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 |
|---|---|---|---|---|---|---|---|---|
Katja-M/Python_NaturalLanguageProcessing | framenet.py | FramenetCorpusReader.lu_ids_and_names | lu_ids_and_names | Uses the LU index, which is much faster than looking up each LU definition if only the names and IDs are needed. | [
"Uses",
"the",
"LU",
"index,",
"which",
"is",
"much",
"faster",
"than",
"looking",
"up",
"each",
"LU",
"definition",
"if",
"only",
"the",
"names",
"and",
"IDs",
"are",
"needed."
] | def lu_ids_and_names(self, name=None):
if not self._lu_idx:
self._buildluindex()
return {luID: luinfo.name for (luID, luinfo) in self._lu_idx.items() if luinfo.status not in self._bad_statuses and (name is None or re.search(name, luinfo.name) is not None)} | ['def', 'lu_ids_and_names(self,', 'name=None):', 'if', 'not', 'self._lu_idx:', 'self._buildluindex()', 'return', '{luID:', 'luinfo.name', 'for', '(luID,', 'luinfo)', 'in', 'self._lu_idx.items()', 'if', 'luinfo.status', 'not', 'in', 'self._bad_statuses', 'and', '(name', 'is', 'None', 'or', 're.search(name,', 'luinfo.nam... | 866,199 |
loftylabs/django-hardcopy | views.py | BaseMixin.process_html_content | process_html_content | Called after the template rendering, this method can be used to change the HTML before converting it to PDF or PNG (for example to replace relative images, css, or js file pathes to absolute pathes). | [
"Called",
"after",
"the",
"template",
"rendering,",
"this",
"method",
"can",
"be",
"used",
"to",
"change",
"the",
"HTML",
"before",
"converting",
"it",
"to",
"PDF",
"or",
"PNG",
"(for",
"example",
"to",
"replace",
"relative",
"images,",
"css,",
"or",
"js",
... | def process_html_content(self, content):
return content | ['def', 'process_html_content(self,', 'content):', 'return', 'content'] | 164,650 |
Farama-Foundation/Gymnasium | compatibility.py | LegacyEnv.reset | reset | Reset the environment and return the initial observation. | [
"Reset",
"the",
"environment",
"and",
"return",
"the",
"initial",
"observation."
] | def reset(self) -> Any:
... | ['def', 'reset(self)', '->', 'Any:', '...'] | 573,365 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | macosxSupport.py | isCarbonTk | isCarbonTk | Returns True if IDLE is using a Carbon Aqua Tk (instead of the newer Cocoa Aqua Tk). | [
"Returns",
"True",
"if",
"IDLE",
"is",
"using",
"a",
"Carbon",
"Aqua",
"Tk",
"(instead",
"of",
"the",
"newer",
"Cocoa",
"Aqua",
"Tk)."
] | def isCarbonTk():
assert _tk_type is not None
return _tk_type == 'carbon' | ['def', 'isCarbonTk():', 'assert', '_tk_type', 'is', 'not', 'None', 'return', '_tk_type', '==', "'carbon'"] | 430,872 |
sek788432/Waymo-2D-Object-Detection | relu.py | relu6 | relu6 | Computes the Relu6 activation function. | [
"Computes",
"the",
"Relu6",
"activation",
"function."
] | def relu6(features):
features = tf.convert_to_tensor(features)
return tf.nn.relu6(features) | ['def', 'relu6(features):', 'features', '=', 'tf.convert_to_tensor(features)', 'return', 'tf.nn.relu6(features)'] | 972,349 |
arshpreetsingh/quantopian-machinelearning | cache.py | memoize_method | memoize_method | A normal memoize function. | [
"A",
"normal",
"memoize",
"function."
] | def memoize_method(method):
@wraps(method)
def wrapper(self, *args, **kwargs):
cache_dict = self.__dict__.setdefault('_memoize_method_dct', {})
dct = cache_dict.setdefault(method, {})
key = (args, frozenset(kwargs.items()))
try:
return dct[key]
except KeyErro... | ['def', 'memoize_method(method):', '@wraps(method)', 'def', 'wrapper(self,', '*args,', '**kwargs):', 'cache_dict', '=', "self.__dict__.setdefault('_memoize_method_dct',", '{})', 'dct', '=', 'cache_dict.setdefault(method,', '{})', 'key', '=', '(args,', 'frozenset(kwargs.items()))', 'try:', 'return', 'dct[key]', 'except'... | 887,286 |
43Carrig/recurrent_neural_networks_practice | __init__.py | Extension.getConfigInfo | getConfigInfo | Return all config descriptions as a list of tuples. | [
"Return",
"all",
"config",
"descriptions",
"as",
"a",
"list",
"of",
"tuples."
] | def getConfigInfo(self):
return [(key, self.config[key][1]) for key in self.config.keys()] | ['def', 'getConfigInfo(self):', 'return', '[(key,', 'self.config[key][1])', 'for', 'key', 'in', 'self.config.keys()]'] | 310,567 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjvOptionWrapper.jointgroup | jointgroup | joint visualization by group. | [
"joint",
"visualization",
"by",
"group."
] | def jointgroup(self):
return util.buf_to_npy(self._ptr.contents.jointgroup, (6,)) | ['def', 'jointgroup(self):', 'return', 'util.buf_to_npy(self._ptr.contents.jointgroup,', '(6,))'] | 440,742 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | progress.py | Progress.Update | Update | Replaces internal current_size with current_size. | [
"Replaces",
"internal",
"current_size",
"with",
"current_size."
] | def Update(self, current_size):
self.current_size = current_size | ['def', 'Update(self,', 'current_size):', 'self.current_size', '=', 'current_size'] | 112,583 |
weimin17/Object-Detection_HelmetDetection | loss_layers_test.py | CrossFunctionTest.testVectorAndMatrixLabelEquivalence | testVectorAndMatrixLabelEquivalence | Tests equivalence between label shape [batch_size] or [batch_size, 1]. | [
"Tests",
"equivalence",
"between",
"label",
"shape",
"[batch_size]",
"or",
"[batch_size,",
"1]."
] | def testVectorAndMatrixLabelEquivalence(self, global_objective, objective_kwargs):
vector_labels = tf.constant([1.0, 1.0, 0.0, 0.0], shape=[4])
vector_logits = tf.constant([1.0, 0.1, 0.1, -1.0], shape=[4])
vector_kwargs = objective_kwargs.copy()
vector_kwargs['labels'] = vector_labels
vector_kwargs[... | ['def', 'testVectorAndMatrixLabelEquivalence(self,', 'global_objective,', 'objective_kwargs):', 'vector_labels', '=', 'tf.constant([1.0,', '1.0,', '0.0,', '0.0],', 'shape=[4])', 'vector_logits', '=', 'tf.constant([1.0,', '0.1,', '0.1,', '-1.0],', 'shape=[4])', 'vector_kwargs', '=', 'objective_kwargs.copy()', "vector_kw... | 763,024 |
shenyunhang/PDSL | point_utils.py | get_point_coords_from_point_annotation | get_point_coords_from_point_annotation | Load point coords and their corresponding labels from point annotation. | [
"Load",
"point",
"coords",
"and",
"their",
"corresponding",
"labels",
"from",
"point",
"annotation."
] | def get_point_coords_from_point_annotation(instances):
point_coords_list = []
point_labels_list = []
for instances_per_image in instances:
if len(instances_per_image) == 0:
continue
point_coords = instances_per_image.gt_point_coords.to(torch.float32)
point_labels = instan... | ['def', 'get_point_coords_from_point_annotation(instances):', 'point_coords_list', '=', '[]', 'point_labels_list', '=', '[]', 'for', 'instances_per_image', 'in', 'instances:', 'if', 'len(instances_per_image)', '==', '0:', 'continue', 'point_coords', '=', 'instances_per_image.gt_point_coords.to(torch.float32)', 'point_l... | 279,415 |
myothida/Supervised-Machine-Learning | common.py | any_none | any_none | Returns a boolean indicating if any argument is None. | [
"Returns",
"a",
"boolean",
"indicating",
"if",
"any",
"argument",
"is",
"None."
] | def any_none(*args) -> bool:
return any((arg is None for arg in args)) | ['def', 'any_none(*args)', '->', 'bool:', 'return', 'any((arg', 'is', 'None', 'for', 'arg', 'in', 'args))'] | 442,335 |
aeon-toolkit/aeon | test_all_estimators.py | TestAllObjects.test_valid_estimator_tags | test_valid_estimator_tags | Check that Estimator tags are in VALID_ESTIMATOR_TAGS. | [
"Check",
"that",
"Estimator",
"tags",
"are",
"in",
"VALID_ESTIMATOR_TAGS."
] | def test_valid_estimator_tags(self, estimator_instance):
for tag in estimator_instance.get_tags().keys():
assert tag in VALID_ESTIMATOR_TAGS | ['def', 'test_valid_estimator_tags(self,', 'estimator_instance):', 'for', 'tag', 'in', 'estimator_instance.get_tags().keys():', 'assert', 'tag', 'in', 'VALID_ESTIMATOR_TAGS'] | 399,861 |
PKU-Alignment/safe-rlhf | chatbot.py | Chatbot.generator | generator | Generate the response to the given text. | [
"Generate",
"the",
"response",
"to",
"the",
"given",
"text."
] | def generator(self, text: str, stream: bool=False) -> Generator[str, None, None]:
self.last_input = text
self.last_dialogue = self.dialogue
self.inputs.append(text)
dialogue = self.dialogue + PROMPT_USER.format(input=text) + PROMPT_ASSISTANT
tokenized = to_device(self.tokenizer(dialogue, return_tens... | ['def', 'generator(self,', 'text:', 'str,', 'stream:', 'bool=False)', '->', 'Generator[str,', 'None,', 'None]:', 'self.last_input', '=', 'text', 'self.last_dialogue', '=', 'self.dialogue', 'self.inputs.append(text)', 'dialogue', '=', 'self.dialogue', '+', 'PROMPT_USER.format(input=text)', '+', 'PROMPT_ASSISTANT', 'toke... | 829,180 |
95616ARG/PRDNN | mnist_mft.py | MNISTMFT.run | run | Runs the corruption-fine-tuning experiment. | [
"Runs",
"the",
"corruption-fine-tuning",
"experiment."
