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 |
|---|---|---|---|---|---|---|---|---|
vturrisi/solo-learn | deepclusterv2.py | DeepClusterV2.update_memory_banks | update_memory_banks | Updates DeepClusterV2's memory banks of indices and features. | [
"Updates",
"DeepClusterV2's",
"memory",
"banks",
"of",
"indices",
"and",
"features."
] | def update_memory_banks(self, idxs: torch.Tensor, z: torch.Tensor, batch_idx: int) -> None:
(start_idx, end_idx) = (batch_idx * self.batch_size, (batch_idx + 1) * self.batch_size)
self.local_memory_index[start_idx:end_idx] = idxs
for (c, z_c) in enumerate(z):
self.local_memory_embeddings[c][start_id... | ['def', 'update_memory_banks(self,', 'idxs:', 'torch.Tensor,', 'z:', 'torch.Tensor,', 'batch_idx:', 'int)', '->', 'None:', '(start_idx,', 'end_idx)', '=', '(batch_idx', '*', 'self.batch_size,', '(batch_idx', '+', '1)', '*', 'self.batch_size)', 'self.local_memory_index[start_idx:end_idx]', '=', 'idxs', 'for', '(c,', 'z_... | 393,613 |
Katja-M/Python_NaturalLanguageProcessing | framenet.py | FramenetCorpusReader.ft_sents | ft_sents | Full-text annotation sentences, optionally filtered by document name. | [
"Full-text",
"annotation",
"sentences,",
"optionally",
"filtered",
"by",
"document",
"name."
] | def ft_sents(self, docNamePattern=None):
return PrettyLazyIteratorList((sent for d in self.docs(docNamePattern) for sent in d.sentence)) | ['def', 'ft_sents(self,', 'docNamePattern=None):', 'return', 'PrettyLazyIteratorList((sent', 'for', 'd', 'in', 'self.docs(docNamePattern)', 'for', 'sent', 'in', 'd.sentence))'] | 866,204 |
techexpert1611/Natural-Language-Processing | vector_embeddings.py | IMDBMovieReviews.apply_label_map | apply_label_map | Converts string labels to indices. | [
"Converts",
"string",
"labels",
"to",
"indices."
] | def apply_label_map(self, data, label_to_idx):
for review in data:
review[L_LABEL] = label_to_idx[review[L_LABEL]] | ['def', 'apply_label_map(self,', 'data,', 'label_to_idx):', 'for', 'review', 'in', 'data:', 'review[L_LABEL]', '=', 'label_to_idx[review[L_LABEL]]'] | 658,031 |
gunthercox/ChatterBot | sessions.py | SessionStore.generate_key | generate_key | Simple function that generates a new session key. | [
"Simple",
"function",
"that",
"generates",
"a",
"new",
"session",
"key."
] | def generate_key(self, salt=None):
return generate_key(salt) | ['def', 'generate_key(self,', 'salt=None):', 'return', 'generate_key(salt)'] | 483,740 |
vinits5/pc_autoencoder | plyfile.py | PlyData.read | read | Read PLY data from a readable file-like object or filename. | [
"Read",
"PLY",
"data",
"from",
"a",
"readable",
"file-like",
"object",
"or",
"filename."
] | def read(stream):
(must_close, stream) = _open_stream(stream, 'read')
try:
data = PlyData._parse_header(stream)
for elt in data:
elt._read(stream, data.text, data.byte_order)
finally:
if must_close:
stream.close()
return data | ['def', 'read(stream):', '(must_close,', 'stream)', '=', '_open_stream(stream,', "'read')", 'try:', 'data', '=', 'PlyData._parse_header(stream)', 'for', 'elt', 'in', 'data:', 'elt._read(stream,', 'data.text,', 'data.byte_order)', 'finally:', 'if', 'must_close:', 'stream.close()', 'return', 'data'] | 765,799 |
vwxyzjn/cleanrl | buffers.py | BaseBuffer.add | add | Add elements to the buffer. | [
"Add",
"elements",
"to",
"the",
"buffer."
] | def add(self, *args, **kwargs) -> None:
raise NotImplementedError() | ['def', 'add(self,', '*args,', '**kwargs)', '->', 'None:', 'raise', 'NotImplementedError()'] | 488,138 |
43Carrig/recurrent_neural_networks_practice | summaries.py | add_histogram_summaries | add_histogram_summaries | Adds a histogram summary for each of the given tensors. | [
"Adds",
"a",
"histogram",
"summary",
"for",
"each",
"of",
"the",
"given",
"tensors."
] | def add_histogram_summaries(tensors, prefix=None):
summary_ops = []
for tensor in tensors:
summary_ops.append(add_histogram_summary(tensor, prefix=prefix))
return summary_ops | ['def', 'add_histogram_summaries(tensors,', 'prefix=None):', 'summary_ops', '=', '[]', 'for', 'tensor', 'in', 'tensors:', 'summary_ops.append(add_histogram_summary(tensor,', 'prefix=prefix))', 'return', 'summary_ops'] | 335,205 |
microsoft/nni | trial_runner.py | TrialRunner.send_heartbeat | send_heartbeat | Send a heartbeat to the other side. | [
"Send",
"a",
"heartbeat",
"to",
"the",
"other",
"side."
] | def send_heartbeat(self) -> float:
current_time = time.time()
command = ReportAwakeCommand(command_type='awake', time=current_time, idle=not self._processing_trials)
self._channel.send(json.dumps(command))
return current_time | ['def', 'send_heartbeat(self)', '->', 'float:', 'current_time', '=', 'time.time()', 'command', '=', "ReportAwakeCommand(command_type='awake',", 'time=current_time,', 'idle=not', 'self._processing_trials)', 'self._channel.send(json.dumps(command))', 'return', 'current_time'] | 728,616 |
sunishsheth2009/ChatterBot | testing.py | make_test_environ_builder | make_test_environ_builder | Creates a new test builder with some application defaults thrown in. | [
"Creates",
"a",
"new",
"test",
"builder",
"with",
"some",
"application",
"defaults",
"thrown",
"in."
] | def make_test_environ_builder(app, path='/', base_url=None, *args, **kwargs):
http_host = app.config.get('SERVER_NAME')
app_root = app.config.get('APPLICATION_ROOT')
if base_url is None:
base_url = 'http://%s/' % (http_host or 'localhost')
if app_root:
base_url += app_root.lstrip... | ['def', 'make_test_environ_builder(app,', "path='/',", 'base_url=None,', '*args,', '**kwargs):', 'http_host', '=', "app.config.get('SERVER_NAME')", 'app_root', '=', "app.config.get('APPLICATION_ROOT')", 'if', 'base_url', 'is', 'None:', 'base_url', '=', "'http://%s/'", '%', '(http_host', 'or', "'localhost')", 'if', 'app... | 528,987 |
calico/basenji | basenji_sat_plot2.py | global_align | global_align | Align two 1-hot encoded sequences. | [
"Align",
"two",
"1-hot",
"encoded",
"sequences."
