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 |
|---|---|---|---|---|---|---|---|---|
mo-cv/pycv | utils.py | createCurveFunc | createCurveFunc | Return a function derived from control points. | [
"Return",
"a",
"function",
"derived",
"from",
"control",
"points."
] | def createCurveFunc(points):
if points is None:
return None
numPoints = len(points)
if numPoints < 2:
return None
(xs, ys) = zip(*points)
if numPoints < 4:
kind = 'linear'
else:
kind = 'cubic'
return scipy.interpolate.interp1d(xs, ys, kind, bounds_error=False) | ['def', 'createCurveFunc(points):', 'if', 'points', 'is', 'None:', 'return', 'None', 'numPoints', '=', 'len(points)', 'if', 'numPoints', '<', '2:', 'return', 'None', '(xs,', 'ys)', '=', 'zip(*points)', 'if', 'numPoints', '<', '4:', 'kind', '=', "'linear'", 'else:', 'kind', '=', "'cubic'", 'return', 'scipy.interpolate.i... | 819,502 |
boat-group/fancy-nlp | ner_predictor.py | NERPredictor.pretty_tag_batch | pretty_tag_batch | Analyze the tagging results of given batch of text predicted by the ner model and return the results in pretty format with detailed information. | [
"Analyze",
"the",
"tagging",
"results",
"of",
"given",
"batch",
"of",
"text",
"predicted",
"by",
"the",
"ner",
"model",
"and",
"return",
"the",
"results",
"in",
"pretty",
"format",
"with",
"detailed",
"information."
] | def pretty_tag_batch(self, texts: Union[List[str], List[List[str]]]) -> List[Dict[str, Any]]:
pred_probs = self.predict_prob_batch(texts)
lengths = [min(len(text), pred_prob.shape[0]) for (text, pred_prob) in zip(texts, pred_probs)]
tags = self.preprocessor.label_decode(pred_probs, lengths)
pred_probs =... | ['def', 'pretty_tag_batch(self,', 'texts:', 'Union[List[str],', 'List[List[str]]])', '->', 'List[Dict[str,', 'Any]]:', 'pred_probs', '=', 'self.predict_prob_batch(texts)', 'lengths', '=', '[min(len(text),', 'pred_prob.shape[0])', 'for', '(text,', 'pred_prob)', 'in', 'zip(texts,', 'pred_probs)]', 'tags', '=', 'self.prep... | 559,222 |
openai/spinningup | serialization_utils.py | convert_json | convert_json | Convert obj to a version which can be serialized with JSON. | [
"Convert",
"obj",
"to",
"a",
"version",
"which",
"can",
"be",
"serialized",
"with",
"JSON."
] | def convert_json(obj):
if is_json_serializable(obj):
return obj
else:
if isinstance(obj, dict):
return {convert_json(k): convert_json(v) for (k, v) in obj.items()}
elif isinstance(obj, tuple):
return (convert_json(x) for x in obj)
elif isinstance(obj, list... | ['def', 'convert_json(obj):', 'if', 'is_json_serializable(obj):', 'return', 'obj', 'else:', 'if', 'isinstance(obj,', 'dict):', 'return', '{convert_json(k):', 'convert_json(v)', 'for', '(k,', 'v)', 'in', 'obj.items()}', 'elif', 'isinstance(obj,', 'tuple):', 'return', '(convert_json(x)', 'for', 'x', 'in', 'obj)', 'elif',... | 371,783 |
bhateharsh/computer_vision | head.py | KerasHead.call | call | The Keras model call will delegate to the `_predict` method. | [
"The",
"Keras",
"model",
"call",
"will",
"delegate",
"to",
"the",
"`_predict`",
"method."
] | def call(self, features):
return self._predict(features) | ['def', 'call(self,', 'features):', 'return', 'self._predict(features)'] | 512,014 |
explosion/spaCy | test_pipe_methods.py | test_disable_pipes_context_restore | test_disable_pipes_context_restore | Test that a disabled component stays disabled after running the context manager. | [
"Test",
"that",
"a",
"disabled",
"component",
"stays",
"disabled",
"after",
"running",
"the",
"context",
"manager."
] | def test_disable_pipes_context_restore(nlp, name):
nlp.add_pipe('new_pipe', name=name)
assert nlp.has_pipe(name)
nlp.disable_pipe(name)
assert not nlp.has_pipe(name)
with nlp.select_pipes(disable=name):
assert not nlp.has_pipe(name)
assert not nlp.has_pipe(name) | ['def', 'test_disable_pipes_context_restore(nlp,', 'name):', "nlp.add_pipe('new_pipe',", 'name=name)', 'assert', 'nlp.has_pipe(name)', 'nlp.disable_pipe(name)', 'assert', 'not', 'nlp.has_pipe(name)', 'with', 'nlp.select_pipes(disable=name):', 'assert', 'not', 'nlp.has_pipe(name)', 'assert', 'not', 'nlp.has_pipe(name)'] | 894,305 |
pipermerriam/flex | decorators.py | skip_if_empty | skip_if_empty | Decorator for validation functions which makes them pass if the value passed in is the EMPTY sentinal value. | [
"Decorator",
"for",
"validation",
"functions",
"which",
"makes",
"them",
"pass",
"if",
"the",
"value",
"passed",
"in",
"is",
"the",
"EMPTY",
"sentinal",
"value."
] | def skip_if_empty(func):
@partial_safe_wraps(func)
def inner(value, *args, **kwargs):
if value is EMPTY:
return
else:
return func(value, *args, **kwargs)
return inner | ['def', 'skip_if_empty(func):', '@partial_safe_wraps(func)', 'def', 'inner(value,', '*args,', '**kwargs):', 'if', 'value', 'is', 'EMPTY:', 'return', 'else:', 'return', 'func(value,', '*args,', '**kwargs)', 'return', 'inner'] | 211,270 |
arshpreetsingh/quantopian-machinelearning | msvc.py | RegistryInfo.vc_for_python | vc_for_python | Microsoft Visual C++ for Python registry key. | [
"Microsoft",
"Visual",
"C++",
"for",
"Python",
"registry",
"key."
] | def vc_for_python(self):
return 'DevDiv\\VCForPython' | ['def', 'vc_for_python(self):', 'return', "'DevDiv\\\\VCForPython'"] | 893,107 |
FreshAirTonight/af2complex | folding.py | compute_violation_metrics | compute_violation_metrics | Compute several metrics to assess the structural violations. | [
"Compute",
"several",
"metrics",
"to",
"assess",
"the",
"structural",
"violations."
] | def compute_violation_metrics(batch: Dict[str, jnp.ndarray], atom14_pred_positions: jnp.ndarray, violations: Dict[str, jnp.ndarray]) -> Dict[str, jnp.ndarray]:
ret = {}
extreme_ca_ca_violations = all_atom.extreme_ca_ca_distance_violations(pred_atom_positions=atom14_pred_positions, pred_atom_mask=batch['atom14_a... | ['def', 'compute_violation_metrics(batch:', 'Dict[str,', 'jnp.ndarray],', 'atom14_pred_positions:', 'jnp.ndarray,', 'violations:', 'Dict[str,', 'jnp.ndarray])', '->', 'Dict[str,', 'jnp.ndarray]:', 'ret', '=', '{}', 'extreme_ca_ca_violations', '=', 'all_atom.extreme_ca_ca_distance_violations(pred_atom_positions=atom14_p... | 400,644 |
omarmhaimdat/twitter_nlp_native_swift | compiler.py | CodeGenerator.pop_assign_tracking | pop_assign_tracking | Pops the topmost level for assignment tracking and updates the context variables if necessary. | [
"Pops",
"the",
"topmost",
"level",
"for",
"assignment",
"tracking",
"and",
"updates",
"the",
"context",
"variables",
"if",
"necessary."
] | def pop_assign_tracking(self, frame):
vars = self._assign_stack.pop()
if not frame.toplevel or not vars:
return
public_names = [x for x in vars if x[:1] != '_']
if len(vars) == 1:
name = next(iter(vars))
ref = frame.symbols.ref(name)
self.writeline('context.vars[%r] = %s'... | ['def', 'pop_assign_tracking(self,', 'frame):', 'vars', '=', 'self._assign_stack.pop()', 'if', 'not', 'frame.toplevel', 'or', 'not', 'vars:', 'return', 'public_names', '=', '[x', 'for', 'x', 'in', 'vars', 'if', 'x[:1]', '!=', "'_']", 'if', 'len(vars)', '==', '1:', 'name', '=', 'next(iter(vars))', 'ref', '=', 'frame.sym... | 953,842 |
joao-montanari/artificial_intelligence | __init__.py | packb | packb | Pack object `o` and return packed bytes See :class:`Packer` for options. | [
"Pack",
"object",
"`o`",
"and",
"return",
"packed",
"bytes",
"See",
":class:`Packer`",
"for",
"options."
] | def packb(o, **kwargs):
return Packer(**kwargs).pack(o) | ['def', 'packb(o,', '**kwargs):', 'return', 'Packer(**kwargs).pack(o)'] | 155,311 |
HCIILAB/DeRPN | cpp_lint.py | ProcessLine | ProcessLine | Processes a single line in the file. | [
"Processes",
"a",
"single",
"line",
"in",
"the",
"file."
] | def ProcessLine(filename, file_extension, clean_lines, line, include_state, function_state, nesting_state, error, extra_check_functions=[]):
raw_lines = clean_lines.raw_lines
ParseNolintSuppressions(filename, raw_lines[line], line, error)
nesting_state.Update(filename, clean_lines, line, error)
if nesti... | ['def', 'ProcessLine(filename,', 'file_extension,', 'clean_lines,', 'line,', 'include_state,', 'function_state,', 'nesting_state,', 'error,', 'extra_check_functions=[]):', 'raw_lines', '=', 'clean_lines.raw_lines', 'ParseNolintSuppressions(filename,', 'raw_lines[line],', 'line,', 'error)', 'nesting_state.Update(filenam... | 184,117 |
QData/deepWordBug | nodes.py | GenericNodeVisitor.default_visit | default_visit | Override for generic, uniform traversals. | [
"Override",
"for",
"generic,",
"uniform",
"traversals."
