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 |
|---|---|---|---|---|---|---|---|---|
shanglianlm0525/CvPytorch | config.py | Configuration.print | print | This function will create a copy of self and removes all the 'data' key for better print format. | [
"This",
"function",
"will",
"create",
"a",
"copy",
"of",
"self",
"and",
"removes",
"all",
"the",
"'data'",
"key",
"for",
"better",
"print",
"format."
] | def print(self):
_self = deepcopy(self)
def _pop_data_key(d, r):
for (k, v) in r.items():
if k == 'data' or k.startswith('_'):
d.pop(k)
if isinstance(v, UserDict):
_pop_data_key(d[k], v)
_pop_data_key(_self, self)
pprint.pprint(_self) | ['def', 'print(self):', '_self', '=', 'deepcopy(self)', 'def', '_pop_data_key(d,', 'r):', 'for', '(k,', 'v)', 'in', 'r.items():', 'if', 'k', '==', "'data'", 'or', "k.startswith('_'):", 'd.pop(k)', 'if', 'isinstance(v,', 'UserDict):', '_pop_data_key(d[k],', 'v)', '_pop_data_key(_self,', 'self)', 'pprint.pprint(_self)'] | 523,621 |
Ruturaj123/Flowchart-Detection | model.py | Model.predict | predict | Predict the labels on a single batch of examples. | [
"Predict",
"the",
"labels",
"on",
"a",
"single",
"batch",
"of",
"examples."
] | def predict(self, sess, x, y=None):
cur_memory = sess.run([self.mem_keys, self.mem_vals, self.mem_age])
outputs = [self.y_preds]
if y is None:
ret = sess.run(outputs, feed_dict={self.x: x})
else:
ret = sess.run(outputs, feed_dict={self.x: x, self.y: y})
sess.run([self.mem_reset_op], ... | ['def', 'predict(self,', 'sess,', 'x,', 'y=None):', 'cur_memory', '=', 'sess.run([self.mem_keys,', 'self.mem_vals,', 'self.mem_age])', 'outputs', '=', '[self.y_preds]', 'if', 'y', 'is', 'None:', 'ret', '=', 'sess.run(outputs,', 'feed_dict={self.x:', 'x})', 'else:', 'ret', '=', 'sess.run(outputs,', 'feed_dict={self.x:',... | 585,817 |
asyml/texar-pytorch | bleu_moses_test.py | BLEUMosesTest.test_sentence_numpy | test_sentence_numpy | Tests with numpy format. | [
"Tests",
"with",
"numpy",
"format."
] | def test_sentence_numpy(self):
hypothesis = 'this is a test sentence to evaluate the good bleu score . è¯Â\x8d'
hypothesis = np.array(hypothesis.split())
references = ['this is a test sentence to evaluate the bleu score .', 'this is a test sentence to evaluate the good score .']
references = np.array(... | ['def', 'test_sentence_numpy(self):', 'hypothesis', '=', "'this", 'is', 'a', 'test', 'sentence', 'to', 'evaluate', 'the', 'good', 'bleu', 'score', '.', "è¯Â\\x8d'", 'hypothesis', '=', 'np.array(hypothesis.split())', 'references', '=', "['this", 'is', 'a', 'test', 'sentence', 'to', 'evaluate', 'the', 'bleu', 'score', ... | 924,899 |
gunthercox/ChatterBot | attributes.py | History.non_added | non_added | Return a collection of unchanged + deleted. | [
"Return",
"a",
"collection",
"of",
"unchanged",
"+",
"deleted."
] | def non_added(self):
return (self.unchanged or []) + (self.deleted or []) | ['def', 'non_added(self):', 'return', '(self.unchanged', 'or', '[])', '+', '(self.deleted', 'or', '[])'] | 534,427 |
mj-will/nessai | test_resume.py | test_checkpoint_resume_integration | test_checkpoint_resume_integration | Integration test for checkpointing the sampler. | [
"Integration",
"test",
"for",
"checkpointing",
"the",
"sampler."
] | def test_checkpoint_resume_integration(complete_sampler, model):
complete_sampler.likelihood_evaluations = [1, 2]
complete_sampler.checkpoint()
resume_file = os.path.join(complete_sampler.output, complete_sampler.resume_file)
assert os.path.exists(resume_file)
ns = NestedSampler.resume(resume_file, ... | ['def', 'test_checkpoint_resume_integration(complete_sampler,', 'model):', 'complete_sampler.likelihood_evaluations', '=', '[1,', '2]', 'complete_sampler.checkpoint()', 'resume_file', '=', 'os.path.join(complete_sampler.output,', 'complete_sampler.resume_file)', 'assert', 'os.path.exists(resume_file)', 'ns', '=', 'Nest... | 293,034 |
xrick/tensorflow_nlp | crf.py | crf_log_norm | crf_log_norm | Computes the normalization for a CRF. | [
"Computes",
"the",
"normalization",
"for",
"a",
"CRF."
] | def crf_log_norm(inputs, sequence_lengths, transition_params):
first_input = array_ops.slice(inputs, [0, 0, 0], [-1, 1, -1])
first_input = array_ops.squeeze(first_input, [1])
rest_of_input = array_ops.slice(inputs, [0, 1, 0], [-1, -1, -1])
forward_cell = CrfForwardRnnCell(transition_params)
(_, alph... | ['def', 'crf_log_norm(inputs,', 'sequence_lengths,', 'transition_params):', 'first_input', '=', 'array_ops.slice(inputs,', '[0,', '0,', '0],', '[-1,', '1,', '-1])', 'first_input', '=', 'array_ops.squeeze(first_input,', '[1])', 'rest_of_input', '=', 'array_ops.slice(inputs,', '[0,', '1,', '0],', '[-1,', '-1,', '-1])', '... | 922,493 |
kornia/kornia | face_detection.py | FaceDetectorResult.top_left | top_left | The [x y] position of the top-left coordinate of the bounding box. | [
"The",
"[x",
"y]",
"position",
"of",
"the",
"top-left",
"coordinate",
"of",
"the",
"bounding",
"box."
] | def top_left(self) -> torch.Tensor:
return self._data[..., (0, 1)] | ['def', 'top_left(self)', '->', 'torch.Tensor:', 'return', 'self._data[...,', '(0,', '1)]'] | 621,592 |
rlworkgroup/garage | gaussian_cnn_baseline.py | GaussianCNNBaseline.predict | predict | Predict ys based on input xs. | [
"Predict",
"ys",
"based",
"on",
"input",
"xs."
] | def predict(self, paths):
xs = paths['observations']
if isinstance(self._env_spec.observation_space, akro.Image) and len(xs[0].shape) < len(self._env_spec.observation_space.shape):
xs = self._env_spec.observation_space.unflatten_n(xs)
return self._f_predict(xs).flatten() | ['def', 'predict(self,', 'paths):', 'xs', '=', "paths['observations']", 'if', 'isinstance(self._env_spec.observation_space,', 'akro.Image)', 'and', 'len(xs[0].shape)', '<', 'len(self._env_spec.observation_space.shape):', 'xs', '=', 'self._env_spec.observation_space.unflatten_n(xs)', 'return', 'self._f_predict(xs).flatt... | 200,538 |
ZumoLabs/zpy | output.py | Output.output_annotations | output_annotations | Output annotations to file. | [
"Output",
"annotations",
"to",
"file."
] | def output_annotations(self, annotation_path: Union[Path, str]=None) -> Path:
if annotation_path is None:
annotation_path = self.annotation_path
log.info(f'Outputting annotation file to {annotation_path}')
annotation_path = zpy.files.verify_path(annotation_path)
return annotation_path | ['def', 'output_annotations(self,', 'annotation_path:', 'Union[Path,', 'str]=None)', '->', 'Path:', 'if', 'annotation_path', 'is', 'None:', 'annotation_path', '=', 'self.annotation_path', "log.info(f'Outputting", 'annotation', 'file', 'to', "{annotation_path}')", 'annotation_path', '=', 'zpy.files.verify_path(annotatio... | 972,090 |
google-research/ssl_detection | model_utils.py | get_shape_str | get_shape_str | Internally used by layer registry, to print shapes of inputs/outputs of layers. | [
"Internally",
"used",
"by",
"layer",
"registry,",
"to",
"print",
"shapes",
"of",
"inputs/outputs",
"of",
"layers."
] | def get_shape_str(tensors):
if isinstance(tensors, (list, tuple)):
for v in tensors:
assert isinstance(v, (tf.Tensor, tf.Variable)), 'Not a tensor: {}'.format(type(v))
shape_str = ', '.join(map(get_shape_str, tensors))
else:
assert isinstance(tensors, (tf.Tensor, tf.Variable)... | ['def', 'get_shape_str(tensors):', 'if', 'isinstance(tensors,', '(list,', 'tuple)):', 'for', 'v', 'in', 'tensors:', 'assert', 'isinstance(v,', '(tf.Tensor,', 'tf.Variable)),', "'Not", 'a', 'tensor:', "{}'.format(type(v))", 'shape_str', '=', "',", "'.join(map(get_shape_str,", 'tensors))', 'else:', 'assert', 'isinstance(... | 382,278 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | figure.py | Figure.get_dpi | get_dpi | Return the resolution in dots per inch as a float. | [
"Return",
"the",
"resolution",
"in",
"dots",
"per",
"inch",
"as",
"a",
"float."
] | def get_dpi(self):
return self.dpi | ['def', 'get_dpi(self):', 'return', 'self.dpi'] | 450,418 |
LeonhardFeiner/sparse_rcnn | bbox.py | calc_start_end | calc_start_end | Converts size and position of boxes to their start and stop coordinates. | [
"Converts",
"size",
"and",
"position",
"of",
"boxes",
"to",
"their",
"start",
"and",
"stop",
"coordinates."
