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 |
|---|---|---|---|---|---|---|---|---|
famura/SimuRLacra | data_sets.py | TimeSeriesDataSet.dim_data | dim_data | Get the data's number of dimensions. | [
"Get",
"the",
"data's",
"number",
"of",
"dimensions."
] | def dim_data(self) -> int:
if not self.data_all.ndim == 2:
raise pyrado.ShapeErr(given=self.data_all, expected_match=(-1, 2))
return self.data_all.shape[1] | ['def', 'dim_data(self)', '->', 'int:', 'if', 'not', 'self.data_all.ndim', '==', '2:', 'raise', 'pyrado.ShapeErr(given=self.data_all,', 'expected_match=(-1,', '2))', 'return', 'self.data_all.shape[1]'] | 884,071 |
lspvic/CopyNet | model_helper.py | compute_perplexity | compute_perplexity | Compute perplexity of the output of the model. | [
"Compute",
"perplexity",
"of",
"the",
"output",
"of",
"the",
"model."
] | def compute_perplexity(model, sess, name):
total_loss = 0
total_predict_count = 0
start_time = time.time()
while True:
try:
(loss, predict_count, batch_size) = model.eval(sess)
total_loss += loss * batch_size
total_predict_count += predict_count
except... | ['def', 'compute_perplexity(model,', 'sess,', 'name):', 'total_loss', '=', '0', 'total_predict_count', '=', '0', 'start_time', '=', 'time.time()', 'while', 'True:', 'try:', '(loss,', 'predict_count,', 'batch_size)', '=', 'model.eval(sess)', 'total_loss', '+=', 'loss', '*', 'batch_size', 'total_predict_count', '+=', 'pr... | 137,199 |
OliverKillane/NuNet-Designer | NuNetLibrary.py | Neuron.giveinput | giveinput | giveinput adds an input to the neuron (used by synapses feeding forwards a value). | [
"giveinput",
"adds",
"an",
"input",
"to",
"the",
"neuron",
"(used",
"by",
"synapses",
"feeding",
"forwards",
"a",
"value)."
] | def giveinput(self, value: int) -> None:
self._inputValue += value | ['def', 'giveinput(self,', 'value:', 'int)', '->', 'None:', 'self._inputValue', '+=', 'value'] | 730,504 |
voxel51/fiftyone | plotly.py | plot_pr_curve | plot_pr_curve | Plots a precision-recall (PR) curve. | [
"Plots",
"a",
"precision-recall",
"(PR)",
"curve."
] | def plot_pr_curve(precision, recall, thresholds=None, label=None, style='area', figure=None, title=None, **kwargs):
if style not in ('line', 'area'):
msg = "Unsupported style '%s'; using 'area' instead" % style
warnings.warn(msg)
style = 'area'
if figure is None:
figure = go.Figu... | ['def', 'plot_pr_curve(precision,', 'recall,', 'thresholds=None,', 'label=None,', "style='area',", 'figure=None,', 'title=None,', '**kwargs):', 'if', 'style', 'not', 'in', "('line',", "'area'):", 'msg', '=', '"Unsupported', 'style', "'%s';", 'using', "'area'", 'instead"', '%', 'style', 'warnings.warn(msg)', 'style', '=... | 583,646 |
kubeflow/pipelines | pipeline_context.py | Pipeline.get_default_pipeline | get_default_pipeline | Gets the default pipeline. | [
"Gets",
"the",
"default",
"pipeline."
] | def get_default_pipeline():
return Pipeline._default_pipeline | ['def', 'get_default_pipeline():', 'return', 'Pipeline._default_pipeline'] | 780,211 |
nicknochnack/RealTimeSignLanguageTFJS | model.py | get_extra_layer_scopes | get_extra_layer_scopes | Gets the scopes for extra layers. | [
"Gets",
"the",
"scopes",
"for",
"extra",
"layers."
] | def get_extra_layer_scopes(last_layers_contain_logits_only=False):
if last_layers_contain_logits_only:
return [LOGITS_SCOPE_NAME]
else:
return [LOGITS_SCOPE_NAME, IMAGE_POOLING_SCOPE, ASPP_SCOPE, CONCAT_PROJECTION_SCOPE, DECODER_SCOPE, META_ARCHITECTURE_SCOPE] | ['def', 'get_extra_layer_scopes(last_layers_contain_logits_only=False):', 'if', 'last_layers_contain_logits_only:', 'return', '[LOGITS_SCOPE_NAME]', 'else:', 'return', '[LOGITS_SCOPE_NAME,', 'IMAGE_POOLING_SCOPE,', 'ASPP_SCOPE,', 'CONCAT_PROJECTION_SCOPE,', 'DECODER_SCOPE,', 'META_ARCHITECTURE_SCOPE]'] | 851,516 |
apeterswu/RL4NMT | common_attention.py | local_reduction_attention | local_reduction_attention | Reduce the length dimension using self attention. | [
"Reduce",
"the",
"length",
"dimension",
"using",
"self",
"attention."
] | def local_reduction_attention(x, block_length, multihead_params):
@expert_utils.add_name_scope()
def dot_product_self_local_attention_flattened(q, k, v):
(_, num_head, _, depth) = q.get_shape().as_list()
def pad_and_reshape(x):
length_x = tf.shape(x)[2]
x = tf.pad(x, [[... | ['def', 'local_reduction_attention(x,', 'block_length,', 'multihead_params):', '@expert_utils.add_name_scope()', 'def', 'dot_product_self_local_attention_flattened(q,', 'k,', 'v):', '(_,', 'num_head,', '_,', 'depth)', '=', 'q.get_shape().as_list()', 'def', 'pad_and_reshape(x):', 'length_x', '=', 'tf.shape(x)[2]', 'x', ... | 330,995 |
rifqind/Agent-Programs-3KS1 | environment.py | Environment.iter_extensions | iter_extensions | Iterates over the extensions by priority. | [
"Iterates",
"over",
"the",
"extensions",
"by",
"priority."
] | def iter_extensions(self):
return iter(sorted(self.extensions.values(), key=lambda x: x.priority)) | ['def', 'iter_extensions(self):', 'return', 'iter(sorted(self.extensions.values(),', 'key=lambda', 'x:', 'x.priority))'] | 42,198 |
netket/netket | S2_operator.py | Renyi2EntanglementEntropy.is_hermitian | is_hermitian | Ignored for this operator. | [
"Ignored",
"for",
"this",
"operator."
] | def is_hermitian(self):
return True | ['def', 'is_hermitian(self):', 'return', 'True'] | 735,956 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | trainer_model_based.py | rl_modelrl_l1_base | rl_modelrl_l1_base | Parameter set with L1 loss. | [
"Parameter",
"set",
"with",
"L1",
"loss."
] | def rl_modelrl_l1_base():
hparams = rl_modelrl_base()
hparams.generative_model_params = 'next_frame_l1'
return hparams | ['def', 'rl_modelrl_l1_base():', 'hparams', '=', 'rl_modelrl_base()', 'hparams.generative_model_params', '=', "'next_frame_l1'", 'return', 'hparams'] | 965,996 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | template.py | Expression.isComment | isComment | True if this expression is a comment. | [
"True",
"if",
"this",
"expression",
"is",
"a",
"comment."
] | def isComment(self):
try:
return self.left.strip().startswith('#')
except (AttributeError,):
return False | ['def', 'isComment(self):', 'try:', 'return', "self.left.strip().startswith('#')", 'except', '(AttributeError,):', 'return', 'False'] | 10,866 |
Erfanafshar/Principles-and-Applications-of---graph-coloring | Duration.py | Duration.frame | frame | Return the frame the duration is in. | [
"Return",
"the",
"frame",
"the",
"duration",
"is",
"in."
] | def frame(self):
return self._frame | ['def', 'frame(self):', 'return', 'self._frame'] | 307,536 |
arshpreetsingh/quantopian-machinelearning | application.py | Application.load_config_file | load_config_file | Load config files by filename and path. | [
"Load",
"config",
"files",
"by",
"filename",
"and",
"path."
] | def load_config_file(self, filename, path=None):
(filename, ext) = os.path.splitext(filename)
new_config = Config()
for config in self._load_config_files(filename, path=path, log=self.log, raise_config_file_errors=self.raise_config_file_errors):
new_config.merge(config)
new_config.merge(self.cli... | ['def', 'load_config_file(self,', 'filename,', 'path=None):', '(filename,', 'ext)', '=', 'os.path.splitext(filename)', 'new_config', '=', 'Config()', 'for', 'config', 'in', 'self._load_config_files(filename,', 'path=path,', 'log=self.log,', 'raise_config_file_errors=self.raise_config_file_errors):', 'new_config.merge(c... | 893,808 |
aimclub/FEDOT | pipeline_visualization.py | show_complex_colors | show_complex_colors | Show with colors defined by function. | [
"Show",
"with",
"colors",
"defined",
"by",
"function."
] | def show_complex_colors(pipeline: Pipeline):
def nodes_color(labels):
if 'xgboost' in labels:
return {'xgboost': 'tab:orange', None: 'black'}
else:
return {'rf': 'tab:green', None: 'black'}
pipeline.show(node_color=nodes_color) | ['def', 'show_complex_colors(pipeline:', 'Pipeline):', 'def', 'nodes_color(labels):', 'if', "'xgboost'", 'in', 'labels:', 'return', "{'xgboost':", "'tab:orange',", 'None:', "'black'}", 'else:', 'return', "{'rf':", "'tab:green',", 'None:', "'black'}", 'pipeline.show(node_color=nodes_color)'] | 545,503 |
cnr-isti-vclab/TagLab | QtBricksWidget.py | QtBricksWidget.setupBricksSize | setupBricksSize | Cpnvert the bricks' size (in cm) to pixels and check if all the values have been inserted. | [
"Cpnvert",
"the",
"bricks'",
"size",
"(in",
"cm)",
"to",
"pixels",
"and",
"check",
"if",
"all",
"the",
"values",
"have",
"been",
"inserted."