] | def default_visit(self, node):
raise NotImplementedError | ['def', 'default_visit(self,', 'node):', 'raise', 'NotImplementedError'] | 542,075 |
tencent-ailab/TriNet | iterators.py | CountingIterator.has_next | has_next | Whether the iterator has been exhausted. | [
"Whether",
"the",
"iterator",
"has",
"been",
"exhausted."
] | def has_next(self):
return self.n < self.total | ['def', 'has_next(self):', 'return', 'self.n', '<', 'self.total'] | 425,154 |
Ruturaj123/Flowchart-Detection | test_utils.py | test_parameter_recovery | test_parameter_recovery | Test that a generative model fits generated data. | [
"Test",
"that",
"a",
"generative",
"model",
"fits",
"generated",
"data."
] | def test_parameter_recovery(generate_fn, generative_model, train_iterations, test_case, seed, learning_rate=0.1, rtol=0.2, atol=0.1, train_loss_tolerance_coeff=0.99, ignore_params_fn=lambda _: (), derived_param_test_fn=lambda _: (), train_input_fn_type=input_pipeline.WholeDatasetInputFn, train_state_manager=state_manag... | ['def', 'test_parameter_recovery(generate_fn,', 'generative_model,', 'train_iterations,', 'test_case,', 'seed,', 'learning_rate=0.1,', 'rtol=0.2,', 'atol=0.1,', 'train_loss_tolerance_coeff=0.99,', 'ignore_params_fn=lambda', '_:', '(),', 'derived_param_test_fn=lambda', '_:', '(),', 'train_input_fn_type=input_pipeline.Wh... | 604,684 |
calico/basenji | sonnet_predict_bed.py | bigwig_open | bigwig_open | Open the bigwig file for writing and write the header. | [
"Open",
"the",
"bigwig",
"file",
"for",
"writing",
"and",
"write",
"the",
"header."
] | def bigwig_open(bw_file, genome_file):
bw_out = pyBigWig.open(bw_file, 'w')
chrom_sizes = []
for line in open(genome_file):
a = line.split()
chrom_sizes.append((a[0], int(a[1])))
bw_out.addHeader(chrom_sizes)
return bw_out | ['def', 'bigwig_open(bw_file,', 'genome_file):', 'bw_out', '=', 'pyBigWig.open(bw_file,', "'w')", 'chrom_sizes', '=', '[]', 'for', 'line', 'in', 'open(genome_file):', 'a', '=', 'line.split()', 'chrom_sizes.append((a[0],', 'int(a[1])))', 'bw_out.addHeader(chrom_sizes)', 'return', 'bw_out'] | 94,847 |
Xianpeng919/MonoCon | builder.py | build_positional_encoding | build_positional_encoding | Builder for Position Encoding. | [
"Builder",
"for",
"Position",
"Encoding."
] | def build_positional_encoding(cfg, default_args=None):
return build_from_cfg(cfg, POSITIONAL_ENCODING, default_args) | ['def', 'build_positional_encoding(cfg,', 'default_args=None):', 'return', 'build_from_cfg(cfg,', 'POSITIONAL_ENCODING,', 'default_args)'] | 654,126 |
sek788432/Waymo-2D-Object-Detection | train_utils.py | parse_configuration | parse_configuration | Parses ExperimentConfig from flags. | [
"Parses",
"ExperimentConfig",
"from",
"flags."
] | def parse_configuration(flags_obj, lock_return=True, print_return=True):
params = exp_factory.get_exp_config(flags_obj.experiment)
for config_file in flags_obj.config_file or []:
params = hyperparams.override_params_dict(params, config_file, is_strict=True)
params.override({'runtime': {'tpu': flags_... | ['def', 'parse_configuration(flags_obj,', 'lock_return=True,', 'print_return=True):', 'params', '=', 'exp_factory.get_exp_config(flags_obj.experiment)', 'for', 'config_file', 'in', 'flags_obj.config_file', 'or', '[]:', 'params', '=', 'hyperparams.override_params_dict(params,', 'config_file,', 'is_strict=True)', "params... | 972,330 |
arshpreetsingh/quantopian-machinelearning | testing.py | HTMLTreeBuilderSmokeTest.assertDoctypeHandled | assertDoctypeHandled | Assert that a given doctype string is handled correctly. | [
"Assert",
"that",
"a",
"given",
"doctype",
"string",
"is",
"handled",
"correctly."
] | def assertDoctypeHandled(self, doctype_fragment):
(doctype_str, soup) = self._document_with_doctype(doctype_fragment)
doctype = soup.contents[0]
self.assertEqual(doctype.__class__, Doctype)
self.assertEqual(doctype, doctype_fragment)
self.assertEqual(str(soup)[:len(doctype_str)], doctype_str)
se... | ['def', 'assertDoctypeHandled(self,', 'doctype_fragment):', '(doctype_str,', 'soup)', '=', 'self._document_with_doctype(doctype_fragment)', 'doctype', '=', 'soup.contents[0]', 'self.assertEqual(doctype.__class__,', 'Doctype)', 'self.assertEqual(doctype,', 'doctype_fragment)', 'self.assertEqual(str(soup)[:len(doctype_st... | 816,520 |
sunishsheth2009/ChatterBot | ma.py | masked_binary_operation.reduce | reduce | Reduce target along the given axis with this function. | [
"Reduce",
"target",
"along",
"the",
"given",
"axis",
"with",
"this",
"function."
] | def reduce(self, target, axis=0, dtype=None):
m = getmask(target)
t = filled(target, self.filly)
if t.shape == ():
t = t.reshape(1)
if m is not nomask:
m = make_mask(m, copy=1)
m.shape = (1,)
if m is nomask:
t = self.f.reduce(t, axis)
else:
t =... | ['def', 'reduce(self,', 'target,', 'axis=0,', 'dtype=None):', 'm', '=', 'getmask(target)', 't', '=', 'filled(target,', 'self.filly)', 'if', 't.shape', '==', '():', 't', '=', 't.reshape(1)', 'if', 'm', 'is', 'not', 'nomask:', 'm', '=', 'make_mask(m,', 'copy=1)', 'm.shape', '=', '(1,)', 'if', 'm', 'is', 'nomask:', 't', '... | 532,341 |
Eric3911/OpenAGI | data_utils.py | ais_cache_base | ais_cache_base | Return path to local cache for AIS. | [
"Return",
"path",
"to",
"local",
"cache",
"for",
"AIS."
] | def ais_cache_base() -> str:
override_dir = os.environ.get(constants.NEMO_ENV_DATA_STORE_CACHE_DIR, '')
if override_dir == '':
cache_dir = resolve_cache_dir().as_posix()
else:
cache_dir = pathlib.Path(override_dir).resolve().as_posix()
if cache_dir.endswith(NEMO_VERSION):
cache_d... | ['def', 'ais_cache_base()', '->', 'str:', 'override_dir', '=', 'os.environ.get(constants.NEMO_ENV_DATA_STORE_CACHE_DIR,', "'')", 'if', 'override_dir', '==', "'':", 'cache_dir', '=', 'resolve_cache_dir().as_posix()', 'else:', 'cache_dir', '=', 'pathlib.Path(override_dir).resolve().as_posix()', 'if', 'cache_dir.endswith(... | 274,167 |
ZhangAoCanada/RADDet | drawer.py | getEllipse | getEllipse | Draw 2D Gaussian Ellipse. | [
"Draw",
"2D",
"Gaussian",
"Ellipse."
] | def getEllipse(color, means, covariances, scale_factor=1):
sign = np.sign(means[0] / means[1])
(eigen, eigen_vec) = np.linalg.eig(covariances)
eigen_root_x = np.sqrt(eigen[0]) * scale_factor
eigen_root_y = np.sqrt(eigen[1]) * scale_factor
theta = np.degrees(np.arctan2(*eigen_vec[:, 0][::-1]))
el... | ['def', 'getEllipse(color,', 'means,', 'covariances,', 'scale_factor=1):', 'sign', '=', 'np.sign(means[0]', '/', 'means[1])', '(eigen,', 'eigen_vec)', '=', 'np.linalg.eig(covariances)', 'eigen_root_x', '=', 'np.sqrt(eigen[0])', '*', 'scale_factor', 'eigen_root_y', '=', 'np.sqrt(eigen[1])', '*', 'scale_factor', 'theta',... | 835,777 |
43Carrig/recurrent_neural_networks_practice | multi_worker_util.py | is_chief | is_chief | Returns whether the given task is chief in the cluster. | [
"Returns",
"whether",
"the",
"given",
"task",
"is",
"chief",
"in",
"the",
"cluster."
] | def is_chief(cluster_spec, task_type, task_id):
cluster_spec = normalize_cluster_spec(cluster_spec)
if task_type not in cluster_spec.jobs:
raise ValueError('The task_type "%s" is not in the `cluster_spec`.' % task_type)
if task_id >= cluster_spec.num_tasks(task_type):
raise ValueError('The `... | ['def', 'is_chief(cluster_spec,', 'task_type,', 'task_id):', 'cluster_spec', '=', 'normalize_cluster_spec(cluster_spec)', 'if', 'task_type', 'not', 'in', 'cluster_spec.jobs:', 'raise', "ValueError('The", 'task_type', '"%s"', 'is', 'not', 'in', 'the', "`cluster_spec`.'", '%', 'task_type)', 'if', 'task_id', '>=', 'cluste... | 336,069 |
sandialabs/bcnn | utils.py | round_down | round_down | Rounds num to next lowest multiple of factor. | [
"Rounds",
"num",
"to",
"next",
"lowest",
"multiple",
"of",
"factor."
] | def round_down(num, factor):
return num // factor * factor | ['def', 'round_down(num,', 'factor):', 'return', 'num', '//', 'factor', '*', 'factor'] | 105,976 |
rifqind/Agent-Programs-3KS1 | guisupport.py | get_app_qt4 | get_app_qt4 | Create a new qt4 app or return an existing one. | [
"Create",
"a",
"new",
"qt4",
"app",
"or",
"return",
"an",
"existing",
"one."