] | def calc_start_end(position: torch.tensor, size: torch.tensor) -> Tuple[torch.tensor, torch.tensor]:
half_size = size / 2
start = position - half_size
end = position + half_size
return (start, end) | ['def', 'calc_start_end(position:', 'torch.tensor,', 'size:', 'torch.tensor)', '->', 'Tuple[torch.tensor,', 'torch.tensor]:', 'half_size', '=', 'size', '/', '2', 'start', '=', 'position', '-', 'half_size', 'end', '=', 'position', '+', 'half_size', 'return', '(start,', 'end)'] | 894,706 |
datature/portal | routes.py | clear_cachelist | clear_cachelist | Clear the cached list of predictions. | [
"Clear",
"the",
"cached",
"list",
"of",
"predictions."
] | def clear_cachelist(model_id) -> tuple:
global_store.clear_predicted_images(model_id)
return Response(status=200) | ['def', 'clear_cachelist(model_id)', '->', 'tuple:', 'global_store.clear_predicted_images(model_id)', 'return', 'Response(status=200)'] | 820,929 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | configHandler.py | IdleUserConfParser.RemoveEmptySections | RemoveEmptySections | Remove any sections that have no options. | [
"Remove",
"any",
"sections",
"that",
"have",
"no",
"options."
] | def RemoveEmptySections(self):
for section in self.sections():
if not self.GetOptionList(section):
self.remove_section(section) | ['def', 'RemoveEmptySections(self):', 'for', 'section', 'in', 'self.sections():', 'if', 'not', 'self.GetOptionList(section):', 'self.remove_section(section)'] | 430,793 |
vmware-archive/salt-contrib | win_update.py | download | download | Cache updates for later install. | [
"Cache",
"updates",
"for",
"later",
"install."
] | def download(name, categories=None, includes=None, retries=10):
ret = {'name': name, 'result': True, 'changes': {}, 'comment': ''}
log.debug('categories to search for are: {0}'.format(str(categories)))
quidditch = PyWinUpdater()
quidditch.SetCategories(categories)
quidditch.SetIncludes(includes)
... | ['def', 'download(name,', 'categories=None,', 'includes=None,', 'retries=10):', 'ret', '=', "{'name':", 'name,', "'result':", 'True,', "'changes':", '{},', "'comment':", "''}", "log.debug('categories", 'to', 'search', 'for', 'are:', "{0}'.format(str(categories)))", 'quidditch', '=', 'PyWinUpdater()', 'quidditch.SetCate... | 328,949 |
microsoft/fastseq | test_api_decorator.py | APIDecoratorTest.test_replace_baseclass | test_replace_baseclass | Test replace() decorator for the base class. | [
"Test",
"replace()",
"decorator",
"for",
"the",
"base",
"class."
] | def test_replace_baseclass(self):
preloaded_classes = {}
preloaded_classes['grandchild'] = Grandchild
child = Child()
grandchild = Grandchild()
preloaded_grandchild = preloaded_classes['grandchild']()
self.assertEqual(child.name(), 'Base')
self.assertEqual(grandchild.name(), 'Base')
self... | ['def', 'test_replace_baseclass(self):', 'preloaded_classes', '=', '{}', "preloaded_classes['grandchild']", '=', 'Grandchild', 'child', '=', 'Child()', 'grandchild', '=', 'Grandchild()', 'preloaded_grandchild', '=', "preloaded_classes['grandchild']()", 'self.assertEqual(child.name(),', "'Base')", 'self.assertEqual(gran... | 559,954 |
BillZito/transfer-learning | tfhub_text_classification_model.py | TFHubTextClassificationModel.predict | predict | Generates predictions for the specified input samples. | [
"Generates",
"predictions",
"for",
"the",
"specified",
"input",
"samples."
] | def predict(self, input_samples):
if self._model is None:
raise ValueError('The model must be trained or loaded before predicting.')
if isinstance(input_samples, str):
input_samples = [input_samples]
return tf.sigmoid(self._model.predict(input_samples)).numpy() | ['def', 'predict(self,', 'input_samples):', 'if', 'self._model', 'is', 'None:', 'raise', "ValueError('The", 'model', 'must', 'be', 'trained', 'or', 'loaded', 'before', "predicting.')", 'if', 'isinstance(input_samples,', 'str):', 'input_samples', '=', '[input_samples]', 'return', 'tf.sigmoid(self._model.predict(input_sa... | 928,540 |
microsoft/maro | zmq_driver.py | ZmqDriver.close | close | Close ZMQ context and sockets. | [
"Close",
"ZMQ",
"context",
"and",
"sockets."
] | def close(self):
self._zmq_context.setsockopt(zmq.LINGER, 0)
self._broadcast_receiver.close()
self._broadcast_sender.close()
self._unicast_receiver.close()
for unicast_sender in self._unicast_sender_dict.values():
unicast_sender.close()
self._zmq_context.term() | ['def', 'close(self):', 'self._zmq_context.setsockopt(zmq.LINGER,', '0)', 'self._broadcast_receiver.close()', 'self._broadcast_sender.close()', 'self._unicast_receiver.close()', 'for', 'unicast_sender', 'in', 'self._unicast_sender_dict.values():', 'unicast_sender.close()', 'self._zmq_context.term()'] | 628,379 |
myothida/Supervised-Machine-Learning | test_format.py | assert_filepath_or_buffer_equals | assert_filepath_or_buffer_equals | Assertion helper for checking filepath_or_buffer. | [
"Assertion",
"helper",
"for",
"checking",
"filepath_or_buffer."
] | def assert_filepath_or_buffer_equals(filepath_or_buffer, filepath_or_buffer_id, encoding):
def _assert_filepath_or_buffer_equals(expected):
if filepath_or_buffer_id == 'string':
with open(filepath_or_buffer, encoding=encoding) as f:
result = f.read()
elif filepath_or_buf... | ['def', 'assert_filepath_or_buffer_equals(filepath_or_buffer,', 'filepath_or_buffer_id,', 'encoding):', 'def', '_assert_filepath_or_buffer_equals(expected):', 'if', 'filepath_or_buffer_id', '==', "'string':", 'with', 'open(filepath_or_buffer,', 'encoding=encoding)', 'as', 'f:', 'result', '=', 'f.read()', 'elif', 'filep... | 443,756 |
deepmind/dm_control | primitive.py | Primitive.touch | touch | Exposing the touch sensor for observations and reward. | [
"Exposing",
"the",
"touch",
"sensor",
"for",
"observations",
"and",
"reward."
] | def touch(self):
return self._touch | ['def', 'touch(self):', 'return', 'self._touch'] | 165,169 |
Kvatsx/Artificial-Intelligence-Assignments | backend_wx.py | MenuButtonWx.getActiveAxes | getActiveAxes | Return a list of the selected axes. | [
"Return",
"a",
"list",
"of",
"the",
"selected",
"axes."
] | def getActiveAxes(self):
active = []
for i in range(len(self._axisId)):
if self._menu.IsChecked(self._axisId[i]):
active.append(i)
return active | ['def', 'getActiveAxes(self):', 'active', '=', '[]', 'for', 'i', 'in', 'range(len(self._axisId)):', 'if', 'self._menu.IsChecked(self._axisId[i]):', 'active.append(i)', 'return', 'active'] | 1,261 |
deepmind/acme | networks.py | add_batch | add_batch | Adds a batch dimension at axis 0 to the leaves of a nested structure. | [
"Adds",
"a",
"batch",
"dimension",
"at",
"axis",
"0",
"to",
"the",
"leaves",
"of",
"a",
"nested",
"structure."
] | def add_batch(nest, batch_size: Optional[int]):
broadcast = lambda x: jnp.broadcast_to(x, (batch_size,) + x.shape)
return jax.tree_map(broadcast, nest) | ['def', 'add_batch(nest,', 'batch_size:', 'Optional[int]):', 'broadcast', '=', 'lambda', 'x:', 'jnp.broadcast_to(x,', '(batch_size,)', '+', 'x.shape)', 'return', 'jax.tree_map(broadcast,', 'nest)'] | 8,147 |
Eric3911/OpenAGI | snapshot.py | Snapshot.full | full | Whether the number of snapshots it keeps track of is greater than the max_size. | [
"Whether",
"the",
"number",
"of",
"snapshots",
"it",
"keeps",
"track",
"of",
"is",
"greater",
"than",
"the",
"max_size."
] | def full(self):
return not self._save_all and len(self.records) > self.max_size | ['def', 'full(self):', 'return', 'not', 'self._save_all', 'and', 'len(self.records)', '>', 'self.max_size'] | 251,859 |
sjtu-marl/malib | offline_dataset_server.py | OfflineDataset.start_consumer_pipe | start_consumer_pipe | Start a consumer pipeline, if there is no such a table that named as `name`, the function will be stucked until the table has been created. | [
"Start",
"a",
"consumer",
"pipeline,",
"if",
"there",
"is",
"no",
"such",
"a",
"table",
"that",
"named",
"as",
"`name`,",
"the",
"function",
"will",
"be",
"stucked",
"until",
"the",
"table",
"has",
"been",
"created."
] | def start_consumer_pipe(self, name: str, batch_size: int) -> Tuple[str, Queue]:
queue_id = f'{name}_{time.time()}'
queue = Queue(actor_options={'num_cpus': 0})
self.reader_queues[queue_id] = queue
while name not in self.buffers:
time.sleep(1)
self.thread_pool.submit(read_table, self.markers[... | ['def', 'start_consumer_pipe(self,', 'name:', 'str,', 'batch_size:', 'int)', '->', 'Tuple[str,', 'Queue]:', 'queue_id', '=', "f'{name}_{time.time()}'", 'queue', '=', "Queue(actor_options={'num_cpus':", '0})', 'self.reader_queues[queue_id]', '=', 'queue', 'while', 'name', 'not', 'in', 'self.buffers:', 'time.sleep(1)', '... | 627,449 |
ForrestPi/ObjectDetectionTricks | util.py | image_idct | image_idct | Inverts image_dct(), by performing a type-III DCT. | [
"Inverts",
"image_dct(),",
"by",
"performing",
"a",
"type-III",
"DCT."