] | def global_align(seq1_1hot, seq2_1hot):
align_opts = {'gap_open_penalty': 10, 'gap_extend_penalty': 1, 'match_score': 5, 'mismatch_score': -4}
seq1_dna = DNA(dna_io.hot1_dna(seq1_1hot))
seq2_dna = DNA(dna_io.hot1_dna(seq2_1hot))
seq_align = global_pairwise_align_nucleotide(seq1_dna, seq2_dna, gap_open_p... | ['def', 'global_align(seq1_1hot,', 'seq2_1hot):', 'align_opts', '=', "{'gap_open_penalty':", '10,', "'gap_extend_penalty':", '1,', "'match_score':", '5,', "'mismatch_score':", '-4}', 'seq1_dna', '=', 'DNA(dna_io.hot1_dna(seq1_1hot))', 'seq2_dna', '=', 'DNA(dna_io.hot1_dna(seq2_1hot))', 'seq_align', '=', 'global_pairwis... | 94,808 |
matsu0228/nlp-jp | bulk.py | _Bulk.add_update | add_update | Create an update document and add it to the list of ops. | [
"Create",
"an",
"update",
"document",
"and",
"add",
"it",
"to",
"the",
"list",
"of",
"ops."
] | def add_update(self, selector, update, multi=False, upsert=False, collation=None):
validate_ok_for_update(update)
cmd = SON([('q', selector), ('u', update), ('multi', multi), ('upsert', upsert)])
collation = validate_collation_or_none(collation)
if collation is not None:
self.uses_collation = Tr... | ['def', 'add_update(self,', 'selector,', 'update,', 'multi=False,', 'upsert=False,', 'collation=None):', 'validate_ok_for_update(update)', 'cmd', '=', "SON([('q',", 'selector),', "('u',", 'update),', "('multi',", 'multi),', "('upsert',", 'upsert)])', 'collation', '=', 'validate_collation_or_none(collation)', 'if', 'col... | 804,719 |
wandb/wandb | test_vertex.py | mock_aiplatform | mock_aiplatform | Patch the aiplatform module with a mock object and return that object. | [
"Patch",
"the",
"aiplatform",
"module",
"with",
"a",
"mock",
"object",
"and",
"return",
"that",
"object."
] | def mock_aiplatform(mocker):
mock = MagicMock()
def _fake_get_module(*args, **kwargs):
return mock
mocker.patch('wandb.sdk.launch.runner.vertex_runner.get_module', side_effect=_fake_get_module)
return mock | ['def', 'mock_aiplatform(mocker):', 'mock', '=', 'MagicMock()', 'def', '_fake_get_module(*args,', '**kwargs):', 'return', 'mock', "mocker.patch('wandb.sdk.launch.runner.vertex_runner.get_module',", 'side_effect=_fake_get_module)', 'return', 'mock'] | 941,308 |
kianak2002/Sentiment-Emotion-Analysis-project | egg_info.py | get_pkg_info_revision | get_pkg_info_revision | Get a -r### off of PKG-INFO Version in case this is an sdist of a subversion revision. | [
"Get",
"a",
"-r###",
"off",
"of",
"PKG-INFO",
"Version",
"in",
"case",
"this",
"is",
"an",
"sdist",
"of",
"a",
"subversion",
"revision."
] | def get_pkg_info_revision():
warnings.warn('get_pkg_info_revision is deprecated.', EggInfoDeprecationWarning)
if os.path.exists('PKG-INFO'):
with io.open('PKG-INFO') as f:
for line in f:
match = re.match('Version:.*-r(\\d+)\\s*$', line)
if match:
... | ['def', 'get_pkg_info_revision():', "warnings.warn('get_pkg_info_revision", 'is', "deprecated.',", 'EggInfoDeprecationWarning)', 'if', "os.path.exists('PKG-INFO'):", 'with', "io.open('PKG-INFO')", 'as', 'f:', 'for', 'line', 'in', 'f:', 'match', '=', "re.match('Version:.*-r(\\\\d+)\\\\s*$',", 'line)', 'if', 'match:', 'r... | 875,722 |
chen742/PiPa | cityscapes.py | CityscapesDataset.evaluate | evaluate | Evaluation in Cityscapes/default protocol. | [
"Evaluation",
"in",
"Cityscapes/default",
"protocol."
] | def evaluate(self, results, metric='mIoU', logger=None, imgfile_prefix=None, efficient_test=False):
eval_results = dict()
metrics = metric.copy() if isinstance(metric, list) else [metric]
if 'cityscapes' in metrics:
eval_results.update(self._evaluate_cityscapes(results, logger, imgfile_prefix))
... | ['def', 'evaluate(self,', 'results,', "metric='mIoU',", 'logger=None,', 'imgfile_prefix=None,', 'efficient_test=False):', 'eval_results', '=', 'dict()', 'metrics', '=', 'metric.copy()', 'if', 'isinstance(metric,', 'list)', 'else', '[metric]', 'if', "'cityscapes'", 'in', 'metrics:', 'eval_results.update(self._evaluate_c... | 305,091 |
ArdaGunay99/Key_Detection_Unsupervised_Learning | transforms.py | Bbox.mutatedx | mutatedx | Return whether the x-limits have changed since init. | [
"Return",
"whether",
"the",
"x-limits",
"have",
"changed",
"since",
"init."
] | def mutatedx(self):
return self._points[0, 0] != self._points_orig[0, 0] or self._points[1, 0] != self._points_orig[1, 0] | ['def', 'mutatedx(self):', 'return', 'self._points[0,', '0]', '!=', 'self._points_orig[0,', '0]', 'or', 'self._points[1,', '0]', '!=', 'self._points_orig[1,', '0]'] | 257,432 |
kubeflow/pipelines | component.py | bigquery_ml_feature_info_job | bigquery_ml_feature_info_job | Launch a BigQuery feature info job and waits for it to finish. | [
"Launch",
"a",
"BigQuery",
"feature",
"info",
"job",
"and",
"waits",
"for",
"it",
"to",
"finish."
] | def bigquery_ml_feature_info_job(model: Input[BQMLModel], feature_info: Output[Artifact], gcp_resources: OutputPath(str), location: str='us-central1', query_parameters: List[str]=[], job_configuration_query: Dict[str, str]={}, labels: Dict[str, str]={}, project: str=_placeholders.PROJECT_ID_PLACEHOLDER):
return Con... | ['def', 'bigquery_ml_feature_info_job(model:', 'Input[BQMLModel],', 'feature_info:', 'Output[Artifact],', 'gcp_resources:', 'OutputPath(str),', 'location:', "str='us-central1',", 'query_parameters:', 'List[str]=[],', 'job_configuration_query:', 'Dict[str,', 'str]={},', 'labels:', 'Dict[str,', 'str]={},', 'project:', 's... | 770,931 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | vgslspecs_test.py | VgslspecsTest.ExpectScaledSize | ExpectScaledSize | Tests that the output of the graph of the given spec has target_shape. | [
"Tests",
"that",
"the",
"output",
"of",
"the",
"graph",
"of",
"the",
"given",
"spec",
"has",
"target_shape."