] | def get_app_qt4(*args, **kwargs):
from IPython.external.qt_for_kernel import QtGui
app = QtGui.QApplication.instance()
if app is None:
if not args:
args = ([''],)
app = QtGui.QApplication(*args, **kwargs)
return app | ['def', 'get_app_qt4(*args,', '**kwargs):', 'from', 'IPython.external.qt_for_kernel', 'import', 'QtGui', 'app', '=', 'QtGui.QApplication.instance()', 'if', 'app', 'is', 'None:', 'if', 'not', 'args:', 'args', '=', "([''],)", 'app', '=', 'QtGui.QApplication(*args,', '**kwargs)', 'return', 'app'] | 41,612 |
devashish-patel/webcam-motion-detector | test_contents_api.py | APITest.delete_file | delete_file | Delete a file at the given path if it exists. | [
"Delete",
"a",
"file",
"at",
"the",
"given",
"path",
"if",
"it",
"exists."
] | def delete_file(self, api_path):
if self.isfile(api_path):
os.unlink(self.to_os_path(api_path)) | ['def', 'delete_file(self,', 'api_path):', 'if', 'self.isfile(api_path):', 'os.unlink(self.to_os_path(api_path))'] | 980,787 |
microsoft/InnerEye-DeepLearning | lightning_container.py | LightningContainer.get_callbacks | get_callbacks | Gets additional callbacks that the trainer should use when training this model. | [
"Gets",
"additional",
"callbacks",
"that",
"the",
"trainer",
"should",
"use",
"when",
"training",
"this",
"model."
] | def get_callbacks(self) -> List[Callback]:
return [] | ['def', 'get_callbacks(self)', '->', 'List[Callback]:', 'return', '[]'] | 612,901 |
sek788432/Waymo-2D-Object-Detection | assemblenet.py | flow_conv_stem | flow_conv_stem | Layers for an optical flow stem. | [
"Layers",
"for",
"an",
"optical",
"flow",
"stem."
] | def flow_conv_stem(inputs, filters, temporal_dilation, bn_decay: float=rf.BATCH_NORM_DECAY, bn_epsilon: float=rf.BATCH_NORM_EPSILON, use_sync_bn: bool=False):
if temporal_dilation < 1:
temporal_dilation = 1
inputs = conv2d_fixed_padding(inputs=inputs, filters=filters, kernel_size=7, strides=2)
input... | ['def', 'flow_conv_stem(inputs,', 'filters,', 'temporal_dilation,', 'bn_decay:', 'float=rf.BATCH_NORM_DECAY,', 'bn_epsilon:', 'float=rf.BATCH_NORM_EPSILON,', 'use_sync_bn:', 'bool=False):', 'if', 'temporal_dilation', '<', '1:', 'temporal_dilation', '=', '1', 'inputs', '=', 'conv2d_fixed_padding(inputs=inputs,', 'filter... | 973,304 |
MegEngine/Transfer-Learning-Library | mdd.py | GeneralModule.step | step | Gradually increase :math:`\lambda` in GRL layer. | [
"Gradually",
"increase",
":math:`\\lambda`",
"in",
"GRL",
"layer."
] | def step(self):
self.grl_layer.step() | ['def', 'step(self):', 'self.grl_layer.step()'] | 921,111 |
RunpeiDong/ACT | indoor3d_util.py | bbox_label_to_obj | bbox_label_to_obj | Visualization of bounding boxes. | [
"Visualization",
"of",
"bounding",
"boxes."
] | def bbox_label_to_obj(input_filename, out_filename_prefix, easy_view=False):
bbox_label = np.loadtxt(input_filename)
bbox = bbox_label[:, 0:6]
label = bbox_label[:, -1].astype(int)
v_cnt = 0
ins_cnt = 0
for i in range(bbox.shape[0]):
if easy_view and label[i] not in g_easy_view_labels:
... | ['def', 'bbox_label_to_obj(input_filename,', 'out_filename_prefix,', 'easy_view=False):', 'bbox_label', '=', 'np.loadtxt(input_filename)', 'bbox', '=', 'bbox_label[:,', '0:6]', 'label', '=', 'bbox_label[:,', '-1].astype(int)', 'v_cnt', '=', '0', 'ins_cnt', '=', '0', 'for', 'i', 'in', 'range(bbox.shape[0]):', 'if', 'eas... | 407,317 |
rifqind/Agent-Programs-3KS1 | agents.py | GraphicEnvironment.run | run | Run the Environment for given number of time steps, but update the GUI too. | [
"Run",
"the",
"Environment",
"for",
"given",
"number",
"of",
"time",
"steps,",
"but",
"update",
"the",
"GUI",
"too."
] | def run(self, steps=1000, delay=1):
for step in range(steps):
self.update(delay)
if self.is_done():
break
self.step()
self.update(delay) | ['def', 'run(self,', 'steps=1000,', 'delay=1):', 'for', 'step', 'in', 'range(steps):', 'self.update(delay)', 'if', 'self.is_done():', 'break', 'self.step()', 'self.update(delay)'] | 40,282 |
HealthML/ContIG | ukb_covariate_prediction.py | load_from_state_dict_img_only | load_from_state_dict_img_only | Loads the model weights from the state dictionary. | [
"Loads",
"the",
"model",
"weights",
"from",
"the",
"state",
"dictionary."
] | def load_from_state_dict_img_only(model, state_dict):
model_keys_prefixes = []
for (okey, oitem) in model.state_dict().items():
model_keys_prefixes.append(okey.split('.')[0])
new_state_dict = {}
index = 0
for (key, item) in state_dict.items():
if (key.startswith('resnet_simclr') or k... | ['def', 'load_from_state_dict_img_only(model,', 'state_dict):', 'model_keys_prefixes', '=', '[]', 'for', '(okey,', 'oitem)', 'in', 'model.state_dict().items():', "model_keys_prefixes.append(okey.split('.')[0])", 'new_state_dict', '=', '{}', 'index', '=', '0', 'for', '(key,', 'item)', 'in', 'state_dict.items():', 'if', ... | 136,479 |
lbkchen/deep-learning | tensor_forest.py | RandomForestGraphs.training_graph | training_graph | Constructs a TF graph for training a random forest. | [
"Constructs",
"a",
"TF",
"graph",
"for",
"training",
"a",
"random",
"forest."
] | def training_graph(self, input_data, input_labels, data_spec=None, epoch=None, **tree_kwargs):
data_spec = [constants.DATA_FLOAT] if data_spec is None else data_spec
tree_graphs = []
for i in range(self.params.num_trees):
with ops.device(self.device_assigner.get_device(i)):
seed = self.p... | ['def', 'training_graph(self,', 'input_data,', 'input_labels,', 'data_spec=None,', 'epoch=None,', '**tree_kwargs):', 'data_spec', '=', '[constants.DATA_FLOAT]', 'if', 'data_spec', 'is', 'None', 'else', 'data_spec', 'tree_graphs', '=', '[]', 'for', 'i', 'in', 'range(self.params.num_trees):', 'with', 'ops.device(self.dev... | 518,667 |
matsu0228/nlp-jp | patches.py | _Style.pprint_styles | pprint_styles | A class method which returns a string of the available styles. | [
"A",
"class",
"method",
"which",
"returns",
"a",
"string",
"of",
"the",
"available",
"styles."
] | def pprint_styles(klass):
return _pprint_styles(klass._style_list) | ['def', 'pprint_styles(klass):', 'return', '_pprint_styles(klass._style_list)'] | 789,055 |
suarez12138/AI-Reversi_IMP_TextDichotomy | offsetbox.py | AuxTransformBox.get_window_extent | get_window_extent | Return the bounding box in display space. | [
"Return",
"the",
"bounding",
"box",
"in",
"display",
"space."
] | def get_window_extent(self, renderer):
(w, h, xd, yd) = self.get_extent(renderer)
(ox, oy) = self.get_offset()
return mtransforms.Bbox.from_bounds(ox - xd, oy - yd, w, h) | ['def', 'get_window_extent(self,', 'renderer):', '(w,', 'h,', 'xd,', 'yd)', '=', 'self.get_extent(renderer)', '(ox,', 'oy)', '=', 'self.get_offset()', 'return', 'mtransforms.Bbox.from_bounds(ox', '-', 'xd,', 'oy', '-', 'yd,', 'w,', 'h)'] | 96,655 |
Gorilla-Lab-SCUT/frustum-convnet | provider_sample_sunrgbd.py | ProviderDataset.get_center_view_box3d | get_center_view_box3d | Frustum rotation of 3D bounding box corners. | [
"Frustum",
"rotation",
"of",
"3D",
"bounding",
"box",
"corners."
] | def get_center_view_box3d(self, index):
box3d = self.box3d_list[index]
box3d_center_view = np.copy(box3d)
return rotate_pc_along_y(box3d_center_view, self.get_center_view_rot_angle(index)) | ['def', 'get_center_view_box3d(self,', 'index):', 'box3d', '=', 'self.box3d_list[index]', 'box3d_center_view', '=', 'np.copy(box3d)', 'return', 'rotate_pc_along_y(box3d_center_view,', 'self.get_center_view_rot_angle(index))'] | 564,805 |
jimtin/Stock_Comparison | testing.py | get_data_path | get_data_path | Return the path of a data file, these are relative to the current test directory. | [
"Return",
"the",
"path",
"of",
"a",
"data",
"file,",
"these",
"are",
"relative",
"to",
"the",
"current",
"test",
"directory."
] | def get_data_path(f=''):
(_, filename, _, _, _, _) = inspect.getouterframes(inspect.currentframe())[1]
base_dir = os.path.abspath(os.path.dirname(filename))
return os.path.join(base_dir, 'data', f) | ['def', "get_data_path(f=''):", '(_,', 'filename,', '_,', '_,', '_,', '_)', '=', 'inspect.getouterframes(inspect.currentframe())[1]', 'base_dir', '=', 'os.path.abspath(os.path.dirname(filename))', 'return', 'os.path.join(base_dir,', "'data',", 'f)'] | 388,351 |
rifqind/Agent-Programs-3KS1 | Py25Queue.py | Queue.full | full | Return True if the queue is full, False otherwise (not reliable!). | [
"Return",
"True",
"if",
"the",
"queue",
"is",
"full,",
"False",
"otherwise",
"(not",
"reliable!)."
] | def full(self):
self.mutex.acquire()
n = self._full()
self.mutex.release()
return n | ['def', 'full(self):', 'self.mutex.acquire()', 'n', '=', 'self._full()', 'self.mutex.release()', 'return', 'n'] | 46,100 |
devashish-patel/webcam-motion-detector | guisupport.py | is_event_loop_running_wx | is_event_loop_running_wx | Is the wx event loop running. | [
"Is",
"the",
"wx",
"event",
"loop",
"running."
