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 |
|---|---|---|---|---|---|---|---|---|
openvinotoolkit/training_extensions | task.py | ClassificationOpenVINOTask.evaluate | evaluate | Evaluate function of ClassificationOpenVINOTask. | [
"Evaluate",
"function",
"of",
"ClassificationOpenVINOTask."
] | def evaluate(self, output_resultset: ResultSetEntity, evaluation_metric: Optional[str]=None):
if evaluation_metric is not None:
logger.warning(f'Requested to use {evaluation_metric} metric,but parameter is ignored. Use accuracy instead.')
output_resultset.performance = MetricsHelper.compute_accuracy(out... | ['def', 'evaluate(self,', 'output_resultset:', 'ResultSetEntity,', 'evaluation_metric:', 'Optional[str]=None):', 'if', 'evaluation_metric', 'is', 'not', 'None:', "logger.warning(f'Requested", 'to', 'use', '{evaluation_metric}', 'metric,but', 'parameter', 'is', 'ignored.', 'Use', 'accuracy', "instead.')", 'output_result... | 904,102 |
PaddlePaddle/Paddle3D | transform.py | limit_period | limit_period | Limit the value into a period for periodic function. | [
"Limit",
"the",
"value",
"into",
"a",
"period",
"for",
"periodic",
"function."
] | def limit_period(val, offset=0.5, period=np.pi):
return val - np.floor(val / period + offset) * period | ['def', 'limit_period(val,', 'offset=0.5,', 'period=np.pi):', 'return', 'val', '-', 'np.floor(val', '/', 'period', '+', 'offset)', '*', 'period'] | 778,010 |
tinyvision/DAMO-YOLO | tta_aug.py | im_detect_bbox | im_detect_bbox | Performs bbox detection on the original image. | [
"Performs",
"bbox",
"detection",
"on",
"the",
"original",
"image."
] | def im_detect_bbox(model, images, target_scale, target_max_size, device, config):
transform = T.Compose([T.Resize(target_scale, target_max_size), T.ToTensor(), T.Normalize(mean=config.dataset.input_pixel_mean, std=config.dataset.input_pixel_std, to_bgr255=config.dataset.input_to_bgr255)])
images = [transform(im... | ['def', 'im_detect_bbox(model,', 'images,', 'target_scale,', 'target_max_size,', 'device,', 'config):', 'transform', '=', 'T.Compose([T.Resize(target_scale,', 'target_max_size),', 'T.ToTensor(),', 'T.Normalize(mean=config.dataset.input_pixel_mean,', 'std=config.dataset.input_pixel_std,', 'to_bgr255=config.dataset.input... | 496,983 |
matsu0228/nlp-jp | connection.py | MWSConnection.get_feed_submission_count | get_feed_submission_count | Returns a count of the feeds submitted in the previous 90 days. | [
"Returns",
"a",
"count",
"of",
"the",
"feeds",
"submitted",
"in",
"the",
"previous",
"90",
"days."
] | def get_feed_submission_count(self, request, response, **kw):
return self._post_request(request, kw, response) | ['def', 'get_feed_submission_count(self,', 'request,', 'response,', '**kw):', 'return', 'self._post_request(request,', 'kw,', 'response)'] | 784,927 |
google-research/rigl | mask_updaters.py | MaskUpdater.generic_mask_update | generic_mask_update | Prunes+grows connections, all tensors same shape. | [
"Prunes+grows",
"connections,",
"all",
"tensors",
"same",
"shape."
] | def generic_mask_update(self, mask, var, score_drop, score_grow, drop_fraction, reinit_when_same=False):
n_total = tf.size(score_drop)
n_ones = tf.cast(tf.reduce_sum(mask), dtype=tf.int32)
n_prune = tf.cast(tf.cast(n_ones, dtype=tf.float32) * drop_fraction, tf.int32)
n_keep = n_ones - n_prune
(_, so... | ['def', 'generic_mask_update(self,', 'mask,', 'var,', 'score_drop,', 'score_grow,', 'drop_fraction,', 'reinit_when_same=False):', 'n_total', '=', 'tf.size(score_drop)', 'n_ones', '=', 'tf.cast(tf.reduce_sum(mask),', 'dtype=tf.int32)', 'n_prune', '=', 'tf.cast(tf.cast(n_ones,', 'dtype=tf.float32)', '*', 'drop_fraction,'... | 841,618 |
ChenhongyiYang/PPAL | detr_head.py | DETRHead.forward_train | forward_train | Forward function for training mode. | [
"Forward",
"function",
"for",
"training",
"mode."
] | def forward_train(self, x, img_metas, gt_bboxes, gt_labels=None, gt_bboxes_ignore=None, proposal_cfg=None, **kwargs):
assert proposal_cfg is None, '"proposal_cfg" must be None'
outs = self(x, img_metas)
if gt_labels is None:
loss_inputs = outs + (gt_bboxes, img_metas)
else:
loss_inputs =... | ['def', 'forward_train(self,', 'x,', 'img_metas,', 'gt_bboxes,', 'gt_labels=None,', 'gt_bboxes_ignore=None,', 'proposal_cfg=None,', '**kwargs):', 'assert', 'proposal_cfg', 'is', 'None,', '\'"proposal_cfg"', 'must', 'be', "None'", 'outs', '=', 'self(x,', 'img_metas)', 'if', 'gt_labels', 'is', 'None:', 'loss_inputs', '='... | 821,528 |
ramabhadraraju/NaturalLanguageProcessing | run_pretraining.py | gather_indexes | gather_indexes | Gathers the vectors at the specific positions over a minibatch. | [
"Gathers",
"the",
"vectors",
"at",
"the",
"specific",
"positions",
"over",
"a",
"minibatch."
] | def gather_indexes(sequence_tensor, positions):
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
... | ['def', 'gather_indexes(sequence_tensor,', 'positions):', 'sequence_shape', '=', 'modeling.get_shape_list(sequence_tensor,', 'expected_rank=3)', 'batch_size', '=', 'sequence_shape[0]', 'seq_length', '=', 'sequence_shape[1]', 'width', '=', 'sequence_shape[2]', 'flat_offsets', '=', 'tf.reshape(tf.range(0,', 'batch_size,'... | 798,683 |
tinyvision/DAMO-YOLO | zero_head.py | ZeroHead.get_target_single | get_target_single | Compute regression, classification targets for anchors in a single image. | [
"Compute",
"regression,",
"classification",
"targets",
"for",
"anchors",
"in",
"a",
"single",
"image."
] | def get_target_single(self, center_priors, cls_scores, bbox_preds, gt_bboxes, gt_labels, unmap_outputs=True, gt_bboxes_ignore=None):
num_valid_center = center_priors.shape[0]
labels = center_priors.new_full((num_valid_center,), self.num_classes, dtype=torch.long)
label_weights = center_priors.new_zeros(num_... | ['def', 'get_target_single(self,', 'center_priors,', 'cls_scores,', 'bbox_preds,', 'gt_bboxes,', 'gt_labels,', 'unmap_outputs=True,', 'gt_bboxes_ignore=None):', 'num_valid_center', '=', 'center_priors.shape[0]', 'labels', '=', 'center_priors.new_full((num_valid_center,),', 'self.num_classes,', 'dtype=torch.long)', 'lab... | 496,972 |
myothida/Supervised-Machine-Learning | tz.py | tzical.keys | keys | Retrieves the available time zones as a list. | [
"Retrieves",
"the",
"available",
"time",
"zones",
"as",
"a",
"list."
] | def keys(self):
return list(self._vtz.keys()) | ['def', 'keys(self):', 'return', 'list(self._vtz.keys())'] | 360,682 |
jimtin/Stock_Comparison | ctypeslib.py | prep_array | prep_array | Given a ctypes array type, construct and attach an __array_interface__ property to it if it does not yet have one. | [
"Given",
"a",
"ctypes",
"array",
"type,",
"construct",
"and",
"attach",
"an",
"__array_interface__",
"property",
"to",
"it",
"if",
"it",
"does",
"not",
"yet",
"have",
"one."
] | def prep_array(array_type):
try:
array_type.__array_interface__
except AttributeError:
pass
else:
return
shape = []
ob = array_type
while type(ob) is _ARRAY_TYPE:
shape.append(ob._length_)
ob = ob._type_
shape = tuple(shape)
ai = ob().__array_inter... | ['def', 'prep_array(array_type):', 'try:', 'array_type.__array_interface__', 'except', 'AttributeError:', 'pass', 'else:', 'return', 'shape', '=', '[]', 'ob', '=', 'array_type', 'while', 'type(ob)', 'is', '_ARRAY_TYPE:', 'shape.append(ob._length_)', 'ob', '=', 'ob._type_', 'shape', '=', 'tuple(shape)', 'ai', '=', 'ob()... | 386,627 |
enuguru/artificial_intelligence_and_machine_learning | support.py | NullTranslations.dpgettext | dpgettext | Like `pgettext()`, but look the message up in the specified `domain`. | [
"Like",
"`pgettext()`,",
"but",
"look",
"the",
"message",
"up",
"in",
"the",
"specified",
"`domain`."
] | def dpgettext(self, domain, context, message):
return self._domains.get(domain, self).pgettext(context, message) | ['def', 'dpgettext(self,', 'domain,', 'context,', 'message):', 'return', 'self._domains.get(domain,', 'self).pgettext(context,', 'message)'] | 157,024 |
triaquae/triaquae | storage.py | Storage.size | size | Returns the total size, in bytes, of the file specified by name. | [
"Returns",
"the",
"total",
"size,",
"in",
"bytes,",
"of",
"the",
"file",
"specified",
"by",
"name."
] | def size(self, name):
raise NotImplementedError() | ['def', 'size(self,', 'name):', 'raise', 'NotImplementedError()'] | 358,287 |
facebookresearch/CompilerGym | random_search.py | random_search | random_search | Run a random search on the given environment. | [
"Run",
"a",
"random",
"search",
"on",
"the",
"given",
"environment."