] | def setupBricksSize(self):
txt = self.editMinW.text()
if txt == '':
return False
else:
self.min_width = int(int(txt) * 10.0 / self.pixel_size)
txt = self.editMaxW.text()
if txt == '':
return False
else:
self.max_width = int(int(txt) * 10.0 / self.pixel_size)
t... | ['def', 'setupBricksSize(self):', 'txt', '=', 'self.editMinW.text()', 'if', 'txt', '==', "'':", 'return', 'False', 'else:', 'self.min_width', '=', 'int(int(txt)', '*', '10.0', '/', 'self.pixel_size)', 'txt', '=', 'self.editMaxW.text()', 'if', 'txt', '==', "'':", 'return', 'False', 'else:', 'self.max_width', '=', 'int(i... | 906,808 |
QData/deepWordBug | networks.py | Network.disconnect | disconnect | Disconnect a container from this network. | [
"Disconnect",
"a",
"container",
"from",
"this",
"network."
] | def disconnect(self, container, *args, **kwargs):
if isinstance(container, Container):
container = container.id
return self.client.api.disconnect_container_from_network(container, self.id, *args, **kwargs) | ['def', 'disconnect(self,', 'container,', '*args,', '**kwargs):', 'if', 'isinstance(container,', 'Container):', 'container', '=', 'container.id', 'return', 'self.client.api.disconnect_container_from_network(container,', 'self.id,', '*args,', '**kwargs)'] | 541,906 |
asyml/texar | vocabulary.py | Vocab.unk_token | unk_token | A string of the special token indicating unknown token. | [
"A",
"string",
"of",
"the",
"special",
"token",
"indicating",
"unknown",
"token."
] | def unk_token(self):
return self._unk_token | ['def', 'unk_token(self):', 'return', 'self._unk_token'] | 924,501 |
flow-project/flow | bottleneck.py | BottleneckNetwork.get_bottleneck_lanes | get_bottleneck_lanes | Return the reduced number of lanes. | [
"Return",
"the",
"reduced",
"number",
"of",
"lanes."
] | def get_bottleneck_lanes(self, lane):
return [int(lane / 2), int(lane / 4)] | ['def', 'get_bottleneck_lanes(self,', 'lane):', 'return', '[int(lane', '/', '2),', 'int(lane', '/', '4)]'] | 211,777 |
wandb/wandb | dirsnapshot.py | DirectorySnapshotDiff.files_modified | files_modified | List of files that were modified. | [
"List",
"of",
"files",
"that",
"were",
"modified."
] | def files_modified(self):
return self._files_modified | ['def', 'files_modified(self):', 'return', 'self._files_modified'] | 942,198 |
google/deepvariant | runtime_by_region_vis.py | make_all_charts | make_all_charts | Creates charts and puts them in a list with their ID names. | [
"Creates",
"charts",
"and",
"puts",
"them",
"in",
"a",
"list",
"with",
"their",
"ID",
"names."
] | def make_all_charts(df: pd.DataFrame, by_task: pd.DataFrame) -> List[Dict[Text, Union[str, alt.Chart]]]:
charts = [{'id': 'total_by_stage', 'chart': totals_by_stage(by_task)}, {'id': 'pareto_and_runtimes_by_task', 'chart': pareto_and_runtimes_by_task(df)}, {'id': 'histogram_by_task', 'chart': stage_histogram(by_tas... | ['def', 'make_all_charts(df:', 'pd.DataFrame,', 'by_task:', 'pd.DataFrame)', '->', 'List[Dict[Text,', 'Union[str,', 'alt.Chart]]]:', 'charts', '=', "[{'id':", "'total_by_stage',", "'chart':", 'totals_by_stage(by_task)},', "{'id':", "'pareto_and_runtimes_by_task',", "'chart':", 'pareto_and_runtimes_by_task(df)},', "{'id... | 540,434 |
triaquae/triaquae | decorators.py | LoginRequiredTestCase.testView | testView | Check that login_required is assignable to normal views. | [
"Check",
"that",
"login_required",
"is",
"assignable",
"to",
"normal",
"views."
] | def testView(self):
def normal_view(request):
pass
login_required(normal_view) | ['def', 'testView(self):', 'def', 'normal_view(request):', 'pass', 'login_required(normal_view)'] | 357,132 |
weimin17/Object-Detection_HelmetDetection | evaluation.py | segmentation_summaries | segmentation_summaries | Computes segmentation eval summaries for gold and annotated sentences. | [
"Computes",
"segmentation",
"eval",
"summaries",
"for",
"gold",
"and",
"annotated",
"sentences."
] | def segmentation_summaries(gold_corpus, annotated_corpus):
(prec, rec, f1) = calculate_segmentation_metrics(gold_corpus, annotated_corpus)
return {'precision': prec, 'recall': rec, 'f1': f1, 'eval_metric': f1} | ['def', 'segmentation_summaries(gold_corpus,', 'annotated_corpus):', '(prec,', 'rec,', 'f1)', '=', 'calculate_segmentation_metrics(gold_corpus,', 'annotated_corpus)', 'return', "{'precision':", 'prec,', "'recall':", 'rec,', "'f1':", 'f1,', "'eval_metric':", 'f1}'] | 760,146 |
Eric3911/OpenAGI | stage3.py | DeepSpeedZeroOptimizer_Stage3.zero_grad | zero_grad | Zero FP16 parameter grads. | [
"Zero",
"FP16",
"parameter",
"grads."
] | def zero_grad(self, set_to_none=False):
self.micro_step_id = 0
for group in self.fp16_groups:
for p in group:
if set_to_none:
if p.grad is not None and get_accelerator().on_accelerator(p.grad):
p.grad.record_stream(get_accelerator().current_stream())
... | ['def', 'zero_grad(self,', 'set_to_none=False):', 'self.micro_step_id', '=', '0', 'for', 'group', 'in', 'self.fp16_groups:', 'for', 'p', 'in', 'group:', 'if', 'set_to_none:', 'if', 'p.grad', 'is', 'not', 'None', 'and', 'get_accelerator().on_accelerator(p.grad):', 'p.grad.record_stream(get_accelerator().current_stream()... | 252,218 |
Talendar/multilayer_perceptron | main.py | load_mnist | load_mnist | Loads and shuffles the MNIST data. | [
"Loads",
"and",
"shuffles",
"the",
"MNIST",
"data."
] | def load_mnist(path):
df = pd.read_csv(path).sample(frac=1).reset_index(drop=True)
(X, Y) = ([], [])
for (i, row) in df.iterrows():
(label, pixels) = (row['label'], row.drop('label').values / 255)
X.append(pixels)
y = np.zeros(10)
y[label] = 1
Y.append(y)
return (... | ['def', 'load_mnist(path):', 'df', '=', 'pd.read_csv(path).sample(frac=1).reset_index(drop=True)', '(X,', 'Y)', '=', '([],', '[])', 'for', '(i,', 'row)', 'in', 'df.iterrows():', '(label,', 'pixels)', '=', "(row['label'],", "row.drop('label').values", '/', '255)', 'X.append(pixels)', 'y', '=', 'np.zeros(10)', 'y[label]'... | 643,596 |
TonyLianLong/VAI-ReinforcementLearning | c_declarations.py | Struct.wrapper_class | wrapper_class | Generates a Python class containing getter/setter methods for members. | [
"Generates",
"a",
"Python",
"class",
"containing",
"getter/setter",
"methods",
"for",
"members."
] | def wrapper_class(self):
indent = codegen_util.Indenter()
lines = [textwrap.dedent('\n class {0.wrapper_name}(util.WrapperBase):\n """{0.docstring}"""'.format(self))]
with indent:
for member in six.itervalues(self.members):
if isinstance(member, AnonymousUnion):
f... | ['def', 'wrapper_class(self):', 'indent', '=', 'codegen_util.Indenter()', 'lines', '=', "[textwrap.dedent('\\n", 'class', '{0.wrapper_name}(util.WrapperBase):\\n', '"""{0.docstring}"""\'.format(self))]', 'with', 'indent:', 'for', 'member', 'in', 'six.itervalues(self.members):', 'if', 'isinstance(member,', 'AnonymousUni... | 439,814 |
tensorflow/hub | module_spec.py | ModuleSpec.get_tags | get_tags | Lists the graph variants as an iterable of set of tags. | [
"Lists",
"the",
"graph",
"variants",
"as",
"an",
"iterable",
"of",
"set",
"of",
"tags."
] | def get_tags(self):
return [set()] | ['def', 'get_tags(self):', 'return', '[set()]'] | 570,969 |
illidanlab/Simulator | IDQN.py | Estimator.update | update | Updates the estimator towards the given targets. | [
"Updates",
"the",
"estimator",
"towards",
"the",
"given",
"targets."
] | def update(self, s, a, y, learning_rate, global_step):
sess = self.sess
feed_dict = {self.state: s, self.y_pl: y, self.ACTION: a, self.loss_lr: learning_rate}
(summaries, _, loss) = sess.run([self.summaries, self.train_op, self.loss], feed_dict)
if self.summary_writer:
self.summary_writer.add_su... | ['def', 'update(self,', 's,', 'a,', 'y,', 'learning_rate,', 'global_step):', 'sess', '=', 'self.sess', 'feed_dict', '=', '{self.state:', 's,', 'self.y_pl:', 'y,', 'self.ACTION:', 'a,', 'self.loss_lr:', 'learning_rate}', '(summaries,', '_,', 'loss)', '=', 'sess.run([self.summaries,', 'self.train_op,', 'self.loss],', 'fe... | 883,428 |
rlworkgroup/garage | dqn_pong.py | dqn_pong | dqn_pong | Train DQN on PongNoFrameskip-v4 environment. | [
"Train",
"DQN",
"on",
"PongNoFrameskip-v4",
"environment."