] | def is_event_loop_running_wx(app=None):
if app is None:
app = get_app_wx()
if hasattr(app, '_in_event_loop'):
return app._in_event_loop
else:
return app.IsMainLoopRunning() | ['def', 'is_event_loop_running_wx(app=None):', 'if', 'app', 'is', 'None:', 'app', '=', 'get_app_wx()', 'if', 'hasattr(app,', "'_in_event_loop'):", 'return', 'app._in_event_loop', 'else:', 'return', 'app.IsMainLoopRunning()'] | 979,171 |
sunishsheth2009/ChatterBot | datastructures.py | ContentRange.unset | unset | Sets the units to `None` which indicates that the header should no longer be used. | [
"Sets",
"the",
"units",
"to",
"`None`",
"which",
"indicates",
"that",
"the",
"header",
"should",
"no",
"longer",
"be",
"used."
] | def unset(self):
self.set(None, None, units=None) | ['def', 'unset(self):', 'self.set(None,', 'None,', 'units=None)'] | 483,077 |
microsoft/InnerEye-DeepLearning | test_scalar_dataset.py | test_load_items_when_channel_missing | test_load_items_when_channel_missing | Test loading file paths from a dataframe when a subject misses a channel. | [
"Test",
"loading",
"file",
"paths",
"from",
"a",
"dataframe",
"when",
"a",
"subject",
"misses",
"a",
"channel."
] | def test_load_items_when_channel_missing() -> None:
csv_string = StringIO('subject,channel,path,value\nS1,image1,img11.nii\nS1,image2,img12.nii,True\nS2,image2,image22.nii,False\n')
df = pd.read_csv(csv_string, sep=',', dtype=str)
items: List[ScalarDataSource] = DataSourceReader(data_frame=df, image_channel... | ['def', 'test_load_items_when_channel_missing()', '->', 'None:', 'csv_string', '=', "StringIO('subject,channel,path,value\\nS1,image1,img11.nii\\nS1,image2,img12.nii,True\\nS2,image2,image22.nii,False\\n')", 'df', '=', 'pd.read_csv(csv_string,', "sep=',',", 'dtype=str)', 'items:', 'List[ScalarDataSource]', '=', 'DataSo... | 613,666 |
openvinotoolkit/training_extensions | run_test_command.py | otx_find_testing | otx_find_testing | Performs several options of available otx find. | [
"Performs",
"several",
"options",
"of",
"available",
"otx",
"find."
] | def otx_find_testing():
command_line = ['otx', 'find', '--template']
check_run(command_line)
for task in find_supported_tasks:
command_line = ['otx', 'find', '--template', '--task', task]
check_run(command_line)
for backbone_backends in find_supported_backends:
command_line = ['o... | ['def', 'otx_find_testing():', 'command_line', '=', "['otx',", "'find',", "'--template']", 'check_run(command_line)', 'for', 'task', 'in', 'find_supported_tasks:', 'command_line', '=', "['otx',", "'find',", "'--template',", "'--task',", 'task]', 'check_run(command_line)', 'for', 'backbone_backends', 'in', 'find_support... | 919,212 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | named_commands.py | operate_and_get_next | operate_and_get_next | Accept the current line for execution and fetch the next line relative to the current line from the history for editing. | [
"Accept",
"the",
"current",
"line",
"for",
"execution",
"and",
"fetch",
"the",
"next",
"line",
"relative",
"to",
"the",
"current",
"line",
"from",
"the",
"history",
"for",
"editing."
] | def operate_and_get_next(event: E) -> None:
buff = event.current_buffer
new_index = buff.working_index + 1
buff.validate_and_handle()
def set_working_index() -> None:
if new_index < len(buff._working_lines):
buff.working_index = new_index
event.app.pre_run_callables.append(set_w... | ['def', 'operate_and_get_next(event:', 'E)', '->', 'None:', 'buff', '=', 'event.current_buffer', 'new_index', '=', 'buff.working_index', '+', '1', 'buff.validate_and_handle()', 'def', 'set_working_index()', '->', 'None:', 'if', 'new_index', '<', 'len(buff._working_lines):', 'buff.working_index', '=', 'new_index', 'even... | 435,271 |
NVIDIA-Omniverse/IsaacGymEnvs | rlgames_utils.py | ComplexObsRLGPUEnv.get_env_info | get_env_info | Gets information on the environment's observation, action, and privileged observation (states) spaces. | [
"Gets",
"information",
"on",
"the",
"environment's",
"observation,",
"action,",
"and",
"privileged",
"observation",
"(states)",
"spaces."
] | def get_env_info(self) -> Dict[str, gym.spaces.Space]:
info = {}
info['action_space'] = self.env.action_space
for (k, v) in self.obs_spec.items():
info[v['space_name']] = self.gen_obs_space(v['names'], v['concat'])
return info | ['def', 'get_env_info(self)', '->', 'Dict[str,', 'gym.spaces.Space]:', 'info', '=', '{}', "info['action_space']", '=', 'self.env.action_space', 'for', '(k,', 'v)', 'in', 'self.obs_spec.items():', "info[v['space_name']]", '=', "self.gen_obs_space(v['names'],", "v['concat'])", 'return', 'info'] | 246,698 |
brain-research/realistic-ssl-evaluation | dataset_utils.py | tf_gcn | tf_gcn | Performs global contrast normalization on a TF tensor of images. | [
"Performs",
"global",
"contrast",
"normalization",
"on",
"a",
"TF",
"tensor",
"of",
"images."
] | def tf_gcn(inp, multiplier=55.0, eps=1e-08):
inp -= tf.reduce_mean(inp, axis=[1, 2, 3], keepdims=True)
denominator = tf.sqrt(tf.reduce_sum(tf.square(inp), axis=[1, 2, 3], keepdims=True))
denominator /= multiplier
denominator = tf.where(tf.less(denominator, tf.constant(eps)), tf.ones_like(denominator), d... | ['def', 'tf_gcn(inp,', 'multiplier=55.0,', 'eps=1e-08):', 'inp', '-=', 'tf.reduce_mean(inp,', 'axis=[1,', '2,', '3],', 'keepdims=True)', 'denominator', '=', 'tf.sqrt(tf.reduce_sum(tf.square(inp),', 'axis=[1,', '2,', '3],', 'keepdims=True))', 'denominator', '/=', 'multiplier', 'denominator', '=', 'tf.where(tf.less(denom... | 308,995 |
zehuichen123/AutoAlignV2 | prediction_kitti_to_waymo.py | KITTI2Waymo.combine | combine | Combine predictions in waymo format for each sample together. | [
"Combine",
"predictions",
"in",
"waymo",
"format",
"for",
"each",
"sample",
"together."
] | def combine(self, pathnames):
combined = metrics_pb2.Objects()
for pathname in pathnames:
objects = metrics_pb2.Objects()
with open(pathname, 'rb') as f:
objects.ParseFromString(f.read())
for o in objects.objects:
combined.objects.append(o)
return combined | ['def', 'combine(self,', 'pathnames):', 'combined', '=', 'metrics_pb2.Objects()', 'for', 'pathname', 'in', 'pathnames:', 'objects', '=', 'metrics_pb2.Objects()', 'with', 'open(pathname,', "'rb')", 'as', 'f:', 'objects.ParseFromString(f.read())', 'for', 'o', 'in', 'objects.objects:', 'combined.objects.append(o)', 'retur... | 416,622 |
rlworkgroup/garage | _dtypes.py | TimeStep.last | last | bool: Whether this step is the last of its episode. | [
"bool:",
"Whether",
"this",
"step",
"is",
"the",
"last",
"of",
"its",
"episode."
] | def last(self):
return self.step_type is StepType.TERMINAL or self.step_type is StepType.TIMEOUT | ['def', 'last(self):', 'return', 'self.step_type', 'is', 'StepType.TERMINAL', 'or', 'self.step_type', 'is', 'StepType.TIMEOUT'] | 200,134 |
tobegit3hub/deep_image_model | flags.py | DEFINE_boolean | DEFINE_boolean | Defines a flag of type 'boolean'. | [
"Defines",
"a",
"flag",
"of",
"type",
"'boolean'."
] | def DEFINE_boolean(flag_name, default_value, docstring):
def str2bool(v):
return v.lower() in ('true', 't', '1')
_global_parser.add_argument('--' + flag_name, nargs='?', const=True, help=docstring, default=default_value, type=str2bool)
_global_parser.add_argument('--no' + flag_name, action='store_f... | ['def', 'DEFINE_boolean(flag_name,', 'default_value,', 'docstring):', 'def', 'str2bool(v):', 'return', 'v.lower()', 'in', "('true',", "'t',", "'1')", "_global_parser.add_argument('--'", '+', 'flag_name,', "nargs='?',", 'const=True,', 'help=docstring,', 'default=default_value,', 'type=str2bool)', "_global_parser.add_arg... | 183,153 |
jinfanhahaha/base-cifar-10-recurrent--.github.io | InceptionNet-v2-6.py | CifarData.next_batch | next_batch | return batch_size examples as a batch. | [
"return",
"batch_size",
"examples",
"as",
"a",
"batch."
] | def next_batch(self, batch_size):
end_indicator = self._indicator + batch_size
if end_indicator > self._num_examples:
if self._need_shuffle:
self._shuffle_data()
self._indicator = 0
end_indicator = batch_size
else:
raise Exception('have no more exa... | ['def', 'next_batch(self,', 'batch_size):', 'end_indicator', '=', 'self._indicator', '+', 'batch_size', 'if', 'end_indicator', '>', 'self._num_examples:', 'if', 'self._need_shuffle:', 'self._shuffle_data()', 'self._indicator', '=', '0', 'end_indicator', '=', 'batch_size', 'else:', 'raise', "Exception('have", 'no', 'mor... | 94,336 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | numpy_.py | PandasDtype.name | name | A bit-width name for this data-type. | [
"A",
"bit-width",
"name",
"for",
"this",
"data-type."