] | def image_idct(dct_x):
dct_x = torch.as_tensor(dct_x)
dct_y = torch_dct.idct(torch.transpose(dct_x, 1, 2), norm='ortho')
image = torch_dct.idct(torch.transpose(dct_y, 1, 2), norm='ortho')
return image | ['def', 'image_idct(dct_x):', 'dct_x', '=', 'torch.as_tensor(dct_x)', 'dct_y', '=', 'torch_dct.idct(torch.transpose(dct_x,', '1,', '2),', "norm='ortho')", 'image', '=', 'torch_dct.idct(torch.transpose(dct_y,', '1,', '2),', "norm='ortho')", 'return', 'image'] | 744,665 |
SvenGronauer/phoenix-drone-simulation | train.py | run_training | run_training | Executes one training loop with given parameters. | [
"Executes",
"one",
"training",
"loop",
"with",
"given",
"parameters."
] | def run_training(args, unparsed_args, exp_name=None):
physical_cores = 2 ** int(np.log2(psutil.cpu_count(logical=False)))
use_number_of_threads = True if args.cores > physical_cores else False
if mpi_fork(args.cores, use_number_of_threads=use_number_of_threads):
sys.exit()
mpi_print('Unknowns:',... | ['def', 'run_training(args,', 'unparsed_args,', 'exp_name=None):', 'physical_cores', '=', '2', '**', 'int(np.log2(psutil.cpu_count(logical=False)))', 'use_number_of_threads', '=', 'True', 'if', 'args.cores', '>', 'physical_cores', 'else', 'False', 'if', 'mpi_fork(args.cores,', 'use_number_of_threads=use_number_of_threa... | 769,051 |
triaquae/triaquae | forms.py | PLREGONField.has_valid_checksum | has_valid_checksum | Calculates a checksum with the provided algorithm. | [
"Calculates",
"a",
"checksum",
"with",
"the",
"provided",
"algorithm."
] | def has_valid_checksum(self, number):
weights = ((8, 9, 2, 3, 4, 5, 6, 7, -1), (2, 4, 8, 5, 0, 9, 7, 3, 6, 1, 2, 4, 8, -1), (8, 9, 2, 3, 4, 5, 6, 7, -1, 0, 0, 0, 0, 0))
weights = [table for table in weights if len(table) == len(number)]
for table in weights:
checksum = sum([int(n) * w for (n, w) in ... | ['def', 'has_valid_checksum(self,', 'number):', 'weights', '=', '((8,', '9,', '2,', '3,', '4,', '5,', '6,', '7,', '-1),', '(2,', '4,', '8,', '5,', '0,', '9,', '7,', '3,', '6,', '1,', '2,', '4,', '8,', '-1),', '(8,', '9,', '2,', '3,', '4,', '5,', '6,', '7,', '-1,', '0,', '0,', '0,', '0,', '0))', 'weights', '=', '[table'... | 358,099 |
sek788432/Waymo-2D-Object-Detection | dataset_loader.py | KittiRaw.load_pose_sequence | load_pose_sequence | Returns a sequence of pose vectors for frames around the target frame. | [
"Returns",
"a",
"sequence",
"of",
"pose",
"vectors",
"for",
"frames",
"around",
"the",
"target",
"frame."
] | def load_pose_sequence(self, frames, target_index):
(target_drive, _, target_frame_id) = frames[target_index].split(' ')
target_pose = self.load_pose_raw(target_drive, target_frame_id)
(start_index, end_index) = get_seq_start_end(target_frame_id, self.seq_length)
pose_seq = []
for index in range(sta... | ['def', 'load_pose_sequence(self,', 'frames,', 'target_index):', '(target_drive,', '_,', 'target_frame_id)', '=', "frames[target_index].split('", "')", 'target_pose', '=', 'self.load_pose_raw(target_drive,', 'target_frame_id)', '(start_index,', 'end_index)', '=', 'get_seq_start_end(target_frame_id,', 'self.seq_length)'... | 975,913 |
googleapis/python-aiplatform | test_model_monitoring.py | TestModelDeploymentMonitoring.test_mdm_two_models_one_valid_config | test_mdm_two_models_one_valid_config | Enable model monitoring on two existing models deployed to the same endpoint. | [
"Enable",
"model",
"monitoring",
"on",
"two",
"existing",
"models",
"deployed",
"to",
"the",
"same",
"endpoint."
] | def test_mdm_two_models_one_valid_config(self, shared_state):
assert len(shared_state['resources']) == 1
self.endpoint = shared_state['resources'][0]
aiplatform.init(project=e2e_base._PROJECT, location=e2e_base._LOCATION)
job = aiplatform.ModelDeploymentMonitoringJob.create(display_name=self._make_displ... | ['def', 'test_mdm_two_models_one_valid_config(self,', 'shared_state):', 'assert', "len(shared_state['resources'])", '==', '1', 'self.endpoint', '=', "shared_state['resources'][0]", 'aiplatform.init(project=e2e_base._PROJECT,', 'location=e2e_base._LOCATION)', 'job', '=', 'aiplatform.ModelDeploymentMonitoringJob.create(d... | 862,960 |
IBM/graph4nlp | bleu_scorer.py | BleuScorer.cook_append | cook_append | called by constructor and __iadd__ to avoid creating new instances. | [
"called",
"by",
"constructor",
"and",
"__iadd__",
"to",
"avoid",
"creating",
"new",
"instances."
] | def cook_append(self, test, refs):
if refs is not None:
self.crefs.append(cook_refs(refs))
if test is not None:
cooked_test = cook_test(test, self.crefs[-1])
self.ctest.append(cooked_test)
else:
self.ctest.append(None)
self._score = None | ['def', 'cook_append(self,', 'test,', 'refs):', 'if', 'refs', 'is', 'not', 'None:', 'self.crefs.append(cook_refs(refs))', 'if', 'test', 'is', 'not', 'None:', 'cooked_test', '=', 'cook_test(test,', 'self.crefs[-1])', 'self.ctest.append(cooked_test)', 'else:', 'self.ctest.append(None)', 'self._score', '=', 'None'] | 580,470 |
llSourcell/AI_Artist | pyparsing.py | ParseResults.insert | insert | Inserts new element at location index in the list of parsed tokens. | [
"Inserts",
"new",
"element",
"at",
"location",
"index",
"in",
"the",
"list",
"of",
"parsed",
"tokens."
] | def insert(self, index, insStr):
self.__toklist.insert(index, insStr)
for (name, occurrences) in self.__tokdict.items():
for (k, (value, position)) in enumerate(occurrences):
occurrences[k] = _ParseResultsWithOffset(value, position + (position > index)) | ['def', 'insert(self,', 'index,', 'insStr):', 'self.__toklist.insert(index,', 'insStr)', 'for', '(name,', 'occurrences)', 'in', 'self.__tokdict.items():', 'for', '(k,', '(value,', 'position))', 'in', 'enumerate(occurrences):', 'occurrences[k]', '=', '_ParseResultsWithOffset(value,', 'position', '+', '(position', '>', '... | 413,690 |
CityU-AIM-Group/SIGMA | wassdistance.py | SinkhornDistance.ave | ave | Barycenter subroutine, used by kinetic acceleration through extrapolation. | [
"Barycenter",
"subroutine,",
"used",
"by",
"kinetic",
"acceleration",
"through",
"extrapolation."
] | def ave(u, u1, tau):
return tau * u + (1 - tau) * u1 | ['def', 'ave(u,', 'u1,', 'tau):', 'return', 'tau', '*', 'u', '+', '(1', '-', 'tau)', '*', 'u1'] | 934,542 |
rudranil723/mini-main | introspection.py | BaseDatabaseIntrospection.sequence_list | sequence_list | Return a list of information about all DB sequences for all models in all apps. | [
"Return",
"a",
"list",
"of",
"information",
"about",
"all",
"DB",
"sequences",
"for",
"all",
"models",
"in",
"all",
"apps."
] | def sequence_list(self):
from django.apps import apps
from django.db import router
sequence_list = []
with self.connection.cursor() as cursor:
for app_config in apps.get_app_configs():
for model in router.get_migratable_models(app_config, self.connection.alias):
if no... | ['def', 'sequence_list(self):', 'from', 'django.apps', 'import', 'apps', 'from', 'django.db', 'import', 'router', 'sequence_list', '=', '[]', 'with', 'self.connection.cursor()', 'as', 'cursor:', 'for', 'app_config', 'in', 'apps.get_app_configs():', 'for', 'model', 'in', 'router.get_migratable_models(app_config,', 'self... | 315,761 |
zihuitang/medical_AI_platform | __init__.py | makeLogRecord | makeLogRecord | Make a LogRecord whose attributes are defined by the specified dictionary, This function is useful for converting a logging event received over a socket connection (which is sent as a dictionary) into a LogRecord instance. | [
"Make",
"a",
"LogRecord",
"whose",
"attributes",
"are",
"defined",
"by",
"the",
"specified",
"dictionary,",
"This",
"function",
"is",
"useful",
"for",
"converting",
"a",
"logging",
"event",
"received",
"over",
"a",
"socket",
"connection",
"(which",
"is",
"sent",... | def makeLogRecord(dict):
rv = _logRecordFactory(None, None, '', 0, '', (), None, None)
rv.__dict__.update(dict)
return rv | ['def', 'makeLogRecord(dict):', 'rv', '=', '_logRecordFactory(None,', 'None,', "'',", '0,', "'',", '(),', 'None,', 'None)', 'rv.__dict__.update(dict)', 'return', 'rv'] | 283,093 |
ZumoLabs/zpy | blender.py | set_seed | set_seed | Set the random seed (sets the python and numpy seed). | [
"Set",
"the",
"random",
"seed",
"(sets",
"the",
"python",
"and",
"numpy",
"seed)."
] | def set_seed(seed: int=0) -> None:
if log.getEffectiveLevel() == logging.DEBUG:
seed = random.randint(1, 100)
log.info(f'Setting random seed to {seed}')
random.seed(seed)
np.random.seed(seed)
mathutils.noise.seed_set(seed) | ['def', 'set_seed(seed:', 'int=0)', '->', 'None:', 'if', 'log.getEffectiveLevel()', '==', 'logging.DEBUG:', 'seed', '=', 'random.randint(1,', '100)', "log.info(f'Setting", 'random', 'seed', 'to', "{seed}')", 'random.seed(seed)', 'np.random.seed(seed)', 'mathutils.noise.seed_set(seed)'] | 971,962 |
Eric3911/OpenAGI | freesound_download.py | get_text_query_with_resource_limit_checks | get_text_query_with_resource_limit_checks | Performs a text query, checks for rate / api limits, and retries. | [
"Performs",
"a",
"text",
"query,",
"checks",
"for",
"rate",
"/",
"api",
"limits,",
"and",
"retries."