] | def ExpectScaledSize(self, spec, target_shape, factor=1):
with tf.Graph().as_default():
with self.test_session() as sess:
self.SetupInputs()
vgsl = vgslspecs.VGSLSpecs(self.ph_widths, self.ph_heights, True)
outputs = vgsl.Build(self.ph_image, spec)
target_widt... | ['def', 'ExpectScaledSize(self,', 'spec,', 'target_shape,', 'factor=1):', 'with', 'tf.Graph().as_default():', 'with', 'self.test_session()', 'as', 'sess:', 'self.SetupInputs()', 'vgsl', '=', 'vgslspecs.VGSLSpecs(self.ph_widths,', 'self.ph_heights,', 'True)', 'outputs', '=', 'vgsl.Build(self.ph_image,', 'spec)', 'target... | 110,657 |
unixpickle/anyrl-py | test_mpi.py | test_mpi_optimizer | test_mpi_optimizer | Test that the MPIOptimizer is equivalent to its encapsulated optimizer. | [
"Test",
"that",
"the",
"MPIOptimizer",
"is",
"equivalent",
"to",
"its",
"encapsulated",
"optimizer."
] | def test_mpi_optimizer(loss_fn):
with tf.Graph().as_default():
x = tf.get_variable('x', shape=[10, 15], dtype=tf.float32, initializer=tf.truncated_normal_initializer())
loss = loss_fn(x)
optim = tf.train.AdamOptimizer(learning_rate=0.1)
mpi_optim = MPIOptimizer(optim, loss)
m... | ['def', 'test_mpi_optimizer(loss_fn):', 'with', 'tf.Graph().as_default():', 'x', '=', "tf.get_variable('x',", 'shape=[10,', '15],', 'dtype=tf.float32,', 'initializer=tf.truncated_normal_initializer())', 'loss', '=', 'loss_fn(x)', 'optim', '=', 'tf.train.AdamOptimizer(learning_rate=0.1)', 'mpi_optim', '=', 'MPIOptimizer... | 33,716 |
maheshbhosle/Natural-Language-Processing | data.py | load_vocabulary | load_vocabulary | Loads vocabulary from vocabulary_path. | [
"Loads",
"vocabulary",
"from",
"vocabulary_path."
] | def load_vocabulary(vocabulary_path: str) -> Tuple[Dict[str, int], Dict[int, str]]:
vocab_id_to_token = {}
vocab_token_to_id = {}
with open(vocabulary_path, 'r', encoding='UTF-8') as file:
for (index, token) in enumerate(file):
token = token.strip()
if not token:
... | ['def', 'load_vocabulary(vocabulary_path:', 'str)', '->', 'Tuple[Dict[str,', 'int],', 'Dict[int,', 'str]]:', 'vocab_id_to_token', '=', '{}', 'vocab_token_to_id', '=', '{}', 'with', 'open(vocabulary_path,', "'r',", "encoding='UTF-8')", 'as', 'file:', 'for', '(index,', 'token)', 'in', 'enumerate(file):', 'token', '=', 't... | 685,236 |
Center-of-Diagnostics-and-Telemedicine/ai-testing-platform | test_longdouble.py | test_array_and_stringlike_roundtrip | test_array_and_stringlike_roundtrip | Test that string representations of long-double roundtrip both for array casting and scalar coercion, see also gh-15608. | [
"Test",
"that",
"string",
"representations",
"of",
"long-double",
"roundtrip",
"both",
"for",
"array",
"casting",
"and",
"scalar",
"coercion,",
"see",
"also",
"gh-15608."
] | def test_array_and_stringlike_roundtrip(strtype):
o = 1 + LD_INFO.eps
if strtype in (np.bytes_, bytes):
o_str = strtype(repr(o).encode('ascii'))
else:
o_str = strtype(repr(o))
assert o == np.longdouble(o_str)
o_strarr = np.asarray([o] * 3, dtype=strtype)
assert (o == o_strarr.ast... | ['def', 'test_array_and_stringlike_roundtrip(strtype):', 'o', '=', '1', '+', 'LD_INFO.eps', 'if', 'strtype', 'in', '(np.bytes_,', 'bytes):', 'o_str', '=', "strtype(repr(o).encode('ascii'))", 'else:', 'o_str', '=', 'strtype(repr(o))', 'assert', 'o', '==', 'np.longdouble(o_str)', 'o_strarr', '=', 'np.asarray([o]', '*', '... | 102,554 |
AgnostiqHQ/covalent | app_test.py | test_db | test_db | Instantiate and return an in-memory database. | [
"Instantiate",
"and",
"return",
"an",
"in-memory",
"database."
] | def test_db():
return MockDataStore(db_URL='sqlite+pysqlite:///:memory:') | ['def', 'test_db():', 'return', "MockDataStore(db_URL='sqlite+pysqlite:///:memory:')"] | 489,754 |
dengfy/cs224d | utils.py | idxs_to_matrix | idxs_to_matrix | Return a matrix X with each row as a word vector for the corresponding index in idxs. | [
"Return",
"a",
"matrix",
"X",
"with",
"each",
"row",
"as",
"a",
"word",
"vector",
"for",
"the",
"corresponding",
"index",
"in",
"idxs."
] | def idxs_to_matrix(idxs, L):
return vstack([L[i] for i in idxs]) | ['def', 'idxs_to_matrix(idxs,', 'L):', 'return', 'vstack([L[i]', 'for', 'i', 'in', 'idxs])'] | 506,474 |
openvinotoolkit/training_extensions | shape_drawer.py | Helpers.set_cursor_pos | set_cursor_pos | Move the cursor to a new position. | [
"Move",
"the",
"cursor",
"to",
"a",
"new",
"position."
] | def set_cursor_pos(self, cursor_pos: Optional[Coordinate]=None):
if cursor_pos is None:
cursor_pos = Coordinate(0, 0)
self.cursor_pos = cursor_pos | ['def', 'set_cursor_pos(self,', 'cursor_pos:', 'Optional[Coordinate]=None):', 'if', 'cursor_pos', 'is', 'None:', 'cursor_pos', '=', 'Coordinate(0,', '0)', 'self.cursor_pos', '=', 'cursor_pos'] | 918,892 |
aivclab/vision | test_video_reader.py | TestVideoReader.test_read_video_from_file_rescale_both_min_max_dimension | test_read_video_from_file_rescale_both_min_max_dimension | Test the case when decoder starts with a video file to decode frames, and video min dimension between height and width is set. | [
"Test",
"the",
"case",
"when",
"decoder",
"starts",
"with",
"a",
"video",
"file",
"to",
"decode",
"frames,",
"and",
"video",
"min",
"dimension",
"between",
"height",
"and",
"width",
"is",
"set."