] | def name(self) -> str:
return self._dtype.name | ['def', 'name(self)', '->', 'str:', 'return', 'self._dtype.name'] | 452,814 |
JahJajaka/afternoon_cleaner | mobilenet_v2.py | mobilenet_base | mobilenet_base | Creates base of the mobilenet (no pooling and no logits) . | [
"Creates",
"base",
"of",
"the",
"mobilenet",
"(no",
"pooling",
"and",
"no",
"logits)",
"."
] | def mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs):
return mobilenet(input_tensor, depth_multiplier=depth_multiplier, base_only=True, **kwargs) | ['def', 'mobilenet_base(input_tensor,', 'depth_multiplier=1.0,', '**kwargs):', 'return', 'mobilenet(input_tensor,', 'depth_multiplier=depth_multiplier,', 'base_only=True,', '**kwargs)'] | 411,902 |
cheng052/BRNet | anchor_3d_generator.py | Anchor3DRangeGenerator.num_levels | num_levels | int: Number of feature levels that the generator is applied to. | [
"int:",
"Number",
"of",
"feature",
"levels",
"that",
"the",
"generator",
"is",
"applied",
"to."
] | def num_levels(self):
return len(self.scales) | ['def', 'num_levels(self):', 'return', 'len(self.scales)'] | 409,604 |
TheCurryMan/MedicAI | debug.py | unspew | unspew | Remove the trace hook installed by spew. | [
"Remove",
"the",
"trace",
"hook",
"installed",
"by",
"spew."
] | def unspew():
sys.settrace(None) | ['def', 'unspew():', 'sys.settrace(None)'] | 648,216 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | win32.py | _Win32Handles.add_win32_handle | add_win32_handle | Add a Win32 handle to the event loop. | [
"Add",
"a",
"Win32",
"handle",
"to",
"the",
"event",
"loop."
] | def add_win32_handle(self, handle: HANDLE, callback: Callable[[], None]) -> None:
handle_value = handle.value
if handle_value is None:
raise ValueError('Invalid handle.')
self.remove_win32_handle(handle)
loop = get_event_loop()
self._handle_callbacks[handle_value] = callback
remove_event... | ['def', 'add_win32_handle(self,', 'handle:', 'HANDLE,', 'callback:', 'Callable[[],', 'None])', '->', 'None:', 'handle_value', '=', 'handle.value', 'if', 'handle_value', 'is', 'None:', 'raise', "ValueError('Invalid", "handle.')", 'self.remove_win32_handle(handle)', 'loop', '=', 'get_event_loop()', 'self._handle_callback... | 435,188 |
accel-brain/accel-brain-code | labeled_csv_extractor.py | LabeledCSVExtractor.get_label_column | get_label_column | getter of `str` of column of label. | [
"getter",
"of",
"`str`",
"of",
"column",
"of",
"label."
] | def get_label_column(self):
return self.__label_column | ['def', 'get_label_column(self):', 'return', 'self.__label_column'] | 6,653 |
arshpreetsingh/quantopian-machinelearning | sparse.py | SparseArray.sp_index | sp_index | The SparseIndex containing the location of non- ``fill_value`` points. | [
"The",
"SparseIndex",
"containing",
"the",
"location",
"of",
"non-",
"``fill_value``",
"points."
] | def sp_index(self):
return self._sparse_index | ['def', 'sp_index(self):', 'return', 'self._sparse_index'] | 889,834 |
ZhangAoCanada/RADDet | loader.py | readStereoLeft | readStereoLeft | read stereo left image for verification. | [
"read",
"stereo",
"left",
"image",
"for",
"verification."
] | def readStereoLeft(img_filename):
if os.path.exists(img_filename):
stereo_image = cv2.imread(img_filename)
left_image = stereo_image[:, :stereo_image.shape[1] // 2, ...][..., ::-1]
return left_image
else:
return None | ['def', 'readStereoLeft(img_filename):', 'if', 'os.path.exists(img_filename):', 'stereo_image', '=', 'cv2.imread(img_filename)', 'left_image', '=', 'stereo_image[:,', ':stereo_image.shape[1]', '//', '2,', '...][...,', '::-1]', 'return', 'left_image', 'else:', 'return', 'None'] | 835,812 |
alibaba/EasyCV | face_keypoint.py | FaceKeypoint.with_keypoint | with_keypoint | Check if has keypoint_head. | [
"Check",
"if",
"has",
"keypoint_head."
] | def with_keypoint(self):
return hasattr(self, 'keypoint_head') | ['def', 'with_keypoint(self):', 'return', 'hasattr(self,', "'keypoint_head')"] | 546,650 |
csjunxu/Noisy-As-Clean-TIP2020 | req_tracker.py | RequirementTracker.remove | remove | Remove an InstallRequirement from build tracking. | [
"Remove",
"an",
"InstallRequirement",
"from",
"build",
"tracking."
] | def remove(self, req):
assert req.link
os.unlink(self._entry_path(req.link))
self._entries.remove(req)
logger.debug('Removed %s from build tracker %r', req, self._root) | ['def', 'remove(self,', 'req):', 'assert', 'req.link', 'os.unlink(self._entry_path(req.link))', 'self._entries.remove(req)', "logger.debug('Removed", '%s', 'from', 'build', 'tracker', "%r',", 'req,', 'self._root)'] | 294,761 |
matsu0228/nlp-jp | storage_uri.py | BucketStorageUri.copy_key | copy_key | Returns newly created key. | [
"Returns",
"newly",
"created",
"key."
] | def copy_key(self, src_bucket_name, src_key_name, metadata=None, src_version_id=None, storage_class='STANDARD', preserve_acl=False, encrypt_key=False, headers=None, query_args=None, src_generation=None):
self._check_object_uri('copy_key')
dst_bucket = self.get_bucket(validate=False, headers=headers)
if src_... | ['def', 'copy_key(self,', 'src_bucket_name,', 'src_key_name,', 'metadata=None,', 'src_version_id=None,', "storage_class='STANDARD',", 'preserve_acl=False,', 'encrypt_key=False,', 'headers=None,', 'query_args=None,', 'src_generation=None):', "self._check_object_uri('copy_key')", 'dst_bucket', '=', 'self.get_bucket(valid... | 783,894 |
coder-mano/Shi-Tomasi-Corner-Detector | misc.py | remove_auth_from_url | remove_auth_from_url | Return a copy of url with 'username:password@' removed. | [
"Return",
"a",
"copy",
"of",
"url",
"with",
"'username:password@'",
"removed."
] | def remove_auth_from_url(url):
return _transform_url(url, _get_netloc)[0] | ['def', 'remove_auth_from_url(url):', 'return', '_transform_url(url,', '_get_netloc)[0]'] | 899,952 |
Kvatsx/Artificial-Intelligence-Assignments | mathtext.py | MathtextBackend.get_hinting_type | get_hinting_type | Get the FreeType hinting type to use with this particular backend. | [
"Get",
"the",
"FreeType",
"hinting",
"type",
"to",
"use",
"with",
"this",
"particular",
"backend."
] | def get_hinting_type(self):
return LOAD_NO_HINTING | ['def', 'get_hinting_type(self):', 'return', 'LOAD_NO_HINTING'] | 623 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | mailbox.py | _mboxMMDF.get_file | get_file | Return a file-like representation or raise a KeyError. | [
"Return",
"a",
"file-like",
"representation",
"or",
"raise",
"a",
"KeyError."
] | def get_file(self, key, from_=False):
(start, stop) = self._lookup(key)
self._file.seek(start)
if not from_:
self._file.readline()
return _PartialFile(self._file, self._file.tell(), stop) | ['def', 'get_file(self,', 'key,', 'from_=False):', '(start,', 'stop)', '=', 'self._lookup(key)', 'self._file.seek(start)', 'if', 'not', 'from_:', 'self._file.readline()', 'return', '_PartialFile(self._file,', 'self._file.tell(),', 'stop)'] | 428,844 |
adamshamsudeen/vision.ai | testtools.py | ContentAccessors.xml | xml | Get an etree if possible. | [
"Get",
"an",
"etree",
"if",
"possible."
] | def xml(self):
if 'xml' not in self.mimetype:
raise AttributeError('Not a XML response (Content-Type: %s)' % self.mimetype)
for module in ['xml.etree.ElementTree', 'ElementTree', 'elementtree.ElementTree']:
etree = import_string(module, silent=True)
if etree is not None:
retu... | ['def', 'xml(self):', 'if', "'xml'", 'not', 'in', 'self.mimetype:', 'raise', "AttributeError('Not", 'a', 'XML', 'response', '(Content-Type:', "%s)'", '%', 'self.mimetype)', 'for', 'module', 'in', "['xml.etree.ElementTree',", "'ElementTree',", "'elementtree.ElementTree']:", 'etree', '=', 'import_string(module,', 'silent... | 944,732 |
Jittor/JDet | coco.py | COCODataset.save_results | save_results | Convert detection results to COCO json style. | [
"Convert",
"detection",
"results",
"to",
"COCO",
"json",
"style."
] | def save_results(self, results, save_file):
def xyxy2xywh(box):
(x1, y1, x2, y2) = box.tolist()
return [x1, y1, x2 - x1, y2 - y1]
json_results = []
for (result, target) in results:
img_id = result['img_id']
for (box, score, label) in zip(result['boxes'], result['scores'], re... | ['def', 'save_results(self,', 'results,', 'save_file):', 'def', 'xyxy2xywh(box):', '(x1,', 'y1,', 'x2,', 'y2)', '=', 'box.tolist()', 'return', '[x1,', 'y1,', 'x2', '-', 'x1,', 'y2', '-', 'y1]', 'json_results', '=', '[]', 'for', '(result,', 'target)', 'in', 'results:', 'img_id', '=', "result['img_id']", 'for', '(box,', ... | 577,642 |
wandb/wandb | prodigy.py | upload_dataset | upload_dataset | Upload dataset from local database to Weights & Biases. | [
"Upload",
"dataset",
"from",
"local",
"database",
"to",
"Weights",
"&",
"Biases."