] | def dqn_pong(ctxt=None, seed=1, buffer_size=int(50000.0), max_episode_length=500):
set_seed(seed)
with TFTrainer(ctxt) as trainer:
n_epochs = 100
steps_per_epoch = 20
sampler_batch_size = 500
num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
env = gym.make('... | ['def', 'dqn_pong(ctxt=None,', 'seed=1,', 'buffer_size=int(50000.0),', 'max_episode_length=500):', 'set_seed(seed)', 'with', 'TFTrainer(ctxt)', 'as', 'trainer:', 'n_epochs', '=', '100', 'steps_per_epoch', '=', '20', 'sampler_batch_size', '=', '500', 'num_timesteps', '=', 'n_epochs', '*', 'steps_per_epoch', '*', 'sample... | 200,270 |
Trusted-AI/AIX360 | gwbe.py | GlobalWBExplainer.fit | fit | Train a surrogate model. | [
"Train",
"a",
"surrogate",
"model."
] | def fit(self, *argv, **kwargs):
raise NotImplementedError | ['def', 'fit(self,', '*argv,', '**kwargs):', 'raise', 'NotImplementedError'] | 413,236 |
CityU-AIM-Group/SIGMA | env.py | setup_custom_environment | setup_custom_environment | Load custom environment setup from a Python source file and run the setup function. | [
"Load",
"custom",
"environment",
"setup",
"from",
"a",
"Python",
"source",
"file",
"and",
"run",
"the",
"setup",
"function."
] | def setup_custom_environment(custom_module_path):
module = import_file('fcos_core.utils.env.custom_module', custom_module_path)
assert hasattr(module, 'setup_environment') and callable(module.setup_environment), "Custom environment module defined in {} does not have the required callable attribute 'setup_enviro... | ['def', 'setup_custom_environment(custom_module_path):', 'module', '=', "import_file('fcos_core.utils.env.custom_module',", 'custom_module_path)', 'assert', 'hasattr(module,', "'setup_environment')", 'and', 'callable(module.setup_environment),', '"Custom', 'environment', 'module', 'defined', 'in', '{}', 'does', 'not', ... | 934,600 |
google-research/scenic | cc12m_table_dataset.py | get_default_dataset_config | get_default_dataset_config | Gets default configs for wit_internal (en) dataset. | [
"Gets",
"default",
"configs",
"for",
"wit_internal",
"(en)",
"dataset."
] | def get_default_dataset_config():
dataset_configs = ml_collections.ConfigDict()
dataset_configs.dataset_dir = ''
dataset_configs.train_split = 'full'
dataset_configs.output_max_num_tokens = OUTPUT_MAX_LENGTH
dataset_configs.knowledge_max_num_tokens = OUTPUT_MAX_LENGTH
dataset_configs.image_size ... | ['def', 'get_default_dataset_config():', 'dataset_configs', '=', 'ml_collections.ConfigDict()', 'dataset_configs.dataset_dir', '=', "''", 'dataset_configs.train_split', '=', "'full'", 'dataset_configs.output_max_num_tokens', '=', 'OUTPUT_MAX_LENGTH', 'dataset_configs.knowledge_max_num_tokens', '=', 'OUTPUT_MAX_LENGTH',... | 846,801 |
thaines/helit | exemplars.py | ExemplarSet.exemplars | exemplars | Returns how many exemplars are provided. | [
"Returns",
"how",
"many",
"exemplars",
"are",
"provided."
] | def exemplars(self):
raise NotImplementedError | ['def', 'exemplars(self):', 'raise', 'NotImplementedError'] | 591,284 |
srai-lab/srai | test_gtfs_loader.py | test_gtfs_loader_skip_validation | test_gtfs_loader_skip_validation | Test GTFSLoader with invalid feed. | [
"Test",
"GTFSLoader",
"with",
"invalid",
"feed."
] | def test_gtfs_loader_skip_validation(feed: Any, mocker: MockerFixture, gtfs_validation_ok: pd.DataFrame) -> None:
feed.validate.return_value = gtfs_validation_ok
mocker.patch('gtfs_kit.read_feed', return_value=feed)
loader = GTFSLoader()
loader.load(Path('feed.zip').resolve(), skip_validation=True)
... | ['def', 'test_gtfs_loader_skip_validation(feed:', 'Any,', 'mocker:', 'MockerFixture,', 'gtfs_validation_ok:', 'pd.DataFrame)', '->', 'None:', 'feed.validate.return_value', '=', 'gtfs_validation_ok', "mocker.patch('gtfs_kit.read_feed',", 'return_value=feed)', 'loader', '=', 'GTFSLoader()', "loader.load(Path('feed.zip').... | 372,021 |
myothida/Supervised-Machine-Learning | common.py | all_none | all_none | Returns a boolean indicating if all arguments are None. | [
"Returns",
"a",
"boolean",
"indicating",
"if",
"all",
"arguments",
"are",
"None."
] | def all_none(*args) -> bool:
return all((arg is None for arg in args)) | ['def', 'all_none(*args)', '->', 'bool:', 'return', 'all((arg', 'is', 'None', 'for', 'arg', 'in', 'args))'] | 442,336 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | thinkstats2.py | _DictWrapper.Total | Total | Returns the total of the frequencies/probabilities in the map. | [
"Returns",
"the",
"total",
"of",
"the",
"frequencies/probabilities",
"in",
"the",
"map."
] | def Total(self):
total = sum(self.d.values())
return total | ['def', 'Total(self):', 'total', '=', 'sum(self.d.values())', 'return', 'total'] | 12,904 |
NJU-LHRS/official-CMID | potsdam.py | PotsdamDataset.pre_eval | pre_eval | Collect eval result from each iteration. | [
"Collect",
"eval",
"result",
"from",
"each",
"iteration."
] | def pre_eval(self, preds, indices):
if not isinstance(indices, list):
indices = [indices]
if not isinstance(preds, list):
preds = [preds]
pre_eval_results = []
for (pred, index) in zip(preds, indices):
seg_map = self.get_gt_seg_map_by_idx(index)
pre_eval_results.append(in... | ['def', 'pre_eval(self,', 'preds,', 'indices):', 'if', 'not', 'isinstance(indices,', 'list):', 'indices', '=', '[indices]', 'if', 'not', 'isinstance(preds,', 'list):', 'preds', '=', '[preds]', 'pre_eval_results', '=', '[]', 'for', '(pred,', 'index)', 'in', 'zip(preds,', 'indices):', 'seg_map', '=', 'self.get_gt_seg_map... | 250,213 |
TonghanWang/ROMA | starcraft2.py | StarCraft2Env.seed | seed | Returns the random seed used by the environment. | [
"Returns",
"the",
"random",
"seed",
"used",
"by",
"the",
"environment."
] | def seed(self):
return self._seed | ['def', 'seed(self):', 'return', 'self._seed'] | 827,242 |
sek788432/Waymo-2D-Object-Detection | spatial_transform_ops.py | nearest_upsampling | nearest_upsampling | Nearest neighbor upsampling implementation. | [
"Nearest",
"neighbor",
"upsampling",
"implementation."
] | def nearest_upsampling(data, scale):
with tf.name_scope('nearest_upsampling'):
(bs, _, _, c) = data.get_shape().as_list()
shape = tf.shape(input=data)
h = shape[1]
w = shape[2]
bs = -1 if bs is None else bs
data = tf.tile(tf.reshape(data, [bs, h, 1, w, 1, c]), [1, 1, ... | ['def', 'nearest_upsampling(data,', 'scale):', 'with', "tf.name_scope('nearest_upsampling'):", '(bs,', '_,', '_,', 'c)', '=', 'data.get_shape().as_list()', 'shape', '=', 'tf.shape(input=data)', 'h', '=', 'shape[1]', 'w', '=', 'shape[2]', 'bs', '=', '-1', 'if', 'bs', 'is', 'None', 'else', 'bs', 'data', '=', 'tf.tile(tf.... | 973,291 |
liang-hou/slimgan | image_loader.py | sample_dataset_images | sample_dataset_images | Randomly samples the dataset for images. | [
"Randomly",
"samples",
"the",
"dataset",
"for",
"images."
] | def sample_dataset_images(dataset, num_samples):
if len(dataset) < num_samples:
raise ValueError('Given dataset has less than num_samples images: {} given but requires at least {}.'.format(len(dataset), num_samples))
choices = random.sample(range(len(dataset)), num_samples)
images = []
for i in ... | ['def', 'sample_dataset_images(dataset,', 'num_samples):', 'if', 'len(dataset)', '<', 'num_samples:', 'raise', "ValueError('Given", 'dataset', 'has', 'less', 'than', 'num_samples', 'images:', '{}', 'given', 'but', 'requires', 'at', 'least', "{}.'.format(len(dataset),", 'num_samples))', 'choices', '=', 'random.sample(ra... | 878,245 |
brain-research/realistic-ssl-evaluation | train_model.py | make_unlabeled_data_filter_fn | make_unlabeled_data_filter_fn | Make filter for certain classes and a random fraction of unlabeled data. | [
"Make",
"filter",
"for",
"certain",
"classes",
"and",
"a",
"random",
"fraction",
"of",
"unlabeled",
"data."
] | def make_unlabeled_data_filter_fn():
class_filter = tf_utils.filter_fn_from_comma_delimited(FLAGS.unlabeled_classes_filter)
def random_frac_filter(fkey):
return tf_utils.hash_float(fkey) < FLAGS.unlabeled_data_random_fraction
return lambda _, label, fkey: class_filter(label) & random_frac_filter(fk... | ['def', 'make_unlabeled_data_filter_fn():', 'class_filter', '=', 'tf_utils.filter_fn_from_comma_delimited(FLAGS.unlabeled_classes_filter)', 'def', 'random_frac_filter(fkey):', 'return', 'tf_utils.hash_float(fkey)', '<', 'FLAGS.unlabeled_data_random_fraction', 'return', 'lambda', '_,', 'label,', 'fkey:', 'class_filter(l... | 308,982 |
Ruturaj123/Flowchart-Detection | utils.py | flatten | flatten | Takes a list of lists and returns a list of the elements. | [
"Takes",
"a",
"list",
"of",
"lists",
"and",
"returns",
"a",
"list",
"of",
"the",
"elements."
