TestGetSet,', 'getset),', '_TestSol... | 203,474 |
thaines/helit | pool.py | Pool.size | size | Returns how many entities are currently stored. | [
"Returns",
"how",
"many",
"entities",
"are",
"currently",
"stored."
] | def size(self):
return len(self.entities) | ['def', 'size(self):', 'return', 'len(self.entities)'] | 591,601 |
LiqunChen0606/Triangle-GAN | triGan_mnist.py | data_network_2 | data_network_2 | Approximate z log data density. | [
"Approximate",
"z",
"log",
"data",
"density."
] | def data_network_2(x, y):
with tf.variable_scope('D2'):
d = discriminator(x, y)
return tf.squeeze(d, squeeze_dims=[1]) | ['def', 'data_network_2(x,', 'y):', 'with', "tf.variable_scope('D2'):", 'd', '=', 'discriminator(x,', 'y)', 'return', 'tf.squeeze(d,', 'squeeze_dims=[1])'] | 951,573 |
PacktPublishing/Hands-On-Artificial--for-Banking | tarfile.py | stn | stn | Convert a string to a null-terminated bytes object. | [
"Convert",
"a",
"string",
"to",
"a",
"null-terminated",
"bytes",
"object."
] | def stn(s, length, encoding, errors):
s = s.encode(encoding, errors)
return s[:length] + (length - len(s)) * NUL | ['def', 'stn(s,', 'length,', 'encoding,', 'errors):', 's', '=', 's.encode(encoding,', 'errors)', 'return', 's[:length]', '+', '(length', '-', 'len(s))', '*', 'NUL'] | 237,854 |
ViTAE-Transformer/ViTDet | transformer.py | DeformableDetrTransformer.get_valid_ratio | get_valid_ratio | Get the valid radios of feature maps of all level. | [
"Get",
"the",
"valid",
"radios",
"of",
"feature",
"maps",
"of",
"all",
"level."
] | def get_valid_ratio(self, mask):
(_, H, W) = mask.shape
valid_H = torch.sum(~mask[:, :, 0], 1)
valid_W = torch.sum(~mask[:, 0, :], 1)
valid_ratio_h = valid_H.float() / H
valid_ratio_w = valid_W.float() / W
valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
return valid_ratio | ['def', 'get_valid_ratio(self,', 'mask):', '(_,', 'H,', 'W)', '=', 'mask.shape', 'valid_H', '=', 'torch.sum(~mask[:,', ':,', '0],', '1)', 'valid_W', '=', 'torch.sum(~mask[:,', '0,', ':],', '1)', 'valid_ratio_h', '=', 'valid_H.float()', '/', 'H', 'valid_ratio_w', '=', 'valid_W.float()', '/', 'W', 'valid_ratio', '=', 'to... | 945,822 |
arijit7978/arijit7978-Artificial-Intelligence-CSE-471--PacMan | solvers.py | GradientDescentSolver.get_updates_with_momentum | get_updates_with_momentum | Question 5: Returns the gradient descent updates when momentum is used. | [
"Question",
"5:",
"Returns",
"the",
"gradient",
"descent",
"updates",
"when",
"momentum",
"is",
"used."
] | def get_updates_with_momentum(self, loss_tensor, param_vars):
grad_tensors = tf.gradients(loss_tensor, param_vars)
vel_vars = [tf.Variable(np.zeros(param_var.get_shape(), dtype=np.float32)) for param_var in param_vars]
tfu.get_session().run([vel_var.initializer for vel_var in vel_vars])
updates = []
... | ['def', 'get_updates_with_momentum(self,', 'loss_tensor,', 'param_vars):', 'grad_tensors', '=', 'tf.gradients(loss_tensor,', 'param_vars)', 'vel_vars', '=', '[tf.Variable(np.zeros(param_var.get_shape(),', 'dtype=np.float32))', 'for', 'param_var', 'in', 'param_vars]', 'tfu.get_session().run([vel_var.initializer', 'for',... | 34,706 |
kiseliu/NaturalLanguageProcessing | run_classifier.py | DataProcessor.get_dev_examples | get_dev_examples | Gets a collection of `InputExample`s for the dev set. | [
"Gets",
"a",
"collection",
"of",
"`InputExample`s",
"for",
"the",
"dev",
"set."
] | def get_dev_examples(self, data_dir):
raise NotImplementedError() | ['def', 'get_dev_examples(self,', 'data_dir):', 'raise', 'NotImplementedError()'] | 713,617 |
tobegit3hub/deep_image_model | debug_data.py | DebugDumpDir.get_tensor_file_paths | get_tensor_file_paths | Get the file paths from a debug-dumped tensor. | [
"Get",
"the",
"file",
"paths",
"from",
"a",
"debug-dumped",
"tensor."
] | def get_tensor_file_paths(self, node_name, output_slot, debug_op):
watch_key = _get_tensor_watch_key(node_name, output_slot, debug_op)
if watch_key not in self._watch_key_to_datum:
raise ValueError('Watch key "%s" does not exist in the debug dump' % watch_key)
return [datum.file_path for datum in se... | ['def', 'get_tensor_file_paths(self,', 'node_name,', 'output_slot,', 'debug_op):', 'watch_key', '=', '_get_tensor_watch_key(node_name,', 'output_slot,', 'debug_op)', 'if', 'watch_key', 'not', 'in', 'self._watch_key_to_datum:', 'raise', "ValueError('Watch", 'key', '"%s"', 'does', 'not', 'exist', 'in', 'the', 'debug', "d... | 182,332 |
huawei-noah/xingtian | starcraft_qmix.py | StarCraftQMix.do_one_interaction | do_one_interaction | Overwrite with obs and global states. | [
"Overwrite",
"with",
"obs",
"and",
"global",
"states."
] | def do_one_interaction(self, raw_state, use_explore=True):
pre_transition_data = {'state': [self.env.get_state()], 'avail_actions': [self.env.get_avail_actions()], 'obs': [self.env.get_obs()]}
self.batch.update(pre_transition_data, ts=self.timestamp_per_agent)
_start0 = time()
actions = self.alg.predict... | ['def', 'do_one_interaction(self,', 'raw_state,', 'use_explore=True):', 'pre_transition_data', '=', "{'state':", '[self.env.get_state()],', "'avail_actions':", '[self.env.get_avail_actions()],', "'obs':", '[self.env.get_obs()]}', 'self.batch.update(pre_transition_data,', 'ts=self.timestamp_per_agent)', '_start0', '=', ... | 962,066 |
enuguru/artificial_intelligence_and_machine_learning | index.py | Index.doc_count | doc_count | Returns the total number of UNDELETED documents in this index. | [
"Returns",
"the",
"total",
"number",
"of",
"UNDELETED",
"documents",
"in",
"this",
"index."