] | def test_read_video_from_file_rescale_both_min_max_dimension(self, test_video):
(width, height, min_dimension, max_dimension) = (0, 0, 64, 85)
(video_start_pts, video_end_pts) = (0, -1)
(video_timebase_num, video_timebase_den) = (0, 1)
(samples, channels) = (0, 0)
(audio_start_pts, audio_end_pts) = ... | ['def', 'test_read_video_from_file_rescale_both_min_max_dimension(self,', 'test_video):', '(width,', 'height,', 'min_dimension,', 'max_dimension)', '=', '(0,', '0,', '64,', '85)', '(video_start_pts,', 'video_end_pts)', '=', '(0,', '-1)', '(video_timebase_num,', 'video_timebase_den)', '=', '(0,', '1)', '(samples,', 'cha... | 958,052 |
Wuziyi616/Artificial_Intelligence_Project1 | image_utils.py | show_gray_image | show_gray_image | Show gray scale image. | [
"Show",
"gray",
"scale",
"image."
] | def show_gray_image(image):
plt.imshow(image, cmap='gray')
plt.show() | ['def', 'show_gray_image(image):', 'plt.imshow(image,', "cmap='gray')", 'plt.show()'] | 92,085 |
thaines/helit | model.py | DocSample.getInstCount | getInstCount | Returns the number of cluster instances in the documents model. | [
"Returns",
"the",
"number",
"of",
"cluster",
"instances",
"in",
"the",
"documents",
"model."
] | def getInstCount(self):
return self.dp.shape[0] | ['def', 'getInstCount(self):', 'return', 'self.dp.shape[0]'] | 591,128 |
Katja-M/Python_NaturalLanguageProcessing | dates.py | weeks | weeks | Return weeks as days. | [
"Return",
"weeks",
"as",
"days."
] | def weeks(w):
return w * DAYS_PER_WEEK | ['def', 'weeks(w):', 'return', 'w', '*', 'DAYS_PER_WEEK'] | 864,488 |
sony/nnabla-rl | test_ppo.py | TestPPO.test_latest_iteration_state | test_latest_iteration_state | Check that latest iteration state has the keys and values we expected. | [
"Check",
"that",
"latest",
"iteration",
"state",
"has",
"the",
"keys",
"and",
"values",
"we",
"expected."
] | def test_latest_iteration_state(self):
dummy_env = E.DummyContinuous()
ppo = A.PPO(dummy_env)
ppo._policy_trainer_state = {'pi_loss': 0.0}
ppo._v_function_trainer_state = {'v_loss': 1.0}
latest_iteration_state = ppo.latest_iteration_state
assert 'pi_loss' in latest_iteration_state['scalar']
... | ['def', 'test_latest_iteration_state(self):', 'dummy_env', '=', 'E.DummyContinuous()', 'ppo', '=', 'A.PPO(dummy_env)', 'ppo._policy_trainer_state', '=', "{'pi_loss':", '0.0}', 'ppo._v_function_trainer_state', '=', "{'v_loss':", '1.0}', 'latest_iteration_state', '=', 'ppo.latest_iteration_state', 'assert', "'pi_loss'", ... | 727,426 |
huawei-noah/xingtian | mean_loss.py | MeanLoss.call | call | Compute loss, mean() to average on multi-gpu. | [
"Compute",
"loss,",
"mean()",
"to",
"average",
"on",
"multi-gpu."
] | def call(self, inputs, targets):
return inputs.mean() | ['def', 'call(self,', 'inputs,', 'targets):', 'return', 'inputs.mean()'] | 962,719 |
sek788432/Waymo-2D-Object-Detection | export_model_utils.py | ExtractGlobalFeatures | ExtractGlobalFeatures | Extract global features for input image. | [
"Extract",
"global",
"features",
"for",
"input",
"image."
] | def ExtractGlobalFeatures(image, image_scales, global_scales_ind, model_fn, multi_scale_pool_type='None', normalize_global_descriptor=False):
original_image_shape_float = tf.gather(tf.dtypes.cast(tf.shape(image), tf.float32), [0, 1])
image_tensor = gld.NormalizeImages(image, pixel_value_offset=128.0, pixel_valu... | ['def', 'ExtractGlobalFeatures(image,', 'image_scales,', 'global_scales_ind,', 'model_fn,', "multi_scale_pool_type='None',", 'normalize_global_descriptor=False):', 'original_image_shape_float', '=', 'tf.gather(tf.dtypes.cast(tf.shape(image),', 'tf.float32),', '[0,', '1])', 'image_tensor', '=', 'gld.NormalizeImages(imag... | 974,301 |
marcsto/rl | transforms.py | Transform.transform_env_device | transform_env_device | Transforms the device of the parent env. | [
"Transforms",
"the",
"device",
"of",
"the",
"parent",
"env."
] | def transform_env_device(self, device: torch.device):
return device | ['def', 'transform_env_device(self,', 'device:', 'torch.device):', 'return', 'device'] | 859,109 |
myothida/Supervised-Machine-Learning | test_lapack.py | test_tzrzf | test_tzrzf | This test performs an RZ decomposition in which an m x n upper trapezoidal array M (m <= n) is factorized as M = [R 0] * Z where R is upper triangular and Z is unitary. | [
"This",
"test",
"performs",
"an",
"RZ",
"decomposition",
"in",
"which",
"an",
"m",
"x",
"n",
"upper",
"trapezoidal",
"array",
"M",
"(m",
"<=",
"n)",
"is",
"factorized",
"as",
"M",
"=",
"[R",
"0]",
"*",
"Z",
"where",
"R",
"is",
"upper",
"triangular",
... | def test_tzrzf():
seed(1234)
(m, n) = (10, 15)
for (ind, dtype) in enumerate(DTYPES):
(tzrzf, tzrzf_lw) = get_lapack_funcs(('tzrzf', 'tzrzf_lwork'), dtype=dtype)
lwork = _compute_lwork(tzrzf_lw, m, n)
if ind < 2:
A = triu(rand(m, n).astype(dtype))
else:
... | ['def', 'test_tzrzf():', 'seed(1234)', '(m,', 'n)', '=', '(10,', '15)', 'for', '(ind,', 'dtype)', 'in', 'enumerate(DTYPES):', '(tzrzf,', 'tzrzf_lw)', '=', "get_lapack_funcs(('tzrzf',", "'tzrzf_lwork'),", 'dtype=dtype)', 'lwork', '=', '_compute_lwork(tzrzf_lw,', 'm,', 'n)', 'if', 'ind', '<', '2:', 'A', '=', 'triu(rand(m... | 445,830 |
wanyao1992/code_summarization_public | getComments.py | generate_pairs | generate_pairs | Loop through the source code and filter comments and their correspondig code. | [
"Loop",
"through",
"the",
"source",
"code",
"and",
"filter",
"comments",
"and",
"their",
"correspondig",
"code."