] | def random_search(env: LlvmEnv) -> None:
patience = int(env.action_space.n * FLAGS.patience_ratio)
workers = [RandomAgentWorker(make_env=lambda : gym.make('llvm-ic-v0', benchmark=env.benchmark), patience=patience) for _ in range(FLAGS.nproc)]
for worker in workers:
worker.start()
sleep(FLAGS.sea... | ['def', 'random_search(env:', 'LlvmEnv)', '->', 'None:', 'patience', '=', 'int(env.action_space.n', '*', 'FLAGS.patience_ratio)', 'workers', '=', '[RandomAgentWorker(make_env=lambda', ':', "gym.make('llvm-ic-v0',", 'benchmark=env.benchmark),', 'patience=patience)', 'for', '_', 'in', 'range(FLAGS.nproc)]', 'for', 'worke... | 135,753 |
Eric3911/OpenAGI | topology.py | ProcessTopology.get_axis_names | get_axis_names | Return a list of the axis names in the ordering of the topology. | [
"Return",
"a",
"list",
"of",
"the",
"axis",
"names",
"in",
"the",
"ordering",
"of",
"the",
"topology."
] | def get_axis_names(self):
return self.axes | ['def', 'get_axis_names(self):', 'return', 'self.axes'] | 252,183 |
tobegit3hub/deep_image_model | k8s_tensorflow.py | ParamServerClusterSpecString | ParamServerClusterSpecString | Generates parameter server spec. | [
"Generates",
"parameter",
"server",
"spec."
] | def ParamServerClusterSpecString(num_workers, num_param_servers, port):
return ClusterSpecString(num_workers, num_param_servers, port) | ['def', 'ParamServerClusterSpecString(num_workers,', 'num_param_servers,', 'port):', 'return', 'ClusterSpecString(num_workers,', 'num_param_servers,', 'port)'] | 183,503 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | visitor.py | MethodContent.acceptForEach | acceptForEach | Accept and process a 'for each' style statement. | [
"Accept",
"and",
"process",
"a",
"'for",
"each'",
"style",
"statement."
] | def acceptForEach(self, node, memo):
forEach = self.factory.statement('for', fs=FS.lsrc, parent=self)
identExpr = forEach.expr.right = self.factory.expr(fs=FS.l + ' in ' + FS.r)
identExpr.walk(node.firstChildOfType(tokens.IDENT), memo)
inExpr = identExpr.right = self.factory.expr()
inExpr.walk(node.... | ['def', 'acceptForEach(self,', 'node,', 'memo):', 'forEach', '=', "self.factory.statement('for',", 'fs=FS.lsrc,', 'parent=self)', 'identExpr', '=', 'forEach.expr.right', '=', 'self.factory.expr(fs=FS.l', '+', "'", 'in', "'", '+', 'FS.r)', 'identExpr.walk(node.firstChildOfType(tokens.IDENT),', 'memo)', 'inExpr', '=', 'i... | 11,207 |
NovemberChopin/RL_Tutorial | TD3.py | PolicyNetwork.evaluate | evaluate | generate action with state for calculating gradients; eval_noise_scale: as the trick of target policy smoothing, for generating noisy actions. | [
"generate",
"action",
"with",
"state",
"for",
"calculating",
"gradients;",
"eval_noise_scale:",
"as",
"the",
"trick",
"of",
"target",
"policy",
"smoothing,",
"for",
"generating",
"noisy",
"actions."
] | def evaluate(self, state, eval_noise_scale):
state = state.astype(np.float32)
action = self.forward(state)
action = self.action_range * action
normal = Normal(0, 1)
noise = normal.sample(action.shape) * eval_noise_scale
eval_noise_clip = 2 * eval_noise_scale
noise = tf.clip_by_value(noise, -... | ['def', 'evaluate(self,', 'state,', 'eval_noise_scale):', 'state', '=', 'state.astype(np.float32)', 'action', '=', 'self.forward(state)', 'action', '=', 'self.action_range', '*', 'action', 'normal', '=', 'Normal(0,', '1)', 'noise', '=', 'normal.sample(action.shape)', '*', 'eval_noise_scale', 'eval_noise_clip', '=', '2'... | 324,818 |
pytorch/rl | generic.py | DistributedDataCollector.update_policy_weights_ | update_policy_weights_ | Updates the weights of the worker nodes. | [
"Updates",
"the",
"weights",
"of",
"the",
"worker",
"nodes."
] | def update_policy_weights_(self, worker_rank=None) -> None:
if worker_rank is not None and worker_rank < 1:
raise RuntimeError('worker_rank must be greater than 1')
workers = range(self.num_workers) if worker_rank is None else [worker_rank - 1]
for i in workers:
rank = i + 1
if self.... | ['def', 'update_policy_weights_(self,', 'worker_rank=None)', '->', 'None:', 'if', 'worker_rank', 'is', 'not', 'None', 'and', 'worker_rank', '<', '1:', 'raise', "RuntimeError('worker_rank", 'must', 'be', 'greater', 'than', "1')", 'workers', '=', 'range(self.num_workers)', 'if', 'worker_rank', 'is', 'None', 'else', '[wor... | 858,606 |
chanyn/Reasoning-RCNN | bbox_head.py | BBoxHead.refine_bboxes | refine_bboxes | Refine bboxes during training. | [
"Refine",
"bboxes",
"during",
"training."
] | def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
img_ids = rois[:, 0].long().unique(sorted=True)
assert img_ids.numel() == len(img_metas)
bboxes_list = []
for i in range(len(img_metas)):
inds = torch.nonzero(rois[:, 0] == i).squeeze()
num_rois = inds.numel()
... | ['def', 'refine_bboxes(self,', 'rois,', 'labels,', 'bbox_preds,', 'pos_is_gts,', 'img_metas):', 'img_ids', '=', 'rois[:,', '0].long().unique(sorted=True)', 'assert', 'img_ids.numel()', '==', 'len(img_metas)', 'bboxes_list', '=', '[]', 'for', 'i', 'in', 'range(len(img_metas)):', 'inds', '=', 'torch.nonzero(rois[:,', '0]... | 831,982 |
rudranil723/mini-main | test_json2csv_corpus.py | TestJSON2CSV.test_file_is_wrong | test_file_is_wrong | Sanity check that file comparison is not giving false positives. | [
"Sanity",
"check",
"that",
"file",
"comparison",
"is",
"not",
"giving",
"false",
"positives."
] | def test_file_is_wrong(self):
ref_fn = os.path.join(self.subdir, 'tweets.20150430-223406.retweet.csv.ref')
with TemporaryDirectory() as tempdir:
outfn = os.path.join(tempdir, 'tweets.20150430-223406.text.csv')
json2csv(self.infile, outfn, ['text'], gzip_compress=False)
self.assertFalse(a... | ['def', 'test_file_is_wrong(self):', 'ref_fn', '=', 'os.path.join(self.subdir,', "'tweets.20150430-223406.retweet.csv.ref')", 'with', 'TemporaryDirectory()', 'as', 'tempdir:', 'outfn', '=', 'os.path.join(tempdir,', "'tweets.20150430-223406.text.csv')", 'json2csv(self.infile,', 'outfn,', "['text'],", 'gzip_compress=Fals... | 321,848 |
famura/SimuRLacra | parameter_exploration_sampler.py | ParameterSamplingResult.mean_returns | mean_returns | Get all parameter sample means return as a N-dim vector, where N is the number of samples. | [
"Get",
"all",
"parameter",
"sample",
"means",
"return",
"as",
"a",
"N-dim",
"vector,",
"where",
"N",
"is",
"the",
"number",
"of",
"samples."
] | def mean_returns(self) -> np.ndarray:
return np.array([s.mean_undiscounted_return for s in self._samples]) | ['def', 'mean_returns(self)', '->', 'np.ndarray:', 'return', 'np.array([s.mean_undiscounted_return', 'for', 's', 'in', 'self._samples])'] | 883,908 |
adamshamsudeen/vision.ai | datastructures.py | MultiDict.values | values | Returns an iterator of the first value on every key's value list. | [
"Returns",
"an",
"iterator",
"of",
"the",
"first",
"value",
"on",
"every",
"key's",
"value",
"list."
] | def values(self):
for values in itervalues(dict, self):
yield values[0] | ['def', 'values(self):', 'for', 'values', 'in', 'itervalues(dict,', 'self):', 'yield', 'values[0]'] | 944,369 |
matsu0228/nlp-jp | posix.py | PosixEventLoop.remove_reader | remove_reader | Remove read file descriptor from the event loop. | [
"Remove",
"read",
"file",
"descriptor",
"from",
"the",
"event",
"loop."
] | def remove_reader(self, fd):
fd = fd_to_int(fd)
if fd in self._read_fds:
del self._read_fds[fd]
self.selector.unregister(fd) | ['def', 'remove_reader(self,', 'fd):', 'fd', '=', 'fd_to_int(fd)', 'if', 'fd', 'in', 'self._read_fds:', 'del', 'self._read_fds[fd]', 'self.selector.unregister(fd)'] | 804,411 |
billstark/receipt-scanner | data_utils.py | FeatureIO.int64_feature | int64_feature | Wrapper for inserting int64 features into Example proto. | [
"Wrapper",
"for",
"inserting",
"int64",
"features",
"into",
"Example",
"proto."
] | def int64_feature(value):
if not isinstance(value, list):
value = [value]
value_tmp = []
is_int = True
for val in value:
if not isinstance(val, int):
is_int = False
value_tmp.append(int(float(val)))
if is_int is False:
value = value_tmp
return tf.t... | ['def', 'int64_feature(value):', 'if', 'not', 'isinstance(value,', 'list):', 'value', '=', '[value]', 'value_tmp', '=', '[]', 'is_int', '=', 'True', 'for', 'val', 'in', 'value:', 'if', 'not', 'isinstance(val,', 'int):', 'is_int', '=', 'False', 'value_tmp.append(int(float(val)))', 'if', 'is_int', 'is', 'False:', 'value'... | 832,077 |
CYBERDEVILZ/artificial- | __init__.py | FCompiler.get_flags_f90 | get_flags_f90 | List of Fortran 90 specific flags. | [
"List",
"of",
"Fortran",
"90",
"specific",
"flags."