] | def doc_count(self):
r = self.reader()
try:
return r.doc_count()
finally:
r.close() | ['def', 'doc_count(self):', 'r', '=', 'self.reader()', 'try:', 'return', 'r.doc_count()', 'finally:', 'r.close()'] | 132,946 |
arshpreetsingh/quantopian-machinelearning | locks.py | Semaphore.release | release | Increment the counter and wake one waiter. | [
"Increment",
"the",
"counter",
"and",
"wake",
"one",
"waiter."
] | def release(self) -> None:
self._value += 1
while self._waiters:
waiter = self._waiters.popleft()
if not waiter.done():
self._value -= 1
waiter.set_result(_ReleasingContextManager(self))
break | ['def', 'release(self)', '->', 'None:', 'self._value', '+=', '1', 'while', 'self._waiters:', 'waiter', '=', 'self._waiters.popleft()', 'if', 'not', 'waiter.done():', 'self._value', '-=', '1', 'waiter.set_result(_ReleasingContextManager(self))', 'break'] | 893,529 |
weimin17/Object-Detection_HelmetDetection | utils.py | visualize_voxel_spectral | visualize_voxel_spectral | Function to visualize voxel (spectral). | [
"Function",
"to",
"visualize",
"voxel",
"(spectral)."
] | def visualize_voxel_spectral(points, vis_size=128):
points = np.rint(points)
points = np.swapaxes(points, 0, 2)
fig = p.figure(figsize=(1, 1), dpi=vis_size)
(verts, faces) = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
ax = fig.add_subplot(111, projection='3d')
ax.plot_tris... | ['def', 'visualize_voxel_spectral(points,', 'vis_size=128):', 'points', '=', 'np.rint(points)', 'points', '=', 'np.swapaxes(points,', '0,', '2)', 'fig', '=', 'p.figure(figsize=(1,', '1),', 'dpi=vis_size)', '(verts,', 'faces)', '=', 'measure.marching_cubes_classic(points,', '0,', 'spacing=(0.1,', '0.1,', '0.1))', 'ax', ... | 759,485 |
ViTAE-Transformer/ViTDet | test_mixins.py | MaskTestMixin.simple_test_mask | simple_test_mask | Simple test for mask head without augmentation. | [
"Simple",
"test",
"for",
"mask",
"head",
"without",
"augmentation."
] | def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False):
ori_shapes = tuple((meta['ori_shape'] for meta in img_metas))
scale_factors = tuple((meta['scale_factor'] for meta in img_metas))
if isinstance(scale_factors[0], float):
warnings.warn('Scale factor in img_metas should b... | ['def', 'simple_test_mask(self,', 'x,', 'img_metas,', 'det_bboxes,', 'det_labels,', 'rescale=False):', 'ori_shapes', '=', "tuple((meta['ori_shape']", 'for', 'meta', 'in', 'img_metas))', 'scale_factors', '=', "tuple((meta['scale_factor']", 'for', 'meta', 'in', 'img_metas))', 'if', 'isinstance(scale_factors[0],', 'float)... | 945,753 |
deephyper/deephyper | space.py | Space.transformed_bounds | transformed_bounds | The dimension bounds, in the warped space. | [
"The",
"dimension",
"bounds,",
"in",
"the",
"warped",
"space."
] | def transformed_bounds(self):
b = []
for dim in self.dimensions:
if dim.transformed_size == 1:
b.append(dim.transformed_bounds)
else:
b.extend(dim.transformed_bounds)
return b | ['def', 'transformed_bounds(self):', 'b', '=', '[]', 'for', 'dim', 'in', 'self.dimensions:', 'if', 'dim.transformed_size', '==', '1:', 'b.append(dim.transformed_bounds)', 'else:', 'b.extend(dim.transformed_bounds)', 'return', 'b'] | 521,058 |
Megvii-BaseDetection/cvpods | transform.py | GridSampleTransform.apply_image | apply_image | Apply grid sampling on the image(s). | [
"Apply",
"grid",
"sampling",
"on",
"the",
"image(s)."
] | def apply_image(self, img: np.ndarray, interp: str=None) -> np.ndarray:
interp_method = interp if interp is not None else self.interp
float_tensor = torch.nn.functional.grid_sample(to_float_tensor(img), torch.from_numpy(self.grid), mode=interp_method, padding_mode='border', align_corners=False)
return to_nu... | ['def', 'apply_image(self,', 'img:', 'np.ndarray,', 'interp:', 'str=None)', '->', 'np.ndarray:', 'interp_method', '=', 'interp', 'if', 'interp', 'is', 'not', 'None', 'else', 'self.interp', 'float_tensor', '=', 'torch.nn.functional.grid_sample(to_float_tensor(img),', 'torch.from_numpy(self.grid),', 'mode=interp_method,'... | 510,885 |
clips/pattern | __init__.py | DatasheetColumn.map | map | Applies the given function to each value in the column. | [
"Applies",
"the",
"given",
"function",
"to",
"each",
"value",
"in",
"the",
"column."
] | def map(self, function=lambda value: value):
for (j, value) in enumerate(self):
self[j] = function(value) | ['def', 'map(self,', 'function=lambda', 'value:', 'value):', 'for', '(j,', 'value)', 'in', 'enumerate(self):', 'self[j]', '=', 'function(value)'] | 764,609 |
PaddlePaddle/PARL | train.py | Learner.run_remote_sample | run_remote_sample | Sample data from remote actor and update parameters of remote actor. | [
"Sample",
"data",
"from",
"remote",
"actor",
"and",
"update",
"parameters",
"of",
"remote",
"actor."
] | def run_remote_sample(self):
remote_actor = Actor(self.config)
cnt = 0
remote_actor.set_weights(self.cache_params)
while True:
batch = remote_actor.sample()
self.sample_data_queue.put(batch)
cnt += 1
if cnt % self.config['get_remote_metrics_interval'] == 0:
me... | ['def', 'run_remote_sample(self):', 'remote_actor', '=', 'Actor(self.config)', 'cnt', '=', '0', 'remote_actor.set_weights(self.cache_params)', 'while', 'True:', 'batch', '=', 'remote_actor.sample()', 'self.sample_data_queue.put(batch)', 'cnt', '+=', '1', 'if', 'cnt', '%', "self.config['get_remote_metrics_interval']", '... | 277,801 |
PacktPublishing/OpenCV-Computer--Projects-with-Python | filters.py | BGRFuncFilter.apply | apply | Apply the filter with a BGR source/destination. | [
"Apply",
"the",
"filter",
"with",
"a",
"BGR",
"source/destination."