] | def generate_pairs(source, codeFile, commentFile, maxBucket, module='<string>'):
if hasattr(source, 'read'):
filename = getattr(source, 'name', module)
module = splitext(basename(filename))[0]
source = source.read()
source = source.splitlines()
normalComments = 0
inlineComments =... | ['def', 'generate_pairs(source,', 'codeFile,', 'commentFile,', 'maxBucket,', "module='<string>'):", 'if', 'hasattr(source,', "'read'):", 'filename', '=', 'getattr(source,', "'name',", 'module)', 'module', '=', 'splitext(basename(filename))[0]', 'source', '=', 'source.read()', 'source', '=', 'source.splitlines()', 'norm... | 495,850 |
devashish-patel/webcam-motion-detector | server.py | BaseHTTPRequestHandler.log_date_time_string | log_date_time_string | Return the current time formatted for logging. | [
"Return",
"the",
"current",
"time",
"formatted",
"for",
"logging."
] | def log_date_time_string(self):
now = time.time()
(year, month, day, hh, mm, ss, x, y, z) = time.localtime(now)
s = '%02d/%3s/%04d %02d:%02d:%02d' % (day, self.monthname[month], year, hh, mm, ss)
return s | ['def', 'log_date_time_string(self):', 'now', '=', 'time.time()', '(year,', 'month,', 'day,', 'hh,', 'mm,', 'ss,', 'x,', 'y,', 'z)', '=', 'time.localtime(now)', 's', '=', "'%02d/%3s/%04d", "%02d:%02d:%02d'", '%', '(day,', 'self.monthname[month],', 'year,', 'hh,', 'mm,', 'ss)', 'return', 's'] | 978,084 |
happinesslz/TANet | fastai_optim.py | OptimWrapper.beta | beta | Set beta (or alpha as makes sense for given optimizer). | [
"Set",
"beta",
"(or",
"alpha",
"as",
"makes",
"sense",
"for",
"given",
"optimizer)."
] | def beta(self, val: float) -> None:
if val is None:
return
if 'betas' in self.opt_keys:
self.set_val('betas', (self._mom, listify(val, self._beta)))
elif 'alpha' in self.opt_keys:
self.set_val('alpha', listify(val, self._beta))
self._beta = listify(val, self._beta) | ['def', 'beta(self,', 'val:', 'float)', '->', 'None:', 'if', 'val', 'is', 'None:', 'return', 'if', "'betas'", 'in', 'self.opt_keys:', "self.set_val('betas',", '(self._mom,', 'listify(val,', 'self._beta)))', 'elif', "'alpha'", 'in', 'self.opt_keys:', "self.set_val('alpha',", 'listify(val,', 'self._beta))', 'self._beta',... | 907,231 |
intel/neural-compressor | utility.py | get_serve_log_workspace | get_serve_log_workspace | Get log workspace for service. | [
"Get",
"log",
"workspace",
"for",
"service."
] | def get_serve_log_workspace(workspace='./'):
return os.path.join(workspace, 'serve_log') | ['def', "get_serve_log_workspace(workspace='./'):", 'return', 'os.path.join(workspace,', "'serve_log')"] | 721,809 |
Multhree/Computer-Vision | resneXt.py | resnext101 | resnext101 | Constructs a ResNeXt-101 model. | [
"Constructs",
"a",
"ResNeXt-101",
"model."
] | def resnext101(**kwargs):
model = ResNeXt(Bottleneck, [3, 4, 23, 3], **kwargs)
return model | ['def', 'resnext101(**kwargs):', 'model', '=', 'ResNeXt(Bottleneck,', '[3,', '4,', '23,', '3],', '**kwargs)', 'return', 'model'] | 460,061 |
intel/neural-compressor | test_graph.py | TestGraph.test_empty_graph_has_no_nodes | test_empty_graph_has_no_nodes | Test if empty graph has no nodes. | [
"Test",
"if",
"empty",
"graph",
"has",
"no",
"nodes."
] | def test_empty_graph_has_no_nodes(self) -> None:
graph = Graph()
self.assertEqual([], graph.nodes) | ['def', 'test_empty_graph_has_no_nodes(self)', '->', 'None:', 'graph', '=', 'Graph()', 'self.assertEqual([],', 'graph.nodes)'] | 721,620 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | pygame_console.py | PyGameConsole.getheightwidth | getheightwidth | Return (height, width) where height and width are the height and width of the terminal window in characters. | [
"Return",
"(height,",
"width)",
"where",
"height",
"and",
"width",
"are",
"the",
"height",
"and",
"width",
"of",
"the",
"terminal",
"window",
"in",
"characters."
] | def getheightwidth(self):
return ((600 - tmargin - bmargin) / self.fh, (800 - lmargin - rmargin) / self.fw) | ['def', 'getheightwidth(self):', 'return', '((600', '-', 'tmargin', '-', 'bmargin)', '/', 'self.fh,', '(800', '-', 'lmargin', '-', 'rmargin)', '/', 'self.fw)'] | 377,435 |
google-research/batch_rl | logged_prioritized_replay_buffer.py | WrappedLoggedPrioritizedReplayBuffer.tf_get_priority | tf_get_priority | Gets the priorities for the given indices. | [
"Gets",
"the",
"priorities",
"for",
"the",
"given",
"indices."
] | def tf_get_priority(self, indices):
return tf.py_func(self.memory.get_priority, [indices], tf.float32, name='prioritized_replay_get_priority_py_func') | ['def', 'tf_get_priority(self,', 'indices):', 'return', 'tf.py_func(self.memory.get_priority,', '[indices],', 'tf.float32,', "name='prioritized_replay_get_priority_py_func')"] | 105,904 |
QData/deepWordBug | math2html.py | HybridSize.getsize | getsize | Read the size for a function and parse it. | [
"Read",
"the",
"size",
"for",
"a",
"function",
"and",
"parse",
"it."
] | def getsize(self, function):
sizestring = self.configsizes[function.command]
for name in function.params:
if name in sizestring:
size = function.params[name].value.computesize()
sizestring = sizestring.replace(name, str(size))
if '$' in sizestring:
Trace.error('Unconv... | ['def', 'getsize(self,', 'function):', 'sizestring', '=', 'self.configsizes[function.command]', 'for', 'name', 'in', 'function.params:', 'if', 'name', 'in', 'sizestring:', 'size', '=', 'function.params[name].value.computesize()', 'sizestring', '=', 'sizestring.replace(name,', 'str(size))', 'if', "'$'", 'in', 'sizestrin... | 542,647 |
sunishsheth2009/ChatterBot | serving.py | generate_adhoc_ssl_context | generate_adhoc_ssl_context | Generates an adhoc SSL context for the development server. | [
"Generates",
"an",
"adhoc",
"SSL",
"context",
"for",
"the",
"development",
"server."