] | def get_flags_f90(self):
return self._get_command_flags('compiler_f90') | ['def', 'get_flags_f90(self):', 'return', "self._get_command_flags('compiler_f90')"] | 168,669 |
enuguru/artificial_intelligence_and_machine_ | migrate_repository.py | move_file | move_file | Moves a file and prints a message. | [
"Moves",
"a",
"file",
"and",
"prints",
"a",
"message."
] | def move_file(src, tgt):
log.info('Moving file %s to %s' % (src, tgt))
if os.path.exists(tgt):
raise Exception('Cannot move file %s because target %s already exists' % (src, tgt))
os.rename(src, tgt) | ['def', 'move_file(src,', 'tgt):', "log.info('Moving", 'file', '%s', 'to', "%s'", '%', '(src,', 'tgt))', 'if', 'os.path.exists(tgt):', 'raise', "Exception('Cannot", 'move', 'file', '%s', 'because', 'target', '%s', 'already', "exists'", '%', '(src,', 'tgt))', 'os.rename(src,', 'tgt)'] | 129,861 |
JonasLandman/QCNN | __init__.py | LockBase.is_locked | is_locked | Tell whether or not the file is locked. | [
"Tell",
"whether",
"or",
"not",
"the",
"file",
"is",
"locked."
] | def is_locked(self):
raise NotImplemented('implement in subclass') | ['def', 'is_locked(self):', 'raise', "NotImplemented('implement", 'in', "subclass')"] | 303,340 |
opendilab/DI-star | actions.py | Arguments.types | types | Create an Arguments of the possible Types. | [
"Create",
"an",
"Arguments",
"of",
"the",
"possible",
"Types."
] | def types(cls, **kwargs):
named = {name: factory(Arguments._fields.index(name), name) for (name, factory) in six.iteritems(kwargs)}
return cls(**named) | ['def', 'types(cls,', '**kwargs):', 'named', '=', '{name:', 'factory(Arguments._fields.index(name),', 'name)', 'for', '(name,', 'factory)', 'in', 'six.iteritems(kwargs)}', 'return', 'cls(**named)'] | 184,672 |
shery322/Lunar-Lander-ANN | mixer_test.py | SoundTypeTest.test_sound | test_sound | Ensure Sound() creation with a filename works. | [
"Ensure",
"Sound()",
"creation",
"with",
"a",
"filename",
"works."
] | def test_sound(self):
filename = example_path(os.path.join('data', 'house_lo.wav'))
sound1 = mixer.Sound(filename)
sound2 = mixer.Sound(file=filename)
self.assertIsInstance(sound1, mixer.Sound)
self.assertIsInstance(sound2, mixer.Sound) | ['def', 'test_sound(self):', 'filename', '=', "example_path(os.path.join('data',", "'house_lo.wav'))", 'sound1', '=', 'mixer.Sound(filename)', 'sound2', '=', 'mixer.Sound(file=filename)', 'self.assertIsInstance(sound1,', 'mixer.Sound)', 'self.assertIsInstance(sound2,', 'mixer.Sound)'] | 619,091 |
zackmcnulty/CSE_446-Machine_Learning | pyparsing.py | ParseExpression.leaveWhitespace | leaveWhitespace | Extends ``leaveWhitespace`` defined in base class, and also invokes ``leaveWhitespace`` on all contained expressions. | [
"Extends",
"``leaveWhitespace``",
"defined",
"in",
"base",
"class,",
"and",
"also",
"invokes",
"``leaveWhitespace``",
"on",
"all",
"contained",
"expressions."
] | def leaveWhitespace(self):
self.skipWhitespace = False
self.exprs = [e.copy() for e in self.exprs]
for e in self.exprs:
e.leaveWhitespace()
return self | ['def', 'leaveWhitespace(self):', 'self.skipWhitespace', '=', 'False', 'self.exprs', '=', '[e.copy()', 'for', 'e', 'in', 'self.exprs]', 'for', 'e', 'in', 'self.exprs:', 'e.leaveWhitespace()', 'return', 'self'] | 196,543 |
scotthuang1989/object_detection_with_tensorflow | vgslspecs.py | VGSLSpecs.Build | Build | Builds a network with input prev_layer from a VGSLSpecs description. | [
"Builds",
"a",
"network",
"with",
"input",
"prev_layer",
"from",
"a",
"VGSLSpecs",
"description."
] | def Build(self, prev_layer, model_str):
self.model_str = model_str
(final_layer, _) = self.BuildFromString(prev_layer, 0)
return final_layer | ['def', 'Build(self,', 'prev_layer,', 'model_str):', 'self.model_str', '=', 'model_str', '(final_layer,', '_)', '=', 'self.BuildFromString(prev_layer,', '0)', 'return', 'final_layer'] | 739,728 |
matsu0228/nlp-jp | __init__.py | Grouper.get_siblings | get_siblings | Returns all of the items joined with *a*, including itself. | [
"Returns",
"all",
"of",
"the",
"items",
"joined",
"with",
"*a*,",
"including",
"itself."
] | def get_siblings(self, a):
self.clean()
siblings = self._mapping.get(ref(a), [ref(a)])
return [x() for x in siblings] | ['def', 'get_siblings(self,', 'a):', 'self.clean()', 'siblings', '=', 'self._mapping.get(ref(a),', '[ref(a)])', 'return', '[x()', 'for', 'x', 'in', 'siblings]'] | 789,767 |
TheCurryMan/MedicAI | runtime.py | unicode_join | unicode_join | Simple args to unicode conversion and concatenation. | [
"Simple",
"args",
"to",
"unicode",
"conversion",
"and",
"concatenation."
] | def unicode_join(seq):
return concat(imap(text_type, seq)) | ['def', 'unicode_join(seq):', 'return', 'concat(imap(text_type,', 'seq))'] | 648,469 |
ludwig-ai/ludwig | llm.py | LLM.save | save | Saves the model to the given path. | [
"Saves",
"the",
"model",
"to",
"the",
"given",
"path."
] | def save(self, save_path):
if self.config_obj.trainer.type != 'none':
weights_save_path = os.path.join(save_path, MODEL_WEIGHTS_FILE_NAME)
self.model.save_pretrained(weights_save_path)
else:
logger.info('Skipped saving LLM without weight adjustments.') | ['def', 'save(self,', 'save_path):', 'if', 'self.config_obj.trainer.type', '!=', "'none':", 'weights_save_path', '=', 'os.path.join(save_path,', 'MODEL_WEIGHTS_FILE_NAME)', 'self.model.save_pretrained(weights_save_path)', 'else:', "logger.info('Skipped", 'saving', 'LLM', 'without', 'weight', "adjustments.')"] | 616,879 |
muhanzhang/D-VAE | test_blocksparse.py | BlockSparse_Gemv_and_Outer.test_sparseblockgemvF | test_sparseblockgemvF | Test the fortan order for W (which can happen in the grad for some graphs). | [
"Test",
"the",
"fortan",
"order",
"for",
"W",
"(which",
"can",
"happen",
"in",
"the",
"grad",
"for",
"some",
"graphs)."
] | def test_sparseblockgemvF(self):
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
o = self.gemv_op(b.take(oIdx, axis=0), tensor.DimShuffle((False, False, False, False), (0, 1, 3, 2))(tensor.as_tensor_variable(W)), h, iIdx, oIdx)
... | ['def', 'test_sparseblockgemvF(self):', 'b', '=', 'tensor.fmatrix()', 'W', '=', 'tensor.ftensor4()', 'h', '=', 'tensor.ftensor3()', 'iIdx', '=', 'tensor.imatrix()', 'oIdx', '=', 'tensor.imatrix()', 'o', '=', 'self.gemv_op(b.take(oIdx,', 'axis=0),', 'tensor.DimShuffle((False,', 'False,', 'False,', 'False),', '(0,', '1,'... | 525,725 |
RasaHQ/rasa | sklearn_intent_classifier.py | SklearnIntentClassifier.get_default_config | get_default_config | The component's default config (see parent class for full docstring). | [
"The",
"component's",
"default",
"config",
"(see",
"parent",
"class",
"for",
"full",
"docstring)."
] | def get_default_config() -> Dict[Text, Any]:
return {'C': [1, 2, 5, 10, 20, 100], 'gamma': [0.1], 'kernels': ['linear'], 'max_cross_validation_folds': 5, 'scoring_function': 'f1_weighted', 'num_threads': 1} | ['def', 'get_default_config()', '->', 'Dict[Text,', 'Any]:', 'return', "{'C':", '[1,', '2,', '5,', '10,', '20,', '100],', "'gamma':", '[0.1],', "'kernels':", "['linear'],", "'max_cross_validation_folds':", '5,', "'scoring_function':", "'f1_weighted',", "'num_threads':", '1}'] | 837,174 |
dornik/reagent | environment.py | expert | expert | Get the expert action in the current state. | [
"Get",
"the",
"expert",
"action",
"in",
"the",
"current",
"state."
] | def expert(pose_source, targets, mode='steady'):
delta_t = targets[:, :3, 3] - pose_source[:, :3, 3]
delta_R = targets[:, :3, :3] @ pose_source[:, :3, :3].transpose(2, 1)
delta_r = tra.matrix_to_euler_angles(delta_R, 'XYZ')
def _get_axis_action(axis_delta, mode='steady'):
lower_idx = (torch.buc... | ['def', 'expert(pose_source,', 'targets,', "mode='steady'):", 'delta_t', '=', 'targets[:,', ':3,', '3]', '-', 'pose_source[:,', ':3,', '3]', 'delta_R', '=', 'targets[:,', ':3,', ':3]', '@', 'pose_source[:,', ':3,', ':3].transpose(2,', '1)', 'delta_r', '=', 'tra.matrix_to_euler_angles(delta_R,', "'XYZ')", 'def', '_get_a... | 849,264 |
Eric3911/OpenAGI | conv_asr.py | ConvASREncoder.input_types | input_types | Returns definitions of module input ports. | [
"Returns",
"definitions",
"of",
"module",
"input",
"ports."