] | def apply(self, src, dst):
(b, g, r) = cv2.split(src)
utils.applyLookupArray(self._bLookupArray, b, b)
utils.applyLookupArray(self._gLookupArray, g, g)
utils.applyLookupArray(self._rLookupArray, r, r)
cv2.merge([b, g, r], dst) | ['def', 'apply(self,', 'src,', 'dst):', '(b,', 'g,', 'r)', '=', 'cv2.split(src)', 'utils.applyLookupArray(self._bLookupArray,', 'b,', 'b)', 'utils.applyLookupArray(self._gLookupArray,', 'g,', 'g)', 'utils.applyLookupArray(self._rLookupArray,', 'r,', 'r)', 'cv2.merge([b,', 'g,', 'r],', 'dst)'] | 756,950 |
aws/sagemaker-training-toolkit | files.py | s3_download | s3_download | Download a file from S3. | [
"Download",
"a",
"file",
"from",
"S3."
] | def s3_download(url, dst):
url = parse.urlparse(url)
if url.scheme != 's3':
raise ValueError("Expecting 's3' scheme, got: %s in %s" % (url.scheme, url))
(bucket, key) = (url.netloc, url.path.lstrip('/'))
region = os.environ.get('AWS_REGION', os.environ.get(params.REGION_NAME_ENV))
endpoint_u... | ['def', 's3_download(url,', 'dst):', 'url', '=', 'parse.urlparse(url)', 'if', 'url.scheme', '!=', "'s3':", 'raise', 'ValueError("Expecting', "'s3'", 'scheme,', 'got:', '%s', 'in', '%s"', '%', '(url.scheme,', 'url))', '(bucket,', 'key)', '=', '(url.netloc,', "url.path.lstrip('/'))", 'region', '=', "os.environ.get('AWS_R... | 845,037 |
PaddlePaddle/PARL | remote_class_serialization.py | load_remote_class | load_remote_class | load a class given related info dumped in the client. | [
"load",
"a",
"class",
"given",
"related",
"info",
"dumped",
"in",
"the",
"client."
] | def load_remote_class(remote_class_info):
(in_notebook, dumped_class_info) = cloudpickle.loads(remote_class_info)
if in_notebook:
cls = dumped_class_info
else:
(file_name, class_name, end_of_file, in_sys_path, client_sys_path) = dumped_class_info
with open(file_name + '.py') as t_fil... | ['def', 'load_remote_class(remote_class_info):', '(in_notebook,', 'dumped_class_info)', '=', 'cloudpickle.loads(remote_class_info)', 'if', 'in_notebook:', 'cls', '=', 'dumped_class_info', 'else:', '(file_name,', 'class_name,', 'end_of_file,', 'in_sys_path,', 'client_sys_path)', '=', 'dumped_class_info', 'with', 'open(f... | 278,117 |
jariasf/GMVAE | utils.py | cluster_acc | cluster_acc | Computes the clustering accuracy metric. | [
"Computes",
"the",
"clustering",
"accuracy",
"metric."
] | def cluster_acc(logits, labels, no_components):
cat_preds = tf.argmax(logits, axis=1)
real_preds = tf.zeros(tf.shape(cat_preds))
for k in xrange(no_components):
idx = tf.equal(cat_preds, k)
lab = tf.boolean_mask(labels, idx)
modes = tf.cond(tf.equal(tf.size(lab), 0), lambda : 0.0, la... | ['def', 'cluster_acc(logits,', 'labels,', 'no_components):', 'cat_preds', '=', 'tf.argmax(logits,', 'axis=1)', 'real_preds', '=', 'tf.zeros(tf.shape(cat_preds))', 'for', 'k', 'in', 'xrange(no_components):', 'idx', '=', 'tf.equal(cat_preds,', 'k)', 'lab', '=', 'tf.boolean_mask(labels,', 'idx)', 'modes', '=', 'tf.cond(tf... | 578,507 |
tianyolanda/derain_dehaze_objdetection | ssd_vgg_preprocessing.py | preprocess_for_eval | preprocess_for_eval | Preprocess an image for evaluation. | [
"Preprocess",
"an",
"image",
"for",
"evaluation."
] | def preprocess_for_eval(image, labels, bboxes, out_shape=EVAL_SIZE, data_format='NHWC', difficults=None, resize=Resize.WARP_RESIZE, scope='ssd_preprocessing_train'):
with tf.name_scope(scope):
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
... | ['def', 'preprocess_for_eval(image,', 'labels,', 'bboxes,', 'out_shape=EVAL_SIZE,', "data_format='NHWC',", 'difficults=None,', 'resize=Resize.WARP_RESIZE,', "scope='ssd_preprocessing_train'):", 'with', 'tf.name_scope(scope):', 'if', 'image.get_shape().ndims', '!=', '3:', 'raise', "ValueError('Input", 'must', 'be', 'of'... | 538,312 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | policy.py | Policy.get_kl | get_kl | Calculate KL between one policy output and another. | [
"Calculate",
"KL",
"between",
"one",
"policy",
"output",
"and",
"another."
] | def get_kl(self, my_logits, other_logits):
kl = []
for (i, (act_dim, act_type)) in enumerate(self.env_spec.act_dims_and_types):
sampling_dim = self.env_spec.sampling_dim(act_dim, act_type)
single_my_logits = my_logits[i]
single_other_logits = other_logits[i]
if self.env_spec.is_d... | ['def', 'get_kl(self,', 'my_logits,', 'other_logits):', 'kl', '=', '[]', 'for', '(i,', '(act_dim,', 'act_type))', 'in', 'enumerate(self.env_spec.act_dims_and_types):', 'sampling_dim', '=', 'self.env_spec.sampling_dim(act_dim,', 'act_type)', 'single_my_logits', '=', 'my_logits[i]', 'single_other_logits', '=', 'other_log... | 58,841 |
thu-ml/tianshou | base.py | ReplayBuffer.reset | reset | Clear all the data in replay buffer and episode statistics. | [
"Clear",
"all",
"the",
"data",
"in",
"replay",
"buffer",
"and",
"episode",
"statistics."
] | def reset(self, keep_statistics: bool=False) -> None:
self.last_index = np.array([0])
self._index = self._size = 0
if not keep_statistics:
(self._ep_rew, self._ep_len, self._ep_idx) = (0.0, 0, 0) | ['def', 'reset(self,', 'keep_statistics:', 'bool=False)', '->', 'None:', 'self.last_index', '=', 'np.array([0])', 'self._index', '=', 'self._size', '=', '0', 'if', 'not', 'keep_statistics:', '(self._ep_rew,', 'self._ep_len,', 'self._ep_idx)', '=', '(0.0,', '0,', '0)'] | 355,207 |
rudranil723/mini-main | formsets.py | BaseFormSet.as_table | as_table | Return this formset rendered as HTML <tr>s -- excluding the <table></table>. | [
"Return",
"this",
"formset",
"rendered",
"as",
"HTML",
"<tr>s",
"--",
"excluding",
"the",
"<table></table>."
] | def as_table(self):
forms = ' '.join((form.as_table() for form in self))
return mark_safe(str(self.management_form) + '\n' + forms) | ['def', 'as_table(self):', 'forms', '=', "'", "'.join((form.as_table()", 'for', 'form', 'in', 'self))', 'return', 'mark_safe(str(self.management_form)', '+', "'\\n'", '+', 'forms)'] | 316,274 |
myothida/Supervised-Machine-Learning | varStore.py | _Encoding.get_gain | get_gain | Maximum possible byte gain from merging this into another characteristic. | [
"Maximum",
"possible",
"byte",
"gain",
"from",
"merging",
"this",
"into",
"another",
"characteristic."