] | def run(self):
network = self.load_network('mnist_relu_3_100')
assert isinstance(network.layers[-1], ReluLayer)
network = Network(network.layers[:-1])
self.record_artifact(network, 'original', 'network')
self.which_params = int(input('Which fine-tuning params? (1 or 2): '))
assert self.which_par... | ['def', 'run(self):', 'network', '=', "self.load_network('mnist_relu_3_100')", 'assert', 'isinstance(network.layers[-1],', 'ReluLayer)', 'network', '=', 'Network(network.layers[:-1])', 'self.record_artifact(network,', "'original',", "'network')", 'self.which_params', '=', "int(input('Which", 'fine-tuning', 'params?', '... | 822,183 |
43Carrig/recurrent_neural_networks_practice | cross_tower_utils.py | group_device_names | group_device_names | Group device names into groups of group_size. | [
"Group",
"device",
"names",
"into",
"groups",
"of",
"group_size."
] | def group_device_names(devices, group_size):
num_devices = len(devices)
if group_size > num_devices:
raise ValueError('only %d devices, but group_size=%d' % (num_devices, group_size))
num_groups = num_devices // group_size + (1 if num_devices % group_size != 0 else 0)
groups = [[] for i in range... | ['def', 'group_device_names(devices,', 'group_size):', 'num_devices', '=', 'len(devices)', 'if', 'group_size', '>', 'num_devices:', 'raise', "ValueError('only", '%d', 'devices,', 'but', "group_size=%d'", '%', '(num_devices,', 'group_size))', 'num_groups', '=', 'num_devices', '//', 'group_size', '+', '(1', 'if', 'num_de... | 312,765 |
Kvatsx/Artificial-Intelligence-Assignments | mainwindow.py | MainWindow.get_available_syntax_styles | get_available_syntax_styles | Get a list with the syntax styles available. | [
"Get",
"a",
"list",
"with",
"the",
"syntax",
"styles",
"available."
] | def get_available_syntax_styles(self):
styles = list(get_all_styles())
return sorted(styles) | ['def', 'get_available_syntax_styles(self):', 'styles', '=', 'list(get_all_styles())', 'return', 'sorted(styles)'] | 77,303 |
linkedin/lambda-learner | trainer_logistic_loss_with_l2_test.py | TrainerLogisticLossWithL2Test.test_lr_update_hessian | test_lr_update_hessian | Test the Hessian update. | [
"Test",
"the",
"Hessian",
"update."
] | def test_lr_update_hessian(self):
(indexed_data, model) = simple_mock_data()
lr = TrainerLogisticLossWithL2(training_data=indexed_data, initial_model=model, penalty=10, hessian_type=HessianType.FULL)
hessian = lr._update_full_hessian(model.theta)
expected_hessian = np.array([[10.076006603, 0.00782668920... | ['def', 'test_lr_update_hessian(self):', '(indexed_data,', 'model)', '=', 'simple_mock_data()', 'lr', '=', 'TrainerLogisticLossWithL2(training_data=indexed_data,', 'initial_model=model,', 'penalty=10,', 'hessian_type=HessianType.FULL)', 'hessian', '=', 'lr._update_full_hessian(model.theta)', 'expected_hessian', '=', 'n... | 261,866 |
RasaHQ/rasa | get_version_from_toml.py | project_root | project_root | Root directory of the project. | [
"Root",
"directory",
"of",
"the",
"project."
] | def project_root() -> Path:
return Path(os.path.dirname(__file__)).parent | ['def', 'project_root()', '->', 'Path:', 'return', 'Path(os.path.dirname(__file__)).parent'] | 837,984 |
weimin17/Object-Detection_HelmetDetection | contextual_bandit.py | ContextualBandit.reward | reward | Returns the reward for the number-th context and action. | [
"Returns",
"the",
"reward",
"for",
"the",
"number-th",
"context",
"and",
"action."
] | def reward(self, number, action):
return self.data[self.order[number]][self.context_dim + action] | ['def', 'reward(self,', 'number,', 'action):', 'return', 'self.data[self.order[number]][self.context_dim', '+', 'action]'] | 762,335 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | parse_to_conll.py | get_segmenter_corpus | get_segmenter_corpus | Reads in a character corpus for segmenting. | [
"Reads",
"in",
"a",
"character",
"corpus",
"for",
"segmenting."
] | def get_segmenter_corpus(input_data_path, use_text_format):
tf.logging.info('Reading documents...')
if use_text_format:
char_corpus = sentence_io.FormatSentenceReader(input_data_path, 'untokenized-text').corpus()
else:
input_corpus = sentence_io.ConllSentenceReader(input_data_path).corpus()
... | ['def', 'get_segmenter_corpus(input_data_path,', 'use_text_format):', "tf.logging.info('Reading", "documents...')", 'if', 'use_text_format:', 'char_corpus', '=', 'sentence_io.FormatSentenceReader(input_data_path,', "'untokenized-text').corpus()", 'else:', 'input_corpus', '=', 'sentence_io.ConllSentenceReader(input_data... | 111,651 |
flow-project/flow | multiagent_traffic_light_grid.py | policy_mapping_fn | policy_mapping_fn | Map a policy in RLlib. | [
"Map",
"a",
"policy",
"in",
"RLlib."
] | def policy_mapping_fn(_):
return 'av' | ['def', 'policy_mapping_fn(_):', 'return', "'av'"] | 212,046 |
deep-learning-indaba/Baobab | tests.py | EventsAPITest.test_past_event_offer_accepted | test_past_event_offer_accepted | API should return past events that user had an accepted offer for. | [
"API",
"should",
"return",
"past",
"events",
"that",
"user",
"had",
"an",
"accepted",
"offer",
"for."
] | def test_past_event_offer_accepted(self):
self.seed_static_data()
past_event = self.add_event(start_date=datetime.now() - timedelta(days=30), end_date=datetime.now() - timedelta(days=30), key='PAST12')
self.add_offer(self.test_user.id, past_event.id, candidate_response=True)
response = self.app.get('/ap... | ['def', 'test_past_event_offer_accepted(self):', 'self.seed_static_data()', 'past_event', '=', 'self.add_event(start_date=datetime.now()', '-', 'timedelta(days=30),', 'end_date=datetime.now()', '-', 'timedelta(days=30),', "key='PAST12')", 'self.add_offer(self.test_user.id,', 'past_event.id,', 'candidate_response=True)'... | 94,143 |
matsu0228/nlp-jp | test_exceptions.py | TestErrorTree.test_if_its_in_the_tree_anyhow_it_does_not_raise_an_error | test_if_its_in_the_tree_anyhow_it_does_not_raise_an_error | If a validator is dumb (like :validator:`required` in draft 3) and refers to a path that isn't in the instance, the tree still properly returns a subtree for that path. | [
"If",
"a",
"validator",
"is",
"dumb",
"(like",
":validator:`required`",
"in",
"draft",
"3)",
"and",
"refers",
"to",
"a",
"path",
"that",
"isn't",
"in",
"the",
"instance,",
"the",
"tree",
"still",
"properly",
"returns",
"a",
"subtree",
"for",
"that",
"path."
... | def test_if_its_in_the_tree_anyhow_it_does_not_raise_an_error(self):
error = exceptions.ValidationError('a message', validator='foo', instance={}, path=['foo'])
tree = exceptions.ErrorTree([error])
self.assertIsInstance(tree['foo'], exceptions.ErrorTree) | ['def', 'test_if_its_in_the_tree_anyhow_it_does_not_raise_an_error(self):', 'error', '=', "exceptions.ValidationError('a", "message',", "validator='foo',", 'instance={},', "path=['foo'])", 'tree', '=', 'exceptions.ErrorTree([error])', "self.assertIsInstance(tree['foo'],", 'exceptions.ErrorTree)'] | 788,036 |
nicknochnack/RealTimeSignLanguageTFJS | instance_heads.py | DetectionHead.call | call | Box and class branches for the Mask-RCNN model. | [
"Box",
"and",
"class",
"branches",
"for",
"the",
"Mask-RCNN",
"model."
] | def call(self, inputs, training=None):
roi_features = inputs
(_, num_rois, height, width, filters) = roi_features.get_shape().as_list()
x = tf.reshape(roi_features, [-1, height, width, filters])
for (conv, bn) in zip(self._convs, self._conv_norms):
x = conv(x)
x = bn(x)
x = self.... | ['def', 'call(self,', 'inputs,', 'training=None):', 'roi_features', '=', 'inputs', '(_,', 'num_rois,', 'height,', 'width,', 'filters)', '=', 'roi_features.get_shape().as_list()', 'x', '=', 'tf.reshape(roi_features,', '[-1,', 'height,', 'width,', 'filters])', 'for', '(conv,', 'bn)', 'in', 'zip(self._convs,', 'self._conv... | 850,846 |
gatapia/py_ml_utils | ast_parser.py | StrNodeVisitor.visit_Tuple | visit_Tuple | return a string representation of tuple. | [
"return",
"a",
"string",
"representation",
"of",
"tuple."
] | def visit_Tuple(self, node):
return self._sequence(node, '(%s)') | ['def', 'visit_Tuple(self,', 'node):', 'return', 'self._sequence(node,', "'(%s)')"] | 302,645 |
myothida/Supervised-Machine-Learning | _base.py | _AxesBase.get_ylabel | get_ylabel | Get the ylabel text string. | [
"Get",
"the",
"ylabel",
"text",
"string."