] | def input_types(self):
return OrderedDict({'audio_signal': NeuralType(('B', 'D', 'T'), SpectrogramType()), 'length': NeuralType(tuple('B'), LengthsType())}) | ['def', 'input_types(self):', 'return', "OrderedDict({'audio_signal':", "NeuralType(('B',", "'D',", "'T'),", 'SpectrogramType()),', "'length':", "NeuralType(tuple('B'),", 'LengthsType())})'] | 272,566 |
tobegit3hub/deep_image_model | alexnet_benchmark.py | time_tensorflow_run | time_tensorflow_run | Run the computation to obtain the target tensor and print timing stats. | [
"Run",
"the",
"computation",
"to",
"obtain",
"the",
"target",
"tensor",
"and",
"print",
"timing",
"stats."
] | def time_tensorflow_run(session, target, info_string):
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target)
duration = time.time() - start_time
if ... | ['def', 'time_tensorflow_run(session,', 'target,', 'info_string):', 'num_steps_burn_in', '=', '10', 'total_duration', '=', '0.0', 'total_duration_squared', '=', '0.0', 'for', 'i', 'in', 'xrange(FLAGS.num_batches', '+', 'num_steps_burn_in):', 'start_time', '=', 'time.time()', '_', '=', 'session.run(target)', 'duration',... | 182,227 |
jankrepl/mildlyoverfitted | src.py | compute_loss | compute_loss | Computer average loss over a dataset. | [
"Computer",
"average",
"loss",
"over",
"a",
"dataset."
] | def compute_loss(cal, net, dataloader):
net.eval()
all_losses = []
for (X_batch, y_batch) in dataloader:
(probs, _, _) = net(X_batch)
all_losses.append(cal(probs, y_batch).item())
return np.mean(all_losses) | ['def', 'compute_loss(cal,', 'net,', 'dataloader):', 'net.eval()', 'all_losses', '=', '[]', 'for', '(X_batch,', 'y_batch)', 'in', 'dataloader:', '(probs,', '_,', '_)', '=', 'net(X_batch)', 'all_losses.append(cal(probs,', 'y_batch).item())', 'return', 'np.mean(all_losses)'] | 670,408 |
rudranil723/mini-main | __init__.py | intercept_channel | intercept_channel | Intercepts a channel through a set of interceptors. | [
"Intercepts",
"a",
"channel",
"through",
"a",
"set",
"of",
"interceptors."
] | def intercept_channel(channel, *interceptors):
from grpc import _interceptor
return _interceptor.intercept_channel(channel, *interceptors) | ['def', 'intercept_channel(channel,', '*interceptors):', 'from', 'grpc', 'import', '_interceptor', 'return', '_interceptor.intercept_channel(channel,', '*interceptors)'] | 318,549 |
StephenLouis/Reinforcement_Learning | Breakout_DQN_class.py | get_copy_var_ops | get_copy_var_ops | ÃÂÂê²Âë¤Ã¸ìÂÂÓ ë©Âì¸ë¤Ã¸ìÂÂÓ Weightê°Âì 복ì¬. | [
"ÃÂÂê²Âë¤Ã¸ìÂÂÓÂÂ",
"ë©Âì¸ë¤Ã¸ìÂÂÓÂÂ",
"Weightê°ÂìÂÂ",
"ë³µì¬."
] | def get_copy_var_ops(*, dest_scope_name='target', src_scope_name='main'):
op_holder = []
src_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=src_scope_name)
dest_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=dest_scope_name)
for (src_var, dest_var) in zip(src_vars, de... | ['def', 'get_copy_var_ops(*,', "dest_scope_name='target',", "src_scope_name='main'):", 'op_holder', '=', '[]', 'src_vars', '=', 'tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,', 'scope=src_scope_name)', 'dest_vars', '=', 'tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,', 'scope=dest_scope_name)', 'for', '(src_... | 345,411 |
sek788432/Waymo-2D-Object-Detection | model_lib_tf1_test.py | ModelLibTest.test_model_fn_in_train_mode_freeze_all_included_variables | test_model_fn_in_train_mode_freeze_all_included_variables | Tests model_fn TRAIN mode with all included variables frozen. | [
"Tests",
"model_fn",
"TRAIN",
"mode",
"with",
"all",
"included",
"variables",
"frozen."
] | def test_model_fn_in_train_mode_freeze_all_included_variables(self):
configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
train_config = configs['train_config']
train_config.update_trainable_variables.append('FeatureExtractor')
train_config.freeze_variables.append('.*')
with self.assertRaisesRegexp... | ['def', 'test_model_fn_in_train_mode_freeze_all_included_variables(self):', 'configs', '=', '_get_configs_for_model(MODEL_NAME_FOR_TEST)', 'train_config', '=', "configs['train_config']", "train_config.update_trainable_variables.append('FeatureExtractor')", "train_config.freeze_variables.append('.*')", 'with', 'self.ass... | 974,605 |
darrellsilver/norc | log.py | AbstractLog.format | format | The format of all log messages. | [
"The",
"format",
"of",
"all",
"log",
"messages."
] | def format(msg, prefix):
return '[%s] %s: %s\n' % (timestamp(), prefix, msg) | ['def', 'format(msg,', 'prefix):', 'return', "'[%s]", '%s:', "%s\\n'", '%', '(timestamp(),', 'prefix,', 'msg)'] | 249,493 |
keya-desai/Natural-Language-Processing | Sentence.py | Sentence.getCorrectSentence | getCorrectSentence | Returns a list of strings with the sentence containing all corrections. | [
"Returns",
"a",
"list",
"of",
"strings",
"with",
"the",
"sentence",
"containing",
"all",
"corrections."
] | def getCorrectSentence(self):
correctSentence = []
for datum in self.data:
correctSentence.append(datum.word)
return correctSentence | ['def', 'getCorrectSentence(self):', 'correctSentence', '=', '[]', 'for', 'datum', 'in', 'self.data:', 'correctSentence.append(datum.word)', 'return', 'correctSentence'] | 683,506 |
xmax1/dvae | dataloader.py | DataLoader.load_data | load_data | Load the data used for training/validation/testing. | [
"Load",
"the",
"data",
"used",
"for",
"training/validation/testing."
] | def load_data(self, **kwargs):
raise NotImplementedError | ['def', 'load_data(self,', '**kwargs):', 'raise', 'NotImplementedError'] | 554,985 |
triaquae/triaquae | models.py | update_last_login | update_last_login | A signal receiver which updates the last_login date for the user logging in. | [
"A",
"signal",
"receiver",
"which",
"updates",
"the",
"last_login",
"date",
"for",
"the",
"user",
"logging",
"in."
] | def update_last_login(sender, user, **kwargs):
user.last_login = timezone.now()
user.save(update_fields=['last_login']) | ['def', 'update_last_login(sender,', 'user,', '**kwargs):', 'user.last_login', '=', 'timezone.now()', "user.save(update_fields=['last_login'])"] | 357,082 |
opendilab/DI-star | renderer_human.py | RendererHuman.select_idle_worker | select_idle_worker | Select an idle worker. | [
"Select",
"an",
"idle",
"worker."
] | def select_idle_worker(self, ctrl, shift):
action = sc_pb.Action()
mod = sc_ui.ActionSelectIdleWorker
if ctrl:
select_worker = mod.AddAll if shift else mod.All
else:
select_worker = mod.Add if shift else mod.Set
action.action_ui.select_idle_worker.type = select_worker
return acti... | ['def', 'select_idle_worker(self,', 'ctrl,', 'shift):', 'action', '=', 'sc_pb.Action()', 'mod', '=', 'sc_ui.ActionSelectIdleWorker', 'if', 'ctrl:', 'select_worker', '=', 'mod.AddAll', 'if', 'shift', 'else', 'mod.All', 'else:', 'select_worker', '=', 'mod.Add', 'if', 'shift', 'else', 'mod.Set', 'action.action_ui.select_i... | 184,779 |
DeepLearnXMU/ABDNMT-RNMT | options.py | parse_static_args | parse_static_args | Parse the args a first time. | [
"Parse",
"the",
"args",
"a",
"first",
"time."
] | def parse_static_args(parser, input_args=None):
no_default_parser = get_no_default_parser(parser)
(args, unknown_args) = no_default_parser.parse_known_args(input_args)
defaults = get_defaults(parser)
return (args, defaults, unknown_args) | ['def', 'parse_static_args(parser,', 'input_args=None):', 'no_default_parser', '=', 'get_no_default_parser(parser)', '(args,', 'unknown_args)', '=', 'no_default_parser.parse_known_args(input_args)', 'defaults', '=', 'get_defaults(parser)', 'return', '(args,', 'defaults,', 'unknown_args)'] | 6,350 |
NifTK/NiftyNet | versioneer_version.py | get_config | get_config | Create, populate and return the VersioneerConfig() object. | [
"Create,",
"populate",
"and",
"return",
"the",
"VersioneerConfig()",
"object."
] | def get_config():
cfg = VersioneerConfig()
cfg.VCS = 'git'
cfg.style = 'pep440'
cfg.tag_prefix = 'v'
cfg.parentdir_prefix = 'None'
cfg.versionfile_source = 'niftynet/utilities/versioneer_version.py'
cfg.verbose = False
return cfg | ['def', 'get_config():', 'cfg', '=', 'VersioneerConfig()', 'cfg.VCS', '=', "'git'", 'cfg.style', '=', "'pep440'", 'cfg.tag_prefix', '=', "'v'", 'cfg.parentdir_prefix', '=', "'None'", 'cfg.versionfile_source', '=', "'niftynet/utilities/versioneer_version.py'", 'cfg.verbose', '=', 'False', 'return', 'cfg'] | 294,336 |
arshpreetsingh/quantopian-machinelearning | html.py | escape_html | escape_html | Escape &, <, > as well as single and double quotes for HTML. | [
"Escape",
"&,",
"<,",
">",
"as",
"well",
"as",
"single",
"and",
"double",
"quotes",
"for",
"HTML."