] | def get_gain(self):
count = len(self.items)
return max(0, self.overhead - count) | ['def', 'get_gain(self):', 'count', '=', 'len(self.items)', 'return', 'max(0,', 'self.overhead', '-', 'count)'] | 361,367 |
vinayvinkumar/Natural-Language-Processing | api.py | SupervisedLoadFile.candidate_weighting | candidate_weighting | Extract features and classify candidates with default parameters. | [
"Extract",
"features",
"and",
"classify",
"candidates",
"with",
"default",
"parameters."
] | def candidate_weighting(self):
if not self.candidates:
return
self.feature_extraction()
self.classify_candidates() | ['def', 'candidate_weighting(self):', 'if', 'not', 'self.candidates:', 'return', 'self.feature_extraction()', 'self.classify_candidates()'] | 658,482 |
facebookresearch/CompilerGym | gcc_env_test.py | test_default_benchmark | test_default_benchmark | Test that we are working with the expected default benchmark. | [
"Test",
"that",
"we",
"are",
"working",
"with",
"the",
"expected",
"default",
"benchmark."
] | def test_default_benchmark(gcc_bin: str):
with gym.make('gcc-v0', gcc_bin=gcc_bin) as env:
assert env.benchmark.proto.uri == 'benchmark://chstone-v0/adpcm' | ['def', 'test_default_benchmark(gcc_bin:', 'str):', 'with', "gym.make('gcc-v0',", 'gcc_bin=gcc_bin)', 'as', 'env:', 'assert', 'env.benchmark.proto.uri', '==', "'benchmark://chstone-v0/adpcm'"] | 125,888 |
Kvatsx/Artificial-Intelligence-Assignments | base.py | TabletCanvas.close | close | Close the tablet device for this window. | [
"Close",
"the",
"tablet",
"device",
"for",
"this",
"window."
] | def close(self):
raise NotImplementedError('abstract') | ['def', 'close(self):', 'raise', "NotImplementedError('abstract')"] | 76,918 |
deepmind/pycolab | rendering.py | BaseObservationRenderer.shape | shape | The 2-D dimensions of this `BaseObservationRenderer`. | [
"The",
"2-D",
"dimensions",
"of",
"this",
"`BaseObservationRenderer`."
] | def shape(self):
return self._board.shape | ['def', 'shape(self):', 'return', 'self._board.shape'] | 819,234 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | data_utils.py | maybe_download_data | maybe_download_data | Download Omniglot repo if it does not exist. | [
"Download",
"Omniglot",
"repo",
"if",
"it",
"does",
"not",
"exist."
] | def maybe_download_data():
if os.path.exists(REPO_DIR):
logging.info('It appears that Git repo already exists.')
else:
logging.info('It appears that Git repo does not exist.')
logging.info('Cloning now.')
subprocess.check_output('git clone %s' % REPO_LOCATION, shell=True)
if ... | ['def', 'maybe_download_data():', 'if', 'os.path.exists(REPO_DIR):', "logging.info('It", 'appears', 'that', 'Git', 'repo', 'already', "exists.')", 'else:', "logging.info('It", 'appears', 'that', 'Git', 'repo', 'does', 'not', "exist.')", "logging.info('Cloning", "now.')", "subprocess.check_output('git", 'clone', "%s'", ... | 49,521 |
kemaloksuz/RankSortLoss | test_assigner.py | test_max_iou_assigner_with_empty_boxes | test_max_iou_assigner_with_empty_boxes | Test corner case where a network might predict no boxes. | [
"Test",
"corner",
"case",
"where",
"a",
"network",
"might",
"predict",
"no",
"boxes."
] | def test_max_iou_assigner_with_empty_boxes():
self = MaxIoUAssigner(pos_iou_thr=0.5, neg_iou_thr=0.5)
bboxes = torch.empty((0, 4))
gt_bboxes = torch.FloatTensor([[0, 0, 10, 9], [0, 10, 10, 19]])
gt_labels = torch.LongTensor([2, 3])
assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
... | ['def', 'test_max_iou_assigner_with_empty_boxes():', 'self', '=', 'MaxIoUAssigner(pos_iou_thr=0.5,', 'neg_iou_thr=0.5)', 'bboxes', '=', 'torch.empty((0,', '4))', 'gt_bboxes', '=', 'torch.FloatTensor([[0,', '0,', '10,', '9],', '[0,', '10,', '10,', '19]])', 'gt_labels', '=', 'torch.LongTensor([2,', '3])', 'assign_result'... | 836,392 |
Cihsaing/RVSL-rvsl-robust-vehicle-similarity-learning--ECCV22 | nvmarker.py | modMarker | modMarker | Returns the stringified extra_repr() of a module. | [
"Returns",
"the",
"stringified",
"extra_repr()",
"of",
"a",
"module."
] | def modMarker(mod, fn_name, args):
assert fn_name == 'forward'
assert len(args) > 0
d = {}
d['mod'] = mod.__name__
d['strRepr'] = args[0].extra_repr()
return str(d) | ['def', 'modMarker(mod,', 'fn_name,', 'args):', 'assert', 'fn_name', '==', "'forward'", 'assert', 'len(args)', '>', '0', 'd', '=', '{}', "d['mod']", '=', 'mod.__name__', "d['strRepr']", '=', 'args[0].extra_repr()', 'return', 'str(d)'] | 327,108 |
devashish-patel/webcam-motion-detector | output.py | output_notebook | output_notebook | Configure the default output state to generate output in notebook cells when :func:`show` is called. | [
"Configure",
"the",
"default",
"output",
"state",
"to",
"generate",
"output",
"in",
"notebook",
"cells",
"when",
":func:`show`",
"is",
"called."
] | def output_notebook(resources=None, verbose=False, hide_banner=False, load_timeout=5000, notebook_type='jupyter'):
curstate().output_notebook(notebook_type)
run_notebook_hook(notebook_type, 'load', resources, verbose, hide_banner, load_timeout) | ['def', 'output_notebook(resources=None,', 'verbose=False,', 'hide_banner=False,', 'load_timeout=5000,', "notebook_type='jupyter'):", 'curstate().output_notebook(notebook_type)', 'run_notebook_hook(notebook_type,', "'load',", 'resources,', 'verbose,', 'hide_banner,', 'load_timeout)'] | 977,359 |
JIA-HONG-CHU/Swin-Transformer-add-EncNet-DaNet-DraNet-for---on-Statelite-Dataset | cityscapes.py | CityscapesDataset.format_results | format_results | Format the results into dir (standard format for Cityscapes evaluation). | [
"Format",
"the",
"results",
"into",
"dir",
"(standard",
"format",
"for",
"Cityscapes",
"evaluation)."