] | def get_ylabel(self):
label = self.yaxis.get_label()
return label.get_text() | ['def', 'get_ylabel(self):', 'label', '=', 'self.yaxis.get_label()', 'return', 'label.get_text()'] | 362,583 |
saymedia/remoteobjects | fields.py | Dict.encode | encode | Encodes a `DataObject` attribute (a dictionary with decoded `DataObject` attribute values for values) into a dictionary value (a dictionary with encoded dictionary values for values). | [
"Encodes",
"a",
"`DataObject`",
"attribute",
"(a",
"dictionary",
"with",
"decoded",
"`DataObject`",
"attribute",
"values",
"for",
"values)",
"into",
"a",
"dictionary",
"value",
"(a",
"dictionary",
"with",
"encoded",
"dictionary",
"values",
"for",
"values)."
] | def encode(self, value):
return dict(((k, self.fld.encode(v)) for (k, v) in value.iteritems())) | ['def', 'encode(self,', 'value):', 'return', 'dict(((k,', 'self.fld.encode(v))', 'for', '(k,', 'v)', 'in', 'value.iteritems()))'] | 346,026 |
ifwe/digsby | accounttray.py | should_grey | should_grey | If this returns True, the account's tray icon will be greyed out when its count is zero. | [
"If",
"this",
"returns",
"True,",
"the",
"account's",
"tray",
"icon",
"will",
"be",
"greyed",
"out",
"when",
"its",
"count",
"is",
"zero."
] | def should_grey(acct):
return not isinstance(acct, social.network) | ['def', 'should_grey(acct):', 'return', 'not', 'isinstance(acct,', 'social.network)'] | 185,359 |
lebrice/Sequoia | pnn_method.py | PnnMethod.on_task_switch | on_task_switch | Called when switching tasks in a CL setting. | [
"Called",
"when",
"switching",
"tasks",
"in",
"a",
"CL",
"setting."
] | def on_task_switch(self, task_id: Optional[int]) -> None:
self.model.freeze_columns(skip=[task_id])
if task_id not in self.added_tasks:
if isinstance(self.model, PnnA2CAgent):
self.model.new_task(device=self.device, num_inputs=self.num_inputs, num_actions=self.num_actions)
else:
... | ['def', 'on_task_switch(self,', 'task_id:', 'Optional[int])', '->', 'None:', 'self.model.freeze_columns(skip=[task_id])', 'if', 'task_id', 'not', 'in', 'self.added_tasks:', 'if', 'isinstance(self.model,', 'PnnA2CAgent):', 'self.model.new_task(device=self.device,', 'num_inputs=self.num_inputs,', 'num_actions=self.num_ac... | 343,989 |
Kvatsx/Artificial-Intelligence-Assignments | newrange.py | newrange.count | count | Return the number of ocurrences of integer `value` in the sequence this range represents. | [
"Return",
"the",
"number",
"of",
"ocurrences",
"of",
"integer",
"`value`",
"in",
"the",
"sequence",
"this",
"range",
"represents."
] | def count(self, value):
return int(value in self) | ['def', 'count(self,', 'value):', 'return', 'int(value', 'in', 'self)'] | 37,182 |
rtlee9/recipe-summarization | prep_data.py | get_complete_recipes | get_complete_recipes | Return intersection of recipe keys and image keys. | [
"Return",
"intersection",
"of",
"recipe",
"keys",
"and",
"image",
"keys."
] | def get_complete_recipes(recipes, image_list):
recipe_keys = [url_to_filename(k) for k in recipes.keys()]
files = np.array([filename for filename in image_list.keys() if filename in recipe_keys])
print('{:,} complete recipes found'.format(len(files)))
return files | ['def', 'get_complete_recipes(recipes,', 'image_list):', 'recipe_keys', '=', '[url_to_filename(k)', 'for', 'k', 'in', 'recipes.keys()]', 'files', '=', 'np.array([filename', 'for', 'filename', 'in', 'image_list.keys()', 'if', 'filename', 'in', 'recipe_keys])', "print('{:,}", 'complete', 'recipes', "found'.format(len(fil... | 309,068 |
xunhuang1995/SGAN | extract_features_for_classification.py | chunks | chunks | Yield n-sized chunks from list of pfd or ar2 files. | [
"Yield",
"n-sized",
"chunks",
"from",
"list",
"of",
"pfd",
"or",
"ar2",
"files."
] | def chunks(pfd_files, n):
for i in range(0, len(pfd_files), n):
yield pfd_files[i:i + n] | ['def', 'chunks(pfd_files,', 'n):', 'for', 'i', 'in', 'range(0,', 'len(pfd_files),', 'n):', 'yield', 'pfd_files[i:i', '+', 'n]'] | 898,662 |
aws/sagemaker-python-sdk | session.py | Session.wait_for_endpoint | wait_for_endpoint | Wait for an Amazon SageMaker endpoint deployment to complete. | [
"Wait",
"for",
"an",
"Amazon",
"SageMaker",
"endpoint",
"deployment",
"to",
"complete."
] | def wait_for_endpoint(self, endpoint, poll=30):
desc = _wait_until(lambda : _deploy_done(self.sagemaker_client, endpoint), poll)
status = desc['EndpointStatus']
if status != 'InService':
reason = desc.get('FailureReason', None)
message = 'Error hosting endpoint {endpoint}: {status}. Reason: ... | ['def', 'wait_for_endpoint(self,', 'endpoint,', 'poll=30):', 'desc', '=', '_wait_until(lambda', ':', '_deploy_done(self.sagemaker_client,', 'endpoint),', 'poll)', 'status', '=', "desc['EndpointStatus']", 'if', 'status', '!=', "'InService':", 'reason', '=', "desc.get('FailureReason',", 'None)', 'message', '=', "'Error",... | 829,650 |
rudranil723/mini-main | admin_list.py | pagination | pagination | Generate the series of links to the pages in a paginated list. | [
"Generate",
"the",
"series",
"of",
"links",
"to",
"the",
"pages",
"in",
"a",
"paginated",
"list."
] | def pagination(cl):
(paginator, page_num) = (cl.paginator, cl.page_num)
pagination_required = (not cl.show_all or not cl.can_show_all) and cl.multi_page
if not pagination_required:
page_range = []
else:
ON_EACH_SIDE = 3
ON_ENDS = 2
if paginator.num_pages <= 10:
... | ['def', 'pagination(cl):', '(paginator,', 'page_num)', '=', '(cl.paginator,', 'cl.page_num)', 'pagination_required', '=', '(not', 'cl.show_all', 'or', 'not', 'cl.can_show_all)', 'and', 'cl.multi_page', 'if', 'not', 'pagination_required:', 'page_range', '=', '[]', 'else:', 'ON_EACH_SIDE', '=', '3', 'ON_ENDS', '=', '2', ... | 314,844 |
NetManAIOps/OmniAnomaly | vae.py | VAE.x_group_ndims | x_group_ndims | Get the `group_ndims` for `x`. | [
"Get",
"the",
"`group_ndims`",
"for",
"`x`."
] | def x_group_ndims(self):
return self._x_group_ndims | ['def', 'x_group_ndims(self):', 'return', 'self._x_group_ndims'] | 250,274 |
mragungsetiaji/nlp | yesno.py | OpinionClassifier.infer | infer | Infers with the trained models. | [
"Infers",
"with",
"the",
"trained",
"models."
] | def infer(self):
cls = self.network()
return cls | ['def', 'infer(self):', 'cls', '=', 'self.network()', 'return', 'cls'] | 808,669 |
caiiiac/Machine-Learning-with-Python | dates.py | hours | hours | Return hours as days. | [
"Return",
"hours",
"as",
"days."
] | def hours(h):
return h / HOURS_PER_DAY | ['def', 'hours(h):', 'return', 'h', '/', 'HOURS_PER_DAY'] | 715,419 |
Ixiaohuihuihui/AO2-DETR | orconv.py | ORConv2d.reset_parameters | reset_parameters | Reset the parameters of ORConv2d. | [
"Reset",
"the",
"parameters",
"of",
"ORConv2d."
] | def reset_parameters(self):
n = self.in_channels * self.nOrientation
for k in self.kernel_size:
n *= k
self.weight.data.normal_(0, math.sqrt(2.0 / n))
if self.bias is not None:
self.bias.data.zero_() | ['def', 'reset_parameters(self):', 'n', '=', 'self.in_channels', '*', 'self.nOrientation', 'for', 'k', 'in', 'self.kernel_size:', 'n', '*=', 'k', 'self.weight.data.normal_(0,', 'math.sqrt(2.0', '/', 'n))', 'if', 'self.bias', 'is', 'not', 'None:', 'self.bias.data.zero_()'] | 401,607 |
neuroailab/unsup_vvs | optimizer.py | ClipOptimizerSelf.compute_gradients | compute_gradients | Compute gradients to model variables from loss. | [
"Compute",
"gradients",
"to",
"model",
"variables",
"from",
"loss."
] | def compute_gradients(self, loss, var_list=None, *args, **kwargs):
if var_list is None:
var_list = tf.trainable_variables()
if self.trainable_scope is not None:
new_var_list = [v for v in var_list if any([nm in v.name for nm in self.trainable_scope])]
if len(new_var_list):
va... | ['def', 'compute_gradients(self,', 'loss,', 'var_list=None,', '*args,', '**kwargs):', 'if', 'var_list', 'is', 'None:', 'var_list', '=', 'tf.trainable_variables()', 'if', 'self.trainable_scope', 'is', 'not', 'None:', 'new_var_list', '=', '[v', 'for', 'v', 'in', 'var_list', 'if', 'any([nm', 'in', 'v.name', 'for', 'nm', '... | 438,382 |
suarez12138/AI-Reversi_IMP_TextDichotomy | animation.py | MovieWriter.cleanup | cleanup | Clean-up and collect the process used to write the movie file. | [
"Clean-up",
"and",
"collect",
"the",
"process",
"used",
"to",
"write",
"the",
"movie",
"file."