] | def escape_html(text, table=_escape_html_table):
return text.translate(table) | ['def', 'escape_html(text,', 'table=_escape_html_table):', 'return', 'text.translate(table)'] | 892,648 |
zcablii/LSKNet | sam_reppoints_head.py | SAMRepPointsHead.loss | loss | Loss function of SAM RepPoints head. | [
"Loss",
"function",
"of",
"SAM",
"RepPoints",
"head."
] | def loss(self, cls_scores, pts_preds_init, pts_preds_refine, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.prior_generator.num_levels
label_channels = self.cls_out_channels if self.use_sigmoid_cls el... | ['def', 'loss(self,', 'cls_scores,', 'pts_preds_init,', 'pts_preds_refine,', 'gt_bboxes,', 'gt_labels,', 'img_metas,', 'gt_bboxes_ignore=None):', 'featmap_sizes', '=', '[featmap.size()[-2:]', 'for', 'featmap', 'in', 'cls_scores]', 'assert', 'len(featmap_sizes)', '==', 'self.prior_generator.num_levels', 'label_channels'... | 616,176 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | app.py | vi_recording_macro | vi_recording_macro | When recording a Vi macro. | [
"When",
"recording",
"a",
"Vi",
"macro."
] | def vi_recording_macro() -> bool:
app = get_app()
if app.editing_mode != EditingMode.VI:
return False
return app.vi_state.recording_register is not None | ['def', 'vi_recording_macro()', '->', 'bool:', 'app', '=', 'get_app()', 'if', 'app.editing_mode', '!=', 'EditingMode.VI:', 'return', 'False', 'return', 'app.vi_state.recording_register', 'is', 'not', 'None'] | 435,141 |
caiiiac/Machine-Learning-with-Python | generate_ufuncs.py | npy_cdouble_from_double_complex | npy_cdouble_from_double_complex | Cast a cython double complex to a numpy cdouble. | [
"Cast",
"a",
"cython",
"double",
"complex",
"to",
"a",
"numpy",
"cdouble."
] | def npy_cdouble_from_double_complex(var):
res = '_complexstuff.npy_cdouble_from_double_complex({})'.format(var)
return res | ['def', 'npy_cdouble_from_double_complex(var):', 'res', '=', "'_complexstuff.npy_cdouble_from_double_complex({})'.format(var)", 'return', 'res'] | 719,993 |
westerberg-science/openscope-glo-stim | sweepstim.py | Stimulus.update | update | Updates the stimulus based on the current frame. | [
"Updates",
"the",
"stimulus",
"based",
"on",
"the",
"current",
"frame."
] | def update(self, frame):
self.current_frame = frame
try:
sweep_number = self.frame_list[frame]
except IndexError:
return
if sweep_number == self._current_sweep:
pass
elif sweep_number == -1:
return
else:
for (k, v) in zip(self.dimnames, self.sweep_table[sw... | ['def', 'update(self,', 'frame):', 'self.current_frame', '=', 'frame', 'try:', 'sweep_number', '=', 'self.frame_list[frame]', 'except', 'IndexError:', 'return', 'if', 'sweep_number', '==', 'self._current_sweep:', 'pass', 'elif', 'sweep_number', '==', '-1:', 'return', 'else:', 'for', '(k,', 'v)', 'in', 'zip(self.dimname... | 757,661 |
rifqind/Agent-Programs-3KS1 | utils.py | Event.remove_handler | remove_handler | Remove a handler from this callback. | [
"Remove",
"a",
"handler",
"from",
"this",
"callback."
] | def remove_handler(self, handler):
if handler in self._handlers:
self._handlers.remove(handler) | ['def', 'remove_handler(self,', 'handler):', 'if', 'handler', 'in', 'self._handlers:', 'self._handlers.remove(handler)'] | 45,032 |
NLPCodebase/PTSGM | seq2seq_model_causaltimebank.py | Seq2SeqModel.predict_sep | predict_sep | Performs predictions on a list of text. | [
"Performs",
"predictions",
"on",
"a",
"list",
"of",
"text."
] | def predict_sep(self, to_predict, decoder_input_token_id):
self._move_model_to_device()
all_outputs = []
for batch in [to_predict[i:i + self.args.eval_batch_size] for i in range(0, len(to_predict), self.args.eval_batch_size)]:
if self.args.model_type == 'marian':
input_ids = self.encoder... | ['def', 'predict_sep(self,', 'to_predict,', 'decoder_input_token_id):', 'self._move_model_to_device()', 'all_outputs', '=', '[]', 'for', 'batch', 'in', '[to_predict[i:i', '+', 'self.args.eval_batch_size]', 'for', 'i', 'in', 'range(0,', 'len(to_predict),', 'self.args.eval_batch_size)]:', 'if', 'self.args.model_type', '=... | 818,540 |
bislara/Object-detection-GUI | autoaugment_utils.py | equalize | equalize | Implements Equalize function from PIL using TF ops. | [
"Implements",
"Equalize",
"function",
"from",
"PIL",
"using",
"TF",
"ops."
] | def equalize(image):
def scale_channel(im, c):
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [-1])
step = (tf.reduce_sum(nonzero_h... | ['def', 'equalize(image):', 'def', 'scale_channel(im,', 'c):', 'im', '=', 'tf.cast(im[:,', ':,', 'c],', 'tf.int32)', 'histo', '=', 'tf.histogram_fixed_width(im,', '[0,', '255],', 'nbins=256)', 'nonzero', '=', 'tf.where(tf.not_equal(histo,', '0))', 'nonzero_histo', '=', 'tf.reshape(tf.gather(histo,', 'nonzero),', '[-1])... | 726,730 |
sarnsdev/social-alignment-data-mining | nn.py | NeuralNetwork.is_initialized | is_initialized | Check if the neural network was setup already. | [
"Check",
"if",
"the",
"neural",
"network",
"was",
"setup",
"already."
] | def is_initialized(self):
return self._backend is not None and self._backend.is_initialized | ['def', 'is_initialized(self):', 'return', 'self._backend', 'is', 'not', 'None', 'and', 'self._backend.is_initialized'] | 392,437 |
bachiraoun/fullrmc | Constraint.py | Constraint.get_constraint_value | get_constraint_value | Method must be overloaded in children classes. | [
"Method",
"must",
"be",
"overloaded",
"in",
"children",
"classes."
] | def get_constraint_value(self):
raise Exception(LOGGER.impl("%s '%s' method must be overloaded" % (self.__class__.__name__, inspect.stack()[0][3]))) | ['def', 'get_constraint_value(self):', 'raise', 'Exception(LOGGER.impl("%s', "'%s'", 'method', 'must', 'be', 'overloaded"', '%', '(self.__class__.__name__,', 'inspect.stack()[0][3])))'] | 213,785 |
matsu0228/nlp-jp | connection.py | MWSConnection.list_inbound_shipments | list_inbound_shipments | Returns a list of inbound shipments based on criteria that you specify. | [
"Returns",
"a",
"list",
"of",
"inbound",
"shipments",
"based",
"on",
"criteria",
"that",
"you",
"specify."
] | def list_inbound_shipments(self, request, response, **kw):
return self._post_request(request, kw, response) | ['def', 'list_inbound_shipments(self,', 'request,', 'response,', '**kw):', 'return', 'self._post_request(request,', 'kw,', 'response)'] | 784,948 |
flairNLP/flair | base.py | BasePlugin.mark_func_as_hook | mark_func_as_hook | Mark method as a hook triggered by the `Pluggable`. | [
"Mark",
"method",
"as",
"a",
"hook",
"triggered",
"by",
"the",
"`Pluggable`."
] | def mark_func_as_hook(cls, func: Callable, *events: EventIdenifier) -> Callable:
if len(events) == 0:
events = (func.__name__,)
func._plugin_hook_events = events
return func | ['def', 'mark_func_as_hook(cls,', 'func:', 'Callable,', '*events:', 'EventIdenifier)', '->', 'Callable:', 'if', 'len(events)', '==', '0:', 'events', '=', '(func.__name__,)', 'func._plugin_hook_events', '=', 'events', 'return', 'func'] | 584,853 |
alugupta/ares | detector.py | CustomDetector.predict | predict | Predict function used to predict bboxes on input images. | [
"Predict",
"function",
"used",
"to",
"predict",
"bboxes",
"on",
"input",
"images."
] | def predict(self, batch_data):
batch_data = self.data_preprocessor(batch_data)
return self.detector(batch_data) | ['def', 'predict(self,', 'batch_data):', 'batch_data', '=', 'self.data_preprocessor(batch_data)', 'return', 'self.detector(batch_data)'] | 402,120 |
cpnota/autonomous-learning-library | approximation.py | Approximation.no_grad | no_grad | Run a forward pass of the model in no_grad mode. | [
"Run",
"a",
"forward",
"pass",
"of",
"the",
"model",
"in",
"no_grad",
"mode."
] | def no_grad(self, *inputs):
with torch.no_grad():
return self.model(*inputs) | ['def', 'no_grad(self,', '*inputs):', 'with', 'torch.no_grad():', 'return', 'self.model(*inputs)'] | 93,548 |
huawei-noah/xingtian | tensorflow_fn.py | softmax | softmax | Apply a softmax function. | [
"Apply",
"a",
"softmax",
"function."
] | def softmax(input, dim=None):
return tf.nn.softmax(input, dim) | ['def', 'softmax(input,', 'dim=None):', 'return', 'tf.nn.softmax(input,', 'dim)'] | 962,831 |
AEProgrammer/object_detection | record_demo.py | convert_from_cls_format | convert_from_cls_format | Convert from the class boxes/segms/keyps format generated by the testing code. | [
"Convert",
"from",
"the",
"class",
"boxes/segms/keyps",
"format",
"generated",
"by",
"the",
"testing",
"code."