] | def format_results(self, results, imgfile_prefix=None, to_label_id=True):
assert isinstance(results, list), 'results must be a list'
assert len(results) == len(self), f'The length of results is not equal to the dataset len: {len(results)} != {len(self)}'
if imgfile_prefix is None:
tmp_dir = tempfile... | ['def', 'format_results(self,', 'results,', 'imgfile_prefix=None,', 'to_label_id=True):', 'assert', 'isinstance(results,', 'list),', "'results", 'must', 'be', 'a', "list'", 'assert', 'len(results)', '==', 'len(self),', "f'The", 'length', 'of', 'results', 'is', 'not', 'equal', 'to', 'the', 'dataset', 'len:', '{len(resul... | 882,841 |
70Shubham07/NaturalLanguageProcessing | run_classifier.py | DataProcessor.get_test_examples | get_test_examples | Gets a collection of `InputExample`s for prediction. | [
"Gets",
"a",
"collection",
"of",
"`InputExample`s",
"for",
"prediction."
] | def get_test_examples(self, data_dir):
raise NotImplementedError() | ['def', 'get_test_examples(self,', 'data_dir):', 'raise', 'NotImplementedError()'] | 713,794 |
KalleHallden/InstaAutomator | compat.py | splituser | splituser | splituser('user[:passwd]@host[:port]') --> 'user[:passwd]', 'host[:port]'. | [
"splituser('user[:passwd]@host[:port]')",
"-->",
"'user[:passwd]',",
"'host[:port]'."
] | def splituser(host):
global _userprog
if _userprog is None:
import re
_userprog = re.compile('^(.*)@(.*)$')
match = _userprog.match(host)
if match:
return match.group(1, 2)
return (None, host) | ['def', 'splituser(host):', 'global', '_userprog', 'if', '_userprog', 'is', 'None:', 'import', 're', '_userprog', '=', "re.compile('^(.*)@(.*)$')", 'match', '=', '_userprog.match(host)', 'if', 'match:', 'return', 'match.group(1,', '2)', 'return', '(None,', 'host)'] | 233,106 |
Eric3911/OpenAGI | data_pipeline.py | GeneratorDynamicItem.reset | reset | Signals that this will not be called any more times on this pipeline call. | [
"Signals",
"that",
"this",
"will",
"not",
"be",
"called",
"any",
"more",
"times",
"on",
"this",
"pipeline",
"call."
] | def reset(self):
if self.current_generator is not None:
self.current_generator.close()
self.current_generator = None
self.num_provided_items = 0 | ['def', 'reset(self):', 'if', 'self.current_generator', 'is', 'not', 'None:', 'self.current_generator.close()', 'self.current_generator', '=', 'None', 'self.num_provided_items', '=', '0'] | 251,356 |
google-research/ssl_detection | scope_utils.py | cached_name_scope | cached_name_scope | Return a context which either opens and caches a new name scope, or reenter an existing one. | [
"Return",
"a",
"context",
"which",
"either",
"opens",
"and",
"caches",
"a",
"new",
"name",
"scope,",
"or",
"reenter",
"an",
"existing",
"one."
] | def cached_name_scope(name, top_level=True):
if not top_level:
current_ns = tf.get_default_graph().get_name_scope()
if current_ns:
name = current_ns + '/' + name
ns = _get_cached_ns(name)
with tf.name_scope(ns):
yield ns | ['def', 'cached_name_scope(name,', 'top_level=True):', 'if', 'not', 'top_level:', 'current_ns', '=', 'tf.get_default_graph().get_name_scope()', 'if', 'current_ns:', 'name', '=', 'current_ns', '+', "'/'", '+', 'name', 'ns', '=', '_get_cached_ns(name)', 'with', 'tf.name_scope(ns):', 'yield', 'ns'] | 382,282 |
jimtin/Stock_Comparison | core.py | _Socket.send | send | send, which will only block current greenlet state_changed always fires exactly once (success or fail) at the end of this method. | [
"send,",
"which",
"will",
"only",
"block",
"current",
"greenlet",
"state_changed",
"always",
"fires",
"exactly",
"once",
"(success",
"or",
"fail)",
"at",
"the",
"end",
"of",
"this",
"method."
] | def send(self, data, flags=0, copy=True, track=False):
if flags & zmq.NOBLOCK:
try:
msg = super(_Socket, self).send(data, flags, copy, track)
finally:
if not self.__in_send_multipart:
self.__state_changed()
return msg
flags |= zmq.NOBLOCK
while... | ['def', 'send(self,', 'data,', 'flags=0,', 'copy=True,', 'track=False):', 'if', 'flags', '&', 'zmq.NOBLOCK:', 'try:', 'msg', '=', 'super(_Socket,', 'self).send(data,', 'flags,', 'copy,', 'track)', 'finally:', 'if', 'not', 'self.__in_send_multipart:', 'self.__state_changed()', 'return', 'msg', 'flags', '|=', 'zmq.NOBLOC... | 359,557 |
deepmind/acme | builder.py | R2D2Builder.make_adder | make_adder | Create an adder which records data generated by the actor/environment. | [
"Create",
"an",
"adder",
"which",
"records",
"data",
"generated",
"by",
"the",
"actor/environment."
] | def make_adder(self, replay_client: reverb.Client, environment_spec: Optional[specs.EnvironmentSpec], policy: Optional[r2d2_actor.R2D2Policy]) -> Optional[adders.Adder]:
if environment_spec is None or policy is None:
raise ValueError('`environment_spec` and `policy` cannot be None.')
dummy_actor_state =... | ['def', 'make_adder(self,', 'replay_client:', 'reverb.Client,', 'environment_spec:', 'Optional[specs.EnvironmentSpec],', 'policy:', 'Optional[r2d2_actor.R2D2Policy])', '->', 'Optional[adders.Adder]:', 'if', 'environment_spec', 'is', 'None', 'or', 'policy', 'is', 'None:', 'raise', "ValueError('`environment_spec`", 'and'... | 8,185 |
omarmhaimdat/twitter_nlp_native_swift | cookiejar.py | DefaultCookiePolicy.allowed_domains | allowed_domains | Return None, or the sequence of allowed domains (as a tuple). | [
"Return",
"None,",
"or",
"the",
"sequence",
"of",
"allowed",
"domains",
"(as",
"a",
"tuple)."
] | def allowed_domains(self):
return self._allowed_domains | ['def', 'allowed_domains(self):', 'return', 'self._allowed_domains'] | 953,490 |
VincentAuriau/Natural-Language-Processing | data.py | generate_batches | generate_batches | Generates and returns batch of tensorized instances in a chunk of batch_size. | [
"Generates",
"and",
"returns",
"batch",
"of",
"tensorized",
"instances",
"in",
"a",
"chunk",
"of",
"batch_size."