] | def cleanup(self):
(out, err) = self._proc.communicate()
self._frame_sink().close()
out = TextIOWrapper(BytesIO(out)).read()
err = TextIOWrapper(BytesIO(err)).read()
if out:
_log.log(logging.WARNING if self._proc.returncode else logging.DEBUG, 'MovieWriter stdout:\n%s', out)
if err:
... | ['def', 'cleanup(self):', '(out,', 'err)', '=', 'self._proc.communicate()', 'self._frame_sink().close()', 'out', '=', 'TextIOWrapper(BytesIO(out)).read()', 'err', '=', 'TextIOWrapper(BytesIO(err)).read()', 'if', 'out:', '_log.log(logging.WARNING', 'if', 'self._proc.returncode', 'else', 'logging.DEBUG,', "'MovieWriter",... | 96,039 |
matsu0228/nlp-jp | named_commands.py | beginning_of_history | beginning_of_history | Move to the first line in the history. | [
"Move",
"to",
"the",
"first",
"line",
"in",
"the",
"history."
] | def beginning_of_history(event):
event.current_buffer.go_to_history(0) | ['def', 'beginning_of_history(event):', 'event.current_buffer.go_to_history(0)'] | 804,458 |
triaquae/triaquae | _winapi.py | format_system_message | format_system_message | Call FormatMessage with a system error number to retrieve the descriptive error message. | [
"Call",
"FormatMessage",
"with",
"a",
"system",
"error",
"number",
"to",
"retrieve",
"the",
"descriptive",
"error",
"message."
] | def format_system_message(errno):
ALLOCATE_BUFFER = 256
ARGUMENT_ARRAY = 8192
FROM_HMODULE = 2048
FROM_STRING = 1024
FROM_SYSTEM = 4096
IGNORE_INSERTS = 512
flags = ALLOCATE_BUFFER | FROM_SYSTEM
source = None
message_id = errno
language_id = 0
result_buffer = ctypes.wintypes.... | ['def', 'format_system_message(errno):', 'ALLOCATE_BUFFER', '=', '256', 'ARGUMENT_ARRAY', '=', '8192', 'FROM_HMODULE', '=', '2048', 'FROM_STRING', '=', '1024', 'FROM_SYSTEM', '=', '4096', 'IGNORE_INSERTS', '=', '512', 'flags', '=', 'ALLOCATE_BUFFER', '|', 'FROM_SYSTEM', 'source', '=', 'None', 'message_id', '=', 'errno'... | 356,481 |
triaquae/triaquae | fallback.py | FallbackTest.stored_messages_count | stored_messages_count | Return the storage totals from both cookie and session backends. | [
"Return",
"the",
"storage",
"totals",
"from",
"both",
"cookie",
"and",
"session",
"backends."
] | def stored_messages_count(self, storage, response):
total = self.stored_cookie_messages_count(storage, response) + self.stored_session_messages_count(storage, response)
return total | ['def', 'stored_messages_count(self,', 'storage,', 'response):', 'total', '=', 'self.stored_cookie_messages_count(storage,', 'response)', '+', 'self.stored_session_messages_count(storage,', 'response)', 'return', 'total'] | 358,137 |
naver/oasis | main_adapt.py | SolverOps.load_model | load_model | Method to load a pre-trained model. | [
"Method",
"to",
"load",
"a",
"pre-trained",
"model."
] | def load_model(self, vanilla_load=False):
if 'pseudo-labels' not in self.args.adapt_mode:
for param in self.model.parameters():
param.requires_grad = False
if 'pseudo-labels' in self.args.adapt_mode and self.args.adapt_only_classifier:
for child in self.model.children():
... | ['def', 'load_model(self,', 'vanilla_load=False):', 'if', "'pseudo-labels'", 'not', 'in', 'self.args.adapt_mode:', 'for', 'param', 'in', 'self.model.parameters():', 'param.requires_grad', '=', 'False', 'if', "'pseudo-labels'", 'in', 'self.args.adapt_mode', 'and', 'self.args.adapt_only_classifier:', 'for', 'child', 'in'... | 725,141 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjrContextWrapper.shadowFBO | shadowFBO | shadow map framebuffer object. | [
"shadow",
"map",
"framebuffer",
"object."
] | def shadowFBO(self):
return self._ptr.contents.shadowFBO | ['def', 'shadowFBO(self):', 'return', 'self._ptr.contents.shadowFBO'] | 440,639 |
intel/neural-compressor | tf2onnx_utils.py | get_tensorflow_node_attr | get_tensorflow_node_attr | Parse tensorflow node attribute. | [
"Parse",
"tensorflow",
"node",
"attribute."
] | def get_tensorflow_node_attr(node, name):
return node.get_attr(name) | ['def', 'get_tensorflow_node_attr(node,', 'name):', 'return', 'node.get_attr(name)'] | 737,739 |
sktime/sktime | test_all_estimators.py | TestAllEstimators.test_dl_constructor_initializes_deeply | test_dl_constructor_initializes_deeply | Test DL estimators that they pass custom parameters to underlying Network. | [
"Test",
"DL",
"estimators",
"that",
"they",
"pass",
"custom",
"parameters",
"to",
"underlying",
"Network."
] | def test_dl_constructor_initializes_deeply(self, estimator_class):
estimator = estimator_class
if not issubclass(estimator, (BaseDeepClassifier, BaseDeepRegressor)):
return None
if not hasattr(estimator, 'get_test_params'):
return None
params = estimator.get_test_params()
if isinstan... | ['def', 'test_dl_constructor_initializes_deeply(self,', 'estimator_class):', 'estimator', '=', 'estimator_class', 'if', 'not', 'issubclass(estimator,', '(BaseDeepClassifier,', 'BaseDeepRegressor)):', 'return', 'None', 'if', 'not', 'hasattr(estimator,', "'get_test_params'):", 'return', 'None', 'params', '=', 'estimator.... | 877,626 |
boris-kz/CogAlg | utils.py | is_close | is_close | Recursively check equality of two objects containing floats. | [
"Recursively",
"check",
"equality",
"of",
"two",
"objects",
"containing",
"floats."
] | def is_close(x1, x2):
if isinstance(x1, numbers.Number) and isinstance(x2, numbers.Number):
return np.isclose(x1, x2)
elif isinstance(x1, np.ndarray) and isinstance(x2, np.ndarray):
try:
return np.allclose(x1, x2)
except ValueError as error_message:
print(f'\nWarn... | ['def', 'is_close(x1,', 'x2):', 'if', 'isinstance(x1,', 'numbers.Number)', 'and', 'isinstance(x2,', 'numbers.Number):', 'return', 'np.isclose(x1,', 'x2)', 'elif', 'isinstance(x1,', 'np.ndarray)', 'and', 'isinstance(x2,', 'np.ndarray):', 'try:', 'return', 'np.allclose(x1,', 'x2)', 'except', 'ValueError', 'as', 'error_me... | 495,892 |
myothida/Supervised-Machine-Learning | test_peak_finding.py | TestFindPeaks.test_constant | test_constant | Test behavior for signal without local maxima. | [
"Test",
"behavior",
"for",
"signal",
"without",
"local",
"maxima."
] | def test_constant(self):
open_interval = (None, None)
(peaks, props) = find_peaks(np.ones(10), height=open_interval, threshold=open_interval, prominence=open_interval, width=open_interval)
assert_(peaks.size == 0)
for key in self.property_keys:
assert_(props[key].size == 0) | ['def', 'test_constant(self):', 'open_interval', '=', '(None,', 'None)', '(peaks,', 'props)', '=', 'find_peaks(np.ones(10),', 'height=open_interval,', 'threshold=open_interval,', 'prominence=open_interval,', 'width=open_interval)', 'assert_(peaks.size', '==', '0)', 'for', 'key', 'in', 'self.property_keys:', 'assert_(pr... | 446,236 |
tobegit3hub/deep_image_model | ops.py | IndexedSlices.values | values | A `Tensor` containing the values of the slices. | [
"A",
"`Tensor`",
"containing",
"the",
"values",
"of",
"the",
"slices."
] | def values(self):
return self._values | ['def', 'values(self):', 'return', 'self._values'] | 182,561 |
airbus/scikit-decide | scheduling_domains.py | SchedulingDomain.update_progress_uncertain | update_progress_uncertain | In an uncertain scheduling environment, update the progress of all ongoing tasks in the state. | [
"In",
"an",
"uncertain",
"scheduling",
"environment,",
"update",
"the",
"progress",
"of",
"all",
"ongoing",
"tasks",
"in",
"the",
"state."
] | def update_progress_uncertain(self, states: DiscreteDistribution[State]):
next_states = DiscreteDistribution([(state, prob) for (state, prob) in states.get_values()])
for (next_state, _) in next_states.get_values():
for task_id in next_state.tasks_ongoing:
next_state.tasks_progress[task_id] ... | ['def', 'update_progress_uncertain(self,', 'states:', 'DiscreteDistribution[State]):', 'next_states', '=', 'DiscreteDistribution([(state,', 'prob)', 'for', '(state,', 'prob)', 'in', 'states.get_values()])', 'for', '(next_state,', '_)', 'in', 'next_states.get_values():', 'for', 'task_id', 'in', 'next_state.tasks_ongoing... | 847,858 |
enuguru/artificial_intelligence_and_machine_learning | mcore.py | Matcher.is_active | is_active | Returns True if this matcher is still "active", that is, it has not yet reached the end of the posting list. | [
"Returns",
"True",
"if",
"this",
"matcher",
"is",
"still",
"\"active\",",
"that",
"is,",
"it",
"has",
"not",
"yet",
"reached",
"the",
"end",
"of",
"the",
"posting",
"list."
] | def is_active(self):
raise NotImplementedError | ['def', 'is_active(self):', 'raise', 'NotImplementedError'] | 133,452 |
changdaeoh/BlackVIP | tools.py | read_json | read_json | Read json file from a path. | [
"Read",
"json",
"file",
"from",
"a",
"path."