] | def get_text_query_with_resource_limit_checks(client, query: str, filters: list, fields: str, page_size: int):
pages = None
attempts = 20
while pages is None:
try:
pages = client.text_search(query=query, filter=' '.join(filters), fields=fields, page_size=str(page_size))
except fr... | ['def', 'get_text_query_with_resource_limit_checks(client,', 'query:', 'str,', 'filters:', 'list,', 'fields:', 'str,', 'page_size:', 'int):', 'pages', '=', 'None', 'attempts', '=', '20', 'while', 'pages', 'is', 'None:', 'try:', 'pages', '=', 'client.text_search(query=query,', "filter='", "'.join(filters),", 'fields=fie... | 274,286 |
haoxiangsnr/A-Convolutional-Recurrent--Network-for-Real-Time-Speech-Enhancement | utils.py | prepare_empty_dir | prepare_empty_dir | if resume experiment, assert the dirs exist, if not resume experiment, make dirs. | [
"if",
"resume",
"experiment,",
"assert",
"the",
"dirs",
"exist,",
"if",
"not",
"resume",
"experiment,",
"make",
"dirs."
] | def prepare_empty_dir(dirs, resume=False):
for dir_path in dirs:
if resume:
assert dir_path.exists()
else:
dir_path.mkdir(parents=True, exist_ok=True) | ['def', 'prepare_empty_dir(dirs,', 'resume=False):', 'for', 'dir_path', 'in', 'dirs:', 'if', 'resume:', 'assert', 'dir_path.exists()', 'else:', 'dir_path.mkdir(parents=True,', 'exist_ok=True)'] | 5,089 |
zackmcnulty/CSE_446-Machine_Learning | _base.py | _AxesBase.get_yaxis | get_yaxis | Return the YAxis instance. | [
"Return",
"the",
"YAxis",
"instance."
] | def get_yaxis(self):
return self.yaxis | ['def', 'get_yaxis(self):', 'return', 'self.yaxis'] | 194,859 |
famura/SimuRLacra | base.py | RecurrentPolicy.hidden_size | hidden_size | Get the number of hidden state variables. | [
"Get",
"the",
"number",
"of",
"hidden",
"state",
"variables."
] | def hidden_size(self) -> int:
raise NotImplementedError | ['def', 'hidden_size(self)', '->', 'int:', 'raise', 'NotImplementedError'] | 883,867 |
triaquae/triaquae | tests.py | AdminSeleniumWebDriverTestCase.admin_login | admin_login | Helper function to log into the admin. | [
"Helper",
"function",
"to",
"log",
"into",
"the",
"admin."
] | def admin_login(self, username, password, login_url='/admin/'):
self.selenium.get('%s%s' % (self.live_server_url, login_url))
username_input = self.selenium.find_element_by_name('username')
username_input.send_keys(username)
password_input = self.selenium.find_element_by_name('password')
password_in... | ['def', 'admin_login(self,', 'username,', 'password,', "login_url='/admin/'):", "self.selenium.get('%s%s'", '%', '(self.live_server_url,', 'login_url))', 'username_input', '=', "self.selenium.find_element_by_name('username')", 'username_input.send_keys(username)', 'password_input', '=', "self.selenium.find_element_by_n... | 357,007 |
quantumiracle/Benchmark-Efficient-Reinforcement--with-Demonstrations | metalearner.py | MetaLearner.adapt | adapt | Adapt the parameters of the policy network to a new task, from sampled trajectories `episodes`, with a one-step gradient update [1]. | [
"Adapt",
"the",
"parameters",
"of",
"the",
"policy",
"network",
"to",
"a",
"new",
"task,",
"from",
"sampled",
"trajectories",
"`episodes`,",
"with",
"a",
"one-step",
"gradient",
"update",
"[1]."
] | def adapt(self, episodes, first_order=False):
self.baseline.fit(episodes)
loss = self.inner_loss(episodes)
print('loss: ', loss)
params = self.policy.update_params(loss, step_size=self.fast_lr, first_order=first_order)
return params | ['def', 'adapt(self,', 'episodes,', 'first_order=False):', 'self.baseline.fit(episodes)', 'loss', '=', 'self.inner_loss(episodes)', "print('loss:", "',", 'loss)', 'params', '=', 'self.policy.update_params(loss,', 'step_size=self.fast_lr,', 'first_order=first_order)', 'return', 'params'] | 433,081 |
intra2net/guibot | test_finder.py | FinderTest.test_deep_cache | test_deep_cache | Test the neural network cached storage of deep finders. | [
"Test",
"the",
"neural",
"network",
"cached",
"storage",
"of",
"deep",
"finders."
] | def test_deep_cache(self):
finder = DeepFinder(synchronize=False)
finder.params['deep']['arch'].value = 'fasterrcnn_resnet50_fpn'
finder.synchronize_backend()
matches = finder.find(Pattern('cat'), Image('coco_cat'))
self.assertEqual(len(matches), 1)
self.assertEqual(len(finder._cache.keys()), 1)... | ['def', 'test_deep_cache(self):', 'finder', '=', 'DeepFinder(synchronize=False)', "finder.params['deep']['arch'].value", '=', "'fasterrcnn_resnet50_fpn'", 'finder.synchronize_backend()', 'matches', '=', "finder.find(Pattern('cat'),", "Image('coco_cat'))", 'self.assertEqual(len(matches),', '1)', 'self.assertEqual(len(fi... | 572,662 |
omonimus1/super-computer- | req_file.py | RequirementsFileParser.parse | parse | Parse a given file, yielding parsed lines. | [
"Parse",
"a",
"given",
"file,",
"yielding",
"parsed",
"lines."
] | def parse(self, filename, constraint):
for line in self._parse_and_recurse(filename, constraint):
yield line | ['def', 'parse(self,', 'filename,', 'constraint):', 'for', 'line', 'in', 'self._parse_and_recurse(filename,', 'constraint):', 'yield', 'line'] | 913,176 |
rlgraph/rlgraph | component.py | Component.propagate_variables | propagate_variables | Propagates all variable from this Component to its parents' variable registries. | [
"Propagates",
"all",
"variable",
"from",
"this",
"Component",
"to",
"its",
"parents'",
"variable",
"registries."
] | def propagate_variables(self, keys=None):
if self.parent_component is None:
return
keys = keys or self.variable_registry.keys()
for key in keys:
if key in self.parent_component.variable_registry:
if self.variable_registry[key] is not self.parent_component.variable_registry[key]:
... | ['def', 'propagate_variables(self,', 'keys=None):', 'if', 'self.parent_component', 'is', 'None:', 'return', 'keys', '=', 'keys', 'or', 'self.variable_registry.keys()', 'for', 'key', 'in', 'keys:', 'if', 'key', 'in', 'self.parent_component.variable_registry:', 'if', 'self.variable_registry[key]', 'is', 'not', 'self.pare... | 862,449 |
ZumoLabs/zpy | cli.py | set_project | set_project | Set project Set global PROJECT uuid. | [
"Set",
"project",
"Set",
"global",
"PROJECT",
"uuid."
] | def set_project(project_uuid):
config = read_config()
old_project_uuid = config.get('PROJECT', None)
config['PROJECT'] = str(project_uuid)
write_config(config)
click.echo('Switched project:')
click.echo(f" {old_project_uuid} -> {config['PROJECT']}") | ['def', 'set_project(project_uuid):', 'config', '=', 'read_config()', 'old_project_uuid', '=', "config.get('PROJECT',", 'None)', "config['PROJECT']", '=', 'str(project_uuid)', 'write_config(config)', "click.echo('Switched", "project:')", 'click.echo(f"', '{old_project_uuid}', '->', '{config[\'PROJECT\']}")'] | 971,908 |
digorithm/ArtificialIntelligenceAlgorithms | design_info.py | DesignInfo.terms | terms | A list of :class:`Terms`, in order, or else None. | [
"A",
"list",
"of",
":class:`Terms`,",
"in",
"order,",
"or",
"else",
"None."
] | def terms(self):
if self.term_slices is None:
return None
return list(self.term_slices) | ['def', 'terms(self):', 'if', 'self.term_slices', 'is', 'None:', 'return', 'None', 'return', 'list(self.term_slices)'] | 91,930 |
PaddlePaddle/PARL | actor.py | Actor.self_play | self_play | Collecting training data by self-play. | [
"Collecting",
"training",
"data",
"by",
"self-play."
] | def self_play(self, current_weights, game_num):
self.current_agent.set_weights(current_weights)
train_examples = []
for i in range(game_num):
logger.info('self play iteration #{}'.format(i))
self.current_mcts = MCTS(self.game, self.current_agent, self.args, dirichlet_noise=True)
trai... | ['def', 'self_play(self,', 'current_weights,', 'game_num):', 'self.current_agent.set_weights(current_weights)', 'train_examples', '=', '[]', 'for', 'i', 'in', 'range(game_num):', "logger.info('self", 'play', 'iteration', "#{}'.format(i))", 'self.current_mcts', '=', 'MCTS(self.game,', 'self.current_agent,', 'self.args,'... | 277,720 |
zihuitang/medical_AI_platform | handler.py | EntityResolver.resolveEntity | resolveEntity | Resolve the system identifier of an entity and return either the system identifier to read from as a string, or an InputSource to read from. | [
"Resolve",
"the",
"system",
"identifier",
"of",
"an",
"entity",
"and",
"return",
"either",
"the",
"system",
"identifier",
"to",
"read",
"from",
"as",
"a",
"string,",
"or",
"an",
"InputSource",
"to",
"read",
"from."
] | def resolveEntity(self, publicId, systemId):
return systemId | ['def', 'resolveEntity(self,', 'publicId,', 'systemId):', 'return', 'systemId'] | 284,604 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | preprocessing.py | crop_image_by_strategy | crop_image_by_strategy | Crops an image according to a strategy defined in config. | [
"Crops",
"an",
"image",
"according",
"to",
"a",
"strategy",
"defined",
"in",
"config."