] | def generate_batches(instances: List[Dict], batch_size) -> List[Dict[str, np.ndarray]]:
def chunk(items: List[Any], num: int):
return [items[index:index + num] for index in range(0, len(items), num)]
batches_of_instances = chunk(instances, batch_size)
batches = []
for batch_of_instances in tqdm... | ['def', 'generate_batches(instances:', 'List[Dict],', 'batch_size)', '->', 'List[Dict[str,', 'np.ndarray]]:', 'def', 'chunk(items:', 'List[Any],', 'num:', 'int):', 'return', '[items[index:index', '+', 'num]', 'for', 'index', 'in', 'range(0,', 'len(items),', 'num)]', 'batches_of_instances', '=', 'chunk(instances,', 'bat... | 685,386 |
TheCurryMan/MedicAI | core.py | MultiCommand.get_command | get_command | Given a context and a command name, this returns a :class:`Command` object if it exists or returns `None`. | [
"Given",
"a",
"context",
"and",
"a",
"command",
"name,",
"this",
"returns",
"a",
":class:`Command`",
"object",
"if",
"it",
"exists",
"or",
"returns",
"`None`."
] | def get_command(self, ctx, cmd_name):
raise NotImplementedError() | ['def', 'get_command(self,', 'ctx,', 'cmd_name):', 'raise', 'NotImplementedError()'] | 648,052 |
clovaai/assembled-cnn | logger.py | config_benchmark_logger | config_benchmark_logger | Config the global benchmark logger. | [
"Config",
"the",
"global",
"benchmark",
"logger."
] | def config_benchmark_logger(flag_obj=None):
_logger_lock.acquire()
try:
global _benchmark_logger
if not flag_obj:
flag_obj = FLAGS
if not hasattr(flag_obj, 'benchmark_logger_type') or flag_obj.benchmark_logger_type == 'BaseBenchmarkLogger':
_benchmark_logger = Bas... | ['def', 'config_benchmark_logger(flag_obj=None):', '_logger_lock.acquire()', 'try:', 'global', '_benchmark_logger', 'if', 'not', 'flag_obj:', 'flag_obj', '=', 'FLAGS', 'if', 'not', 'hasattr(flag_obj,', "'benchmark_logger_type')", 'or', 'flag_obj.benchmark_logger_type', '==', "'BaseBenchmarkLogger':", '_benchmark_logger... | 92,452 |
Katja-M/Python_NaturalLanguageProcessing | text.py | ConcordanceIndex.find_concordance | find_concordance | Find all concordance lines given the query word. | [
"Find",
"all",
"concordance",
"lines",
"given",
"the",
"query",
"word."
] | def find_concordance(self, word, width=80):
half_width = (width - len(word) - 2) // 2
context = width // 4
concordance_list = []
offsets = self.offsets(word)
if offsets:
for i in offsets:
query_word = self._tokens[i]
left_context = self._tokens[max(0, i - context):i]
... | ['def', 'find_concordance(self,', 'word,', 'width=80):', 'half_width', '=', '(width', '-', 'len(word)', '-', '2)', '//', '2', 'context', '=', 'width', '//', '4', 'concordance_list', '=', '[]', 'offsets', '=', 'self.offsets(word)', 'if', 'offsets:', 'for', 'i', 'in', 'offsets:', 'query_word', '=', 'self._tokens[i]', 'le... | 865,878 |
sek788432/Waymo-2D-Object-Detection | autoaugment_utils.py | rotate | rotate | Rotates the image by degrees either clockwise or counterclockwise. | [
"Rotates",
"the",
"image",
"by",
"degrees",
"either",
"clockwise",
"or",
"counterclockwise."
] | def rotate(image, degrees, replace):
degrees_to_radians = math.pi / 180.0
radians = degrees * degrees_to_radians
image = contrib_image.rotate(wrap(image), radians)
return unwrap(image, replace) | ['def', 'rotate(image,', 'degrees,', 'replace):', 'degrees_to_radians', '=', 'math.pi', '/', '180.0', 'radians', '=', 'degrees', '*', 'degrees_to_radians', 'image', '=', 'contrib_image.rotate(wrap(image),', 'radians)', 'return', 'unwrap(image,', 'replace)'] | 975,333 |
googleapis/python-aiplatform | client.py | ModelServiceClient.parse_common_billing_account_path | parse_common_billing_account_path | Parse a billing_account path into its component segments. | [
"Parse",
"a",
"billing_account",
"path",
"into",
"its",
"component",
"segments."
] | def parse_common_billing_account_path(path: str) -> Dict[str, str]:
m = re.match('^billingAccounts/(?P<billing_account>.+?)$', path)
return m.groupdict() if m else {} | ['def', 'parse_common_billing_account_path(path:', 'str)', '->', 'Dict[str,', 'str]:', 'm', '=', "re.match('^billingAccounts/(?P<billing_account>.+?)$',", 'path)', 'return', 'm.groupdict()', 'if', 'm', 'else', '{}'] | 813,600 |
bm777/object_detection | oid_od_challenge_evaluation_utils.py | build_predictions_dictionary | build_predictions_dictionary | Builds a predictions dictionary from predictions data in CSV file. | [
"Builds",
"a",
"predictions",
"dictionary",
"from",
"predictions",
"data",
"in",
"CSV",
"file."
] | def build_predictions_dictionary(data, class_label_map):
return {standard_fields.DetectionResultFields.detection_boxes: data[['YMin', 'XMin', 'YMax', 'XMax']].as_matrix(), standard_fields.DetectionResultFields.detection_classes: data['LabelName'].map(lambda x: class_label_map[x]).as_matrix(), standard_fields.Detect... | ['def', 'build_predictions_dictionary(data,', 'class_label_map):', 'return', '{standard_fields.DetectionResultFields.detection_boxes:', "data[['YMin',", "'XMin',", "'YMax',", "'XMax']].as_matrix(),", 'standard_fields.DetectionResultFields.detection_classes:', "data['LabelName'].map(lambda", 'x:', 'class_label_map[x]).a... | 774,432 |
implus/GFocalV2 | dynamic_roi_head.py | DynamicRoIHead.forward_train | forward_train | Forward function for training. | [
"Forward",
"function",
"for",
"training."
] | def forward_train(self, x, img_metas, proposal_list, gt_bboxes, gt_labels, gt_bboxes_ignore=None, gt_masks=None):
if self.with_bbox or self.with_mask:
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
... | ['def', 'forward_train(self,', 'x,', 'img_metas,', 'proposal_list,', 'gt_bboxes,', 'gt_labels,', 'gt_bboxes_ignore=None,', 'gt_masks=None):', 'if', 'self.with_bbox', 'or', 'self.with_mask:', 'num_imgs', '=', 'len(img_metas)', 'if', 'gt_bboxes_ignore', 'is', 'None:', 'gt_bboxes_ignore', '=', '[None', 'for', '_', 'in', '... | 557,734 |
Abtinz/Artificial-Intelligence | utils.py | remove_all | remove_all | Return a copy of seq (or string) with all occurrences of item removed. | [
"Return",
"a",
"copy",
"of",
"seq",
"(or",
"string)",
"with",
"all",
"occurrences",
"of",
"item",
"removed."