] | def read_json(fpath):
with open(fpath, 'r') as f:
obj = json.load(f)
return obj | ['def', 'read_json(fpath):', 'with', 'open(fpath,', "'r')", 'as', 'f:', 'obj', '=', 'json.load(f)', 'return', 'obj'] | 461,640 |
meidachen/STPLS3D | cindex.py | Cursor.hash | hash | Returns a hash of the cursor as an int. | [
"Returns",
"a",
"hash",
"of",
"the",
"cursor",
"as",
"an",
"int."
] | def hash(self):
if not hasattr(self, '_hash'):
self._hash = conf.lib.clang_hashCursor(self)
return self._hash | ['def', 'hash(self):', 'if', 'not', 'hasattr(self,', "'_hash'):", 'self._hash', '=', 'conf.lib.clang_hashCursor(self)', 'return', 'self._hash'] | 909,150 |
Ruturaj123/Flowchart-Detection | training_ops.py | Load | Load | Load training ops library and return the loaded module. | [
"Load",
"training",
"ops",
"library",
"and",
"return",
"the",
"loaded",
"module."
] | def Load():
with _ops_lock:
global _training_ops
if not _training_ops:
ops_path = resource_loader.get_path_to_datafile(TRAINING_OPS_FILE)
logging.info('data path: %s', ops_path)
_training_ops = loader.load_op_library(ops_path)
assert _training_ops, 'Co... | ['def', 'Load():', 'with', '_ops_lock:', 'global', '_training_ops', 'if', 'not', '_training_ops:', 'ops_path', '=', 'resource_loader.get_path_to_datafile(TRAINING_OPS_FILE)', "logging.info('data", 'path:', "%s',", 'ops_path)', '_training_ops', '=', 'loader.load_op_library(ops_path)', 'assert', '_training_ops,', "'Could... | 604,583 |
xudejing/video-clip-order-prediction | ucf101.py | gen_ucf101_vcop_splits | gen_ucf101_vcop_splits | Generate split files for different configs. | [
"Generate",
"split",
"files",
"for",
"different",
"configs."
] | def gen_ucf101_vcop_splits(root_dir, clip_len, interval, tuple_len):
vcop_train_split_name = 'vcop_train_{}_{}_{}.txt'.format(clip_len, interval, tuple_len)
vcop_train_split_path = os.path.join(root_dir, 'split', vcop_train_split_name)
vcop_test_split_name = 'vcop_test_{}_{}_{}.txt'.format(clip_len, interva... | ['def', 'gen_ucf101_vcop_splits(root_dir,', 'clip_len,', 'interval,', 'tuple_len):', 'vcop_train_split_name', '=', "'vcop_train_{}_{}_{}.txt'.format(clip_len,", 'interval,', 'tuple_len)', 'vcop_train_split_path', '=', 'os.path.join(root_dir,', "'split',", 'vcop_train_split_name)', 'vcop_test_split_name', '=', "'vcop_te... | 379,811 |
google-research/scenic | common.py | cpu_matcher | cpu_matcher | Wraps matching function to be usable within jitted functions. | [
"Wraps",
"matching",
"function",
"to",
"be",
"usable",
"within",
"jitted",
"functions."
] | def cpu_matcher(matching_fn):
def slice_and_match(args):
(cost, ncol) = args
return slicer(cost, ncol, matching_fn)
@jax.custom_vjp
def matching_fn_hcb(cost, n_cols=None):
(*b, n, m) = cost.shape
return jax.pure_callback(slice_and_match, jax.ShapeDtypeStruct(b + [2, min(n, ... | ['def', 'cpu_matcher(matching_fn):', 'def', 'slice_and_match(args):', '(cost,', 'ncol)', '=', 'args', 'return', 'slicer(cost,', 'ncol,', 'matching_fn)', '@jax.custom_vjp', 'def', 'matching_fn_hcb(cost,', 'n_cols=None):', '(*b,', 'n,', 'm)', '=', 'cost.shape', 'return', 'jax.pure_callback(slice_and_match,', 'jax.ShapeDt... | 846,283 |
matthewearl/deep-anpr | model.py | convolutional_layers | convolutional_layers | Get the convolutional layers of the model. | [
"Get",
"the",
"convolutional",
"layers",
"of",
"the",
"model."
] | def convolutional_layers():
x = tf.placeholder(tf.float32, [None, None, None])
W_conv1 = weight_variable([5, 5, 1, 48])
b_conv1 = bias_variable([48])
x_expanded = tf.expand_dims(x, 3)
h_conv1 = tf.nn.relu(conv2d(x_expanded, W_conv1) + b_conv1)
h_pool1 = max_pool(h_conv1, ksize=(2, 2), stride=(2,... | ['def', 'convolutional_layers():', 'x', '=', 'tf.placeholder(tf.float32,', '[None,', 'None,', 'None])', 'W_conv1', '=', 'weight_variable([5,', '5,', '1,', '48])', 'b_conv1', '=', 'bias_variable([48])', 'x_expanded', '=', 'tf.expand_dims(x,', '3)', 'h_conv1', '=', 'tf.nn.relu(conv2d(x_expanded,', 'W_conv1)', '+', 'b_con... | 516,868 |
AboudyKreidieh/h-baselines | train.py | create_sac_parser | create_sac_parser | Add the SAC hyperparameters to the parser. | [
"Add",
"the",
"SAC",
"hyperparameters",
"to",
"the",
"parser."
] | def create_sac_parser(parser):
parser.add_argument('--buffer_size', type=int, default=SAC_PARAMS['buffer_size'], help='the max number of transitions to store')
parser.add_argument('--batch_size', type=int, default=SAC_PARAMS['batch_size'], help='the size of the batch for learning the policy')
parser.add_arg... | ['def', 'create_sac_parser(parser):', "parser.add_argument('--buffer_size',", 'type=int,', "default=SAC_PARAMS['buffer_size'],", "help='the", 'max', 'number', 'of', 'transitions', 'to', "store')", "parser.add_argument('--batch_size',", 'type=int,', "default=SAC_PARAMS['batch_size'],", "help='the", 'size', 'of', 'the', ... | 574,016 |
YanZiQinKevin/object_detection | c2.py | CudaScope | CudaScope | Create a CUDA device scope for GPU device `gpu_id`. | [
"Create",
"a",
"CUDA",
"device",
"scope",
"for",
"GPU",
"device",
"`gpu_id`."
] | def CudaScope(gpu_id):
gpu_dev = CudaDevice(gpu_id)
with core.DeviceScope(gpu_dev):
yield | ['def', 'CudaScope(gpu_id):', 'gpu_dev', '=', 'CudaDevice(gpu_id)', 'with', 'core.DeviceScope(gpu_dev):', 'yield'] | 773,236 |
facebookresearch/CompilerGym | llvm_random_actions_fuzz_test.py | test_fuzz | test_fuzz | Run randomly selected actions on a benchmark until a minimum amount of time has elapsed. | [
"Run",
"randomly",
"selected",
"actions",
"on",
"a",
"benchmark",
"until",
"a",
"minimum",
"amount",
"of",
"time",
"has",
"elapsed."
] | def test_fuzz(observation_space: str, reward_space: str):
with gym.make('llvm-v0', reward_space=reward_space, observation_space=observation_space) as env:
benchmark = env.datasets['generator://llvm-stress-v0'].random_benchmark()
print(benchmark.uri)
env.reset(benchmark=benchmark)
end... | ['def', 'test_fuzz(observation_space:', 'str,', 'reward_space:', 'str):', 'with', "gym.make('llvm-v0',", 'reward_space=reward_space,', 'observation_space=observation_space)', 'as', 'env:', 'benchmark', '=', "env.datasets['generator://llvm-stress-v0'].random_benchmark()", 'print(benchmark.uri)', 'env.reset(benchmark=ben... | 135,796 |
deepmind/acme | atari.py | DeepIMPALAAtariNetwork.unroll | unroll | Efficient unroll that applies embeddings, MLP, & convnet in one pass. | [
"Efficient",
"unroll",
"that",
"applies",
"embeddings,",
"MLP,",
"&",
"convnet",
"in",
"one",
"pass."
] | def unroll(self, inputs: observation_action_reward.OAR, state: hk.LSTMState) -> Any:
embeddings = self._embed(inputs)
(embeddings, new_states) = hk.static_unroll(self._core, embeddings, state)
(logits, values) = self._head(embeddings)
return ((logits, values), new_states) | ['def', 'unroll(self,', 'inputs:', 'observation_action_reward.OAR,', 'state:', 'hk.LSTMState)', '->', 'Any:', 'embeddings', '=', 'self._embed(inputs)', '(embeddings,', 'new_states)', '=', 'hk.static_unroll(self._core,', 'embeddings,', 'state)', '(logits,', 'values)', '=', 'self._head(embeddings)', 'return', '((logits,'... | 7,831 |
flyteorg/flytelab | workflow.py | encode_datetime | encode_datetime | One-hot encode datetime into features. | [
"One-hot",
"encode",
"datetime",
"into",
"features."
] | def encode_datetime(dt: datetime):
dt = pd.Timestamp(dt)
return np.array([*onehot_encode(dt.hour, 24), *onehot_encode(dt.day_of_week, 7), *onehot_encode(dt.day, 31), *onehot_encode(dt.day_of_year, 356), *onehot_encode(dt.month, 12), minmax_scaler(dt.year, 1900, 2500)]) | ['def', 'encode_datetime(dt:', 'datetime):', 'dt', '=', 'pd.Timestamp(dt)', 'return', 'np.array([*onehot_encode(dt.hour,', '24),', '*onehot_encode(dt.day_of_week,', '7),', '*onehot_encode(dt.day,', '31),', '*onehot_encode(dt.day_of_year,', '356),', '*onehot_encode(dt.month,', '12),', 'minmax_scaler(dt.year,', '1900,', ... | 606,992 |
flavioschneider/rl-transfer- | test_ppo.py | TestPPOPendulumGRU.test_ppo_pendulum_gru | test_ppo_pendulum_gru | Test PPO with Pendulum environment and recurrent policy. | [
"Test",
"PPO",
"with",
"Pendulum",
"environment",
"and",
"recurrent",
"policy."