] | def crop_image_by_strategy(image, cropping):
strategy_to_method = {'crop_center': crop_center, 'pad': pad, 'pad200': pad_200, 'pad_crop_central': pad_crop_central}
tf.logging.info('Cropping strategy: %s.' % cropping)
if cropping not in strategy_to_method:
raise ValueError('Unknown cropping strategy:... | ['def', 'crop_image_by_strategy(image,', 'cropping):', 'strategy_to_method', '=', "{'crop_center':", 'crop_center,', "'pad':", 'pad,', "'pad200':", 'pad_200,', "'pad_crop_central':", 'pad_crop_central}', "tf.logging.info('Cropping", 'strategy:', "%s.'", '%', 'cropping)', 'if', 'cropping', 'not', 'in', 'strategy_to_meth... | 112,254 |
suarez12138/AI-Reversi_IMP_TextDichotomy | figure.py | Figure.sca | sca | Set the current axes to be *a* and return *a*. | [
"Set",
"the",
"current",
"axes",
"to",
"be",
"*a*",
"and",
"return",
"*a*."
] | def sca(self, a):
self._axstack.bubble(a)
self._axobservers.process('_axes_change_event', self)
return a | ['def', 'sca(self,', 'a):', 'self._axstack.bubble(a)', "self._axobservers.process('_axes_change_event',", 'self)', 'return', 'a'] | 96,475 |
facebookresearch/fvcore | test_transform.py | TestTransforms.test_noop_transform_no_register | test_noop_transform_no_register | NoOpTransform does not need register - it's by default no-op. | [
"NoOpTransform",
"does",
"not",
"need",
"register",
"-",
"it's",
"by",
"default",
"no-op."
] | def test_noop_transform_no_register(self):
t = T.NoOpTransform()
self.assertEqual(t.apply_anything(1), 1) | ['def', 'test_noop_transform_no_register(self):', 't', '=', 'T.NoOpTransform()', 'self.assertEqual(t.apply_anything(1),', '1)'] | 566,016 |
calico/basenji | basenji_sad_multi.py | job_completed | job_completed | Check whether a specific job has generated its output file. | [
"Check",
"whether",
"a",
"specific",
"job",
"has",
"generated",
"its",
"output",
"file."
] | def job_completed(options, pi):
out_file = '%s/job%d/sad.h5' % (options.out_dir, pi)
return os.path.isfile(out_file) or os.path.isdir(out_file) | ['def', 'job_completed(options,', 'pi):', 'out_file', '=', "'%s/job%d/sad.h5'", '%', '(options.out_dir,', 'pi)', 'return', 'os.path.isfile(out_file)', 'or', 'os.path.isdir(out_file)'] | 94,794 |
FeiGSSS/DySAT_pytorch | random_walk.py | Graph_RandomWalk.preprocess_transition_probs | preprocess_transition_probs | Preprocessing of transition probabilities for guiding the random walks. | [
"Preprocessing",
"of",
"transition",
"probabilities",
"for",
"guiding",
"the",
"random",
"walks."
] | def preprocess_transition_probs(self):
G = self.G
is_directed = self.is_directed
alias_nodes = {}
for node in G.nodes():
unnormalized_probs = [G[node][nbr]['weight'] for nbr in sorted(G.neighbors(node))]
norm_const = sum(unnormalized_probs)
normalized_probs = [float(u_prob) / nor... | ['def', 'preprocess_transition_probs(self):', 'G', '=', 'self.G', 'is_directed', '=', 'self.is_directed', 'alias_nodes', '=', '{}', 'for', 'node', 'in', 'G.nodes():', 'unnormalized_probs', '=', "[G[node][nbr]['weight']", 'for', 'nbr', 'in', 'sorted(G.neighbors(node))]', 'norm_const', '=', 'sum(unnormalized_probs)', 'no... | 555,376 |
sjtu-marl/malib | manager.py | validate_strategy_specs | validate_strategy_specs | Validate a dict of strategy specs that whether the prob list is legal. | [
"Validate",
"a",
"dict",
"of",
"strategy",
"specs",
"that",
"whether",
"the",
"prob",
"list",
"is",
"legal."
] | def validate_strategy_specs(specs: Dict[str, StrategySpec]):
for (rid, spec) in specs.items():
if len(spec) < 1:
raise ValueError(f'Empty spec for runtime_id={rid}')
expected_prob_list = spec.meta_data.get('prob_list', [1 / len(spec)] * len(spec))
if expected_prob_list is None:
... | ['def', 'validate_strategy_specs(specs:', 'Dict[str,', 'StrategySpec]):', 'for', '(rid,', 'spec)', 'in', 'specs.items():', 'if', 'len(spec)', '<', '1:', 'raise', "ValueError(f'Empty", 'spec', 'for', "runtime_id={rid}')", 'expected_prob_list', '=', "spec.meta_data.get('prob_list',", '[1', '/', 'len(spec)]', '*', 'len(sp... | 627,545 |
robustness-gym/robustness-gym | metrics.py | f1_macro | f1_macro | Calculate macro F1 score for multi-class classification. | [
"Calculate",
"macro",
"F1",
"score",
"for",
"multi-class",
"classification."
] | def f1_macro(predictions: Union[list, np.array, torch.Tensor], labels: Union[list, np.array, torch.Tensor]):
return f1_score(y_true=labels, y_pred=predictions, average='macro') | ['def', 'f1_macro(predictions:', 'Union[list,', 'np.array,', 'torch.Tensor],', 'labels:', 'Union[list,', 'np.array,', 'torch.Tensor]):', 'return', 'f1_score(y_true=labels,', 'y_pred=predictions,', "average='macro')"] | 826,263 |
weimin17/Object-Detection_HelmetDetection | adversarial_attack.py | generate_pgd_common | generate_pgd_common | Common code for generating PGD adversarial examples. | [
"Common",
"code",
"for",
"generating",
"PGD",
"adversarial",
"examples."
] | def generate_pgd_common(x, bounds, model_fn, attack_params, one_hot_labels, perturbation_multiplier):
params_list = attack_params.split('_')
if len(params_list) != 3:
raise ValueError('Invalid parameters of PGD attack: %s' % attack_params)
epsilon = int(params_list[0])
step_size = int(params_lis... | ['def', 'generate_pgd_common(x,', 'bounds,', 'model_fn,', 'attack_params,', 'one_hot_labels,', 'perturbation_multiplier):', 'params_list', '=', "attack_params.split('_')", 'if', 'len(params_list)', '!=', '3:', 'raise', "ValueError('Invalid", 'parameters', 'of', 'PGD', 'attack:', "%s'", '%', 'attack_params)', 'epsilon',... | 761,399 |
Djaizz/Djaizz | base.py | _PreTrainedMLModelABC.loader | loader | Loader method to load the Model's native object. | [
"Loader",
"method",
"to",
"load",
"the",
"Model's",
"native",
"object."
] | def loader(self) -> callable:
return import_obj(self.loader_module_and_qualname) | ['def', 'loader(self)', '->', 'callable:', 'return', 'import_obj(self.loader_module_and_qualname)'] | 189,450 |
allenai/deepfigures-open | file_util.py | extract_tarfile | extract_tarfile | Extract a tarfile at 'src' to 'dst'. | [
"Extract",
"a",
"tarfile",
"at",
"'src'",
"to",
"'dst'."
] | def extract_tarfile(src: str, dst: str, streaming=True) -> None:
src = _expand(src)
dst = _expand(dst)
with open(src, mode='rb', streaming=streaming) as f:
b = f.read()
extract_tarfile_from_bytes(b, dst) | ['def', 'extract_tarfile(src:', 'str,', 'dst:', 'str,', 'streaming=True)', '->', 'None:', 'src', '=', '_expand(src)', 'dst', '=', '_expand(dst)', 'with', 'open(src,', "mode='rb',", 'streaming=streaming)', 'as', 'f:', 'b', '=', 'f.read()', 'extract_tarfile_from_bytes(b,', 'dst)'] | 520,510 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | visitor.py | Expression.makeAcceptPrePost | makeAcceptPrePost | Make an accept method for pre- and post- assignment expressions. | [
"Make",
"an",
"accept",
"method",
"for",
"pre-",
"and",
"post-",
"assignment",
"expressions."
] | def makeAcceptPrePost(suffix, pre):
def acceptPrePost(self, node, memo):
factory = self.factory.expr
if node.withinExpr:
name = node.firstChildOfType(tokens.IDENT).text
handler = self.configHandler('VariableNaming')
rename = handler(name)
block = self... | ['def', 'makeAcceptPrePost(suffix,', 'pre):', 'def', 'acceptPrePost(self,', 'node,', 'memo):', 'factory', '=', 'self.factory.expr', 'if', 'node.withinExpr:', 'name', '=', 'node.firstChildOfType(tokens.IDENT).text', 'handler', '=', "self.configHandler('VariableNaming')", 'rename', '=', 'handler(name)', 'block', '=', 'se... | 17,347 |
sek788432/Waymo-2D-Object-Detection | create_coco_tf_record.py | generate_annotations | generate_annotations | Generator for COCO annotations. | [
"Generator",
"for",
"COCO",
"annotations."
] | def generate_annotations(images, image_dir, img_to_obj_annotation=None, img_to_caption_annotation=None, id_to_name_map=None, include_masks=False):
for image in images:
object_annotation = img_to_obj_annotation.get(image['id'], None) if img_to_obj_annotation else None
caption_annotaion = img_to_capti... | ['def', 'generate_annotations(images,', 'image_dir,', 'img_to_obj_annotation=None,', 'img_to_caption_annotation=None,', 'id_to_name_map=None,', 'include_masks=False):', 'for', 'image', 'in', 'images:', 'object_annotation', '=', "img_to_obj_annotation.get(image['id'],", 'None)', 'if', 'img_to_obj_annotation', 'else', 'N... | 973,046 |
CentML/DeepView.Profile | Beam.py | Beam.get_the_best_score_and_idx | get_the_best_score_and_idx | Get the score of the best in the beam. | [
"Get",
"the",
"score",
"of",
"the",
"best",
"in",
"the",
"beam."