] | def upload_dataset(dataset_name):
if wandb.run is None:
raise ValueError('You must call wandb.init() before upload_dataset()')
with wb_telemetry.context(run=wandb.run) as tel:
tel.feature.prodigy = True
prodigy_db = util.get_module('prodigy.components.db', required='`prodigy` library is requ... | ['def', 'upload_dataset(dataset_name):', 'if', 'wandb.run', 'is', 'None:', 'raise', "ValueError('You", 'must', 'call', 'wandb.init()', 'before', "upload_dataset()')", 'with', 'wb_telemetry.context(run=wandb.run)', 'as', 'tel:', 'tel.feature.prodigy', '=', 'True', 'prodigy_db', '=', "util.get_module('prodigy.components.... | 941,554 |
wanhch/CS181-Artificial-Intelligence-I | test_inference.py | InferenceModule.setGhostPositions | setGhostPositions | Sets the position of all ghosts to the values in ghostPositions. | [
"Sets",
"the",
"position",
"of",
"all",
"ghosts",
"to",
"the",
"values",
"in",
"ghostPositions."
] | def setGhostPositions(self, gameState, ghostPositions):
for (index, pos) in enumerate(ghostPositions):
conf = game.Configuration(pos, game.Directions.STOP)
gameState.data.agentStates[index + 1] = game.AgentState(conf, False)
return gameState | ['def', 'setGhostPositions(self,', 'gameState,', 'ghostPositions):', 'for', '(index,', 'pos)', 'in', 'enumerate(ghostPositions):', 'conf', '=', 'game.Configuration(pos,', 'game.Directions.STOP)', 'gameState.data.agentStates[index', '+', '1]', '=', 'game.AgentState(conf,', 'False)', 'return', 'gameState'] | 220,282 |
intelligent-environments-lab/CityLearn | wrappers.py | DiscreteActionWrapper.action_space | action_space | Returns action space for discretized actions. | [
"Returns",
"action",
"space",
"for",
"discretized",
"actions."
] | def action_space(self) -> List[spaces.MultiDiscrete]:
if self.env.central_agent:
bin_sizes = []
for b in self.bin_sizes:
for (_, v) in b.items():
bin_sizes.append(v)
action_space = [spaces.MultiDiscrete(bin_sizes)]
else:
action_space = [spaces.MultiDis... | ['def', 'action_space(self)', '->', 'List[spaces.MultiDiscrete]:', 'if', 'self.env.central_agent:', 'bin_sizes', '=', '[]', 'for', 'b', 'in', 'self.bin_sizes:', 'for', '(_,', 'v)', 'in', 'b.items():', 'bin_sizes.append(v)', 'action_space', '=', '[spaces.MultiDiscrete(bin_sizes)]', 'else:', 'action_space', '=', '[spaces... | 105,492 |
aeon-toolkit/aeon | test_datagen.py | test_piecewise_poisson | test_piecewise_poisson | Test piecewise_poisson fuction returns the expected Poisson distributed array. | [
"Test",
"piecewise_poisson",
"fuction",
"returns",
"the",
"expected",
"Poisson",
"distributed",
"array."
] | def test_piecewise_poisson(lambdas, lengths, random_state, output):
assert array_equal(piecewise_poisson(lambdas, lengths, random_state), output) | ['def', 'test_piecewise_poisson(lambdas,', 'lengths,', 'random_state,', 'output):', 'assert', 'array_equal(piecewise_poisson(lambdas,', 'lengths,', 'random_state),', 'output)'] | 399,095 |
Alexander-Parker/youtube_nlp | message.py | _GetMore.get_message | get_message | Get a getmore message. | [
"Get",
"a",
"getmore",
"message."
] | def get_message(self, dummy0, sock_info, use_cmd=False):
ns = _UJOIN % (self.db, self.coll)
ctx = sock_info.compression_context
if use_cmd:
spec = self.as_command(sock_info)[0]
if sock_info.op_msg_enabled:
(request_id, msg, size, _) = _op_msg(0, spec, self.db, ReadPreference.PRIM... | ['def', 'get_message(self,', 'dummy0,', 'sock_info,', 'use_cmd=False):', 'ns', '=', '_UJOIN', '%', '(self.db,', 'self.coll)', 'ctx', '=', 'sock_info.compression_context', 'if', 'use_cmd:', 'spec', '=', 'self.as_command(sock_info)[0]', 'if', 'sock_info.op_msg_enabled:', '(request_id,', 'msg,', 'size,', '_)', '=', '_op_m... | 970,463 |
microsoft/InnerEye-DeepLearning | test_scalar_model.py | test_run_ml_with_segmentation_model | test_run_ml_with_segmentation_model | Test training and testing of segmentation models, when it is started together via run_ml. | [
"Test",
"training",
"and",
"testing",
"of",
"segmentation",
"models,",
"when",
"it",
"is",
"started",
"together",
"via",
"run_ml."
] | def test_run_ml_with_segmentation_model(test_output_dirs: OutputFolderForTests) -> None:
config = DummyModel()
config.num_dataload_workers = 0
config.restrict_subjects = '1'
config.test_crop_size = (75, 75, 75)
config.inference_on_train_set = False
config.inference_on_val_set = True
config.i... | ['def', 'test_run_ml_with_segmentation_model(test_output_dirs:', 'OutputFolderForTests)', '->', 'None:', 'config', '=', 'DummyModel()', 'config.num_dataload_workers', '=', '0', 'config.restrict_subjects', '=', "'1'", 'config.test_crop_size', '=', '(75,', '75,', '75)', 'config.inference_on_train_set', '=', 'False', 'con... | 613,697 |
gunthercox/ChatterBot | sourcedstring.py | SourcedStringStream.close | close | Close the underlying stream. | [
"Close",
"the",
"underlying",
"stream."
] | def close(self):
self.stream.close() | ['def', 'close(self):', 'self.stream.close()'] | 527,354 |
PaddlePaddle/PARL | policy_distribution.py | PolicyDistribution.entropy | entropy | The entropy of the policy distribution. | [
"The",
"entropy",
"of",
"the",
"policy",
"distribution."
] | def entropy(self):
raise NotImplementedError | ['def', 'entropy(self):', 'raise', 'NotImplementedError'] | 278,051 |
eora-ai/torchok | base_backbone.py | BaseBackbone.out_encoder_channels | out_encoder_channels | Number of output feature channels - channels after forward_features method. | [
"Number",
"of",
"output",
"feature",
"channels",
"-",
"channels",
"after",
"forward_features",
"method."
] | def out_encoder_channels(self) -> Tuple[int]:
if self._out_encoder_channels is None:
raise ValueError('TorchOk Backbones must have self._out_feature_channels attribute.')
return tuple(self._out_encoder_channels) | ['def', 'out_encoder_channels(self)', '->', 'Tuple[int]:', 'if', 'self._out_encoder_channels', 'is', 'None:', 'raise', "ValueError('TorchOk", 'Backbones', 'must', 'have', 'self._out_feature_channels', "attribute.')", 'return', 'tuple(self._out_encoder_channels)'] | 903,067 |
ouwei-guo/mit-6.034 | lab5.py | hamming_distance | hamming_distance | Given two Points, computes and returns the Hamming distance between them. | [
"Given",
"two",
"Points,",
"computes",
"and",
"returns",
"the",
"Hamming",
"distance",
"between",
"them."
] | def hamming_distance(point1, point2):
return sum((v1 != v2 for (v1, v2) in zip(point1.coords, point2.coords))) | ['def', 'hamming_distance(point1,', 'point2):', 'return', 'sum((v1', '!=', 'v2', 'for', '(v1,', 'v2)', 'in', 'zip(point1.coords,', 'point2.coords)))'] | 238,759 |
jimtin/Stock_Comparison | garbage.py | GarbageCollector.is_alive | is_alive | Is the garbage collection thread currently running? Includes checks for process shutdown or fork. | [
"Is",
"the",
"garbage",
"collection",
"thread",
"currently",
"running?",
"Includes",
"checks",
"for",
"process",
"shutdown",
"or",
"fork."
] | def is_alive(self):
if getpid is None or getpid() != self.pid or self.thread is None or (not self.thread.is_alive()):
return False
return True | ['def', 'is_alive(self):', 'if', 'getpid', 'is', 'None', 'or', 'getpid()', '!=', 'self.pid', 'or', 'self.thread', 'is', 'None', 'or', '(not', 'self.thread.is_alive()):', 'return', 'False', 'return', 'True'] | 359,663 |
mpeychev/disentangled-autoencoders | util.py | get_classifier_data_dir | get_classifier_data_dir | Returns the directory of the data to be used for training the linear classifier which evaluates the disentanglement level. | [
"Returns",
"the",
"directory",
"of",
"the",
"data",
"to",
"be",
"used",
"for",
"training",
"the",
"linear",
"classifier",
"which",
"evaluates",
"the",
"disentanglement",
"level."
] | def get_classifier_data_dir():
return os.path.join(get_data_dir(), 'classifier') | ['def', 'get_classifier_data_dir():', 'return', 'os.path.join(get_data_dir(),', "'classifier')"] | 552,042 |
Xianpeng919/MonoCon | transforms.py | bbox_mapping | bbox_mapping | Map bboxes from the original image scale to testing scale. | [
"Map",
"bboxes",
"from",
"the",
"original",
"image",
"scale",
"to",
"testing",
"scale."
] | def bbox_mapping(bboxes, img_shape, scale_factor, flip, flip_direction='horizontal'):
new_bboxes = bboxes * bboxes.new_tensor(scale_factor)
if flip:
new_bboxes = bbox_flip(new_bboxes, img_shape, flip_direction)
return new_bboxes | ['def', 'bbox_mapping(bboxes,', 'img_shape,', 'scale_factor,', 'flip,', "flip_direction='horizontal'):", 'new_bboxes', '=', 'bboxes', '*', 'bboxes.new_tensor(scale_factor)', 'if', 'flip:', 'new_bboxes', '=', 'bbox_flip(new_bboxes,', 'img_shape,', 'flip_direction)', 'return', 'new_bboxes'] | 653,628 |
michellesri/cs188 | inference.py | MarginalInference.elapseTime | elapseTime | Predict beliefs for a time step elapsing from a gameState. | [
"Predict",
"beliefs",
"for",
"a",
"time",
"step",
"elapsing",
"from",
"a",
"gameState."