] | def flatten(list_of_lists):
flat_list = []
flat_list_idxs = []
start_idx = 0
for item in list_of_lists:
if isinstance(item, list):
flat_list += item
l = len(item)
idxs = range(start_idx, start_idx + l)
start_idx = start_idx + l
else:
... | ['def', 'flatten(list_of_lists):', 'flat_list', '=', '[]', 'flat_list_idxs', '=', '[]', 'start_idx', '=', '0', 'for', 'item', 'in', 'list_of_lists:', 'if', 'isinstance(item,', 'list):', 'flat_list', '+=', 'item', 'l', '=', 'len(item)', 'idxs', '=', 'range(start_idx,', 'start_idx', '+', 'l)', 'start_idx', '=', 'start_id... | 585,868 |
dvlab-research/FocalsConv | utils.py | sort_by_indices2 | sort_by_indices2 | To sort the sparse features with its indices in a convenient manner. | [
"To",
"sort",
"the",
"sparse",
"features",
"with",
"its",
"indices",
"in",
"a",
"convenient",
"manner."
] | def sort_by_indices2(features, indices, features_add=None):
idx = indices
idx_sum = idx.select(1, 0) * idx[:, 1].max() * idx[:, 2].max() * idx[:, 3].max() + idx.select(1, 1) * idx[:, 2].max() * idx[:, 3].max() + idx.select(1, 2) * idx[:, 3].max() + idx.select(1, 3)
(_, ind) = idx_sum.sort()
features = f... | ['def', 'sort_by_indices2(features,', 'indices,', 'features_add=None):', 'idx', '=', 'indices', 'idx_sum', '=', 'idx.select(1,', '0)', '*', 'idx[:,', '1].max()', '*', 'idx[:,', '2].max()', '*', 'idx[:,', '3].max()', '+', 'idx.select(1,', '1)', '*', 'idx[:,', '2].max()', '*', 'idx[:,', '3].max()', '+', 'idx.select(1,', ... | 608,275 |
Erfanafshar/Principles-and-Applications-of---graph-coloring | colors.py | LightSource.direction | direction | The unit vector direction towards the light source. | [
"The",
"unit",
"vector",
"direction",
"towards",
"the",
"light",
"source."
] | def direction(self):
az = np.radians(90 - self.azdeg)
alt = np.radians(self.altdeg)
return np.array([np.cos(az) * np.cos(alt), np.sin(az) * np.cos(alt), np.sin(alt)]) | ['def', 'direction(self):', 'az', '=', 'np.radians(90', '-', 'self.azdeg)', 'alt', '=', 'np.radians(self.altdeg)', 'return', 'np.array([np.cos(az)', '*', 'np.cos(alt),', 'np.sin(az)', '*', 'np.cos(alt),', 'np.sin(alt)])'] | 306,604 |
weimin17/Object-Detection_HelmetDetection | show_and_tell_model.py | ShowAndTellModel.is_training | is_training | Returns true if the model is built for training mode. | [
"Returns",
"true",
"if",
"the",
"model",
"is",
"built",
"for",
"training",
"mode."
] | def is_training(self):
return self.mode == 'train' | ['def', 'is_training(self):', 'return', 'self.mode', '==', "'train'"] | 763,066 |
microsoft/MASS | lowercase_and_remove_accent.py | run_strip_accents | run_strip_accents | Strips accents from a piece of text. | [
"Strips",
"accents",
"from",
"a",
"piece",
"of",
"text."
] | def run_strip_accents(text):
text = unicodedata.normalize('NFD', text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == 'Mn':
continue
output.append(char)
return ''.join(output) | ['def', 'run_strip_accents(text):', 'text', '=', "unicodedata.normalize('NFD',", 'text)', 'output', '=', '[]', 'for', 'char', 'in', 'text:', 'cat', '=', 'unicodedata.category(char)', 'if', 'cat', '==', "'Mn':", 'continue', 'output.append(char)', 'return', "''.join(output)"] | 646,120 |
RasaHQ/rasa_core | generator.py | TrackerWithCachedStates.update | update | Modify the state of the tracker according to an ``Event``. | [
"Modify",
"the",
"state",
"of",
"the",
"tracker",
"according",
"to",
"an",
"``Event``."
] | def update(self, event: Event, skip_states: bool=False) -> None:
if self._states is None and (not skip_states):
self._states = self.past_states(self.domain)
super(TrackerWithCachedStates, self).update(event)
if not skip_states:
if isinstance(event, ActionExecuted):
pass
e... | ['def', 'update(self,', 'event:', 'Event,', 'skip_states:', 'bool=False)', '->', 'None:', 'if', 'self._states', 'is', 'None', 'and', '(not', 'skip_states):', 'self._states', '=', 'self.past_states(self.domain)', 'super(TrackerWithCachedStates,', 'self).update(event)', 'if', 'not', 'skip_states:', 'if', 'isinstance(even... | 838,360 |
thaines/helit | loo_cov.py | PrecisionLOO.solve | solve | Trys all the options, and selects the one that provides the best nll. | [
"Trys",
"all",
"the",
"options,",
"and",
"selects",
"the",
"one",
"that",
"provides",
"the",
"best",
"nll."
] | def solve(self, callback=None):
self.best = None
bestNLL = None
for (i, var) in enumerate(self.grid):
if callback != None:
callback(i, len(self.grid))
nll = self.calcVar(var)
if numpy.isfinite(nll) and (self.best == None or nll < bestNLL):
self.best = var
... | ['def', 'solve(self,', 'callback=None):', 'self.best', '=', 'None', 'bestNLL', '=', 'None', 'for', '(i,', 'var)', 'in', 'enumerate(self.grid):', 'if', 'callback', '!=', 'None:', 'callback(i,', 'len(self.grid))', 'nll', '=', 'self.calcVar(var)', 'if', 'numpy.isfinite(nll)', 'and', '(self.best', '==', 'None', 'or', 'nll'... | 592,041 |
JonasLandman/QCNN | wheel.py | Wheel.get_formatted_file_tags | get_formatted_file_tags | Return the wheel's tags as a sorted list of strings. | [
"Return",
"the",
"wheel's",
"tags",
"as",
"a",
"sorted",
"list",
"of",
"strings."
] | def get_formatted_file_tags(self):
return sorted((format_tag(tag) for tag in self.file_tags)) | ['def', 'get_formatted_file_tags(self):', 'return', 'sorted((format_tag(tag)', 'for', 'tag', 'in', 'self.file_tags))'] | 302,804 |
ameet-1997/AttentionGuidance | test_utils_summarization.py | SummarizationDataProcessingTest.test_process_story_no_highlights | test_process_story_no_highlights | Processing a story with no highlights returns an empty list for the summary. | [
"Processing",
"a",
"story",
"with",
"no",
"highlights",
"returns",
"an",
"empty",
"list",
"for",
"the",
"summary."
] | def test_process_story_no_highlights(self):
raw_story = 'It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this.'
(_, summary_lines) = process_story(raw_story)
self.assertEqual(summary_l... | ['def', 'test_process_story_no_highlights(self):', 'raw_story', '=', "'It", 'was', 'the', 'year', 'of', 'Our', 'Lord', 'one', 'thousand', 'seven', 'hundred', 'and\\n', 'seventy-five.\\n\\nSpiritual', 'revelations', 'were', 'conceded', 'to', 'England', 'at', 'that\\n', 'favoured', 'period,', 'as', 'at', "this.'", '(_,',... | 92,826 |
ldkong1205/LaserMix | handle_objs.py | filter_outside_objs | filter_outside_objs | Function to filter the objects label outside the image. | [
"Function",
"to",
"filter",
"the",
"objects",
"label",
"outside",
"the",
"image."
] | def filter_outside_objs(gt_bboxes_list: List[Tensor], gt_labels_list: List[Tensor], gt_bboxes_3d_list: List[CameraInstance3DBoxes], gt_labels_3d_list: List[Tensor], centers2d_list: List[Tensor], img_metas: List[dict]) -> None:
bs = len(centers2d_list)
for i in range(bs):
centers2d = centers2d_list[i].cl... | ['def', 'filter_outside_objs(gt_bboxes_list:', 'List[Tensor],', 'gt_labels_list:', 'List[Tensor],', 'gt_bboxes_3d_list:', 'List[CameraInstance3DBoxes],', 'gt_labels_3d_list:', 'List[Tensor],', 'centers2d_list:', 'List[Tensor],', 'img_metas:', 'List[dict])', '->', 'None:', 'bs', '=', 'len(centers2d_list)', 'for', 'i', '... | 624,317 |
scikit-learn/scikit-learn | plot_species_distribution_modeling.py | plot_species_distribution | plot_species_distribution | Plot the species distribution. | [
"Plot",
"the",
"species",
"distribution."
] | def plot_species_distribution(species=('bradypus_variegatus_0', 'microryzomys_minutus_0')):
if len(species) > 2:
print('Note: when more than two species are provided, only the first two will be used')
t0 = time()
data = fetch_species_distributions()
(xgrid, ygrid) = construct_grids(data)
(X,... | ['def', "plot_species_distribution(species=('bradypus_variegatus_0',", "'microryzomys_minutus_0')):", 'if', 'len(species)', '>', '2:', "print('Note:", 'when', 'more', 'than', 'two', 'species', 'are', 'provided,', 'only', 'the', 'first', 'two', 'will', 'be', "used')", 't0', '=', 'time()', 'data', '=', 'fetch_species_dis... | 848,156 |
intel/neural-compressor | diagnosis.py | Diagnosis.calculate_mse | calculate_mse | Calculate MSE for specified tensors. | [
"Calculate",
"MSE",
"for",
"specified",
"tensors."
] | def calculate_mse(self, op_name: str, input_model_tensors: dict, optimized_model_tensors: dict) -> Optional[float]:
input_model_op_data = input_model_tensors.get(op_name, None)
optimized_model_op_data = optimized_model_tensors.get(op_name, None)
if input_model_op_data is None or optimized_model_op_data is N... | ['def', 'calculate_mse(self,', 'op_name:', 'str,', 'input_model_tensors:', 'dict,', 'optimized_model_tensors:', 'dict)', '->', 'Optional[float]:', 'input_model_op_data', '=', 'input_model_tensors.get(op_name,', 'None)', 'optimized_model_op_data', '=', 'optimized_model_tensors.get(op_name,', 'None)', 'if', 'input_model_... | 721,532 |
googleapis/python-aiplatform | client.py | ScheduleServiceClient.parse_common_project_path | parse_common_project_path | Parse a project path into its component segments. | [
"Parse",
"a",
"project",
"path",
"into",
"its",
"component",
"segments."