] | def get_the_best_score_and_idx(self):
(scores, ids) = self.sort_scores()
return (scores[1], ids[1]) | ['def', 'get_the_best_score_and_idx(self):', '(scores,', 'ids)', '=', 'self.sort_scores()', 'return', '(scores[1],', 'ids[1])'] | 540,892 |
ahthie7u/cockpit | test_tic.py | AutogradTICTrace.create_graph | create_graph | Return whether access to the forward pass computation graph is needed. | [
"Return",
"whether",
"access",
"to",
"the",
"forward",
"pass",
"computation",
"graph",
"is",
"needed."
] | def create_graph(self, global_step):
return self.should_compute(global_step) | ['def', 'create_graph(self,', 'global_step):', 'return', 'self.should_compute(global_step)'] | 492,884 |
gunthercox/ChatterBot | highlight.py | SHORTER | SHORTER | Sort shorter passages first. | [
"Sort",
"shorter",
"passages",
"first."
] | def SHORTER(fragment):
return len(fragment) | ['def', 'SHORTER(fragment):', 'return', 'len(fragment)'] | 482,850 |
lebrice/Sequoia | setting_test.py | TestIncrementalRLSetting.test_monsterkong | test_monsterkong | Checks that the MonsterKong env works fine with pixel and state input. | [
"Checks",
"that",
"the",
"MonsterKong",
"env",
"works",
"fine",
"with",
"pixel",
"and",
"state",
"input."
] | def test_monsterkong(self, state: bool):
setting = self.Setting(dataset='StateMetaMonsterKong-v0' if state else 'PixelMetaMonsterKong-v0', nb_tasks=5, train_max_steps=500, test_max_steps=500, train_transforms=[], test_transforms=[], val_transforms=[], max_episode_steps=10)
if state:
assert setting.obser... | ['def', 'test_monsterkong(self,', 'state:', 'bool):', 'setting', '=', "self.Setting(dataset='StateMetaMonsterKong-v0'", 'if', 'state', 'else', "'PixelMetaMonsterKong-v0',", 'nb_tasks=5,', 'train_max_steps=500,', 'test_max_steps=500,', 'train_transforms=[],', 'test_transforms=[],', 'val_transforms=[],', 'max_episode_ste... | 344,540 |
shiwt03/SSformer | isaid.py | iSAID_convert_from_color | iSAID_convert_from_color | RGB-color encoding to grayscale labels. | [
"RGB-color",
"encoding",
"to",
"grayscale",
"labels."
] | def iSAID_convert_from_color(arr_3d, palette=iSAID_invert_palette):
arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)
for (c, i) in palette.items():
m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)
arr_2d[m] = i
return arr_2d | ['def', 'iSAID_convert_from_color(arr_3d,', 'palette=iSAID_invert_palette):', 'arr_2d', '=', 'np.zeros((arr_3d.shape[0],', 'arr_3d.shape[1]),', 'dtype=np.uint8)', 'for', '(c,', 'i)', 'in', 'palette.items():', 'm', '=', 'np.all(arr_3d', '==', 'np.array(c).reshape(1,', '1,', '3),', 'axis=2)', 'arr_2d[m]', '=', 'i', 'retu... | 872,031 |
rudranil723/mini-main | band.py | GDALBand.mean | mean | Return the mean of all pixel values of this band. | [
"Return",
"the",
"mean",
"of",
"all",
"pixel",
"values",
"of",
"this",
"band."
] | def mean(self):
return self.statistics()[2] | ['def', 'mean(self):', 'return', 'self.statistics()[2]'] | 315,224 |
nicknochnack/RealTimeSignLanguageTFJS | center_net_meta_arch_tf2_test.py | get_fake_mask_params | get_fake_mask_params | Returns the fake mask estimation parameter namedtuple. | [
"Returns",
"the",
"fake",
"mask",
"estimation",
"parameter",
"namedtuple."
] | def get_fake_mask_params():
return cnma.MaskParams(classification_loss=losses.WeightedSoftmaxClassificationLoss(), task_loss_weight=1.0, mask_height=4, mask_width=4) | ['def', 'get_fake_mask_params():', 'return', 'cnma.MaskParams(classification_loss=losses.WeightedSoftmaxClassificationLoss(),', 'task_loss_weight=1.0,', 'mask_height=4,', 'mask_width=4)'] | 852,392 |
enuguru/artificial_intelligence_and_machine_ | debug.py | translate_exception | translate_exception | If passed an exc_info it will automatically rewrite the exceptions all the way down to the correct line numbers and frames. | [
"If",
"passed",
"an",
"exc_info",
"it",
"will",
"automatically",
"rewrite",
"the",
"exceptions",
"all",
"the",
"way",
"down",
"to",
"the",
"correct",
"line",
"numbers",
"and",
"frames."
] | def translate_exception(exc_info, initial_skip=0):
tb = exc_info[2]
frames = []
for x in range(initial_skip):
if tb is not None:
tb = tb.tb_next
initial_tb = tb
while tb is not None:
if tb.tb_frame.f_code in internal_code:
tb = tb.tb_next
continue
... | ['def', 'translate_exception(exc_info,', 'initial_skip=0):', 'tb', '=', 'exc_info[2]', 'frames', '=', '[]', 'for', 'x', 'in', 'range(initial_skip):', 'if', 'tb', 'is', 'not', 'None:', 'tb', '=', 'tb.tb_next', 'initial_tb', '=', 'tb', 'while', 'tb', 'is', 'not', 'None:', 'if', 'tb.tb_frame.f_code', 'in', 'internal_code:... | 158,215 |
kykiefer/depression-detect | cnn.py | preprocess | preprocess | Convert from float64 to float32 and normalize normalize to decibels relative to full scale (dBFS) for the 4 sec clip. | [
"Convert",
"from",
"float64",
"to",
"float32",
"and",
"normalize",
"normalize",
"to",
"decibels",
"relative",
"to",
"full",
"scale",
"(dBFS)",
"for",
"the",
"4",
"sec",
"clip."
] | def preprocess(X_train, X_test):
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train = np.array([(X - X.min()) / (X.max() - X.min()) for X in X_train])
X_test = np.array([(X - X.min()) / (X.max() - X.min()) for X in X_test])
return (X_train, X_test) | ['def', 'preprocess(X_train,', 'X_test):', 'X_train', '=', "X_train.astype('float32')", 'X_test', '=', "X_test.astype('float32')", 'X_train', '=', 'np.array([(X', '-', 'X.min())', '/', '(X.max()', '-', 'X.min())', 'for', 'X', 'in', 'X_train])', 'X_test', '=', 'np.array([(X', '-', 'X.min())', '/', '(X.max()', '-', 'X.mi... | 183,935 |
matsu0228/nlp-jp | topology_description.py | TopologyDescription.reset_server | reset_server | A copy of this description, with one server marked Unknown. | [
"A",
"copy",
"of",
"this",
"description,",
"with",
"one",
"server",
"marked",
"Unknown."
] | def reset_server(self, address):
return updated_topology_description(self, ServerDescription(address)) | ['def', 'reset_server(self,', 'address):', 'return', 'updated_topology_description(self,', 'ServerDescription(address))'] | 805,085 |
tonybeltramelli/Graphics-And-Vision | Camera.py | Camera.Height | Height | Set a new height value to captured images. | [
"Set",
"a",
"new",
"height",
"value",
"to",
"captured",
"images."
] | def Height(self, value):
self.__camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, int(value)) | ['def', 'Height(self,', 'value):', 'self.__camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT,', 'int(value))'] | 580,563 |
gunthercox/ChatterBot | checkers.py | python_format | python_format | Verify the format string placeholders in the translation. | [
"Verify",
"the",
"format",
"string",
"placeholders",
"in",
"the",
"translation."
] | def python_format(catalog, message):
if 'python-format' not in message.flags:
return
msgids = message.id
if not isinstance(msgids, (list, tuple)):
msgids = (msgids,)
msgstrs = message.string
if not isinstance(msgstrs, (list, tuple)):
msgstrs = (msgstrs,)
for (msgid, msgst... | ['def', 'python_format(catalog,', 'message):', 'if', "'python-format'", 'not', 'in', 'message.flags:', 'return', 'msgids', '=', 'message.id', 'if', 'not', 'isinstance(msgids,', '(list,', 'tuple)):', 'msgids', '=', '(msgids,)', 'msgstrs', '=', 'message.string', 'if', 'not', 'isinstance(msgstrs,', '(list,', 'tuple)):', '... | 478,660 |
sunishsheth2009/ChatterBot | syntax.py | SyntaxNode.set_range | set_range | Sets the character range associated with this node. | [
"Sets",
"the",
"character",
"range",
"associated",
"with",
"this",
"node."
] | def set_range(self, startchar, endchar):
self.startchar = startchar
self.endchar = endchar
return self | ['def', 'set_range(self,', 'startchar,', 'endchar):', 'self.startchar', '=', 'startchar', 'self.endchar', '=', 'endchar', 'return', 'self'] | 526,938 |
BlissChapman/ICW-fMRI-GAN | mask.py | Masker.remove | remove | Remove one or more layers from the stack of masking layers. | [
"Remove",
"one",
"or",
"more",
"layers",
"from",
"the",
"stack",
"of",
"masking",
"layers."
] | def remove(self, layers):
if not isinstance(layers, list):
layers = [layers]
for l in layers:
if isinstance(l, string_types):
if l not in self.layers:
raise ValueError("There's no image/layer named '%s' in the masking stack!" % l)
self.stack.remove(l)
... | ['def', 'remove(self,', 'layers):', 'if', 'not', 'isinstance(layers,', 'list):', 'layers', '=', '[layers]', 'for', 'l', 'in', 'layers:', 'if', 'isinstance(l,', 'string_types):', 'if', 'l', 'not', 'in', 'self.layers:', 'raise', 'ValueError("There\'s', 'no', 'image/layer', 'named', "'%s'", 'in', 'the', 'masking', 'stack!... | 597,090 |
RasaHQ/rasa | x.py | rasa_x | rasa_x | Run Rasa with the `x` subcommand. | [
"Run",
"Rasa",
"with",
"the",
"`x`",
"subcommand."