] | def convert_from_cls_format(cls_boxes, cls_segms, cls_keyps):
box_list = [b for b in cls_boxes if len(b) > 0]
if len(box_list) > 0:
boxes = np.concatenate(box_list)
else:
boxes = None
if cls_segms is not None:
segms = [s for slist in cls_segms for s in slist]
else:
se... | ['def', 'convert_from_cls_format(cls_boxes,', 'cls_segms,', 'cls_keyps):', 'box_list', '=', '[b', 'for', 'b', 'in', 'cls_boxes', 'if', 'len(b)', '>', '0]', 'if', 'len(box_list)', '>', '0:', 'boxes', '=', 'np.concatenate(box_list)', 'else:', 'boxes', '=', 'None', 'if', 'cls_segms', 'is', 'not', 'None:', 'segms', '=', '[... | 773,776 |
akandykeller/NeuralWaveMachines | game_dynamics.py | ZeroSumGame.generate_trajectories | generate_trajectories | Generates trajectories of the system in phase space. | [
"Generates",
"trajectories",
"of",
"the",
"system",
"in",
"phase",
"space."
] | def generate_trajectories(self, x0: jnp.ndarray, t0: utils.FloatArray, t_eval: jnp.ndarray) -> jnp.ndarray:
if self.method == 'scipy':
x0_shape = x0.shape
def fun(_, y):
y = y.reshape(x0_shape)
y_next = np.apply_along_axis(self.dynamics, -1, y)
return y_next.resh... | ['def', 'generate_trajectories(self,', 'x0:', 'jnp.ndarray,', 't0:', 'utils.FloatArray,', 't_eval:', 'jnp.ndarray)', '->', 'jnp.ndarray:', 'if', 'self.method', '==', "'scipy':", 'x0_shape', '=', 'x0.shape', 'def', 'fun(_,', 'y):', 'y', '=', 'y.reshape(x0_shape)', 'y_next', '=', 'np.apply_along_axis(self.dynamics,', '-1... | 293,597 |
palmettos/neat-autoencoders | test_config.py | test_nonexistent_config | test_nonexistent_config | Check that attempting to open a non-existent config file raises an Exception with appropriate message. | [
"Check",
"that",
"attempting",
"to",
"open",
"a",
"non-existent",
"config",
"file",
"raises",
"an",
"Exception",
"with",
"appropriate",
"message."
] | def test_nonexistent_config():
passed = False
try:
c = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, 'wubba-lubba-dub-dub')
except Exception as e:
passed = 'No such config file' in str(e)
assert passed | ['def', 'test_nonexistent_config():', 'passed', '=', 'False', 'try:', 'c', '=', 'neat.Config(neat.DefaultGenome,', 'neat.DefaultReproduction,', 'neat.DefaultSpeciesSet,', 'neat.DefaultStagnation,', "'wubba-lubba-dub-dub')", 'except', 'Exception', 'as', 'e:', 'passed', '=', "'No", 'such', 'config', "file'", 'in', 'str(e... | 735,223 |
MycroftAI/mycroft-core | skill_tester.py | temporary_handler | temporary_handler | Context manager to replace the default logger with a temporary logger. | [
"Context",
"manager",
"to",
"replace",
"the",
"default",
"logger",
"with",
"a",
"temporary",
"logger."
] | def temporary_handler(log, handler):
old_handler = log.handler
log.handler = handler
yield
log.handler = old_handler | ['def', 'temporary_handler(log,', 'handler):', 'old_handler', '=', 'log.handler', 'log.handler', '=', 'handler', 'yield', 'log.handler', '=', 'old_handler'] | 290,788 |
Farama-Foundation/Gymnasium | frame_stack.py | FrameStack.reset | reset | Reset the environment with kwargs. | [
"Reset",
"the",
"environment",
"with",
"kwargs."
] | def reset(self, **kwargs):
(obs, info) = self.env.reset(**kwargs)
[self.frames.append(obs) for _ in range(self.num_stack)]
return (self.observation(None), info) | ['def', 'reset(self,', '**kwargs):', '(obs,', 'info)', '=', 'self.env.reset(**kwargs)', '[self.frames.append(obs)', 'for', '_', 'in', 'range(self.num_stack)]', 'return', '(self.observation(None),', 'info)'] | 573,377 |
JinliangLu96/CL_UNMT | evaluator.py | eval_moses_bleu | eval_moses_bleu | Given a file of hypothesis and reference files, evaluate the BLEU score using Moses scripts. | [
"Given",
"a",
"file",
"of",
"hypothesis",
"and",
"reference",
"files,",
"evaluate",
"the",
"BLEU",
"score",
"using",
"Moses",
"scripts."
] | def eval_moses_bleu(ref, hyp):
assert os.path.isfile(hyp)
assert os.path.isfile(BLEU_SCRIPT_PATH)
command = BLEU_SCRIPT_PATH + ' %s < %s'
p = subprocess.Popen(command % (ref, hyp), stdout=subprocess.PIPE, shell=True)
result = p.communicate()[0].decode('utf-8')
if result.startswith('BLEU'):
... | ['def', 'eval_moses_bleu(ref,', 'hyp):', 'assert', 'os.path.isfile(hyp)', 'assert', 'os.path.isfile(BLEU_SCRIPT_PATH)', 'command', '=', 'BLEU_SCRIPT_PATH', '+', "'", '%s', '<', "%s'", 'p', '=', 'subprocess.Popen(command', '%', '(ref,', 'hyp),', 'stdout=subprocess.PIPE,', 'shell=True)', 'result', '=', "p.communicate()[0... | 123,268 |
EvanWY/CARLASemSeg | tcp.py | TCPClient.disconnect | disconnect | Disconnect any active connection. | [
"Disconnect",
"any",
"active",
"connection."
] | def disconnect(self):
if self._socket is not None:
logging.debug('%sdisconnecting', self._logprefix)
self._socket.close()
self._socket = None | ['def', 'disconnect(self):', 'if', 'self._socket', 'is', 'not', 'None:', "logging.debug('%sdisconnecting',", 'self._logprefix)', 'self._socket.close()', 'self._socket', '=', 'None'] | 456,029 |
clips/pattern | __init__.py | Application.elapsed | elapsed | Yields the elapsed time since the start of the request. | [
"Yields",
"the",
"elapsed",
"time",
"since",
"the",
"start",
"of",
"the",
"request."
] | def elapsed(self):
return time.time() - cp.request.time | ['def', 'elapsed(self):', 'return', 'time.time()', '-', 'cp.request.time'] | 764,717 |
ahthie7u/cockpit | _utils_deepobs.py | _DeepOBSRunner.training | training | Training loop for this runner. | [
"Training",
"loop",
"for",
"this",
"runner."
] | def training(self, tproblem, hyperparams, num_epochs, print_train_iter, train_log_interval, tb_log, tb_log_dir, **training_params):
opt = self._optimizer_class(tproblem.net.parameters(), **hyperparams)
lr_sched = training_params['lr_schedule'](num_epochs)
scheduler = LambdaLR(opt, lr_lambda=lr_sched)
lo... | ['def', 'training(self,', 'tproblem,', 'hyperparams,', 'num_epochs,', 'print_train_iter,', 'train_log_interval,', 'tb_log,', 'tb_log_dir,', '**training_params):', 'opt', '=', 'self._optimizer_class(tproblem.net.parameters(),', '**hyperparams)', 'lr_sched', '=', "training_params['lr_schedule'](num_epochs)", 'scheduler',... | 492,740 |
ADLab3Ds/TiG-BEV | nuscenes_mono_dataset.py | nusc_box_to_cam_box3d | nusc_box_to_cam_box3d | Convert boxes from :obj:`NuScenesBox` to :obj:`CameraInstance3DBoxes`. | [
"Convert",
"boxes",
"from",
":obj:`NuScenesBox`",
"to",
":obj:`CameraInstance3DBoxes`."
] | def nusc_box_to_cam_box3d(boxes):
locs = torch.Tensor([b.center for b in boxes]).view(-1, 3)
dims = torch.Tensor([b.wlh for b in boxes]).view(-1, 3)
rots = torch.Tensor([b.orientation.yaw_pitch_roll[0] for b in boxes]).view(-1, 1)
velocity = torch.Tensor([b.velocity[:2] for b in boxes]).view(-1, 2)
... | ['def', 'nusc_box_to_cam_box3d(boxes):', 'locs', '=', 'torch.Tensor([b.center', 'for', 'b', 'in', 'boxes]).view(-1,', '3)', 'dims', '=', 'torch.Tensor([b.wlh', 'for', 'b', 'in', 'boxes]).view(-1,', '3)', 'rots', '=', 'torch.Tensor([b.orientation.yaw_pitch_roll[0]', 'for', 'b', 'in', 'boxes]).view(-1,', '1)', 'velocity'... | 916,935 |
NoGameNoLife00/mybolg | tbtools.py | Frame.get_annotated_lines | get_annotated_lines | Helper function that returns lines with extra information. | [
"Helper",
"function",
"that",
"returns",
"lines",
"with",
"extra",
"information."
] | def get_annotated_lines(self):
lines = [Line(idx + 1, x) for (idx, x) in enumerate(self.sourcelines)]
if hasattr(self.code, 'co_firstlineno'):
lineno = self.code.co_firstlineno - 1
while lineno > 0:
if _funcdef_re.match(lines[lineno].code):
break
lineno -=... | ['def', 'get_annotated_lines(self):', 'lines', '=', '[Line(idx', '+', '1,', 'x)', 'for', '(idx,', 'x)', 'in', 'enumerate(self.sourcelines)]', 'if', 'hasattr(self.code,', "'co_firstlineno'):", 'lineno', '=', 'self.code.co_firstlineno', '-', '1', 'while', 'lineno', '>', '0:', 'if', '_funcdef_re.match(lines[lineno].code):... | 290,096 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | data.py | plot_images | plot_images | Images should be a (N_images x pixels) matrix. | [
"Images",
"should",
"be",
"a",
"(N_images",
"x",
"pixels)",
"matrix."