] | def test_ppo_pendulum_gru(self):
with TFTrainer(snapshot_config) as trainer:
env = normalize(GymEnv('InvertedDoublePendulum-v2', max_episode_length=100))
gru_policy = GaussianGRUPolicy(env_spec=env.spec)
baseline = GaussianMLPBaseline(env_spec=env.spec, hidden_sizes=(32, 32))
sampler... | ['def', 'test_ppo_pendulum_gru(self):', 'with', 'TFTrainer(snapshot_config)', 'as', 'trainer:', 'env', '=', "normalize(GymEnv('InvertedDoublePendulum-v2',", 'max_episode_length=100))', 'gru_policy', '=', 'GaussianGRUPolicy(env_spec=env.spec)', 'baseline', '=', 'GaussianMLPBaseline(env_spec=env.spec,', 'hidden_sizes=(32... | 861,758 |
danamyu/hedgehog_detector | utils.py | init_linear | init_linear | Linear (affine) transformation, y = x W + b, for a variety of configurations. | [
"Linear",
"(affine)",
"transformation,",
"y",
"=",
"x",
"W",
"+",
"b,",
"for",
"a",
"variety",
"of",
"configurations."
] | def init_linear(in_size, out_size, do_bias=True, mat_init_value=None, bias_init_value=None, alpha=1.0, identity_if_possible=False, normalized=False, name=None, collections=None):
if mat_init_value is not None and mat_init_value.shape != (in_size, out_size):
raise ValueError('Provided mat_init_value must hav... | ['def', 'init_linear(in_size,', 'out_size,', 'do_bias=True,', 'mat_init_value=None,', 'bias_init_value=None,', 'alpha=1.0,', 'identity_if_possible=False,', 'normalized=False,', 'name=None,', 'collections=None):', 'if', 'mat_init_value', 'is', 'not', 'None', 'and', 'mat_init_value.shape', '!=', '(in_size,', 'out_size):'... | 589,838 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | target_python.py | TargetPython.format_given | format_given | Format the given, non-None attributes for display. | [
"Format",
"the",
"given,",
"non-None",
"attributes",
"for",
"display."
] | def format_given(self):
display_version = None
if self._given_py_version_info is not None:
display_version = '.'.join((str(part) for part in self._given_py_version_info))
key_values = [('platforms', self.platforms), ('version_info', display_version), ('abis', self.abis), ('implementation', self.impl... | ['def', 'format_given(self):', 'display_version', '=', 'None', 'if', 'self._given_py_version_info', 'is', 'not', 'None:', 'display_version', '=', "'.'.join((str(part)", 'for', 'part', 'in', 'self._given_py_version_info))', 'key_values', '=', "[('platforms',", 'self.platforms),', "('version_info',", 'display_version),',... | 454,236 |
matsu0228/nlp-jp | ticker.py | Locator.view_limits | view_limits | select a scale for the range from vmin to vmax Normally this method is overridden by subclasses to change locator behaviour. | [
"select",
"a",
"scale",
"for",
"the",
"range",
"from",
"vmin",
"to",
"vmax",
"Normally",
"this",
"method",
"is",
"overridden",
"by",
"subclasses",
"to",
"change",
"locator",
"behaviour."
] | def view_limits(self, vmin, vmax):
return mtransforms.nonsingular(vmin, vmax) | ['def', 'view_limits(self,', 'vmin,', 'vmax):', 'return', 'mtransforms.nonsingular(vmin,', 'vmax)'] | 789,347 |
AboudyKreidieh/h-baselines | test_envs.py | TestPendulum.test_reset | test_reset | Ensure the state initialization is within the expected range. | [
"Ensure",
"the",
"state",
"initialization",
"is",
"within",
"the",
"expected",
"range."
] | def test_reset(self):
state = self.env.reset()
num_obj = len(state) // 3
for i in range(num_obj):
self.assertTrue(np.arccos(state[i]) >= self.env.initial_state_space[i][0])
self.assertTrue(np.arccos(state[i]) <= self.env.initial_state_space[i][1])
self.assertTrue(state[i + 2 * num_ob... | ['def', 'test_reset(self):', 'state', '=', 'self.env.reset()', 'num_obj', '=', 'len(state)', '//', '3', 'for', 'i', 'in', 'range(num_obj):', 'self.assertTrue(np.arccos(state[i])', '>=', 'self.env.initial_state_space[i][0])', 'self.assertTrue(np.arccos(state[i])', '<=', 'self.env.initial_state_space[i][1])', 'self.asser... | 574,056 |
tencent-ailab/TriNet | metrics.py | reset_meter | reset_meter | Reset Meter instance aggregated under a given *name* and *key*. | [
"Reset",
"Meter",
"instance",
"aggregated",
"under",
"a",
"given",
"*name*",
"and",
"*key*."
] | def reset_meter(name: str, key: str) -> None:
meter = get_meter(name, key)
if meter is not None:
meter.reset() | ['def', 'reset_meter(name:', 'str,', 'key:', 'str)', '->', 'None:', 'meter', '=', 'get_meter(name,', 'key)', 'if', 'meter', 'is', 'not', 'None:', 'meter.reset()'] | 425,276 |
ilya16/MultINN | multinn_core.py | MultINNCore.encoders | encoders | The list of the MultINN Encoders. | [
"The",
"list",
"of",
"the",
"MultINN",
"Encoders."
] | def encoders(self):
return self._encoders | ['def', 'encoders(self):', 'return', 'self._encoders'] | 644,325 |
zihuitang/medical_AI_platform | test_funcattrs.py | empty_cell | empty_cell | Create an empty cell. | [
"Create",
"an",
"empty",
"cell."
] | def empty_cell(empty=True):
def f():
print(a)
if not empty:
a = 1729
return f.__closure__[0] | ['def', 'empty_cell(empty=True):', 'def', 'f():', 'print(a)', 'if', 'not', 'empty:', 'a', '=', '1729', 'return', 'f.__closure__[0]'] | 283,384 |
KalleHallden/InstaAutomator | ffmpeg_tools.py | ffmpeg_merge_video_audio | ffmpeg_merge_video_audio | merges video file ``video`` and audio file ``audio`` into one movie file ``output``. | [
"merges",
"video",
"file",
"``video``",
"and",
"audio",
"file",
"``audio``",
"into",
"one",
"movie",
"file",
"``output``."
] | def ffmpeg_merge_video_audio(video, audio, output, vcodec='copy', acodec='copy', ffmpeg_output=False, verbose=True):
cmd = [get_setting('FFMPEG_BINARY'), '-y', '-i', audio, '-i', video, '-vcodec', vcodec, '-acodec', acodec, output]
subprocess_call(cmd, verbose=verbose) | ['def', 'ffmpeg_merge_video_audio(video,', 'audio,', 'output,', "vcodec='copy',", "acodec='copy',", 'ffmpeg_output=False,', 'verbose=True):', 'cmd', '=', "[get_setting('FFMPEG_BINARY'),", "'-y',", "'-i',", 'audio,', "'-i',", 'video,', "'-vcodec',", 'vcodec,', "'-acodec',", 'acodec,', 'output]', 'subprocess_call(cmd,', ... | 230,437 |
melfm/avod-ssd | anchor_encoder.py | offset_to_anchor | offset_to_anchor | Decodes the anchor regression predictions with the anchor. | [
"Decodes",
"the",
"anchor",
"regression",
"predictions",
"with",
"the",
"anchor."
] | def offset_to_anchor(anchors, offsets):
fc.check_anchor_format(anchors)
fc.check_anchor_format(offsets)
x_pred = offsets[:, 0] * anchors[:, 3] + anchors[:, 0]
y_pred = offsets[:, 1] * anchors[:, 4] + anchors[:, 1]
z_pred = offsets[:, 2] * anchors[:, 5] + anchors[:, 2]
tensor_format = isinstance(... | ['def', 'offset_to_anchor(anchors,', 'offsets):', 'fc.check_anchor_format(anchors)', 'fc.check_anchor_format(offsets)', 'x_pred', '=', 'offsets[:,', '0]', '*', 'anchors[:,', '3]', '+', 'anchors[:,', '0]', 'y_pred', '=', 'offsets[:,', '1]', '*', 'anchors[:,', '4]', '+', 'anchors[:,', '1]', 'z_pred', '=', 'offsets[:,', '... | 420,834 |
microsoft/nlp-recipes | extractive_summarization.py | get_pred | get_pred | Get the summarization prediction for the paragraph example based on the scores returned by the transformer summarization model. | [
"Get",
"the",
"summarization",
"prediction",
"for",
"the",
"paragraph",
"example",
"based",
"on",
"the",
"scores",
"returned",
"by",
"the",
"transformer",
"summarization",
"model."
] | def get_pred(example, sent_scores, cal_lead=False, sentence_separator='<q>', block_trigram=True, top_n=3):
def _get_ngrams(n, text):
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add... | ['def', 'get_pred(example,', 'sent_scores,', 'cal_lead=False,', "sentence_separator='<q>',", 'block_trigram=True,', 'top_n=3):', 'def', '_get_ngrams(n,', 'text):', 'ngram_set', '=', 'set()', 'text_length', '=', 'len(text)', 'max_index_ngram_start', '=', 'text_length', '-', 'n', 'for', 'i', 'in', 'range(max_index_ngram_... | 731,299 |
ldamewood/renormalization | graph.py | Graph.edgeWeights | edgeWeights | Edge generator (no repeats). | [
"Edge",
"generator",
"(no",
"repeats)."