] | def rasa_x(args: argparse.Namespace) -> None:
from rasa.cli.utils import signal_handler
signal.signal(signal.SIGINT, signal_handler)
if args.production:
run_in_enterprise_connection_mode(args)
else:
rasa.shared.utils.io.raise_warning('Running Rasa X in local mode is no longer supported a... | ['def', 'rasa_x(args:', 'argparse.Namespace)', '->', 'None:', 'from', 'rasa.cli.utils', 'import', 'signal_handler', 'signal.signal(signal.SIGINT,', 'signal_handler)', 'if', 'args.production:', 'run_in_enterprise_connection_mode(args)', 'else:', "rasa.shared.utils.io.raise_warning('Running", 'Rasa', 'X', 'in', 'local', ... | 836,639 |
kPsarakis/Image-Forgery-Detection-CNN | mask_extraction.py | extract_masks | extract_masks | Extracts and saves all the masks. | [
"Extracts",
"and",
"saves",
"all",
"the",
"masks."
] | def extract_masks():
save_dir = 'masks'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
au_pic_list = glob('..' + os.sep + '..' + os.sep + 'data' + os.sep + 'CASIA2' + os.sep + 'Au' + os.sep + '*')
sp_pic_list = glob('..' + os.sep + '..' + os.sep + 'data' + os.sep + 'CASIA2' + os.sep + 'T... | ['def', 'extract_masks():', 'save_dir', '=', "'masks'", 'if', 'not', 'os.path.exists(save_dir):', 'os.makedirs(save_dir)', 'au_pic_list', '=', "glob('..'", '+', 'os.sep', '+', "'..'", '+', 'os.sep', '+', "'data'", '+', 'os.sep', '+', "'CASIA2'", '+', 'os.sep', '+', "'Au'", '+', 'os.sep', '+', "'*')", 'sp_pic_list', '='... | 229,243 |
AtlantixJJ/LinearGAN | helper.py | build_extractor | build_extractor | Builds feature extractor by architecture name. | [
"Builds",
"feature",
"extractor",
"by",
"architecture",
"name."
] | def build_extractor(architecture, spatial_feature=False, imagenet_logits=False):
if architecture not in PREDICTOR_POOL:
raise ValueError(f'Feature extractor with architecture `{architecture}` is not registered in `PREDICTOR_POOL` in `predictor_settings.py`!')
return FeatureExtractor(architecture, spatia... | ['def', 'build_extractor(architecture,', 'spatial_feature=False,', 'imagenet_logits=False):', 'if', 'architecture', 'not', 'in', 'PREDICTOR_POOL:', 'raise', "ValueError(f'Feature", 'extractor', 'with', 'architecture', '`{architecture}`', 'is', 'not', 'registered', 'in', '`PREDICTOR_POOL`', 'in', "`predictor_settings.py... | 602,678 |
lalwanii26/openscope-barcodingstim | translator.py | TrialTranslator.find_dx | find_dx | Finds wheel rotation for each frame. | [
"Finds",
"wheel",
"rotation",
"for",
"each",
"frame."
] | def find_dx(self, exp_data):
return exp_data['items']['behavior']['encoders'][0]['dx'] | ['def', 'find_dx(self,', 'exp_data):', 'return', "exp_data['items']['behavior']['encoders'][0]['dx']"] | 757,552 |
jimtin/Stock_Comparison | mpltools.py | get_spine_visible | get_spine_visible | Return some spine parameters for the spine, `spine_key`. | [
"Return",
"some",
"spine",
"parameters",
"for",
"the",
"spine,",
"`spine_key`."
] | def get_spine_visible(ax, spine_key):
spine = ax.spines[spine_key]
ax_frame_on = ax.get_frame_on()
spine_frame_like = spine.is_frame_like()
if not spine.get_visible():
return False
elif not spine._edgecolor[-1]:
return False
elif not ax_frame_on and spine_frame_like:
retu... | ['def', 'get_spine_visible(ax,', 'spine_key):', 'spine', '=', 'ax.spines[spine_key]', 'ax_frame_on', '=', 'ax.get_frame_on()', 'spine_frame_like', '=', 'spine.is_frame_like()', 'if', 'not', 'spine.get_visible():', 'return', 'False', 'elif', 'not', 'spine._edgecolor[-1]:', 'return', 'False', 'elif', 'not', 'ax_frame_on'... | 389,249 |
jxhe/unify-parameter-efficient-tuning | lm_seqs_dataset.py | LmSeqsDataset.remove_unknown_sequences | remove_unknown_sequences | Remove sequences with a (too) high level of unknown tokens. | [
"Remove",
"sequences",
"with",
"a",
"(too)",
"high",
"level",
"of",
"unknown",
"tokens."
] | def remove_unknown_sequences(self):
if 'unk_token' not in self.params.special_tok_ids:
return
else:
unk_token_id = self.params.special_tok_ids['unk_token']
init_size = len(self)
unk_occs = np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
indices = unk_occs / se... | ['def', 'remove_unknown_sequences(self):', 'if', "'unk_token'", 'not', 'in', 'self.params.special_tok_ids:', 'return', 'else:', 'unk_token_id', '=', "self.params.special_tok_ids['unk_token']", 'init_size', '=', 'len(self)', 'unk_occs', '=', 'np.array([np.count_nonzero(a', '==', 'unk_token_id)', 'for', 'a', 'in', 'self.... | 948,138 |
jariasf/GMVAE | utils.py | mode_tensor | mode_tensor | Computes the mode of the float Tensor x. | [
"Computes",
"the",
"mode",
"of",
"the",
"float",
"Tensor",
"x."
] | def mode_tensor(x):
(y, idx, count) = tf.unique_with_counts(x)
mode = y[tf.argmax(count)]
return tf.cast(mode, dtype=tf.float32) | ['def', 'mode_tensor(x):', '(y,', 'idx,', 'count)', '=', 'tf.unique_with_counts(x)', 'mode', '=', 'y[tf.argmax(count)]', 'return', 'tf.cast(mode,', 'dtype=tf.float32)'] | 578,505 |
43Carrig/recurrent_neural_networks_practice | test_util.py | ConstantMinimizationProblem.objective | objective | Returns the objective function. | [
"Returns",
"the",
"objective",
"function."
] | def objective(self):
return self._objective | ['def', 'objective(self):', 'return', 'self._objective'] | 312,630 |
PacktPublishing/Hands-On-Artificial--for-Banking | conftest.py | python_parser_only | python_parser_only | Fixture all of the CSV parsers using the Python engine. | [
"Fixture",
"all",
"of",
"the",
"CSV",
"parsers",
"using",
"the",
"Python",
"engine."
] | def python_parser_only(request):
return request.param | ['def', 'python_parser_only(request):', 'return', 'request.param'] | 237,279 |
RLE-Foundation/rllte | impala.py | IMPALA.update | update | Update the learner model. | [
"Update",
"the",
"learner",
"model."
] | def update(self, batch: Dict, lock=threading.Lock()) -> Dict[str, Any]:
with lock:
learner_outputs = self.policy.learner(batch)
bootstrap_value = learner_outputs['baselines'][-1]
batch = {key: tensor[1:] for (key, tensor) in batch.items()}
learner_outputs = {key: tensor[:-1] for (key... | ['def', 'update(self,', 'batch:', 'Dict,', 'lock=threading.Lock())', '->', 'Dict[str,', 'Any]:', 'with', 'lock:', 'learner_outputs', '=', 'self.policy.learner(batch)', 'bootstrap_value', '=', "learner_outputs['baselines'][-1]", 'batch', '=', '{key:', 'tensor[1:]', 'for', '(key,', 'tensor)', 'in', 'batch.items()}', 'lea... | 333,451 |
Kvatsx/Artificial-Intelligence-Assignments | console_widget.py | ConsoleWidget.can_copy | can_copy | Returns whether text can be copied to the clipboard. | [
"Returns",
"whether",
"text",
"can",
"be",
"copied",
"to",
"the",
"clipboard."
] | def can_copy(self):
return self._control.textCursor().hasSelection() | ['def', 'can_copy(self):', 'return', 'self._control.textCursor().hasSelection()'] | 77,238 |
PacktPublishing/Hands-On-Artificial--for-Banking | test_lapack.py | TestTbtrs.test_invalid_matrix_shapes | test_invalid_matrix_shapes | Test ?tbtrs fails correctly if shapes are invalid. | [
"Test",
"?tbtrs",
"fails",
"correctly",
"if",
"shapes",
"are",
"invalid."
] | def test_invalid_matrix_shapes(self, ldab, n, ldb, nrhs):
ab = np.ones((ldab, n), dtype=float)
b = np.ones((ldb, nrhs), dtype=float)
tbtrs = get_lapack_funcs('tbtrs', dtype=float)
assert_raises(Exception, tbtrs, ab, b) | ['def', 'test_invalid_matrix_shapes(self,', 'ldab,', 'n,', 'ldb,', 'nrhs):', 'ab', '=', 'np.ones((ldab,', 'n),', 'dtype=float)', 'b', '=', 'np.ones((ldb,', 'nrhs),', 'dtype=float)', 'tbtrs', '=', "get_lapack_funcs('tbtrs',", 'dtype=float)', 'assert_raises(Exception,', 'tbtrs,', 'ab,', 'b)'] | 238,617 |
IBM/mi-prometheus | json_to_img.py | convert_to_grid | convert_to_grid | Given a x-y coordinate, return the target activity for a grid of neurons. | [
"Given",
"a",
"x-y",
"coordinate,",
"return",
"the",
"target",
"activity",
"for",
"a",
"grid",
"of",
"neurons."