] | def generate_adhoc_ssl_context():
from OpenSSL import SSL
(cert, pkey) = generate_adhoc_ssl_pair()
ctx = SSL.Context(SSL.SSLv23_METHOD)
ctx.use_privatekey(pkey)
ctx.use_certificate(cert)
return ctx | ['def', 'generate_adhoc_ssl_context():', 'from', 'OpenSSL', 'import', 'SSL', '(cert,', 'pkey)', '=', 'generate_adhoc_ssl_pair()', 'ctx', '=', 'SSL.Context(SSL.SSLv23_METHOD)', 'ctx.use_privatekey(pkey)', 'ctx.use_certificate(cert)', 'return', 'ctx'] | 483,340 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | layers.py | cl_logits_subgraph | cl_logits_subgraph | Construct multiple ReLU layers with dropout and a linear layer. | [
"Construct",
"multiple",
"ReLU",
"layers",
"with",
"dropout",
"and",
"a",
"linear",
"layer."
] | def cl_logits_subgraph(layer_sizes, input_size, num_classes, keep_prob=1.0):
subgraph = K.models.Sequential(name='cl_logits')
for (i, layer_size) in enumerate(layer_sizes):
if i == 0:
subgraph.add(K.layers.Dense(layer_size, activation='relu', input_dim=input_size))
else:
... | ['def', 'cl_logits_subgraph(layer_sizes,', 'input_size,', 'num_classes,', 'keep_prob=1.0):', 'subgraph', '=', "K.models.Sequential(name='cl_logits')", 'for', '(i,', 'layer_size)', 'in', 'enumerate(layer_sizes):', 'if', 'i', '==', '0:', 'subgraph.add(K.layers.Dense(layer_size,', "activation='relu',", 'input_dim=input_si... | 14,268 |
google-research/fixmatch | ema.py | assign_ema_vars_from_initial_values | assign_ema_vars_from_initial_values | Assign EMA variables from initial values. | [
"Assign",
"EMA",
"variables",
"from",
"initial",
"values."
] | def assign_ema_vars_from_initial_values(ema_variables, initial_values):
def _assign_one_var_fn(ema_var, value):
ema_var.assign(value)
def _assign_all_in_cross_replica_context_fn(strategy, ema_vars, values):
for (ema_var, value) in zip(ema_vars, values):
value = strategy.extended.re... | ['def', 'assign_ema_vars_from_initial_values(ema_variables,', 'initial_values):', 'def', '_assign_one_var_fn(ema_var,', 'value):', 'ema_var.assign(value)', 'def', '_assign_all_in_cross_replica_context_fn(strategy,', 'ema_vars,', 'values):', 'for', '(ema_var,', 'value)', 'in', 'zip(ema_vars,', 'values):', 'value', '=', ... | 211,033 |
lakshaygoyal425/Computer-Vision | keras_darknet19.py | darknet19 | darknet19 | Generate Darknet-19 model for Imagenet classification. | [
"Generate",
"Darknet-19",
"model",
"for",
"Imagenet",
"classification."
] | def darknet19(inputs):
body = darknet_body()(inputs)
logits = DarknetConv2D(1000, (1, 1), activation='softmax')(body)
return Model(inputs, logits) | ['def', 'darknet19(inputs):', 'body', '=', 'darknet_body()(inputs)', 'logits', '=', 'DarknetConv2D(1000,', '(1,', '1),', "activation='softmax')(body)", 'return', 'Model(inputs,', 'logits)'] | 469,675 |
nicknochnack/RealTimeSignLanguageTFJS | box_ops.py | encode_boxes | encode_boxes | Encode boxes to targets. | [
"Encode",
"boxes",
"to",
"targets."
] | def encode_boxes(boxes, anchors, weights=None):
if boxes.shape[-1] != 4:
raise ValueError('boxes.shape[-1] is {:d}, but must be 4.'.format(boxes.shape[-1]))
with tf.name_scope('encode_boxes'):
boxes = tf.cast(boxes, dtype=anchors.dtype)
ymin = boxes[..., 0:1]
xmin = boxes[..., 1:... | ['def', 'encode_boxes(boxes,', 'anchors,', 'weights=None):', 'if', 'boxes.shape[-1]', '!=', '4:', 'raise', "ValueError('boxes.shape[-1]", 'is', '{:d},', 'but', 'must', 'be', "4.'.format(boxes.shape[-1]))", 'with', "tf.name_scope('encode_boxes'):", 'boxes', '=', 'tf.cast(boxes,', 'dtype=anchors.dtype)', 'ymin', '=', 'bo... | 850,869 |
ctogle/chunktagger | util.py | gather | gather | Provide namespace of all run options specified. | [
"Provide",
"namespace",
"of",
"all",
"run",
"options",
"specified."
] | def gather():
cachedir = os.path.join(os.getcwd(), '.cache')
vectorcache = os.path.join(cachedir, 'input_vectors.%s.pt')
modelcache = os.path.join(cachedir, 'model_snapshot.pt')
parser = argparse.ArgumentParser()
parser.add_argument('--cachedir', type=str, default=cachedir)
parser.add_argument('... | ['def', 'gather():', 'cachedir', '=', 'os.path.join(os.getcwd(),', "'.cache')", 'vectorcache', '=', 'os.path.join(cachedir,', "'input_vectors.%s.pt')", 'modelcache', '=', 'os.path.join(cachedir,', "'model_snapshot.pt')", 'parser', '=', 'argparse.ArgumentParser()', "parser.add_argument('--cachedir',", 'type=str,', 'defa... | 105,231 |
myothida/Supervised-Machine-Learning | _win32_console.py | GetConsoleScreenBufferInfo | GetConsoleScreenBufferInfo | Retrieves information about the specified console screen buffer. | [
"Retrieves",
"information",
"about",
"the",
"specified",
"console",
"screen",
"buffer."
] | def GetConsoleScreenBufferInfo(std_handle: wintypes.HANDLE) -> CONSOLE_SCREEN_BUFFER_INFO:
console_screen_buffer_info = CONSOLE_SCREEN_BUFFER_INFO()
_GetConsoleScreenBufferInfo(std_handle, byref(console_screen_buffer_info))
return console_screen_buffer_info | ['def', 'GetConsoleScreenBufferInfo(std_handle:', 'wintypes.HANDLE)', '->', 'CONSOLE_SCREEN_BUFFER_INFO:', 'console_screen_buffer_info', '=', 'CONSOLE_SCREEN_BUFFER_INFO()', '_GetConsoleScreenBufferInfo(std_handle,', 'byref(console_screen_buffer_info))', 'return', 'console_screen_buffer_info'] | 445,166 |
THU-BPM/PairSCL | losses.py | SupConLoss.forward | forward | Compute loss for model. | [
"Compute",
"loss",
"for",
"model."