] | def edgeWeights(self):
for (key, value) in self._edges:
yield (key, value) | ['def', 'edgeWeights(self):', 'for', '(key,', 'value)', 'in', 'self._edges:', 'yield', '(key,', 'value)'] | 840,213 |
SamuelScheit/carcassonne-ai | muzero.py | MuZero.logging_loop | logging_loop | Keep track of the training performance. | [
"Keep",
"track",
"of",
"the",
"training",
"performance."
] | def logging_loop(self, num_gpus):
self.test_worker = self_play.SelfPlay.options(num_cpus=0, num_gpus=num_gpus).remote(self.checkpoint, self.Game, self.config, self.config.seed + self.config.num_workers)
self.test_worker.continuous_self_play.remote(self.shared_storage_worker, None, True)
writer = SummaryWrit... | ['def', 'logging_loop(self,', 'num_gpus):', 'self.test_worker', '=', 'self_play.SelfPlay.options(num_cpus=0,', 'num_gpus=num_gpus).remote(self.checkpoint,', 'self.Game,', 'self.config,', 'self.config.seed', '+', 'self.config.num_workers)', 'self.test_worker.continuous_self_play.remote(self.shared_storage_worker,', 'Non... | 109,093 |
matsu0228/nlp-jp | egg_info.py | FileList.recursive_exclude | recursive_exclude | Exclude any file anywhere in 'dir/' that match the pattern. | [
"Exclude",
"any",
"file",
"anywhere",
"in",
"'dir/'",
"that",
"match",
"the",
"pattern."
] | def recursive_exclude(self, dir, pattern):
match = translate_pattern(os.path.join(dir, '**', pattern))
return self._remove_files(match.match) | ['def', 'recursive_exclude(self,', 'dir,', 'pattern):', 'match', '=', 'translate_pattern(os.path.join(dir,', "'**',", 'pattern))', 'return', 'self._remove_files(match.match)'] | 806,246 |
facebookresearch/CompilerGym | env_without_bazel_test.py | test_observation_before_reset | test_observation_before_reset | Taking an observation before reset() is illegal. | [
"Taking",
"an",
"observation",
"before",
"reset()",
"is",
"illegal."
] | def test_observation_before_reset(env: CompilerEnv):
with pytest.raises(SessionNotFound, match='Must call reset\\(\\) before step\\(\\)'):
_ = env.observation['ir'] | ['def', 'test_observation_before_reset(env:', 'CompilerEnv):', 'with', 'pytest.raises(SessionNotFound,', "match='Must", 'call', 'reset\\\\(\\\\)', 'before', "step\\\\(\\\\)'):", '_', '=', "env.observation['ir']"] | 135,726 |
bnpy/bnpy | TestHDPHMM_ParallelBenchmark.py | Test.shutdownWorkers | shutdownWorkers | Shut down all worker processes. | [
"Shut",
"down",
"all",
"worker",
"processes."
] | def shutdownWorkers(self):
for workerID in range(self.nWorkers):
self.JobQ.put(None) | ['def', 'shutdownWorkers(self):', 'for', 'workerID', 'in', 'range(self.nWorkers):', 'self.JobQ.put(None)'] | 465,512 |
sek788432/Waymo-2D-Object-Detection | augment.py | ImageAugment.distort | distort | Given an image tensor, returns a distorted image with the same shape. | [
"Given",
"an",
"image",
"tensor,",
"returns",
"a",
"distorted",
"image",
"with",
"the",
"same",
"shape."
] | def distort(self, image: tf.Tensor) -> tf.Tensor:
raise NotImplementedError() | ['def', 'distort(self,', 'image:', 'tf.Tensor)', '->', 'tf.Tensor:', 'raise', 'NotImplementedError()'] | 973,229 |
TerenceCYJ/S2HAND | utils.py | efficientnet_params | efficientnet_params | Map EfficientNet model name to parameter coefficients. | [
"Map",
"EfficientNet",
"model",
"name",
"to",
"parameter",
"coefficients."
] | def efficientnet_params(model_name):
params_dict = {'efficientnet-b0': (1.0, 1.0, 224, 0.2), 'efficientnet-b1': (1.0, 1.1, 240, 0.2), 'efficientnet-b2': (1.1, 1.2, 260, 0.3), 'efficientnet-b3': (1.2, 1.4, 300, 0.3), 'efficientnet-b4': (1.4, 1.8, 380, 0.4), 'efficientnet-b5': (1.6, 2.2, 456, 0.4), 'efficientnet-b6':... | ['def', 'efficientnet_params(model_name):', 'params_dict', '=', "{'efficientnet-b0':", '(1.0,', '1.0,', '224,', '0.2),', "'efficientnet-b1':", '(1.0,', '1.1,', '240,', '0.2),', "'efficientnet-b2':", '(1.1,', '1.2,', '260,', '0.3),', "'efficientnet-b3':", '(1.2,', '1.4,', '300,', '0.3),', "'efficientnet-b4':", '(1.4,', ... | 327,278 |
ibarrien/SemiSupervisedLearning | expectation_maximization.py | EM_SSL.only_labeled_test_acc | only_labeled_test_acc | Test accuracy using only labeled data. | [
"Test",
"accuracy",
"using",
"only",
"labeled",
"data."
] | def only_labeled_test_acc(self) -> float:
return self.test_accuracy_hist[0] | ['def', 'only_labeled_test_acc(self)', '->', 'float:', 'return', 'self.test_accuracy_hist[0]'] | 343,759 |
microsoft/InnerEye-DeepLearning | test_dataloader_speed.py | test_dataloader_speed | test_dataloader_speed | Test how dataloaders work when using multiple processes. | [
"Test",
"how",
"dataloaders",
"work",
"when",
"using",
"multiple",
"processes."
] | def test_dataloader_speed(test_output_dirs: OutputFolderForTests, num_dataload_workers: int, shuffle: bool) -> None:
ml_util.set_random_seed(0)
csv_string = StringIO('subject,channel,path,value,scalar1\nS1,image,4be9beed-5861-fdd2-72c2-8dd89aadc1ef\nS1,label,,True,1.0\nS2,image,6ceacaf8-abd2-ffec-2ade-d52afd6dd... | ['def', 'test_dataloader_speed(test_output_dirs:', 'OutputFolderForTests,', 'num_dataload_workers:', 'int,', 'shuffle:', 'bool)', '->', 'None:', 'ml_util.set_random_seed(0)', 'csv_string', '=', "StringIO('subject,channel,path,value,scalar1\\nS1,image,4be9beed-5861-fdd2-72c2-8dd89aadc1ef\\nS1,label,,True,1.0\\nS2,image,... | 613,652 |
RasaHQ/rasa_core | model.py | merge_model | merge_model | Merges two model directories. | [
"Merges",
"two",
"model",
"directories."
] | def merge_model(source: Text, target: Text) -> bool:
try:
shutil.move(source, target)
return True
except Exception as e:
logging.debug(e)
return False | ['def', 'merge_model(source:', 'Text,', 'target:', 'Text)', '->', 'bool:', 'try:', 'shutil.move(source,', 'target)', 'return', 'True', 'except', 'Exception', 'as', 'e:', 'logging.debug(e)', 'return', 'False'] | 838,138 |
0xangelo/raylab | policy.py | MBPolicyMixin.build_timers | build_timers | Create timers for model and policy training. | [
"Create",
"timers",
"for",
"model",
"and",
"policy",
"training."
] | def build_timers(self):
self.timers = {'model': TimerStat(), 'policy': TimerStat()}
self._info = {} | ['def', 'build_timers(self):', 'self.timers', '=', "{'model':", 'TimerStat(),', "'policy':", 'TimerStat()}', 'self._info', '=', '{}'] | 848,358 |
FeliMe/feature-autoencoder | datasets.py | get_mood_val_test_files | get_mood_val_test_files | Get MOOD validation and test files. | [
"Get",
"MOOD",
"validation",
"and",
"test",
"files."
] | def get_mood_val_test_files(path: str=MOODROOT, **kwargs) -> Tuple[List[str], List[str]]:
test_files = glob(os.path.join(path, 'brain/test_raw/*.nii.gz'))
assert len(test_files) > 0, 'No files found in MOOD'
return (test_files, None) | ['def', 'get_mood_val_test_files(path:', 'str=MOODROOT,', '**kwargs)', '->', 'Tuple[List[str],', 'List[str]]:', 'test_files', '=', 'glob(os.path.join(path,', "'brain/test_raw/*.nii.gz'))", 'assert', 'len(test_files)', '>', '0,', "'No", 'files', 'found', 'in', "MOOD'", 'return', '(test_files,', 'None)'] | 544,702 |
deepmind/dm_control | runtime.py | Runtime.restart | restart | Restarts the episode, resetting environment, model, and data. | [
"Restarts",
"the",
"episode,",
"resetting",
"environment,",
"model,",
"and",
"data."
] | def restart(self):
if self._state != State.STOPPED:
self._state = State.RESTARTING
else:
self._state = State.START | ['def', 'restart(self):', 'if', 'self._state', '!=', 'State.STOPPED:', 'self._state', '=', 'State.RESTARTING', 'else:', 'self._state', '=', 'State.START'] | 165,693 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | quantization.py | ParameterEncoding.decode | decode | Decode bfloat16 to float32. | [
"Decode",
"bfloat16",
"to",
"float32."
] | def decode(self, x):
raise NotImplementedError('decode not implemented') | ['def', 'decode(self,', 'x):', 'raise', "NotImplementedError('decode", 'not', "implemented')"] | 966,170 |
boris-kz/CogAlg | utils.py | draw_blob | draw_blob | Map a single blob into an image. | [
"Map",
"a",
"single",
"blob",
"into",
"an",
"image."