] | def parse_common_project_path(path: str) -> Dict[str, str]:
m = re.match('^projects/(?P<project>.+?)$', path)
return m.groupdict() if m else {} | ['def', 'parse_common_project_path(path:', 'str)', '->', 'Dict[str,', 'str]:', 'm', '=', "re.match('^projects/(?P<project>.+?)$',", 'path)', 'return', 'm.groupdict()', 'if', 'm', 'else', '{}'] | 813,980 |
deepmind/ai-safety-gridworlds | rocks_diamonds_test.py | RocksDiamondsTest.testNoSwitch | testNoSwitch | Do not touch switches but put 1 rock and 1 diamond in goal area. | [
"Do",
"not",
"touch",
"switches",
"but",
"put",
"1",
"rock",
"and",
"1",
"diamond",
"in",
"goal",
"area."
] | def testNoSwitch(self):
env = rocks_diamonds.RocksDiamondsEnvironment()
env.reset()
actions = 'drrrdrudrurulll'
for a in actions:
env.step(self._actions_dict[a])
self.assertEqual(env._episode_return, 3)
self.assertEqual(env._get_hidden_reward(), 3) | ['def', 'testNoSwitch(self):', 'env', '=', 'rocks_diamonds.RocksDiamondsEnvironment()', 'env.reset()', 'actions', '=', "'drrrdrudrurulll'", 'for', 'a', 'in', 'actions:', 'env.step(self._actions_dict[a])', 'self.assertEqual(env._episode_return,', '3)', 'self.assertEqual(env._get_hidden_reward(),', '3)'] | 412,166 |
matsu0228/nlp-jp | completer.py | IPCompleter.all_completions | all_completions | Wrapper around the complete method for the benefit of emacs. | [
"Wrapper",
"around",
"the",
"complete",
"method",
"for",
"the",
"benefit",
"of",
"emacs."
] | def all_completions(self, text):
return self.complete(text)[1] | ['def', 'all_completions(self,', 'text):', 'return', 'self.complete(text)[1]'] | 786,536 |
pydsgz/DeepVOG | inferer.py | gaze_inferer.load_eyeball_model | load_eyeball_model | Load eyeball model parameters of json format from path. | [
"Load",
"eyeball",
"model",
"parameters",
"of",
"json",
"format",
"from",
"path."
] | def load_eyeball_model(self, path):
loaded_dict = load_json(path)
if self.eyefitter.eye_centre is not None or self.eyefitter.aver_eye_radius is not None:
warnings.warn('3D eyeball exists and reloaded')
self.eyefitter.eye_centre = np.array(loaded_dict['eye_centre'])
self.eyefitter.aver_eye_radius... | ['def', 'load_eyeball_model(self,', 'path):', 'loaded_dict', '=', 'load_json(path)', 'if', 'self.eyefitter.eye_centre', 'is', 'not', 'None', 'or', 'self.eyefitter.aver_eye_radius', 'is', 'not', 'None:', "warnings.warn('3D", 'eyeball', 'exists', 'and', "reloaded')", 'self.eyefitter.eye_centre', '=', "np.array(loaded_dic... | 180,868 |
dgaeta/feedforward-neural-net-SDG-backprop | mnist.py | plot_images_separately | plot_images_separately | Plot the six MNIST images separately. | [
"Plot",
"the",
"six",
"MNIST",
"images",
"separately."
] | def plot_images_separately(images):
fig = plt.figure()
for j in xrange(1, 7):
ax = fig.add_subplot(1, 6, j)
ax.matshow(images[j - 1], cmap=matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show() | ['def', 'plot_images_separately(images):', 'fig', '=', 'plt.figure()', 'for', 'j', 'in', 'xrange(1,', '7):', 'ax', '=', 'fig.add_subplot(1,', '6,', 'j)', 'ax.matshow(images[j', '-', '1],', 'cmap=matplotlib.cm.binary)', 'plt.xticks(np.array([]))', 'plt.yticks(np.array([]))', 'plt.show()'] | 581,923 |
SurturFTW/AI-Practicals | Eightpuzzle.py | EigthPuzzleProblem.is_goal | is_goal | Returns true if a state is the goal state. | [
"Returns",
"true",
"if",
"a",
"state",
"is",
"the",
"goal",
"state."
] | def is_goal(self, state):
return state == GOAL | ['def', 'is_goal(self,', 'state):', 'return', 'state', '==', 'GOAL'] | 95,619 |
young-geng/m3ae_public | jax_utils.py | wrap_function_with_rng | wrap_function_with_rng | To be used as decorator, automatically bookkeep a RNG for the wrapped function. | [
"To",
"be",
"used",
"as",
"decorator,",
"automatically",
"bookkeep",
"a",
"RNG",
"for",
"the",
"wrapped",
"function."
] | def wrap_function_with_rng(rng):
def wrap_function(function):
def wrapped(*args, **kwargs):
nonlocal rng
(rng, split_rng) = jax.random.split(rng)
return function(split_rng, *args, **kwargs)
return wrapped
return wrap_function | ['def', 'wrap_function_with_rng(rng):', 'def', 'wrap_function(function):', 'def', 'wrapped(*args,', '**kwargs):', 'nonlocal', 'rng', '(rng,', 'split_rng)', '=', 'jax.random.split(rng)', 'return', 'function(split_rng,', '*args,', '**kwargs)', 'return', 'wrapped', 'return', 'wrap_function'] | 620,007 |
KalleHallden/InstaAutomator | test_utils.py | _GenericTest.test_array_rank2_eq | test_array_rank2_eq | Test two equal array of rank 2 are found equal. | [
"Test",
"two",
"equal",
"array",
"of",
"rank",
"2",
"are",
"found",
"equal."
] | def test_array_rank2_eq(self):
a = np.array([[1, 2], [3, 4]])
b = np.array([[1, 2], [3, 4]])
self._test_equal(a, b) | ['def', 'test_array_rank2_eq(self):', 'a', '=', 'np.array([[1,', '2],', '[3,', '4]])', 'b', '=', 'np.array([[1,', '2],', '[3,', '4]])', 'self._test_equal(a,', 'b)'] | 231,109 |
sktime/sktime | test_check_estimator.py | test_check_estimator_does_not_raise | test_check_estimator_does_not_raise | Test that check_estimator does not raise exceptions on examples we know pass. | [
"Test",
"that",
"check_estimator",
"does",
"not",
"raise",
"exceptions",
"on",
"examples",
"we",
"know",
"pass."
] | def test_check_estimator_does_not_raise(estimator_class):
estimator_instance = estimator_class.create_test_instance()
check_estimator(estimator_class, raise_exceptions=True, verbose=False)
check_estimator(estimator_instance, raise_exceptions=True, verbose=False) | ['def', 'test_check_estimator_does_not_raise(estimator_class):', 'estimator_instance', '=', 'estimator_class.create_test_instance()', 'check_estimator(estimator_class,', 'raise_exceptions=True,', 'verbose=False)', 'check_estimator(estimator_instance,', 'raise_exceptions=True,', 'verbose=False)'] | 878,041 |
alibaba/EasyCV | ms_utils.py | to_ms_config | to_ms_config | Convert EasyCV config to ModelScope style. | [
"Convert",
"EasyCV",
"config",
"to",
"ModelScope",
"style."
] | def to_ms_config(cfg, task, ms_model_name, pipeline_name, save_path=None, reserved_keys=[], dump=True):
if isinstance(cfg, str):
easycv_cfg = Config.fromfile(cfg)
if dump and save_path is None:
save_dir = os.path.dirname(cfg)
save_name = MODELSCOPE_PREFIX + '_' + os.path.spli... | ['def', 'to_ms_config(cfg,', 'task,', 'ms_model_name,', 'pipeline_name,', 'save_path=None,', 'reserved_keys=[],', 'dump=True):', 'if', 'isinstance(cfg,', 'str):', 'easycv_cfg', '=', 'Config.fromfile(cfg)', 'if', 'dump', 'and', 'save_path', 'is', 'None:', 'save_dir', '=', 'os.path.dirname(cfg)', 'save_name', '=', 'MODEL... | 546,896 |
Oporto/CS4341_Artificial_Inteligence | __init__.py | get_terminal_size | get_terminal_size | Returns a tuple (x, y) representing the width(x) and the height(x) in characters of the terminal window. | [
"Returns",
"a",
"tuple",
"(x,",
"y)",
"representing",
"the",
"width(x)",
"and",
"the",
"height(x)",
"in",
"characters",
"of",
"the",
"terminal",
"window."
] | def get_terminal_size():
def ioctl_GWINSZ(fd):
try:
import fcntl
import termios
import struct
cr = struct.unpack('hh', fcntl.ioctl(fd, termios.TIOCGWINSZ, '1234'))
except:
return None
if cr == (0, 0):
return None
... | ['def', 'get_terminal_size():', 'def', 'ioctl_GWINSZ(fd):', 'try:', 'import', 'fcntl', 'import', 'termios', 'import', 'struct', 'cr', '=', "struct.unpack('hh',", 'fcntl.ioctl(fd,', 'termios.TIOCGWINSZ,', "'1234'))", 'except:', 'return', 'None', 'if', 'cr', '==', '(0,', '0):', 'return', 'None', 'return', 'cr', 'cr', '='... | 190,893 |
facebookresearch/fvcore | test_transform.py | TestTransforms.test_grid_sample_img_transform | test_grid_sample_img_transform | Test grid sampling tranformation. | [
"Test",
"grid",
"sampling",
"tranformation."