] | def forward(self, features, labels=None, mask=None):
device = torch.device('cuda') if features.is_cuda else torch.device('cpu')
batch_size = features.shape[0]
if labels is not None and mask is not None:
raise ValueError('Cannot define both `labels` and `mask`')
elif labels is not None:
l... | ['def', 'forward(self,', 'features,', 'labels=None,', 'mask=None):', 'device', '=', "torch.device('cuda')", 'if', 'features.is_cuda', 'else', "torch.device('cpu')", 'batch_size', '=', 'features.shape[0]', 'if', 'labels', 'is', 'not', 'None', 'and', 'mask', 'is', 'not', 'None:', 'raise', "ValueError('Cannot", 'define', ... | 277,478 |
tobegit3hub/deep_image_model | set_ops.py | set_size | set_size | Compute number of unique elements along last dimension of `a`. | [
"Compute",
"number",
"of",
"unique",
"elements",
"along",
"last",
"dimension",
"of",
"`a`."
] | def set_size(a, validate_indices=True):
a = tensor_util.convert_to_tensor_or_sparse_tensor(a, name='a')
if not isinstance(a, sparse_tensor.SparseTensor):
raise TypeError('Expected `SparseTensor`, got %s.' % a)
if a.values.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError('Invalid dtype ... | ['def', 'set_size(a,', 'validate_indices=True):', 'a', '=', 'tensor_util.convert_to_tensor_or_sparse_tensor(a,', "name='a')", 'if', 'not', 'isinstance(a,', 'sparse_tensor.SparseTensor):', 'raise', "TypeError('Expected", '`SparseTensor`,', 'got', "%s.'", '%', 'a)', 'if', 'a.values.dtype.base_dtype', 'not', 'in', '_VALID... | 181,959 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | model.py | Model.max_pool_views | max_pool_views | Max pool across all nets in spatial dimensions. | [
"Max",
"pool",
"across",
"all",
"nets",
"in",
"spatial",
"dimensions."
] | def max_pool_views(self, nets_list):
(batch_size, height, width, num_features) = [d.value for d in nets_list[0].get_shape().dims]
xy_flat_shape = (batch_size, 1, height * width, num_features)
nets_for_merge = []
with tf.variable_scope('max_pool_views', values=nets_list):
for net in nets_list:
... | ['def', 'max_pool_views(self,', 'nets_list):', '(batch_size,', 'height,', 'width,', 'num_features)', '=', '[d.value', 'for', 'd', 'in', 'nets_list[0].get_shape().dims]', 'xy_flat_shape', '=', '(batch_size,', '1,', 'height', '*', 'width,', 'num_features)', 'nets_for_merge', '=', '[]', 'with', "tf.variable_scope('max_poo... | 14,553 |
denisyarats/exorl | quadruped.py | Physics.self_to_ball_distance | self_to_ball_distance | Returns horizontal distance from the quadruped workspace to the ball. | [
"Returns",
"horizontal",
"distance",
"from",
"the",
"quadruped",
"workspace",
"to",
"the",
"ball."
] | def self_to_ball_distance(self):
self_to_ball = self.named.data.site_xpos['workspace'] - self.named.data.xpos['ball']
return np.linalg.norm(self_to_ball[:2]) | ['def', 'self_to_ball_distance(self):', 'self_to_ball', '=', "self.named.data.site_xpos['workspace']", '-', "self.named.data.xpos['ball']", 'return', 'np.linalg.norm(self_to_ball[:2])'] | 563,600 |
lebrice/Sequoia | setting.py | IncrementalSLSetting.current_task_classes | current_task_classes | Gives back the labels present in the current task. | [
"Gives",
"back",
"the",
"labels",
"present",
"in",
"the",
"current",
"task."
] | def current_task_classes(self, train: bool) -> List[int]:
return self.task_classes(self._current_task_id, train) | ['def', 'current_task_classes(self,', 'train:', 'bool)', '->', 'List[int]:', 'return', 'self.task_classes(self._current_task_id,', 'train)'] | 349,691 |
LorenzoCassano/TablutChallenge22-23 | games.py | Backgammon.compute_utility | compute_utility | If 'W' wins with this move, return 1; if 'B' wins return -1; else return 0. | [
"If",
"'W'",
"wins",
"with",
"this",
"move,",
"return",
"1;",
"if",
"'B'",
"wins",
"return",
"-1;",
"else",
"return",
"0."
] | def compute_utility(self, board, move, player):
util = {'W': 1, 'B': -1}
for idx in range(0, 24):
if board[idx][player] > 0:
return 0
return util[player] | ['def', 'compute_utility(self,', 'board,', 'move,', 'player):', 'util', '=', "{'W':", '1,', "'B':", '-1}', 'for', 'idx', 'in', 'range(0,', '24):', 'if', 'board[idx][player]', '>', '0:', 'return', '0', 'return', 'util[player]'] | 365,184 |
SALT-NLP/Adaptive-Compositional-Modules | model_mixin.py | ModelAdaptersMixin.add_adapter | add_adapter | Adds a new adapter module of the specified type to the model. | [
"Adds",
"a",
"new",
"adapter",
"module",
"of",
"the",
"specified",
"type",
"to",
"the",
"model."
] | def add_adapter(self, adapter_name: str, config=None):
if isinstance(config, dict):
config = AdapterConfig.from_dict(config)
self.config.adapters.add(adapter_name, config=config)
self.base_model._add_adapter(adapter_name) | ['def', 'add_adapter(self,', 'adapter_name:', 'str,', 'config=None):', 'if', 'isinstance(config,', 'dict):', 'config', '=', 'AdapterConfig.from_dict(config)', 'self.config.adapters.add(adapter_name,', 'config=config)', 'self.base_model._add_adapter(adapter_name)'] | 408,500 |
deepmind/trfl | action_value_ops_test.py | QVTest.testTarget | testTarget | Tests that target value == r_t + pcont_t * q_t[a_t]. | [
"Tests",
"that",
"target",
"value",
"==",
"r_t",
"+",
"pcont_t",
"*",
"q_t[a_t]."