] | def elapseTime(self, gameState):
if self.index == 1:
jointInference.elapseTime(gameState) | ['def', 'elapseTime(self,', 'gameState):', 'if', 'self.index', '==', '1:', 'jointInference.elapseTime(gameState)'] | 223,854 |
AiIsBetter/computer_vision | torch_ssd_object.py | detect | detect | Inputs will be, a frame, a ssd neural network, and a transformation to be applied on the images, and that will return the frame with the detector rectangle. | [
"Inputs",
"will",
"be,",
"a",
"frame,",
"a",
"ssd",
"neural",
"network,",
"and",
"a",
"transformation",
"to",
"be",
"applied",
"on",
"the",
"images,",
"and",
"that",
"will",
"return",
"the",
"frame",
"with",
"the",
"detector",
"rectangle."
] | def detect(frame, net, transform):
(height, width) = frame.shape[:2]
frame_t = transform(frame)[0]
x = torch.from_numpy(frame_t).permute(2, 0, 1)
x = Variable(x.unsqueeze(0))
y = net(x)
detections = y.data
scale = torch.Tensor([width, height, width, height])
for i in range(detections.siz... | ['def', 'detect(frame,', 'net,', 'transform):', '(height,', 'width)', '=', 'frame.shape[:2]', 'frame_t', '=', 'transform(frame)[0]', 'x', '=', 'torch.from_numpy(frame_t).permute(2,', '0,', '1)', 'x', '=', 'Variable(x.unsqueeze(0))', 'y', '=', 'net(x)', 'detections', '=', 'y.data', 'scale', '=', 'torch.Tensor([width,', ... | 503,028 |
yanqi1811/transfer-learning | pytorch_hf_text_classification_model.py | PyTorchHFTextClassificationModel.train | train | Trains the model using the specified text classification dataset. | [
"Trains",
"the",
"model",
"using",
"the",
"specified",
"text",
"classification",
"dataset."
] | def train(self, dataset, output_dir: str, epochs: int=1, initial_checkpoints=None, learning_rate: float=1e-05, do_eval: bool=True, early_stopping: bool=False, lr_decay: bool=True, seed: int=None, extra_layers: list=None, device: str='cpu', ipex_optimize: bool=True, use_trainer: bool=False, force_download: bool=False, d... | ['def', 'train(self,', 'dataset,', 'output_dir:', 'str,', 'epochs:', 'int=1,', 'initial_checkpoints=None,', 'learning_rate:', 'float=1e-05,', 'do_eval:', 'bool=True,', 'early_stopping:', 'bool=False,', 'lr_decay:', 'bool=True,', 'seed:', 'int=None,', 'extra_layers:', 'list=None,', 'device:', "str='cpu',", 'ipex_optimiz... | 928,459 |
Katja-M/Python_NaturalLanguageProcessing | arlstem.py | ARLSTem.suff | suff | remove suffixes from the word's end. | [
"remove",
"suffixes",
"from",
"the",
"word's",
"end."
] | def suff(self, token):
if token.endswith('ك') and len(token) > 3:
return token[:-1]
if len(token) > 4:
for s2 in self.su2:
if token.endswith(s2):
return token[:-2]
if len(token) > 5:
for s3 in self.su3:
if token.endswith(s3):
re... | ['def', 'suff(self,', 'token):', 'if', "token.endswith('ك')", 'and', 'len(token)', '>', '3:', 'return', 'token[:-1]', 'if', 'len(token)', '>', '4:', 'for', 's2', 'in', 'self.su2:', 'if', 'token.endswith(s2):', 'return', 'token[:-2]', 'if', 'len(token)', '>', '5:', 'for', 's3', 'in', 'self.su3:', 'if', 'token.endswith(s... | 866,941 |
angsten/pianonet | run.py | Run.checkpoint_method_creator | checkpoint_method_creator | Saves all relevant parts of the current run's training session and state to files within the run directory as an exact checkpoint from which a future run can be restarted without any change in the training outcome. | [
"Saves",
"all",
"relevant",
"parts",
"of",
"the",
"current",
"run's",
"training",
"session",
"and",
"state",
"to",
"files",
"within",
"the",
"run",
"directory",
"as",
"an",
"exact",
"checkpoint",
"from",
"which",
"a",
"future",
"run",
"can",
"be",
"restarted... | def checkpoint_method_creator(self):
def checkpoint(batch=None, logs=None):
save_dictionary_to_json_file(dictionary=self.run_description, json_file_path=self.get_run_description_path(run_index=self.get_run_index()))
self.save_state()
self.save_model()
self.save_generator_state()
... | ['def', 'checkpoint_method_creator(self):', 'def', 'checkpoint(batch=None,', 'logs=None):', 'save_dictionary_to_json_file(dictionary=self.run_description,', 'json_file_path=self.get_run_description_path(run_index=self.get_run_index()))', 'self.save_state()', 'self.save_model()', 'self.save_generator_state()', 'return',... | 769,467 |
weimin17/Object-Detection_HelmetDetection | loss_layers_test.py | PrecisionAtRecallTest.testLagrangeMultiplierUpdateDirectionWithMultipleRecalls | testLagrangeMultiplierUpdateDirectionWithMultipleRecalls | Runs Lagrange multiplier test with multiple recall values. | [
"Runs",
"Lagrange",
"multiplier",
"test",
"with",
"multiple",
"recall",
"values."
] | def testLagrangeMultiplierUpdateDirectionWithMultipleRecalls(self):
target_recall = [0.34, 0.66]
for surrogate_type in ['xent', 'hinge']:
scope_str = 'p-at-r_{}_{}'.format('_'.join([str(recall) for recall in target_recall]), surrogate_type)
kwargs = {'target_recall': target_recall, 'dual_rate_fa... | ['def', 'testLagrangeMultiplierUpdateDirectionWithMultipleRecalls(self):', 'target_recall', '=', '[0.34,', '0.66]', 'for', 'surrogate_type', 'in', "['xent',", "'hinge']:", 'scope_str', '=', "'p-at-r_{}_{}'.format('_'.join([str(recall)", 'for', 'recall', 'in', 'target_recall]),', 'surrogate_type)', 'kwargs', '=', "{'tar... | 763,014 |
apeterswu/RL4NMT | common_layers.py | conv_block_downsample | conv_block_downsample | Implements a downwards-striding conv block, like Xception exit flow. | [
"Implements",
"a",
"downwards-striding",
"conv",
"block,",
"like",
"Xception",
"exit",
"flow."
] | def conv_block_downsample(x, kernel, strides, padding, separability=0, name=None, reuse=None):
with tf.variable_scope(name, default_name='conv_block_downsample', values=[x], reuse=reuse):
hidden_size = int(x.get_shape()[-1])
res = conv_block(x, int(1.25 * hidden_size), [((1, 1), kernel)], padding=pa... | ['def', 'conv_block_downsample(x,', 'kernel,', 'strides,', 'padding,', 'separability=0,', 'name=None,', 'reuse=None):', 'with', 'tf.variable_scope(name,', "default_name='conv_block_downsample',", 'values=[x],', 'reuse=reuse):', 'hidden_size', '=', 'int(x.get_shape()[-1])', 'res', '=', 'conv_block(x,', 'int(1.25', '*', ... | 331,044 |
marcsto/rl | utils.py | generate_exp_name | generate_exp_name | Generates an ID (str) for the described experiment using UUID and current date. | [
"Generates",
"an",
"ID",
"(str)",
"for",
"the",
"described",
"experiment",
"using",
"UUID",
"and",
"current",
"date."
] | def generate_exp_name(model_name: str, experiment_name: str) -> str:
exp_name = '_'.join((model_name, experiment_name, str(uuid.uuid4())[:8], datetime.now().strftime('%y_%m_%d-%H_%M_%S')))
return exp_name | ['def', 'generate_exp_name(model_name:', 'str,', 'experiment_name:', 'str)', '->', 'str:', 'exp_name', '=', "'_'.join((model_name,", 'experiment_name,', 'str(uuid.uuid4())[:8],', "datetime.now().strftime('%y_%m_%d-%H_%M_%S')))", 'return', 'exp_name'] | 859,480 |
tusen-ai/SST | groupfree3d_bbox_coder.py | GroupFree3DBBoxCoder.decode | decode | Decode predicted parts to bbox3d. | [
"Decode",
"predicted",
"parts",
"to",
"bbox3d."
] | def decode(self, bbox_out, prefix=''):
center = bbox_out[f'{prefix}center']
(batch_size, num_proposal) = center.shape[:2]
if self.with_rot:
dir_class = torch.argmax(bbox_out[f'{prefix}dir_class'], -1)
dir_res = torch.gather(bbox_out[f'{prefix}dir_res'], 2, dir_class.unsqueeze(-1))
di... | ['def', 'decode(self,', 'bbox_out,', "prefix=''):", 'center', '=', "bbox_out[f'{prefix}center']", '(batch_size,', 'num_proposal)', '=', 'center.shape[:2]', 'if', 'self.with_rot:', 'dir_class', '=', "torch.argmax(bbox_out[f'{prefix}dir_class'],", '-1)', 'dir_res', '=', "torch.gather(bbox_out[f'{prefix}dir_res'],", '2,',... | 872,166 |
SemiUnsupervisedLearning/DGMs_for_semi-unsupervised_ | utils.py | softmax | softmax | Compute the softmax of each element along an axis of X. | [
"Compute",
"the",
"softmax",
"of",
"each",
"element",
"along",
"an",
"axis",
"of",
"X."
] | def softmax(X, theta=1.0, axis=None):
y = np.atleast_2d(X)
if axis is None:
axis = next((j[0] for j in enumerate(y.shape) if j[1] > 1))
y = y * float(theta)
y = y - np.expand_dims(np.max(y, axis=axis), axis)
y = np.exp(y)
ax_sum = np.expand_dims(np.sum(y, axis=axis), axis)
p = y / ax... | ['def', 'softmax(X,', 'theta=1.0,', 'axis=None):', 'y', '=', 'np.atleast_2d(X)', 'if', 'axis', 'is', 'None:', 'axis', '=', 'next((j[0]', 'for', 'j', 'in', 'enumerate(y.shape)', 'if', 'j[1]', '>', '1))', 'y', '=', 'y', '*', 'float(theta)', 'y', '=', 'y', '-', 'np.expand_dims(np.max(y,', 'axis=axis),', 'axis)', 'y', '=',... | 184,420 |
openvinotoolkit/training_extensions | ir.py | check_if_quantized | check_if_quantized | Checks if OpenVINO model is already quantized. | [
"Checks",
"if",
"OpenVINO",
"model",
"is",
"already",
"quantized."