] | def remove_all(item, seq):
if isinstance(seq, str):
return seq.replace(item, '')
elif isinstance(seq, set):
rest = seq.copy()
rest.remove(item)
return rest
else:
return [x for x in seq if x != item] | ['def', 'remove_all(item,', 'seq):', 'if', 'isinstance(seq,', 'str):', 'return', 'seq.replace(item,', "'')", 'elif', 'isinstance(seq,', 'set):', 'rest', '=', 'seq.copy()', 'rest.remove(item)', 'return', 'rest', 'else:', 'return', '[x', 'for', 'x', 'in', 'seq', 'if', 'x', '!=', 'item]'] | 121,890 |
janluke/cs188 | search.py | solution | solution | Returns a list of actions, following parent pointers. | [
"Returns",
"a",
"list",
"of",
"actions,",
"following",
"parent",
"pointers."
] | def solution(node):
if node.parent == None:
return []
ls = solution(node.parent)
ls.extend([node.action])
return ls | ['def', 'solution(node):', 'if', 'node.parent', '==', 'None:', 'return', '[]', 'ls', '=', 'solution(node.parent)', 'ls.extend([node.action])', 'return', 'ls'] | 224,446 |
PJLab-ADG/LoGoNet | yolact_head.py | InterpolateModule.forward | forward | Forward features from the upstream network. | [
"Forward",
"features",
"from",
"the",
"upstream",
"network."
] | def forward(self, x):
return F.interpolate(x, *self.args, **self.kwargs) | ['def', 'forward(self,', 'x):', 'return', 'F.interpolate(x,', '*self.args,', '**self.kwargs)'] | 615,279 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | test_inputsplitter.py | assemble | assemble | Assemble a block into multi-line sub-blocks. | [
"Assemble",
"a",
"block",
"into",
"multi-line",
"sub-blocks."
] | def assemble(block):
return ['\n'.join(sub_block) + '\n' for sub_block in block] | ['def', 'assemble(block):', 'return', "['\\n'.join(sub_block)", '+', "'\\n'", 'for', 'sub_block', 'in', 'block]'] | 448,503 |
myothida/Supervised-Machine-Learning | misc.py | build_url_from_netloc | build_url_from_netloc | Build a full URL from a netloc. | [
"Build",
"a",
"full",
"URL",
"from",
"a",
"netloc."
] | def build_url_from_netloc(netloc: str, scheme: str='https') -> str:
if netloc.count(':') >= 2 and '@' not in netloc and ('[' not in netloc):
netloc = f'[{netloc}]'
return f'{scheme}://{netloc}' | ['def', 'build_url_from_netloc(netloc:', 'str,', 'scheme:', "str='https')", '->', 'str:', 'if', "netloc.count(':')", '>=', '2', 'and', "'@'", 'not', 'in', 'netloc', 'and', "('['", 'not', 'in', 'netloc):', 'netloc', '=', "f'[{netloc}]'", 'return', "f'{scheme}://{netloc}'"] | 444,294 |
ADLab3Ds/TiG-BEV | utils.py | rotation_3d_in_axis | rotation_3d_in_axis | Rotate points by angles according to axis. | [
"Rotate",
"points",
"by",
"angles",
"according",
"to",
"axis."
] | def rotation_3d_in_axis(points, angles, axis=0):
rot_sin = torch.sin(angles)
rot_cos = torch.cos(angles)
ones = torch.ones_like(rot_cos)
zeros = torch.zeros_like(rot_cos)
if axis == 1:
rot_mat_T = torch.stack([torch.stack([rot_cos, zeros, -rot_sin]), torch.stack([zeros, ones, zeros]), torch.... | ['def', 'rotation_3d_in_axis(points,', 'angles,', 'axis=0):', 'rot_sin', '=', 'torch.sin(angles)', 'rot_cos', '=', 'torch.cos(angles)', 'ones', '=', 'torch.ones_like(rot_cos)', 'zeros', '=', 'torch.zeros_like(rot_cos)', 'if', 'axis', '==', '1:', 'rot_mat_T', '=', 'torch.stack([torch.stack([rot_cos,', 'zeros,', '-rot_si... | 916,791 |
tobegit3hub/deep_image_model | factorization_ops.py | WALSModel.initialize_col_update_op | initialize_col_update_op | Op to initialize worker state before starting column updates. | [
"Op",
"to",
"initialize",
"worker",
"state",
"before",
"starting",
"column",
"updates."
] | def initialize_col_update_op(self):
return self._col_updates_init | ['def', 'initialize_col_update_op(self):', 'return', 'self._col_updates_init'] | 181,259 |
openai/gym | test_mujoco.py | test_mujoco_incompatible_v3_to_v2 | test_mujoco_incompatible_v3_to_v2 | Checks that the v3 environment are slightly different from v2, (v3 has additional info keys that v2 does not). | [
"Checks",
"that",
"the",
"v3",
"environment",
"are",
"slightly",
"different",
"from",
"v2,",
"(v3",
"has",
"additional",
"info",
"keys",
"that",
"v2",
"does",
"not)."
] | def test_mujoco_incompatible_v3_to_v2(env_name: str):
with pytest.raises(KeyError):
verify_environments_match(f'{env_name}-v3', f'{env_name}-v2') | ['def', 'test_mujoco_incompatible_v3_to_v2(env_name:', 'str):', 'with', 'pytest.raises(KeyError):', "verify_environments_match(f'{env_name}-v3',", "f'{env_name}-v2')"] | 234,365 |
chrischoy/3D-R2N2 | read_mesh.py | generate_materials | generate_materials | Generate JS array of materials objects JS material objects are basically prettified one-to-one mappings of MTL properties in JSON format. | [
"Generate",
"JS",
"array",
"of",
"materials",
"objects",
"JS",
"material",
"objects",
"are",
"basically",
"prettified",
"one-to-one",
"mappings",
"of",
"MTL",
"properties",
"in",
"JSON",
"format."
] | def generate_materials(mtl, materials):
mtl_array = []
for m in mtl:
if m in materials:
index = materials[m]
mtl[m]['DbgName'] = m
mtl[m]['DbgIndex'] = index
mtl[m]['DbgColor'] = generate_color(index)
if BAKE_COLORS:
mtl[m]['ver... | ['def', 'generate_materials(mtl,', 'materials):', 'mtl_array', '=', '[]', 'for', 'm', 'in', 'mtl:', 'if', 'm', 'in', 'materials:', 'index', '=', 'materials[m]', "mtl[m]['DbgName']", '=', 'm', "mtl[m]['DbgIndex']", '=', 'index', "mtl[m]['DbgColor']", '=', 'generate_color(index)', 'if', 'BAKE_COLORS:', "mtl[m]['vertexCol... | 4,515 |
muhanzhang/D-VAE | conv.py | gen_conv_code_unroll_batch_kern | gen_conv_code_unroll_batch_kern | c_code for ConvOp that unroll the batch size loop. | [
"c_code",
"for",
"ConvOp",
"that",
"unroll",
"the",
"batch",
"size",
"loop."