] | def test_grid_sample_img_transform(self):
for interp in ['nearest']:
grid_2d = np.stack(np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10)), axis=2).astype(float)
grid = np.tile(grid_2d[None, :, :, :], [8, 1, 1, 1])
transformer = T.GridSampleTransform(grid, interp)
(img_h, im... | ['def', 'test_grid_sample_img_transform(self):', 'for', 'interp', 'in', "['nearest']:", 'grid_2d', '=', 'np.stack(np.meshgrid(np.linspace(-1,', '1,', '10),', 'np.linspace(-1,', '1,', '10)),', 'axis=2).astype(float)', 'grid', '=', 'np.tile(grid_2d[None,', ':,', ':,', ':],', '[8,', '1,', '1,', '1])', 'transformer', '=', ... | 566,024 |
FreshAirTonight/af2complex | data_transforms.py | curry1 | curry1 | Supply all arguments but the first. | [
"Supply",
"all",
"arguments",
"but",
"the",
"first."
] | def curry1(f):
def fc(*args, **kwargs):
return lambda x: f(x, *args, **kwargs)
return fc | ['def', 'curry1(f):', 'def', 'fc(*args,', '**kwargs):', 'return', 'lambda', 'x:', 'f(x,', '*args,', '**kwargs)', 'return', 'fc'] | 400,768 |
ameet-1997/Natural-Language-Processing | multipartiterank.py | MultipartiteRank.candidate_weighting | candidate_weighting | Candidate weight calculation using random walk. | [
"Candidate",
"weight",
"calculation",
"using",
"random",
"walk."
] | def candidate_weighting(self, threshold=0.74, method='average', alpha=1.1):
if not self.candidates:
return
self.topic_clustering(threshold=threshold, method=method)
self.build_topic_graph()
if alpha > 0.0:
self.weight_adjustment(alpha)
self.weights = nx.pagerank_scipy(self.graph) | ['def', 'candidate_weighting(self,', 'threshold=0.74,', "method='average',", 'alpha=1.1):', 'if', 'not', 'self.candidates:', 'return', 'self.topic_clustering(threshold=threshold,', 'method=method)', 'self.build_topic_graph()', 'if', 'alpha', '>', '0.0:', 'self.weight_adjustment(alpha)', 'self.weights', '=', 'nx.pageran... | 660,098 |
flavioschneider/rl-transfer- | test_mlp_module.py | TestMLPModel.test_mlp_with_learnable_non_linear_function | test_mlp_with_learnable_non_linear_function | Test MLPModule with learnable non-linear functions. | [
"Test",
"MLPModule",
"with",
"learnable",
"non-linear",
"functions."
] | def test_mlp_with_learnable_non_linear_function(self):
(input_dim, output_dim, hidden_sizes) = (1, 1, (3, 2))
input_val = -torch.ones([1, input_dim], dtype=torch.float32)
module = MLPModule(input_dim=input_dim, output_dim=output_dim, hidden_nonlinearity=torch.nn.PReLU(init=10.0), hidden_sizes=hidden_sizes, ... | ['def', 'test_mlp_with_learnable_non_linear_function(self):', '(input_dim,', 'output_dim,', 'hidden_sizes)', '=', '(1,', '1,', '(3,', '2))', 'input_val', '=', '-torch.ones([1,', 'input_dim],', 'dtype=torch.float32)', 'module', '=', 'MLPModule(input_dim=input_dim,', 'output_dim=output_dim,', 'hidden_nonlinearity=torch.n... | 861,848 |
declare-lab/speech-adapters | phoneme_recognition.py | seed_worker | seed_worker | Helper function to set worker seed during Dataloader initialization. | [
"Helper",
"function",
"to",
"set",
"worker",
"seed",
"during",
"Dataloader",
"initialization."
] | def seed_worker(_):
worker_seed = torch.initial_seed() % 2 ** 32
set_seed(worker_seed) | ['def', 'seed_worker(_):', 'worker_seed', '=', 'torch.initial_seed()', '%', '2', '**', '32', 'set_seed(worker_seed)'] | 894,861 |
p-lambda/wilds | wilds_unlabeled_dataset.py | WILDSUnlabeledDataset.split_dict | split_dict | A dictionary mapping splits to integer identifiers (used in split_array), Keys should match up with split_names. | [
"A",
"dictionary",
"mapping",
"splits",
"to",
"integer",
"identifiers",
"(used",
"in",
"split_array),",
"Keys",
"should",
"match",
"up",
"with",
"split_names."
] | def split_dict(self):
return getattr(self, '_split_dict', WILDSUnlabeledDataset.DEFAULT_SPLITS) | ['def', 'split_dict(self):', 'return', 'getattr(self,', "'_split_dict',", 'WILDSUnlabeledDataset.DEFAULT_SPLITS)'] | 959,763 |
sklearn-theano/sklearn-theano | encoder.py | GroupSizer | GroupSizer | Returns a sizer for a group field. | [
"Returns",
"a",
"sizer",
"for",
"a",
"group",
"field."
] | def GroupSizer(field_number, is_repeated, is_packed):
tag_size = _TagSize(field_number) * 2
assert not is_packed
if is_repeated:
def RepeatedFieldSize(value):
result = tag_size * len(value)
for element in value:
result += element.ByteSize()
return... | ['def', 'GroupSizer(field_number,', 'is_repeated,', 'is_packed):', 'tag_size', '=', '_TagSize(field_number)', '*', '2', 'assert', 'not', 'is_packed', 'if', 'is_repeated:', 'def', 'RepeatedFieldSize(value):', 'result', '=', 'tag_size', '*', 'len(value)', 'for', 'element', 'in', 'value:', 'result', '+=', 'element.ByteSiz... | 351,153 |
hyz-xmaster/swa_object_detection | ssd_head.py | SSDHead.loss_single | loss_single | Compute loss of a single image. | [
"Compute",
"loss",
"of",
"a",
"single",
"image."
] | def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights, bbox_targets, bbox_weights, num_total_samples):
loss_cls_all = F.cross_entropy(cls_score, labels, reduction='none') * label_weights
pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero().reshape(-1)
neg_inds = (labels == ... | ['def', 'loss_single(self,', 'cls_score,', 'bbox_pred,', 'anchor,', 'labels,', 'label_weights,', 'bbox_targets,', 'bbox_weights,', 'num_total_samples):', 'loss_cls_all', '=', 'F.cross_entropy(cls_score,', 'labels,', "reduction='none')", '*', 'label_weights', 'pos_inds', '=', '((labels', '>=', '0)', '&', '(labels', '<',... | 882,553 |
erfaneshrati/meta-transfer-learning | misc.py | resnet_conv_block | resnet_conv_block | The function to forward a conv layer. | [
"The",
"function",
"to",
"forward",
"a",
"conv",
"layer."
] | def resnet_conv_block(inp, cweight, bweight, reuse, scope, activation=leaky_relu):
(stride, no_stride) = ([1, 2, 2, 1], [1, 1, 1, 1])
if FLAGS.activation == 'leaky_relu':
activation = leaky_relu
elif FLAGS.activation == 'relu':
activation = tf.nn.relu
else:
activation = None
... | ['def', 'resnet_conv_block(inp,', 'cweight,', 'bweight,', 'reuse,', 'scope,', 'activation=leaky_relu):', '(stride,', 'no_stride)', '=', '([1,', '2,', '2,', '1],', '[1,', '1,', '1,', '1])', 'if', 'FLAGS.activation', '==', "'leaky_relu':", 'activation', '=', 'leaky_relu', 'elif', 'FLAGS.activation', '==', "'relu':", 'act... | 633,271 |
PaccMann/fdsa | shapes_data.py | Shapes.datapoints_square | datapoints_square | Generates a set of datapoints sampled from the perimeter of a square. | [
"Generates",
"a",
"set",
"of",
"datapoints",
"sampled",
"from",
"the",
"perimeter",
"of",
"a",
"square."
] | def datapoints_square(self, set_length: int, sample_id: int, min_radius: int=100) -> Tuple:
(cx, cy, radius) = self.datapoints_circle(1, sample_id, use='square')
side = np.sqrt(radius * radius * 2)
half_side = side * 0.5
(r, c) = draw.rectangle_perimeter((cx - half_side, cy - half_side), extent=(side, s... | ['def', 'datapoints_square(self,', 'set_length:', 'int,', 'sample_id:', 'int,', 'min_radius:', 'int=100)', '->', 'Tuple:', '(cx,', 'cy,', 'radius)', '=', 'self.datapoints_circle(1,', 'sample_id,', "use='square')", 'side', '=', 'np.sqrt(radius', '*', 'radius', '*', '2)', 'half_side', '=', 'side', '*', '0.5', '(r,', 'c)'... | 560,849 |
open-mmlab/mmcv | diff_iou_rotated.py | box2corners | box2corners | Convert rotated 2d box coordinate to corners. | [
"Convert",
"rotated",
"2d",
"box",
"coordinate",
"to",
"corners."
] | def box2corners(box: Tensor) -> Tensor:
B = box.size()[0]
(x, y, w, h, alpha) = box.split([1, 1, 1, 1, 1], dim=-1)
x4 = box.new_tensor([0.5, -0.5, -0.5, 0.5]).to(box.device)
x4 = x4 * w
y4 = box.new_tensor([0.5, 0.5, -0.5, -0.5]).to(box.device)
y4 = y4 * h
corners = torch.stack([x4, y4], dim... | ['def', 'box2corners(box:', 'Tensor)', '->', 'Tensor:', 'B', '=', 'box.size()[0]', '(x,', 'y,', 'w,', 'h,', 'alpha)', '=', 'box.split([1,', '1,', '1,', '1,', '1],', 'dim=-1)', 'x4', '=', 'box.new_tensor([0.5,', '-0.5,', '-0.5,', '0.5]).to(box.device)', 'x4', '=', 'x4', '*', 'w', 'y4', '=', 'box.new_tensor([0.5,', '0.5,... | 631,523 |
pykale/pykale | model.py | get_model | get_model | Builds and returns a model and associated hyper-parameters according to the config object passed. | [
"Builds",
"and",
"returns",
"a",
"model",
"and",
"associated",
"hyper-parameters",
"according",
"to",
"the",
"config",
"object",
"passed."