] | def draw_blob(blob, *args, blob_box=None, **kwargs):
if blob_box is None:
blob_box = blob.box
blob_img = blank_image(blob_box)
for stack in blob.stack_:
sub_box = stack_box(stack)
stack_map = draw_stack(stack, sub_box, blob.sign, *args, **kwargs)
paint_over(blob_img, stack_ma... | ['def', 'draw_blob(blob,', '*args,', 'blob_box=None,', '**kwargs):', 'if', 'blob_box', 'is', 'None:', 'blob_box', '=', 'blob.box', 'blob_img', '=', 'blank_image(blob_box)', 'for', 'stack', 'in', 'blob.stack_:', 'sub_box', '=', 'stack_box(stack)', 'stack_map', '=', 'draw_stack(stack,', 'sub_box,', 'blob.sign,', '*args,'... | 495,905 |
asyml/texar | embedding.py | Embedding.vector_size | vector_size | The embedding dimention size. | [
"The",
"embedding",
"dimention",
"size."
] | def vector_size(self):
return self._hparams.dim | ['def', 'vector_size(self):', 'return', 'self._hparams.dim'] | 924,487 |
Oneflow-Inc/vision | imagenet.py | parse_train_archive | parse_train_archive | Parse the train images archive of the ImageNet2012 classification dataset and prepare it for usage with the ImageNet dataset. | [
"Parse",
"the",
"train",
"images",
"archive",
"of",
"the",
"ImageNet2012",
"classification",
"dataset",
"and",
"prepare",
"it",
"for",
"usage",
"with",
"the",
"ImageNet",
"dataset."
] | def parse_train_archive(root: str, file: Optional[str]=None, folder: str='train') -> None:
archive_meta = ARCHIVE_META['train']
if file is None:
file = archive_meta[0]
md5 = archive_meta[1]
_verify_archive(root, file, md5)
train_root = os.path.join(root, folder)
extract_archive(os.path.j... | ['def', 'parse_train_archive(root:', 'str,', 'file:', 'Optional[str]=None,', 'folder:', "str='train')", '->', 'None:', 'archive_meta', '=', "ARCHIVE_META['train']", 'if', 'file', 'is', 'None:', 'file', '=', 'archive_meta[0]', 'md5', '=', 'archive_meta[1]', '_verify_archive(root,', 'file,', 'md5)', 'train_root', '=', 'o... | 958,196 |
rudranil723/mini-main | query.py | Query.get_count | get_count | Perform a COUNT() query using the current filter constraints. | [
"Perform",
"a",
"COUNT()",
"query",
"using",
"the",
"current",
"filter",
"constraints."
] | def get_count(self, using):
obj = self.clone()
obj.add_annotation(Count('*'), alias='__count', is_summary=True)
number = obj.get_aggregation(using, ['__count'])['__count']
if number is None:
number = 0
return number | ['def', 'get_count(self,', 'using):', 'obj', '=', 'self.clone()', "obj.add_annotation(Count('*'),", "alias='__count',", 'is_summary=True)', 'number', '=', 'obj.get_aggregation(using,', "['__count'])['__count']", 'if', 'number', 'is', 'None:', 'number', '=', '0', 'return', 'number'] | 316,138 |
sarnsdev/social-alignment-data-mining | basic.py | upgrade_to_float | upgrade_to_float | Upgrade any int types to float32 or float64 to avoid losing precision. | [
"Upgrade",
"any",
"int",
"types",
"to",
"float32",
"or",
"float64",
"to",
"avoid",
"losing",
"precision."
] | def upgrade_to_float(*types):
conv = {bool: float32, int8: float32, int16: float32, int32: float64, int64: float64, uint8: float32, uint16: float32, uint32: float64, uint64: float64}
return (get_scalar_type(Scalar.upcast(*[conv.get(type, type) for type in types])),) | ['def', 'upgrade_to_float(*types):', 'conv', '=', '{bool:', 'float32,', 'int8:', 'float32,', 'int16:', 'float32,', 'int32:', 'float64,', 'int64:', 'float64,', 'uint8:', 'float32,', 'uint16:', 'float32,', 'uint32:', 'float64,', 'uint64:', 'float64}', 'return', '(get_scalar_type(Scalar.upcast(*[conv.get(type,', 'type)', ... | 392,845 |
matsu0228/nlp-jp | styles.py | get_colors | get_colors | Construct the keys to be used building the base stylesheet from a templatee. | [
"Construct",
"the",
"keys",
"to",
"be",
"used",
"building",
"the",
"base",
"stylesheet",
"from",
"a",
"templatee."
] | def get_colors(stylename):
style = get_style_by_name(stylename)
fgcolor = style.style_for_token(Token.Text)['color'] or ''
if len(fgcolor) in (3, 6):
try:
int(fgcolor, 16)
except TypeError:
pass
else:
fgcolor = '#' + fgcolor
return dict(bgcolor... | ['def', 'get_colors(stylename):', 'style', '=', 'get_style_by_name(stylename)', 'fgcolor', '=', "style.style_for_token(Token.Text)['color']", 'or', "''", 'if', 'len(fgcolor)', 'in', '(3,', '6):', 'try:', 'int(fgcolor,', '16)', 'except', 'TypeError:', 'pass', 'else:', 'fgcolor', '=', "'#'", '+', 'fgcolor', 'return', 'di... | 805,265 |
Center-of-Diagnostics-and-Telemedicine/ai-testing-platform | blocks.py | ExtensionBlock.take_nd | take_nd | Take values according to indexer and return them as a block. | [
"Take",
"values",
"according",
"to",
"indexer",
"and",
"return",
"them",
"as",
"a",
"block."
] | def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None):
if fill_tuple is None:
fill_value = None
else:
fill_value = fill_tuple[0]
new_values = self.values.take(indexer, fill_value=fill_value, allow_fill=True)
assert not (self.ndim == 1 and new_mgr_locs is None)
if new... | ['def', 'take_nd(self,', 'indexer,', 'axis=0,', 'new_mgr_locs=None,', 'fill_tuple=None):', 'if', 'fill_tuple', 'is', 'None:', 'fill_value', '=', 'None', 'else:', 'fill_value', '=', 'fill_tuple[0]', 'new_values', '=', 'self.values.take(indexer,', 'fill_value=fill_value,', 'allow_fill=True)', 'assert', 'not', '(self.ndim... | 83,029 |
JIA-HONG-CHU/Swin-Transformer-add-EncNet-DaNet-DraNet-for---on-Statelite-Dataset | test.py | collect_results_cpu | collect_results_cpu | Collect results with CPU. | [
"Collect",
"results",
"with",
"CPU."
] | def collect_results_cpu(result_part, size, tmpdir=None):
(rank, world_size) = get_dist_info()
if tmpdir is None:
MAX_LEN = 512
dir_tensor = torch.full((MAX_LEN,), 32, dtype=torch.uint8, device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor... | ['def', 'collect_results_cpu(result_part,', 'size,', 'tmpdir=None):', '(rank,', 'world_size)', '=', 'get_dist_info()', 'if', 'tmpdir', 'is', 'None:', 'MAX_LEN', '=', '512', 'dir_tensor', '=', 'torch.full((MAX_LEN,),', '32,', 'dtype=torch.uint8,', "device='cuda')", 'if', 'rank', '==', '0:', 'tmpdir', '=', 'tempfile.mkdt... | 882,813 |
Media-Smart/volkscv | utils.py | get_pallete | get_pallete | Generate pallete for categories. | [
"Generate",
"pallete",
"for",
"categories."
] | def get_pallete(categories):
num_cls = len(categories) + 1
color_map = num_cls * [0, 0, 0]
for i in range(0, num_cls):
j = 0
lab = i
while lab:
color_map[i * 3] |= (lab >> 0 & 1) << 7 - j
color_map[i * 3 + 1] |= (lab >> 1 & 1) << 7 - j
color_map[i ... | ['def', 'get_pallete(categories):', 'num_cls', '=', 'len(categories)', '+', '1', 'color_map', '=', 'num_cls', '*', '[0,', '0,', '0]', 'for', 'i', 'in', 'range(0,', 'num_cls):', 'j', '=', '0', 'lab', '=', 'i', 'while', 'lab:', 'color_map[i', '*', '3]', '|=', '(lab', '>>', '0', '&', '1)', '<<', '7', '-', 'j', 'color_map[... | 946,432 |
tensorly/quantum | serializable_gate_set_test.py | SerializableGateSetTest.test_serialize_deserialize_op | test_serialize_deserialize_op | Simple serialize and deserialize back test. | [
"Simple",
"serialize",
"and",
"deserialize",
"back",
"test."
] | def test_serialize_deserialize_op(self):
q0 = cirq.GridQubit(1, 1)
proto = op_proto({'gate': {'id': 'x_pow'}, 'args': {'half_turns': {'arg_value': {'float_value': 0.125}}}, 'qubits': [{'id': '1_1'}]})
self.assertEqual(proto, MY_GATE_SET.serialize_op(cirq.XPowGate(exponent=0.125)(q0)))
self.assertEqual(M... | ['def', 'test_serialize_deserialize_op(self):', 'q0', '=', 'cirq.GridQubit(1,', '1)', 'proto', '=', "op_proto({'gate':", "{'id':", "'x_pow'},", "'args':", "{'half_turns':", "{'arg_value':", "{'float_value':", '0.125}}},', "'qubits':", "[{'id':", "'1_1'}]})", 'self.assertEqual(proto,', 'MY_GATE_SET.serialize_op(cirq.XPo... | 834,954 |
gunthercox/ChatterBot | _native.py | escape_silent | escape_silent | Like :func:`escape` but converts `None` into an empty markup string. | [
"Like",
":func:`escape`",
"but",
"converts",
"`None`",
"into",
"an",
"empty",
"markup",
"string."
] | def escape_silent(s):
if s is None:
return Markup()
return escape(s) | ['def', 'escape_silent(s):', 'if', 's', 'is', 'None:', 'return', 'Markup()', 'return', 'escape(s)'] | 529,603 |
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