] | def gen_conv_code_unroll_batch_kern(d, unroll_bsize=1, unroll_ksize=1):
assert unroll_bsize > 0 and unroll_ksize > 0
if 'unroll_bsize' in d or 'unroll_ksize' in d or 'unroll_iter' in d or ('unroll_biter' in d) or ('unroll_kiter' in d):
raise Exception("We can't use this dictionnary as we will overwrite ... | ['def', 'gen_conv_code_unroll_batch_kern(d,', 'unroll_bsize=1,', 'unroll_ksize=1):', 'assert', 'unroll_bsize', '>', '0', 'and', 'unroll_ksize', '>', '0', 'if', "'unroll_bsize'", 'in', 'd', 'or', "'unroll_ksize'", 'in', 'd', 'or', "'unroll_iter'", 'in', 'd', 'or', "('unroll_biter'", 'in', 'd)', 'or', "('unroll_kiter'", ... | 525,679 |
chenbinghui1/DSL | test_ga_anchor_head.py | test_ga_anchor_head_loss | test_ga_anchor_head_loss | Tests anchor head loss when truth is empty and non-empty. | [
"Tests",
"anchor",
"head",
"loss",
"when",
"truth",
"is",
"empty",
"and",
"non-empty."
] | def test_ga_anchor_head_loss():
s = 256
img_metas = [{'img_shape': (s, s, 3), 'scale_factor': 1, 'pad_shape': (s, s, 3)}]
cfg = mmcv.Config(dict(assigner=dict(type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, match_low_quality=True, ignore_iof_thr=-1), sampler=dict(type='RandomSample... | ['def', 'test_ga_anchor_head_loss():', 's', '=', '256', 'img_metas', '=', "[{'img_shape':", '(s,', 's,', '3),', "'scale_factor':", '1,', "'pad_shape':", '(s,', 's,', '3)}]', 'cfg', '=', "mmcv.Config(dict(assigner=dict(type='MaxIoUAssigner',", 'pos_iou_thr=0.7,', 'neg_iou_thr=0.3,', 'min_pos_iou=0.3,', 'match_low_qualit... | 167,998 |
flow-project/flow | util.py | makexml | makexml | Create an xml file. | [
"Create",
"an",
"xml",
"file."
] | def makexml(name, nsl):
xsi = 'http://www.w3.org/2001/XMLSchema-instance'
ns = {'xsi': xsi}
attr = {'{%s}noNamespaceSchemaLocation' % xsi: nsl}
t = etree.Element(name, attrib=attr, nsmap=ns)
return t | ['def', 'makexml(name,', 'nsl):', 'xsi', '=', "'http://www.w3.org/2001/XMLSchema-instance'", 'ns', '=', "{'xsi':", 'xsi}', 'attr', '=', "{'{%s}noNamespaceSchemaLocation'", '%', 'xsi:', 'nsl}', 't', '=', 'etree.Element(name,', 'attrib=attr,', 'nsmap=ns)', 'return', 't'] | 211,566 |
zzndream/ShipRSImageNet | pytorch2onnx.py | preprocess_example_input | preprocess_example_input | Prepare an example input image for ``generate_inputs_and_wrap_model``. | [
"Prepare",
"an",
"example",
"input",
"image",
"for",
"``generate_inputs_and_wrap_model``."
] | def preprocess_example_input(input_config):
input_path = input_config['input_path']
input_shape = input_config['input_shape']
one_img = mmcv.imread(input_path)
one_img = mmcv.imresize(one_img, input_shape[2:][::-1])
show_img = one_img.copy()
if 'normalize_cfg' in input_config.keys():
nor... | ['def', 'preprocess_example_input(input_config):', 'input_path', '=', "input_config['input_path']", 'input_shape', '=', "input_config['input_shape']", 'one_img', '=', 'mmcv.imread(input_path)', 'one_img', '=', 'mmcv.imresize(one_img,', 'input_shape[2:][::-1])', 'show_img', '=', 'one_img.copy()', 'if', "'normalize_cfg'"... | 901,189 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | configHandler.py | IdleConf.CreateConfigHandlers | CreateConfigHandlers | Populate default and user config parser dictionaries. | [
"Populate",
"default",
"and",
"user",
"config",
"parser",
"dictionaries."
] | def CreateConfigHandlers(self):
if __name__ != '__main__':
idleDir = os.path.dirname(__file__)
else:
idleDir = os.path.abspath(sys.path[0])
userDir = self.GetUserCfgDir()
defCfgFiles = {}
usrCfgFiles = {}
for cfgType in self.config_types:
defCfgFiles[cfgType] = os.path.jo... | ['def', 'CreateConfigHandlers(self):', 'if', '__name__', '!=', "'__main__':", 'idleDir', '=', 'os.path.dirname(__file__)', 'else:', 'idleDir', '=', 'os.path.abspath(sys.path[0])', 'userDir', '=', 'self.GetUserCfgDir()', 'defCfgFiles', '=', '{}', 'usrCfgFiles', '=', '{}', 'for', 'cfgType', 'in', 'self.config_types:', 'd... | 430,799 |
43Carrig/recurrent_neural_networks_practice | gen_prediction_ops.py | gradient_trees_prediction_verbose | gradient_trees_prediction_verbose | Runs multiple additive regression forests predictors on input instances and computes the final prediction for each class, and outputs a matrix of leaf ids per each tree in an ensemble. | [
"Runs",
"multiple",
"additive",
"regression",
"forests",
"predictors",
"on",
"input",
"instances",
"and",
"computes",
"the",
"final",
"prediction",
"for",
"each",
"class,",
"and",
"outputs",
"a",
"matrix",
"of",
"leaf",
"ids",
"per",
"each",
"tree",
"in",
"an"... | def gradient_trees_prediction_verbose(tree_ensemble_handle, seed, dense_float_features, sparse_float_feature_indices, sparse_float_feature_values, sparse_float_feature_shapes, sparse_int_feature_indices, sparse_int_feature_values, sparse_int_feature_shapes, learner_config, apply_dropout, apply_averaging, center_bias, r... | ['def', 'gradient_trees_prediction_verbose(tree_ensemble_handle,', 'seed,', 'dense_float_features,', 'sparse_float_feature_indices,', 'sparse_float_feature_values,', 'sparse_float_feature_shapes,', 'sparse_int_feature_indices,', 'sparse_int_feature_values,', 'sparse_int_feature_shapes,', 'learner_config,', 'apply_dropo... | 312,507 |
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