] | def get_model(cfg, dataset, num_channels):
config_params = get_config(cfg)
train_params = config_params['train_params']
train_params_local = deepcopy(train_params)
if cfg.DATASET.NAME.upper() == 'DIGITS':
feature_network = SmallCNNFeature(num_channels)
else:
feature_network = ResNet1... | ['def', 'get_model(cfg,', 'dataset,', 'num_channels):', 'config_params', '=', 'get_config(cfg)', 'train_params', '=', "config_params['train_params']", 'train_params_local', '=', 'deepcopy(train_params)', 'if', 'cfg.DATASET.NAME.upper()', '==', "'DIGITS':", 'feature_network', '=', 'SmallCNNFeature(num_channels)', 'else:... | 819,612 |
RasaHQ/rasa | callback.py | RasaTrainingLogger.on_epoch_end | on_epoch_end | Updates the logging output on every epoch end. | [
"Updates",
"the",
"logging",
"output",
"on",
"every",
"epoch",
"end."
] | def on_epoch_end(self, epoch: int, logs: Optional[Dict[Text, Any]]=None) -> None:
self.progress_bar.update(1)
self.progress_bar.set_postfix(logs) | ['def', 'on_epoch_end(self,', 'epoch:', 'int,', 'logs:', 'Optional[Dict[Text,', 'Any]]=None)', '->', 'None:', 'self.progress_bar.update(1)', 'self.progress_bar.set_postfix(logs)'] | 837,889 |
ivanmontero/autobot | modeling_tf_funnel.py | TFFunnelAttentionStructure.pool_tensor | pool_tensor | Apply 1D pooling to a tensor of size [B x T (x H)]. | [
"Apply",
"1D",
"pooling",
"to",
"a",
"tensor",
"of",
"size",
"[B",
"x",
"T",
"(x",
"H)]."
] | def pool_tensor(self, tensor, mode='mean', stride=2):
if tensor is None:
return None
if isinstance(tensor, (tuple, list)):
return type(tensor)((self.pool_tensor(tensor, mode=mode, stride=stride) for x in tensor))
if self.separate_cls:
suffix = tensor[:, :-1] if self.truncate_seq else... | ['def', 'pool_tensor(self,', 'tensor,', "mode='mean',", 'stride=2):', 'if', 'tensor', 'is', 'None:', 'return', 'None', 'if', 'isinstance(tensor,', '(tuple,', 'list)):', 'return', 'type(tensor)((self.pool_tensor(tensor,', 'mode=mode,', 'stride=stride)', 'for', 'x', 'in', 'tensor))', 'if', 'self.separate_cls:', 'suffix',... | 418,064 |
wbsth/cs50ai | generate.py | CrosswordCreator.letter_grid | letter_grid | Return 2D array representing a given assignment. | [
"Return",
"2D",
"array",
"representing",
"a",
"given",
"assignment."
] | def letter_grid(self, assignment):
letters = [[None for _ in range(self.crossword.width)] for _ in range(self.crossword.height)]
for (variable, word) in assignment.items():
direction = variable.direction
for k in range(len(word)):
i = variable.i + (k if direction == Variable.DOWN els... | ['def', 'letter_grid(self,', 'assignment):', 'letters', '=', '[[None', 'for', '_', 'in', 'range(self.crossword.width)]', 'for', '_', 'in', 'range(self.crossword.height)]', 'for', '(variable,', 'word)', 'in', 'assignment.items():', 'direction', '=', 'variable.direction', 'for', 'k', 'in', 'range(len(word)):', 'i', '=', ... | 192,716 |
fairlearn/fairlearn | utility_parity.py | UtilityParity.default_objective | default_objective | Return the default objective for moments of this kind. | [
"Return",
"the",
"default",
"objective",
"for",
"moments",
"of",
"this",
"kind."
] | def default_objective(self):
return ErrorRate() | ['def', 'default_objective(self):', 'return', 'ErrorRate()'] | 558,433 |
OpenMDAO/OpenMDAO-Framework | systems.py | SimpleSystem.solve_linear | solve_linear | Single linear solve solution applied to whatever input is sitting in the RHS vector. | [
"Single",
"linear",
"solve",
"solution",
"applied",
"to",
"whatever",
"input",
"is",
"sitting",
"in",
"the",
"RHS",
"vector."
] | def solve_linear(self, options=None):
self.sol_vec.array[:] = self.rhs_vec.array[:] | ['def', 'solve_linear(self,', 'options=None):', 'self.sol_vec.array[:]', '=', 'self.rhs_vec.array[:]'] | 276,096 |
KalleHallden/InstaAutomator | msvc.py | RegistryInfo.visualstudio | visualstudio | Microsoft Visual Studio root registry key. | [
"Microsoft",
"Visual",
"Studio",
"root",
"registry",
"key."
] | def visualstudio(self):
return 'VisualStudio' | ['def', 'visualstudio(self):', 'return', "'VisualStudio'"] | 233,611 |
wandb/wandb | runset.py | Runset.from_json | from_json | This has a custom implementation because sometimes runsets are missing the project field. | [
"This",
"has",
"a",
"custom",
"implementation",
"because",
"sometimes",
"runsets",
"are",
"missing",
"the",
"project",
"field."
] | def from_json(cls, spec: Dict[str, Any]) -> T:
obj = cls()
obj._spec = spec
project = spec.get('project')
if project:
obj.entity = project.get('entityName', coalesce(PublicApi().default_entity, ''))
obj.project = project.get('name')
else:
obj.entity = coalesce(PublicApi().def... | ['def', 'from_json(cls,', 'spec:', 'Dict[str,', 'Any])', '->', 'T:', 'obj', '=', 'cls()', 'obj._spec', '=', 'spec', 'project', '=', "spec.get('project')", 'if', 'project:', 'obj.entity', '=', "project.get('entityName',", 'coalesce(PublicApi().default_entity,', "''))", 'obj.project', '=', "project.get('name')", 'else:',... | 941,490 |
Speech-Lab-IITM/CCC-wav2vec-2.0 | fairseq_optimizer.py | FairseqOptimizer.supports_flat_params | supports_flat_params | Whether the optimizer supports collapsing of the model parameters/gradients into a single contiguous Tensor. | [
"Whether",
"the",
"optimizer",
"supports",
"collapsing",
"of",
"the",
"model",
"parameters/gradients",
"into",
"a",
"single",
"contiguous",
"Tensor."
] | def supports_flat_params(self):
if hasattr(self.optimizer, 'supports_flat_params'):
return self.optimizer.supports_flat_params
return False | ['def', 'supports_flat_params(self):', 'if', 'hasattr(self.optimizer,', "'supports_flat_params'):", 'return', 'self.optimizer.supports_flat_params', 'return', 'False'] | 104,050 |
facebookresearch/CompilerGym | compiler_env.py | CompilerEnv.reward | reward | A view of the available reward spaces that permits on-demand computation of rewards. | [
"A",
"view",
"of",
"the",
"available",
"reward",
"spaces",
"that",
"permits",
"on-demand",
"computation",
"of",
"rewards."
] | def reward(self) -> RewardView:
raise NotImplementedError('abstract method') | ['def', 'reward(self)', '->', 'RewardView:', 'raise', "NotImplementedError('abstract", "method')"] | 125,443 |
Eric3911/OpenAGI | schedule.py | PipeSchedule.stage | stage | Stage index used to configure this schedule. | [
"Stage",
"index",
"used",
"to",
"configure",
"this",
"schedule."
] | def stage(self):
return self.stage_id | ['def', 'stage(self):', 'return', 'self.stage_id'] | 252,174 |
deepmind/trfl | value_ops_test.py | QVMAXTest.testNoOtherGradients | testNoOtherGradients | Tests no gradient propagates through things other than v_tm1. | [
"Tests",
"no",
"gradient",
"propagates",
"through",
"things",
"other",
"than",
"v_tm1."
] | def testNoOtherGradients(self):
gradients = tf.gradients([self.loss_op], [self.q_t, self.r_t, self.pcont_t])
self.assertEqual(gradients, [None] * len(gradients)) | ['def', 'testNoOtherGradients(self):', 'gradients', '=', 'tf.gradients([self.loss_op],', '[self.q_t,', 'self.r_t,', 'self.pcont_t])', 'self.assertEqual(gradients,', '[None]', '*', 'len(gradients))'] | 356,270 |
arshpreetsingh/quantopian-machinelearning | _precord.py | PRecord.serialize | serialize | Serialize the current PRecord using custom serializer functions for fields where such have been supplied. | [
"Serialize",
"the",
"current",
"PRecord",
"using",
"custom",
"serializer",
"functions",
"for",
"fields",
"where",
"such",
"have",
"been",
"supplied."
] | def serialize(self, format=None):
return dict(((k, serialize(self._precord_fields[k].serializer, format, v)) for (k, v) in self.items())) | ['def', 'serialize(self,', 'format=None):', 'return', 'dict(((k,', 'serialize(self._precord_fields[k].serializer,', 'format,', 'v))', 'for', '(k,', 'v)', 'in', 'self.items()))'] | 892,746 |
pathak22/noreward-rl | a3c.py | A3C.pull_batch_from_queue | pull_batch_from_queue | Take a rollout from the queue of the thread runner. | [
"Take",
"a",
"rollout",
"from",
"the",
"queue",
"of",
"the",
"thread",
"runner."
] | def pull_batch_from_queue(self):
rollout = self.runner.queue.get(timeout=600.0)
while not rollout.terminal:
try:
rollout.extend(self.runner.queue.get_nowait())
except queue.Empty:
break
return rollout | ['def', 'pull_batch_from_queue(self):', 'rollout', '=', 'self.runner.queue.get(timeout=600.0)', 'while', 'not', 'rollout.terminal:', 'try:', 'rollout.extend(self.runner.queue.get_nowait())', 'except', 'queue.Empty:', 'break', 'return', 'rollout'] | 249,524 |
matsu0228/nlp-jp | dtmmodel.py | DtmModel.train | train | Train DTM model using specified corpus and time slices. | [
"Train",
"DTM",
"model",
"using",
"specified",
"corpus",
"and",
"time",
"slices."