] | def check_if_quantized(model: Any) -> bool:
nodes = model.get_ops()
for op in nodes:
if 'FakeQuantize' == op.get_type_name():
return True
return False | ['def', 'check_if_quantized(model:', 'Any)', '->', 'bool:', 'nodes', '=', 'model.get_ops()', 'for', 'op', 'in', 'nodes:', 'if', "'FakeQuantize'", '==', 'op.get_type_name():', 'return', 'True', 'return', 'False'] | 918,015 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | style_transformation.py | SwapLightAndDarkStyleTransformation.transform_attrs | transform_attrs | Return the `Attrs` used when opposite luminosity should be used. | [
"Return",
"the",
"`Attrs`",
"used",
"when",
"opposite",
"luminosity",
"should",
"be",
"used."
] | def transform_attrs(self, attrs: Attrs) -> Attrs:
attrs = attrs._replace(color=get_opposite_color(attrs.color))
attrs = attrs._replace(bgcolor=get_opposite_color(attrs.bgcolor))
return attrs | ['def', 'transform_attrs(self,', 'attrs:', 'Attrs)', '->', 'Attrs:', 'attrs', '=', 'attrs._replace(color=get_opposite_color(attrs.color))', 'attrs', '=', 'attrs._replace(bgcolor=get_opposite_color(attrs.bgcolor))', 'return', 'attrs'] | 435,494 |
Katja-M/Python_NaturalLanguageProcessing | transforms.py | BboxBase.containsy | containsy | Return whether *y* is in the closed (:attr:`y0`, :attr:`y1`) interval. | [
"Return",
"whether",
"*y*",
"is",
"in",
"the",
"closed",
"(:attr:`y0`,",
":attr:`y1`)",
"interval."
] | def containsy(self, y):
(y0, y1) = self.intervaly
return y0 <= y <= y1 or y0 >= y >= y1 | ['def', 'containsy(self,', 'y):', '(y0,', 'y1)', '=', 'self.intervaly', 'return', 'y0', '<=', 'y', '<=', 'y1', 'or', 'y0', '>=', 'y', '>=', 'y1'] | 864,963 |
sek788432/Waymo-2D-Object-Detection | seq_example_util.py | context_float_feature | context_float_feature | Converts a numpy float array to a context float feature. | [
"Converts",
"a",
"numpy",
"float",
"array",
"to",
"a",
"context",
"float",
"feature."
] | def context_float_feature(ndarray):
feature = tf.train.Feature()
for val in ndarray:
feature.float_list.value.append(val)
return feature | ['def', 'context_float_feature(ndarray):', 'feature', '=', 'tf.train.Feature()', 'for', 'val', 'in', 'ndarray:', 'feature.float_list.value.append(val)', 'return', 'feature'] | 974,956 |
TonyLianLong/VAI-ReinforcementLearning | egl_renderer.py | create_initialized_headless_egl_display | create_initialized_headless_egl_display | Creates an initialized EGL display directly on a device. | [
"Creates",
"an",
"initialized",
"EGL",
"display",
"directly",
"on",
"a",
"device."
] | def create_initialized_headless_egl_display():
all_devices = EGL.eglQueryDevicesEXT()
selected_device = os.environ.get('EGL_DEVICE_ID', None)
if selected_device is None:
candidates = all_devices
else:
device_idx = int(selected_device)
if not 0 <= device_idx < len(all_devices):
... | ['def', 'create_initialized_headless_egl_display():', 'all_devices', '=', 'EGL.eglQueryDevicesEXT()', 'selected_device', '=', "os.environ.get('EGL_DEVICE_ID',", 'None)', 'if', 'selected_device', 'is', 'None:', 'candidates', '=', 'all_devices', 'else:', 'device_idx', '=', 'int(selected_device)', 'if', 'not', '0', '<=', ... | 441,154 |
rifqind/Agent-Programs-3KS1 | mixer_test.py | SoundTypeTest.test_sound__without_arg | test_sound__without_arg | Ensure exception raised for Sound() creation with no argument. | [
"Ensure",
"exception",
"raised",
"for",
"Sound()",
"creation",
"with",
"no",
"argument."
] | def test_sound__without_arg(self):
with self.assertRaises(TypeError):
mixer.Sound() | ['def', 'test_sound__without_arg(self):', 'with', 'self.assertRaises(TypeError):', 'mixer.Sound()'] | 45,903 |
enuguru/artificial_intelligence_and_machine_ | flask_login.py | login_fresh | login_fresh | This returns ``True`` if the current login is fresh. | [
"This",
"returns",
"``True``",
"if",
"the",
"current",
"login",
"is",
"fresh."
] | def login_fresh():
return session.get('_fresh', False) | ['def', 'login_fresh():', 'return', "session.get('_fresh',", 'False)'] | 156,509 |
zachgitt/computer-vision-panorama | uiutils.py | ClickableImageWidget.push_click | push_click | Draws a point if it is in bounds and adds it to the internal list. | [
"Draws",
"a",
"point",
"if",
"it",
"is",
"in",
"bounds",
"and",
"adds",
"it",
"to",
"the",
"internal",
"list."
] | def push_click(self, y, x):
if self.in_bounds(y, x):
self.clicked_points.append((y, x))
self.draw_all_points() | ['def', 'push_click(self,', 'y,', 'x):', 'if', 'self.in_bounds(y,', 'x):', 'self.clicked_points.append((y,', 'x))', 'self.draw_all_points()'] | 470,350 |
zehuichen123/AutoAlignV2 | depth_points.py | DepthPoints.flip | flip | Flip the boxes in BEV along given BEV direction. | [
"Flip",
"the",
"boxes",
"in",
"BEV",
"along",
"given",
"BEV",
"direction."
] | def flip(self, bev_direction='horizontal'):
if bev_direction == 'horizontal':
self.tensor[:, 0] = -self.tensor[:, 0]
elif bev_direction == 'vertical':
self.tensor[:, 1] = -self.tensor[:, 1] | ['def', 'flip(self,', "bev_direction='horizontal'):", 'if', 'bev_direction', '==', "'horizontal':", 'self.tensor[:,', '0]', '=', '-self.tensor[:,', '0]', 'elif', 'bev_direction', '==', "'vertical':", 'self.tensor[:,', '1]', '=', '-self.tensor[:,', '1]'] | 416,649 |
GatorEducator/GatorMiner | streamlit_web.py | student_senti | student_senti | Page for display individual student's sentiment. | [
"Page",
"for",
"display",
"individual",
"student's",
"sentiment."
] | def student_senti(input_df):
students = st.multiselect(label='Select specific students below:', options=input_df[stu_id].unique())
plots_range = st.sidebar.slider('Select the number of plots per row', 1, 5, value=3)
df_selected_stu = ut.return_assignment(input_df, stu_id, students)
if len(students) != 0... | ['def', 'student_senti(input_df):', 'students', '=', "st.multiselect(label='Select", 'specific', 'students', "below:',", 'options=input_df[stu_id].unique())', 'plots_range', '=', "st.sidebar.slider('Select", 'the', 'number', 'of', 'plots', 'per', "row',", '1,', '5,', 'value=3)', 'df_selected_stu', '=', 'ut.return_assig... | 567,422 |
megvii-research/TreeEnergyLoss | video_helper.py | VideoHelper.cut_video | cut_video | Cut a clip from a video. | [
"Cut",
"a",
"clip",
"from",
"a",
"video."
] | def cut_video(in_file, out_file, start=None, end=None, vcodec=None, acodec=None, log_level='info', print_cmd=False, **kwargs):
options = {'log_level': log_level}
if vcodec is None:
options['vcodec'] = 'copy'
if acodec is None:
options['acodec'] = 'copy'
if start:
options['ss'] = ... | ['def', 'cut_video(in_file,', 'out_file,', 'start=None,', 'end=None,', 'vcodec=None,', 'acodec=None,', "log_level='info',", 'print_cmd=False,', '**kwargs):', 'options', '=', "{'log_level':", 'log_level}', 'if', 'vcodec', 'is', 'None:', "options['vcodec']", '=', "'copy'", 'if', 'acodec', 'is', 'None:', "options['acodec'... | 951,459 |
nlp-uoregon/trankit | modeling_tf_utils.py | BeamHypotheses.add | add | Add a new hypothesis to the list. | [
"Add",
"a",
"new",
"hypothesis",
"to",
"the",
"list."
] | def add(self, hyp, sum_logprobs):
score = sum_logprobs / len(hyp) ** self.length_penalty
if len(self) < self.num_beams or score > self.worst_score:
self.beams.append((score, hyp))
if len(self) > self.num_beams:
sorted_scores = sorted([(s, idx) for (idx, (s, _)) in enumerate(self.beam... | ['def', 'add(self,', 'hyp,', 'sum_logprobs):', 'score', '=', 'sum_logprobs', '/', 'len(hyp)', '**', 'self.length_penalty', 'if', 'len(self)', '<', 'self.num_beams', 'or', 'score', '>', 'self.worst_score:', 'self.beams.append((score,', 'hyp))', 'if', 'len(self)', '>', 'self.num_beams:', 'sorted_scores', '=', 'sorted([(s... | 920,180 |
DYCI2/Dicy2-python | factor_oracle_model.py | FactorOracle.follow_suffix_links_from | follow_suffix_links_from | Suffix path from a given index. | [
"Suffix",
"path",
"from",
"a",
"given",
"index."
] | def follow_suffix_links_from(self, index_state: int, include_init_state: bool=True) -> List[int]:
index_pointed_by_suffix_link = self.suffix_links.get(index_state)
if index_pointed_by_suffix_link is None:
return []
elif index_pointed_by_suffix_link == 0:
if include_init_state:
re... | ['def', 'follow_suffix_links_from(self,', 'index_state:', 'int,', 'include_init_state:', 'bool=True)', '->', 'List[int]:', 'index_pointed_by_suffix_link', '=', 'self.suffix_links.get(index_state)', 'if', 'index_pointed_by_suffix_link', 'is', 'None:', 'return', '[]', 'elif', 'index_pointed_by_suffix_link', '==', '0:', '... | 550,330 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.