] | def convert_to_grid(xy_coord, prefs):
sigma2 = 0.02
activity = np.exp(-((xy_coord[:, 0:1] - prefs[:, 0]) ** 2 + (xy_coord[:, 1:2] - prefs[:, 1]) ** 2) / sigma2)
activity = (activity.T / np.sum(activity, axis=1)).T
return activity | ['def', 'convert_to_grid(xy_coord,', 'prefs):', 'sigma2', '=', '0.02', 'activity', '=', 'np.exp(-((xy_coord[:,', '0:1]', '-', 'prefs[:,', '0])', '**', '2', '+', '(xy_coord[:,', '1:2]', '-', 'prefs[:,', '1])', '**', '2)', '/', 'sigma2)', 'activity', '=', '(activity.T', '/', 'np.sum(activity,', 'axis=1)).T', 'return', 'a... | 635,725 |
ratschlab/dpsom | somvae_model.py | conv2d_transposed | conv2d_transposed | Creates a transposed convolutional layer simimar to conv2d. | [
"Creates",
"a",
"transposed",
"convolutional",
"layer",
"simimar",
"to",
"conv2d."
] | def conv2d_transposed(x, shape, outshape, name, strides=[1, 1, 1, 1]):
weight = weight_variable(shape, '{}_W'.format(name))
bias = bias_variable([shape[-2]], '{}_b'.format(name))
return tf.nn.conv2d_transpose(x, weight, output_shape=outshape, strides=strides, padding='SAME', name=name) + bias | ['def', 'conv2d_transposed(x,', 'shape,', 'outshape,', 'name,', 'strides=[1,', '1,', '1,', '1]):', 'weight', '=', 'weight_variable(shape,', "'{}_W'.format(name))", 'bias', '=', 'bias_variable([shape[-2]],', "'{}_b'.format(name))", 'return', 'tf.nn.conv2d_transpose(x,', 'weight,', 'output_shape=outshape,', 'strides=stri... | 167,005 |
thaines/helit | model.py | Sample.cleanZeros | cleanZeros | Goes through and removes anything that has a zero reference count, adjusting all indices accordingly. | [
"Goes",
"through",
"and",
"removes",
"anything",
"that",
"has",
"a",
"zero",
"reference",
"count,",
"adjusting",
"all",
"indices",
"accordingly."
] | def cleanZeros(self):
newTopicCount = 0
topicMap = dict()
for t in xrange(self.topicUse.shape[0]):
if self.topicUse[t] != 0:
topicMap[t] = newTopicCount
newTopicCount += 1
if newTopicCount != self.topicUse.shape[0]:
newTopicWord = numpy.zeros((newTopicCount, self.... | ['def', 'cleanZeros(self):', 'newTopicCount', '=', '0', 'topicMap', '=', 'dict()', 'for', 't', 'in', 'xrange(self.topicUse.shape[0]):', 'if', 'self.topicUse[t]', '!=', '0:', 'topicMap[t]', '=', 'newTopicCount', 'newTopicCount', '+=', '1', 'if', 'newTopicCount', '!=', 'self.topicUse.shape[0]:', 'newTopicWord', '=', 'num... | 591,173 |
sunishsheth2009/ChatterBot | parsing.py | date_from_duration | date_from_duration | Find dates from duration Eg: 20 days from now Currently does not support strings like "20 days from last monday". | [
"Find",
"dates",
"from",
"duration",
"Eg:",
"20",
"days",
"from",
"now",
"Currently",
"does",
"not",
"support",
"strings",
"like",
"\"20",
"days",
"from",
"last",
"monday\"."
] | def date_from_duration(base_date, number_as_string, unit, duration, base_time=None):
if base_time is not None:
base_date = date_from_adverb(base_date, base_time)
num = convert_string_to_number(number_as_string)
if unit in day_variations:
args = {'days': num}
elif unit in minute_variation... | ['def', 'date_from_duration(base_date,', 'number_as_string,', 'unit,', 'duration,', 'base_time=None):', 'if', 'base_time', 'is', 'not', 'None:', 'base_date', '=', 'date_from_adverb(base_date,', 'base_time)', 'num', '=', 'convert_string_to_number(number_as_string)', 'if', 'unit', 'in', 'day_variations:', 'args', '=', "{... | 478,041 |
microsoft/UniSpeech | utils.py | broadcast_object | broadcast_object | Broadcast an arbitrary Python object to other workers. | [
"Broadcast",
"an",
"arbitrary",
"Python",
"object",
"to",
"other",
"workers."
] | def broadcast_object(obj: Any, src_rank: int, group: object, dist_device: Optional[torch.device]=None) -> Any:
if dist_device is None:
if torch.distributed.get_backend(group) == 'nccl':
dist_device = torch.device('cuda')
else:
dist_device = torch.device('cpu')
if get_rank... | ['def', 'broadcast_object(obj:', 'Any,', 'src_rank:', 'int,', 'group:', 'object,', 'dist_device:', 'Optional[torch.device]=None)', '->', 'Any:', 'if', 'dist_device', 'is', 'None:', 'if', 'torch.distributed.get_backend(group)', '==', "'nccl':", 'dist_device', '=', "torch.device('cuda')", 'else:', 'dist_device', '=', "to... | 378,329 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjuiThemeColorWrapper.decorinactive2 | decorinactive2 | inactive slider color 2. | [
"inactive",
"slider",
"color",
"2."
] | def decorinactive2(self):
return util.buf_to_npy(self._ptr.contents.decorinactive2, (3,)) | ['def', 'decorinactive2(self):', 'return', 'util.buf_to_npy(self._ptr.contents.decorinactive2,', '(3,))'] | 440,679 |
tensorflow/quantum | serializer_test.py | SerializerTest.test_deserialize_circuit_wrong_type | test_deserialize_circuit_wrong_type | Attempt to deserialize invalid objects types. | [
"Attempt",
"to",
"deserialize",
"invalid",
"objects",
"types."
] | def test_deserialize_circuit_wrong_type(self, inp):
with self.assertRaises(TypeError):
serializer.deserialize_circuit(input) | ['def', 'test_deserialize_circuit_wrong_type(self,', 'inp):', 'with', 'self.assertRaises(TypeError):', 'serializer.deserialize_circuit(input)'] | 835,028 |
enuguru/artificial_intelligence_and_machine_learning | collector.py | Collector.tracer_name | tracer_name | Return the class name of the tracer we're using. | [
"Return",
"the",
"class",
"name",
"of",
"the",
"tracer",
"we're",
"using."
] | def tracer_name(self):
return self._trace_class.__name__ | ['def', 'tracer_name(self):', 'return', 'self._trace_class.__name__'] | 157,233 |
pramodiperera/virtual-keyboard | search_scope.py | SearchScope.create | create | Create a SearchScope object after normalizing the `find_links`. | [
"Create",
"a",
"SearchScope",
"object",
"after",
"normalizing",
"the",
"`find_links`."
] | def create(cls, find_links: List[str], index_urls: List[str]) -> 'SearchScope':
built_find_links: List[str] = []
for link in find_links:
if link.startswith('~'):
new_link = normalize_path(link)
if os.path.exists(new_link):
link = new_link
built_find_links.... | ['def', 'create(cls,', 'find_links:', 'List[str],', 'index_urls:', 'List[str])', '->', "'SearchScope':", 'built_find_links:', 'List[str]', '=', '[]', 'for', 'link', 'in', 'find_links:', 'if', "link.startswith('~'):", 'new_link', '=', 'normalize_path(link)', 'if', 'os.path.exists(new_link):', 'link', '=', 'new_link', 'b... | 931,957 |
PKU-Alignment/safe-rlhf | utils.py | is_main_process | is_main_process | Check if the current process is the main process. | [
"Check",
"if",
"the",
"current",
"process",
"is",
"the",
"main",
"process."
] | def is_main_process() -> bool:
return not dist.is_initialized() or dist.get_rank() == 0 | ['def', 'is_main_process()', '->', 'bool:', 'return', 'not', 'dist.is_initialized()', 'or', 'dist.get_rank()', '==', '0'] | 829,125 |
mfbx9da4/neuron-astrocyte-networks | genotypes.py | Genotype.get_fitness_fail | get_fitness_fail | This function returns the fitness value that constitutes failure as assigned by the parent grammatical evolution. | [
"This",
"function",
"returns",
"the",
"fitness",
"value",
"that",
"constitutes",
"failure",
"as",
"assigned",
"by",
"the",
"parent",
"grammatical",
"evolution."
] | def get_fitness_fail(self):
return self._fitness_fail | ['def', 'get_fitness_fail(self):', 'return', 'self._fitness_fail'] | 722,900 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | plot_directive.py | PlotDirective.run | run | Run the plot directive. | [
"Run",
"the",
"plot",
"directive."
] | def run(self):
return run(self.arguments, self.content, self.options, self.state_machine, self.state, self.lineno) | ['def', 'run(self):', 'return', 'run(self.arguments,', 'self.content,', 'self.options,', 'self.state_machine,', 'self.state,', 'self.lineno)'] | 451,237 |
thu-ml/ares | trainer.py | Trainer.before_eval | before_eval | Do something before evaluating. | [
"Do",
"something",
"before",
"evaluating."
] | def before_eval(self):
self.model.eval() if not self.is_distributed else self.model.module.eval()
self.test_dataloader.sampler.shuffle = False | ['def', 'before_eval(self):', 'self.model.eval()', 'if', 'not', 'self.is_distributed', 'else', 'self.model.module.eval()', 'self.test_dataloader.sampler.shuffle', '=', 'False'] | 402,077 |
Vill-Lab/2021-TIP-IGOAS | dataset.py | Dataset.download_dataset | download_dataset | Downloads and extracts dataset. | [
"Downloads",
"and",
"extracts",
"dataset."
] | def download_dataset(self, dataset_dir, dataset_url):
if osp.exists(dataset_dir):
return
if dataset_url is None:
raise RuntimeError('{} dataset needs to be manually prepared, please follow the document to prepare this dataset'.format(self.__class__.__name__))
print('Creating directory "{}"'.... | ['def', 'download_dataset(self,', 'dataset_dir,', 'dataset_url):', 'if', 'osp.exists(dataset_dir):', 'return', 'if', 'dataset_url', 'is', 'None:', 'raise', "RuntimeError('{}", 'dataset', 'needs', 'to', 'be', 'manually', 'prepared,', 'please', 'follow', 'the', 'document', 'to', 'prepare', 'this', "dataset'.format(self._... | 375,440 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.