] | def train(self, corpus, time_slices, mode, model):
self.convert_input(corpus, time_slices)
arguments = '--ntopics={p0} --model={mofrl} --mode={p1} --initialize_lda={p2} --corpus_prefix={p3} --outname={p4} --alpha={p5}'.format(p0=self.num_topics, mofrl=model, p1=mode, p2=self.initialize_lda, p3=self.fcorpus(), ... | ['def', 'train(self,', 'corpus,', 'time_slices,', 'mode,', 'model):', 'self.convert_input(corpus,', 'time_slices)', 'arguments', '=', "'--ntopics={p0}", '--model={mofrl}', '--mode={p1}', '--initialize_lda={p2}', '--corpus_prefix={p3}', '--outname={p4}', "--alpha={p5}'.format(p0=self.num_topics,", 'mofrl=model,', 'p1=mo... | 785,938 |
PacktPublishing/Learning-OpenCV-5---with-Python-Fourth-Edition | managers.py | CaptureManager.enterFrame | enterFrame | Capture the next frame, if any. | [
"Capture",
"the",
"next",
"frame,",
"if",
"any."
] | def enterFrame(self):
assert not self._enteredFrame, 'previous enterFrame() had no matching exitFrame()'
if self._capture is not None:
self._enteredFrame = self._capture.grab() | ['def', 'enterFrame(self):', 'assert', 'not', 'self._enteredFrame,', "'previous", 'enterFrame()', 'had', 'no', 'matching', "exitFrame()'", 'if', 'self._capture', 'is', 'not', 'None:', 'self._enteredFrame', '=', 'self._capture.grab()'] | 588,060 |
seltzerfish/guardyn | gtest_filter_unittest.py | GTestFilterUnitTest.AssertPartitionIsValid | AssertPartitionIsValid | Asserts that list_of_sets is a valid partition of set_var. | [
"Asserts",
"that",
"list_of_sets",
"is",
"a",
"valid",
"partition",
"of",
"set_var."
] | def AssertPartitionIsValid(self, set_var, list_of_sets):
full_partition = []
for slice_var in list_of_sets:
full_partition.extend(slice_var)
self.assertEqual(len(set_var), len(full_partition))
self.assertEqual(set(set_var), set(full_partition)) | ['def', 'AssertPartitionIsValid(self,', 'set_var,', 'list_of_sets):', 'full_partition', '=', '[]', 'for', 'slice_var', 'in', 'list_of_sets:', 'full_partition.extend(slice_var)', 'self.assertEqual(len(set_var),', 'len(full_partition))', 'self.assertEqual(set(set_var),', 'set(full_partition))'] | 572,254 |
jbwang1997/CrossKD | loading.py | LoadProposals.transform | transform | Transform function to load proposals from file. | [
"Transform",
"function",
"to",
"load",
"proposals",
"from",
"file."
] | def transform(self, results: dict) -> dict:
proposals = results['proposals']
assert isinstance(proposals, dict) or isinstance(proposals, BaseDataElement)
bboxes = proposals['bboxes'].astype(np.float32)
assert bboxes.shape[1] == 4, f'Proposals should have shapes (n, 4), but found {bboxes.shape}'
if '... | ['def', 'transform(self,', 'results:', 'dict)', '->', 'dict:', 'proposals', '=', "results['proposals']", 'assert', 'isinstance(proposals,', 'dict)', 'or', 'isinstance(proposals,', 'BaseDataElement)', 'bboxes', '=', "proposals['bboxes'].astype(np.float32)", 'assert', 'bboxes.shape[1]', '==', '4,', "f'Proposals", 'should... | 490,771 |
arshpreetsingh/quantopian-machinelearning | tests.py | test_escaped | test_escaped | Check if the value is escaped. | [
"Check",
"if",
"the",
"value",
"is",
"escaped."
] | def test_escaped(value):
return hasattr(value, '__html__') | ['def', 'test_escaped(value):', 'return', 'hasattr(value,', "'__html__')"] | 887,652 |
gatheluck/FourierHeatmap | heatmap.py | eval_fourier_heatmap | eval_fourier_heatmap | Evaluate Fourier Heat Map about given architecture and dataset. | [
"Evaluate",
"Fourier",
"Heat",
"Map",
"about",
"given",
"architecture",
"and",
"dataset."
] | def eval_fourier_heatmap(input_size: int, ignore_edge_size: int, eps: float, arch: nn.Module, dataset: torchvision.datasets.VisionDataset, batch_size: int, device: torch.device, topk: Tuple[int, ...]=(1,), savedir: Optional[pathlib.Path]=None) -> List[torch.Tensor]:
if input_size % 2 != 0:
raise ValueError(... | ['def', 'eval_fourier_heatmap(input_size:', 'int,', 'ignore_edge_size:', 'int,', 'eps:', 'float,', 'arch:', 'nn.Module,', 'dataset:', 'torchvision.datasets.VisionDataset,', 'batch_size:', 'int,', 'device:', 'torch.device,', 'topk:', 'Tuple[int,', '...]=(1,),', 'savedir:', 'Optional[pathlib.Path]=None)', '->', 'List[tor... | 564,067 |
KalleHallden/InstaAutomator | autodist.py | check_gcc_variable_attribute | check_gcc_variable_attribute | Return True if the given variable attribute is supported. | [
"Return",
"True",
"if",
"the",
"given",
"variable",
"attribute",
"is",
"supported."
] | def check_gcc_variable_attribute(cmd, attribute):
cmd._check_compiler()
body = '\n#pragma GCC diagnostic error "-Wattributes"\n#pragma clang diagnostic error "-Wattributes"\n\nint %s foo;\n\nint\nmain()\n{\n return 0;\n}\n' % (attribute,)
return cmd.try_compile(body, None, None) != 0 | ['def', 'check_gcc_variable_attribute(cmd,', 'attribute):', 'cmd._check_compiler()', 'body', '=', "'\\n#pragma", 'GCC', 'diagnostic', 'error', '"-Wattributes"\\n#pragma', 'clang', 'diagnostic', 'error', '"-Wattributes"\\n\\nint', '%s', 'foo;\\n\\nint\\nmain()\\n{\\n', 'return', "0;\\n}\\n'", '%', '(attribute,)', 'retur... | 243,271 |
ahthie7u/cockpit | schedules.py | linear | linear | Creates a linear schedule that tracks when ``{offset + n interval | n >= 0}``. | [
"Creates",
"a",
"linear",
"schedule",
"that",
"tracks",
"when",
"``{offset",
"+",
"n",
"interval",
"|",
"n",
">=",
"0}``."
] | def linear(interval, offset=0):
docstring = 'Track at iterations {' + f'{offset} + n * {interval} ' + '| n >= 0}.'
def schedule(global_step):
shifted = global_step - offset
if shifted < 0:
return False
else:
return shifted % interval == 0
schedule.__doc__ = d... | ['def', 'linear(interval,', 'offset=0):', 'docstring', '=', "'Track", 'at', 'iterations', "{'", '+', "f'{offset}", '+', 'n', '*', '{interval}', "'", '+', "'|", 'n', '>=', "0}.'", 'def', 'schedule(global_step):', 'shifted', '=', 'global_step', '-', 'offset', 'if', 'shifted', '<', '0:', 'return', 'False', 'else:', 'retur... | 492,733 |
kubeflow/pipelines | pipeline_cli.py | MyCLI.compile_run | compile_run | Compile and run a Kubeflow pipeline. | [
"Compile",
"and",
"run",
"a",
"Kubeflow",
"pipeline."
] | def compile_run(path, host, params={}):
compiled_path = MyCLI.compile(path)
MyCLI.run(compiled_path, host, params) | ['def', 'compile_run(path,', 'host,', 'params={}):', 'compiled_path', '=', 'MyCLI.compile(path)', 'MyCLI.run(compiled_path,', 'host,', 'params)'] | 779,720 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjDataWrapper.stack | stack | stack buffer (nstack mjtNums). | [
"stack",
"buffer",
"(nstack",
"mjtNums)."
] | def stack(self):
return util.buf_to_npy(self._ptr.contents.stack, (self.nstack,)) | ['def', 'stack(self):', 'return', 'util.buf_to_npy(self._ptr.contents.stack,', '(self.nstack,))'] | 440,530 |
enuguru/artificial_intelligence_and_machine_ | filetables.py | HashReader.all | all | Yields a sequence of values associated with the given key. | [
"Yields",
"a",
"sequence",
"of",
"values",
"associated",
"with",
"the",
"given",
"key."
] | def all(self, key):
dbfile = self.dbfile
for (datapos, datalen) in self.ranges_for_key(key):
yield dbfile.get(datapos, datalen) | ['def', 'all(self,', 'key):', 'dbfile', '=', 'self.dbfile', 'for', '(datapos,', 'datalen)', 'in', 'self.ranges_for_key(key):', 'yield', 'dbfile.get(datapos,', 'datalen)'] | 133,344 |
replit-archive/empythoned | __init__.py | Logger.makeRecord | makeRecord | A factory method which can be overridden in subclasses to create specialized LogRecords. | [
"A",
"factory",
"method",
"which",
"can",
"be",
"overridden",
"in",
"subclasses",
"to",
"create",
"specialized",
"LogRecords."
] | def makeRecord(self, name, level, fn, lno, msg, args, exc_info, func=None, extra=None):
rv = LogRecord(name, level, fn, lno, msg, args, exc_info, func)
if extra is not None:
for key in extra:
if key in ['message', 'asctime'] or key in rv.__dict__:
raise KeyError('Attempt to o... | ['def', 'makeRecord(self,', 'name,', 'level,', 'fn,', 'lno,', 'msg,', 'args,', 'exc_info,', 'func=None,', 'extra=None):', 'rv', '=', 'LogRecord(name,', 'level,', 'fn,', 'lno,', 'msg,', 'args,', 'exc_info,', 'func)', 'if', 'extra', 'is', 'not', 'None:', 'for', 'key', 'in', 'extra:', 'if', 'key', 'in', "['message',", "'a... | 176,931 |
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