] | def plot_images(images, ax, ims_per_row=5, padding=5, digit_dimensions=(28, 28), cmap=matplotlib.cm.binary, vmin=None, vmax=None):
N_images = images.shape[0]
N_rows = (N_images - 1) // ims_per_row + 1
pad_value = np.min(images.ravel())
concat_images = np.full(((digit_dimensions[0] + padding) * N_rows + ... | ['def', 'plot_images(images,', 'ax,', 'ims_per_row=5,', 'padding=5,', 'digit_dimensions=(28,', '28),', 'cmap=matplotlib.cm.binary,', 'vmin=None,', 'vmax=None):', 'N_images', '=', 'images.shape[0]', 'N_rows', '=', '(N_images', '-', '1)', '//', 'ims_per_row', '+', '1', 'pad_value', '=', 'np.min(images.ravel())', 'concat_... | 12,204 |
ivanmontero/autobot | tokenization_utils_base.py | PreTrainedTokenizerBase.batch_decode | batch_decode | Convert a list of lists of token ids into a list of strings by calling decode. | [
"Convert",
"a",
"list",
"of",
"lists",
"of",
"token",
"ids",
"into",
"a",
"list",
"of",
"strings",
"by",
"calling",
"decode."
] | def batch_decode(self, sequences: List[List[int]], skip_special_tokens: bool=False, clean_up_tokenization_spaces: bool=True) -> List[str]:
return [self.decode(seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces) for seq in sequences] | ['def', 'batch_decode(self,', 'sequences:', 'List[List[int]],', 'skip_special_tokens:', 'bool=False,', 'clean_up_tokenization_spaces:', 'bool=True)', '->', 'List[str]:', 'return', '[self.decode(seq,', 'skip_special_tokens=skip_special_tokens,', 'clean_up_tokenization_spaces=clean_up_tokenization_spaces)', 'for', 'seq',... | 418,414 |
MattZhao/cs188-projects | search_hyperparams.py | search_hyperparams | search_hyperparams | Question 8: Evaluate various setups of hyperparameter and find the best one. | [
"Question",
"8:",
"Evaluate",
"various",
"setups",
"of",
"hyperparameter",
"and",
"find",
"the",
"best",
"one."
] | def search_hyperparams(train_data, train_labels, val_data, val_labels, learning_rates, momentums, batch_sizes, iterations, model_class, init_param_values=None, use_bn=False):
hyperparams = [learning_rates, momentums, batch_sizes]
for hyperparam in hyperparams:
if len(hyperparam) != len(hyperparams[0]):
... | ['def', 'search_hyperparams(train_data,', 'train_labels,', 'val_data,', 'val_labels,', 'learning_rates,', 'momentums,', 'batch_sizes,', 'iterations,', 'model_class,', 'init_param_values=None,', 'use_bn=False):', 'hyperparams', '=', '[learning_rates,', 'momentums,', 'batch_sizes]', 'for', 'hyperparam', 'in', 'hyperparam... | 226,119 |
tusen-ai/SST | kitti2d_dataset.py | Kitti2DDataset.reformat_bbox | reformat_bbox | Reformat bounding boxes to KITTI 2D styles. | [
"Reformat",
"bounding",
"boxes",
"to",
"KITTI",
"2D",
"styles."
] | def reformat_bbox(self, outputs, out=None):
from mmdet3d.core.bbox.transforms import bbox2result_kitti2d
sample_idx = [info['image']['image_idx'] for info in self.data_infos]
result_files = bbox2result_kitti2d(outputs, self.CLASSES, sample_idx, out)
return result_files | ['def', 'reformat_bbox(self,', 'outputs,', 'out=None):', 'from', 'mmdet3d.core.bbox.transforms', 'import', 'bbox2result_kitti2d', 'sample_idx', '=', "[info['image']['image_idx']", 'for', 'info', 'in', 'self.data_infos]', 'result_files', '=', 'bbox2result_kitti2d(outputs,', 'self.CLASSES,', 'sample_idx,', 'out)', 'retur... | 872,369 |
intel/neural-compressor | patterns.py | Pattern.get_masks_local | get_masks_local | Obtain layers' local masks. | [
"Obtain",
"layers'",
"local",
"masks."
] | def get_masks_local(self, scores, target_sparsity_ratio, pre_masks, max_sparsity_ratio_per_layer):
masks = {}
if isinstance(self, PatternNxM) and (not isinstance(self.block_size, dict)):
self.block_size = self.get_block_size_dict(pre_masks)
for key in scores.keys():
score = {key: scores[key]... | ['def', 'get_masks_local(self,', 'scores,', 'target_sparsity_ratio,', 'pre_masks,', 'max_sparsity_ratio_per_layer):', 'masks', '=', '{}', 'if', 'isinstance(self,', 'PatternNxM)', 'and', '(not', 'isinstance(self.block_size,', 'dict)):', 'self.block_size', '=', 'self.get_block_size_dict(pre_masks)', 'for', 'key', 'in', '... | 738,663 |
loicmarie/hands-detection | data_utils.py | basic_tokenizer | basic_tokenizer | Very basic tokenizer: split the sentence into a list of tokens. | [
"Very",
"basic",
"tokenizer:",
"split",
"the",
"sentence",
"into",
"a",
"list",
"of",
"tokens."
] | def basic_tokenizer(sentence):
words = []
for space_separated_fragment in sentence.strip().split():
words.extend(_WORD_SPLIT.split(space_separated_fragment))
return [w for w in words if w] | ['def', 'basic_tokenizer(sentence):', 'words', '=', '[]', 'for', 'space_separated_fragment', 'in', 'sentence.strip().split():', 'words.extend(_WORD_SPLIT.split(space_separated_fragment))', 'return', '[w', 'for', 'w', 'in', 'words', 'if', 'w]'] | 575,600 |
devashish-patel/webcam-motion-detector | script.py | ScriptMagics.killbgscripts | killbgscripts | Kill all BG processes started by %%script and its family. | [
"Kill",
"all",
"BG",
"processes",
"started",
"by",
"%%script",
"and",
"its",
"family."
] | def killbgscripts(self, _nouse_=''):
self.kill_bg_processes()
print('All background processes were killed.') | ['def', 'killbgscripts(self,', "_nouse_=''):", 'self.kill_bg_processes()', "print('All", 'background', 'processes', 'were', "killed.')"] | 978,930 |
google-research/batch-ppo | configs.py | hopper | hopper | Configuration for MuJoCo's hopper task. | [
"Configuration",
"for",
"MuJoCo's",
"hopper",
"task."
] | def hopper():
locals().update(default())
env = 'Hopper-v2'
max_length = 1000
steps = 10000000.0
update_every = 60
return locals() | ['def', 'hopper():', 'locals().update(default())', 'env', '=', "'Hopper-v2'", 'max_length', '=', '1000', 'steps', '=', '10000000.0', 'update_every', '=', '60', 'return', 'locals()'] | 95,015 |
43Carrig/recurrent_neural_networks_practice | well_known_types.py | Duration.FromTimedelta | FromTimedelta | Converts timedelta to Duration. | [
"Converts",
"timedelta",
"to",
"Duration."
] | def FromTimedelta(self, td):
self._NormalizeDuration(td.seconds + td.days * _SECONDS_PER_DAY, td.microseconds * _NANOS_PER_MICROSECOND) | ['def', 'FromTimedelta(self,', 'td):', 'self._NormalizeDuration(td.seconds', '+', 'td.days', '*', '_SECONDS_PER_DAY,', 'td.microseconds', '*', '_NANOS_PER_MICROSECOND)'] | 310,020 |
43Carrig/recurrent_neural_networks_practice | callbacks.py | TensorBoard.on_epoch_begin | on_epoch_begin | Add histogram op to Model test_function callbacks, reset batch count. | [
"Add",
"histogram",
"op",
"to",
"Model",
"test_function",
"callbacks,",
"reset",
"batch",
"count."
] | def on_epoch_begin(self, epoch, logs=None):
if self.histogram_freq and epoch % self.histogram_freq == 0:
self._epoch = epoch
self._current_val_batch = 0
if self.merged not in self.model.test_function.fetches:
self.model.test_function.fetches.append(self.merged)
self.m... | ['def', 'on_epoch_begin(self,', 'epoch,', 'logs=None):', 'if', 'self.histogram_freq', 'and', 'epoch', '%', 'self.histogram_freq', '==', '0:', 'self._epoch', '=', 'epoch', 'self._current_val_batch', '=', '0', 'if', 'self.merged', 'not', 'in', 'self.model.test_function.fetches:', 'self.model.test_function.fetches.append(... | 336,815 |
HuiGuanLab/HiCo | meters.py | TrainMeter.iter_toc | iter_toc | Stop to record time. | [
"Stop",
"to",
"record",
"time."
] | def iter_toc(self):
self.iter_timer.pause() | ['def', 'iter_toc(self):', 'self.iter_timer.pause()'] | 206,248 |
myothida/Supervised-Machine-Learning | format.py | DataFrameRenderer.to_csv | to_csv | Render dataframe as comma-separated file. | [
"Render",
"dataframe",
"as",
"comma-separated",
"file."
] | def to_csv(self, path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None=None, encoding: str | None=None, sep: str=',', columns: Sequence[Hashable] | None=None, index_label: IndexLabel | None=None, mode: str='w', compression: CompressionOptions='infer', quoting: int | None=None, quotechar: str='"', lineter... | ['def', 'to_csv(self,', 'path_or_buf:', 'FilePath', '|', 'WriteBuffer[bytes]', '|', 'WriteBuffer[str]', '|', 'None=None,', 'encoding:', 'str', '|', 'None=None,', 'sep:', "str=',',", 'columns:', 'Sequence[Hashable]', '|', 'None=None,', 'index_label:', 'IndexLabel', '|', 'None=None,', 'mode:', "str='w',", 'compression:',... | 443,382 |
lord-alfred/dnlp | preprocess.py | replace_urls | replace_urls | Replace all URLs in ``text`` str with ``replace_with`` str. | [
"Replace",
"all",
"URLs",
"in",
"``text``",
"str",
"with",
"``replace_with``",
"str."
] | def replace_urls(text: str, replace_with: str='*URL*') -> str:
return URL_REGEX.sub(replace_with, SHORT_URL_REGEX.sub(replace_with, text)) | ['def', 'replace_urls(text:', 'str,', 'replace_with:', "str='*URL*')", '->', 'str:', 'return', 'URL_REGEX.sub(replace_with,', 'SHORT_URL_REGEX.sub(replace_with,', 'text))'] | 522,469 |
rlgraph/rlgraph | test_ppo_agent_short_task_learning.py | TestPPOShortTaskLearning.test_ppo_on_lunar_lander | test_ppo_on_lunar_lander | Creates a PPO Agent and runs it via a Runner on the Pendulum env. | [
"Creates",
"a",
"PPO",
"Agent",
"and",
"runs",
"it",
"via",
"a",
"Runner",
"on",
"the",
"Pendulum",
"env."
] | def test_ppo_on_lunar_lander(self):
env = OpenAIGymEnv('LunarLander-v2')
agent = PPOAgent.from_spec(config_from_path('configs/ppo_agent_for_pendulum.json'), state_space=env.state_space, action_space=env.action_space)
worker = SingleThreadedWorker(env_spec=lambda : env, agent=agent, worker_executes_preproces... | ['def', 'test_ppo_on_lunar_lander(self):', 'env', '=', "OpenAIGymEnv('LunarLander-v2')", 'agent', '=', "PPOAgent.from_spec(config_from_path('configs/ppo_agent_for_pendulum.json'),", 'state_space=env.state_space,', 'action_space=env.action_space)', 'worker', '=', 'SingleThreadedWorker(env_spec=lambda', ':', 'env,', 'age... | 862,713 |
chinmayjog13/Computer-Vision | visualization_utils.py | draw_bounding_boxes_on_image_tensors | draw_bounding_boxes_on_image_tensors | Draws bounding boxes, masks, and keypoints on batch of image tensors. | [
"Draws",
"bounding",
"boxes,",
"masks,",
"and",
"keypoints",
"on",
"batch",
"of",
"image",
"tensors."
] | def draw_bounding_boxes_on_image_tensors(images, boxes, classes, scores, category_index, original_image_spatial_shape=None, true_image_shape=None, instance_masks=None, keypoints=None, track_ids=None, max_boxes_to_draw=20, min_score_thresh=0.2, use_normalized_coordinates=True):
if images.shape[3] > 3:
images... | ['def', 'draw_bounding_boxes_on_image_tensors(images,', 'boxes,', 'classes,', 'scores,', 'category_index,', 'original_image_spatial_shape=None,', 'true_image_shape=None,', 'instance_masks=None,', 'keypoints=None,', 'track_ids=None,', 'max_boxes_to_draw=20,', 'min_score_thresh=0.2,', 'use_normalized_coordinates=True):',... | 459,008 |
calico/basenji | layers.py | positional_features_central_mask | positional_features_central_mask | Positional features using a central mask (allow only central features). | [
"Positional",
"features",
"using",
"a",
"central",
"mask",
"(allow",
"only",
"central",
"features)."
] | def positional_features_central_mask(positions: tf.Tensor, feature_size: int, seq_length: int):
pow_rate = np.exp(np.log(seq_length + 1) / feature_size).astype('float32')
center_widths = tf.pow(pow_rate, tf.range(1, feature_size + 1, dtype=tf.float32))
center_widths = center_widths - 1
center_widths = _... | ['def', 'positional_features_central_mask(positions:', 'tf.Tensor,', 'feature_size:', 'int,', 'seq_length:', 'int):', 'pow_rate', '=', 'np.exp(np.log(seq_length', '+', '1)', '/', "feature_size).astype('float32')", 'center_widths', '=', 'tf.pow(pow_rate,', 'tf.range(1,', 'feature_size', '+', '1,', 'dtype=tf.float32))', ... | 94,574 |
arshpreetsingh/quantopian-machinelearning | img.py | FontManager.get_char_size | get_char_size | Get the character size. | [
"Get",
"the",
"character",
"size."
] | def get_char_size(self):
return self.fonts['NORMAL'].getsize('M') | ['def', 'get_char_size(self):', 'return', "self.fonts['NORMAL'].getsize('M')"] | 892,652 |
mfbx9da4/neuron-astrocyte-networks | temp_node1.py | ProtoNode.activate | activate | This function applies the activation function to the value of the node. | [
"This",
"function",
"applies",
"the",
"activation",
"function",
"to",
"the",
"value",
"of",
"the",
"node."
] | def activate(self):
return self._activate(self._value) | ['def', 'activate(self):', 'return', 'self._activate(self._value)'] | 722,834 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | pygments.py | PygmentsLexer.lex_document | lex_document | Create a lexer function that takes a line number and returns the list of (style_str, text) tuples as the Pygments lexer returns for that line. | [
"Create",
"a",
"lexer",
"function",
"that",
"takes",
"a",
"line",
"number",
"and",
"returns",
"the",
"list",
"of",
"(style_str,",
"text)",
"tuples",
"as",
"the",
"Pygments",
"lexer",
"returns",
"for",
"that",
"line."
] | def lex_document(self, document: Document) -> Callable[[int], StyleAndTextTuples]:
LineGenerator = Generator[Tuple[int, StyleAndTextTuples], None, None]
cache: Dict[int, StyleAndTextTuples] = {}
line_generators: Dict[LineGenerator, int] = {}
def get_syntax_sync() -> SyntaxSync:
if self.sync_fro... | ['def', 'lex_document(self,', 'document:', 'Document)', '->', 'Callable[[int],', 'StyleAndTextTuples]:', 'LineGenerator', '=', 'Generator[Tuple[int,', 'StyleAndTextTuples],', 'None,', 'None]', 'cache:', 'Dict[int,', 'StyleAndTextTuples]', '=', '{}', 'line_generators:', 'Dict[LineGenerator,', 'int]', '=', '{}', 'def', '... | 435,402 |
jelgun/Artificial-Intelligence | utils.py | matrix_multiplication | matrix_multiplication | Return a matrix as a matrix-multiplication of x and arbitrary number of matrices *y. | [
"Return",
"a",
"matrix",
"as",
"a",
"matrix-multiplication",
"of",
"x",
"and",
"arbitrary",
"number",
"of",
"matrices",
"*y."
] | def matrix_multiplication(x, *y):
result = x
for _y in y:
result = np.matmul(result, _y)
return result | ['def', 'matrix_multiplication(x,', '*y):', 'result', '=', 'x', 'for', '_y', 'in', 'y:', 'result', '=', 'np.matmul(result,', '_y)', 'return', 'result'] | 121,809 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | options.py | OptionParser.print_help | print_help | Prints all the command line options to stderr (or another file). | [
"Prints",
"all",
"the",
"command",
"line",
"options",
"to",
"stderr",
"(or",
"another",
"file)."
] | def print_help(self, file: Optional[TextIO]=None) -> None:
if file is None:
file = sys.stderr
print('Usage: %s [OPTIONS]' % sys.argv[0], file=file)
print('\nOptions:\n', file=file)
by_group = {}
for option in self._options.values():
by_group.setdefault(option.group_name, []).append(o... | ['def', 'print_help(self,', 'file:', 'Optional[TextIO]=None)', '->', 'None:', 'if', 'file', 'is', 'None:', 'file', '=', 'sys.stderr', "print('Usage:", '%s', "[OPTIONS]'", '%', 'sys.argv[0],', 'file=file)', "print('\\nOptions:\\n',", 'file=file)', 'by_group', '=', '{}', 'for', 'option', 'in', 'self._options.values():', ... | 437,623 |
sithu31296/self-supervised-learning | vicreg.py | off_diagonal | off_diagonal | Returns the off-diagonal elements of a square matrix. | [
"Returns",
"the",
"off-diagonal",
"elements",
"of",
"a",
"square",
"matrix."
] | def off_diagonal(tensor: torch.Tensor) -> torch.Tensor:
(n, m) = tensor.shape
assert n == m, 'Not a square tensor'
return tensor.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten() | ['def', 'off_diagonal(tensor:', 'torch.Tensor)', '->', 'torch.Tensor:', '(n,', 'm)', '=', 'tensor.shape', 'assert', 'n', '==', 'm,', "'Not", 'a', 'square', "tensor'", 'return', 'tensor.flatten()[:-1].view(n', '-', '1,', 'n', '+', '1)[:,', '1:].flatten()'] | 342,054 |
chen742/PiPa | cityscapes.py | CityscapesDataset.results2img | results2img | Write the segmentation results to images. | [
"Write",
"the",
"segmentation",
"results",
"to",
"images."
] | def results2img(self, results, imgfile_prefix, to_label_id):
mmcv.mkdir_or_exist(imgfile_prefix)
result_files = []
prog_bar = mmcv.ProgressBar(len(self))
for idx in range(len(self)):
result = results[idx]
if to_label_id:
result = self._convert_to_label_id(result)
file... | ['def', 'results2img(self,', 'results,', 'imgfile_prefix,', 'to_label_id):', 'mmcv.mkdir_or_exist(imgfile_prefix)', 'result_files', '=', '[]', 'prog_bar', '=', 'mmcv.ProgressBar(len(self))', 'for', 'idx', 'in', 'range(len(self)):', 'result', '=', 'results[idx]', 'if', 'to_label_id:', 'result', '=', 'self._convert_to_la... | 305,198 |
facebookresearch/ReAgent | oss_data_fetcher.py | misc_column_preprocessing | misc_column_preprocessing | Miscellaneous columns are step, time_diff, sequence_number, not_terminal. | [
"Miscellaneous",
"columns",
"are",
"step,",
"time_diff,",
"sequence_number,",
"not_terminal."
] | def misc_column_preprocessing(df, multi_steps: Optional[int]):
df = df.withColumn('step', make_get_step_udf(multi_steps)('next_state_features'))
next_long_udf = make_next_udf(multi_steps, LongType())
df = df.withColumn('time_diff', next_long_udf('time_diff'))
df = df.withColumn('sequence_number', col('s... | ['def', 'misc_column_preprocessing(df,', 'multi_steps:', 'Optional[int]):', 'df', '=', "df.withColumn('step',", "make_get_step_udf(multi_steps)('next_state_features'))", 'next_long_udf', '=', 'make_next_udf(multi_steps,', 'LongType())', 'df', '=', "df.withColumn('time_diff',", "next_long_udf('time_diff'))", 'df', '=', ... | 304,497 |
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