] | def testTarget(self):
with self.test_session() as sess:
self.assertAllClose(sess.run(self.extra_ops.target), [1, 4]) | ['def', 'testTarget(self):', 'with', 'self.test_session()', 'as', 'sess:', 'self.assertAllClose(sess.run(self.extra_ops.target),', '[1,', '4])'] | 356,184 |
caiiiac/Machine-Learning-with-Python | ridge.py | _BaseRidgeCV.fit | fit | Fit Ridge regression model Parameters ---------- X : array-like, shape = [n_samples, n_features] Training data y : array-like, shape = [n_samples] or [n_samples, n_targets] Target values sample_weight : float or array-like of shape [n_samples] Sample weight Returns ------- self : Returns self. | [
"Fit",
"Ridge",
"regression",
"model",
"Parameters",
"----------",
"X",
":",
"array-like,",
"shape",
"=",
"[n_samples,",
"n_features]",
"Training",
"data",
"y",
":",
"array-like,",
"shape",
"=",
"[n_samples]",
"or",
"[n_samples,",
"n_targets]",
"Target",
"values",
... | def fit(self, X, y, sample_weight=None):
if self.cv is None:
estimator = _RidgeGCV(self.alphas, fit_intercept=self.fit_intercept, normalize=self.normalize, scoring=self.scoring, gcv_mode=self.gcv_mode, store_cv_values=self.store_cv_values)
estimator.fit(X, y, sample_weight=sample_weight)
sel... | ['def', 'fit(self,', 'X,', 'y,', 'sample_weight=None):', 'if', 'self.cv', 'is', 'None:', 'estimator', '=', '_RidgeGCV(self.alphas,', 'fit_intercept=self.fit_intercept,', 'normalize=self.normalize,', 'scoring=self.scoring,', 'gcv_mode=self.gcv_mode,', 'store_cv_values=self.store_cv_values)', 'estimator.fit(X,', 'y,', 's... | 720,899 |
stefan-rz/udacity-aind | solution.py | naked_twins | naked_twins | Eliminate values using the naked twins strategy. | [
"Eliminate",
"values",
"using",
"the",
"naked",
"twins",
"strategy."
] | def naked_twins(values):
naked_twin = dict(((unitlist.index(u), [s for s in u for x in u if s != x and len(values[s]) == 2 and (values[s] == values[x])]) for u in unitlist))
for i in naked_twin:
boxes = naked_twin.get(i)
if boxes is not None and len(boxes) >= 2:
digits = values[boxes... | ['def', 'naked_twins(values):', 'naked_twin', '=', 'dict(((unitlist.index(u),', '[s', 'for', 's', 'in', 'u', 'for', 'x', 'in', 'u', 'if', 's', '!=', 'x', 'and', 'len(values[s])', '==', '2', 'and', '(values[s]', '==', 'values[x])])', 'for', 'u', 'in', 'unitlist))', 'for', 'i', 'in', 'naked_twin:', 'boxes', '=', 'naked_t... | 427,909 |
tensorflow/agents | utils.py | SquashToSpecNormal.sample | sample | Generates samples from the wrapped TransformedDistribution. | [
"Generates",
"samples",
"from",
"the",
"wrapped",
"TransformedDistribution."
] | def sample(self, sample_shape=(), seed=None, name='sample'):
return self._squashed_distribution.sample(sample_shape, seed, name) | ['def', 'sample(self,', 'sample_shape=(),', 'seed=None,', "name='sample'):", 'return', 'self._squashed_distribution.sample(sample_shape,', 'seed,', 'name)'] | 22,665 |
matsu0228/nlp-jp | pyplot.py | get_plot_commands | get_plot_commands | Get a sorted list of all of the plotting commands. | [
"Get",
"a",
"sorted",
"list",
"of",
"all",
"of",
"the",
"plotting",
"commands."
] | def get_plot_commands():
import inspect
exclude = {'colormaps', 'colors', 'connect', 'disconnect', 'get_plot_commands', 'get_current_fig_manager', 'ginput', 'plotting', 'waitforbuttonpress'}
exclude |= set(colormaps())
this_module = inspect.getmodule(get_plot_commands)
commands = set()
for (name... | ['def', 'get_plot_commands():', 'import', 'inspect', 'exclude', '=', "{'colormaps',", "'colors',", "'connect',", "'disconnect',", "'get_plot_commands',", "'get_current_fig_manager',", "'ginput',", "'plotting',", "'waitforbuttonpress'}", 'exclude', '|=', 'set(colormaps())', 'this_module', '=', 'inspect.getmodule(get_plo... | 789,139 |
Erfanafshar/Principles-and-Applications-of---graph-coloring | plot_directive.py | unescape_doctest | unescape_doctest | Extract code from a piece of text, which contains either Python code or doctests. | [
"Extract",
"code",
"from",
"a",
"piece",
"of",
"text,",
"which",
"contains",
"either",
"Python",
"code",
"or",
"doctests."
] | def unescape_doctest(text):
if not contains_doctest(text):
return text
code = ''
for line in text.split('\n'):
m = re.match('^\\s*(>>>|\\.\\.\\.) (.*)$', line)
if m:
code += m.group(2) + '\n'
elif line.strip():
code += '# ' + line.strip() + '\n'
... | ['def', 'unescape_doctest(text):', 'if', 'not', 'contains_doctest(text):', 'return', 'text', 'code', '=', "''", 'for', 'line', 'in', "text.split('\\n'):", 'm', '=', "re.match('^\\\\s*(>>>|\\\\.\\\\.\\\\.)", "(.*)$',", 'line)', 'if', 'm:', 'code', '+=', 'm.group(2)', '+', "'\\n'", 'elif', 'line.strip():', 'code', '+=', ... | 307,511 |
liziwl/AI-Lab | go_solve.py | eat_dead | eat_dead | eat all the specific color chess which is dead :param go_arr: the numpy array contains the chess board :param color_type: -1 is black, 1 is white. | [
"eat",
"all",
"the",
"specific",
"color",
"chess",
"which",
"is",
"dead",
":param",
"go_arr:",
"the",
"numpy",
"array",
"contains",
"the",
"chess",
"board",
":param",
"color_type:",
"-1",
"is",
"black,",
"1",
"is",
"white."
] | def eat_dead(go_arr, color_type):
wait_del = []
optional = np.zeros(go_arr.shape)
tmp_indx = np.where(go_arr == color_type)
optional[tmp_indx] = 6
for i in range(go_arr.shape[0]):
for j in range(go_arr.shape[1]):
if optional[i, j] == 6:
wait_del = wait_del + which... | ['def', 'eat_dead(go_arr,', 'color_type):', 'wait_del', '=', '[]', 'optional', '=', 'np.zeros(go_arr.shape)', 'tmp_indx', '=', 'np.where(go_arr', '==', 'color_type)', 'optional[tmp_indx]', '=', '6', 'for', 'i', 'in', 'range(go_arr.shape[0]):', 'for', 'j', 'in', 'range(go_arr.shape[1]):', 'if', 'optional[i,', 'j]', '=='... | 24,619 |
sunishsheth2009/ChatterBot | sourcedstring.py | SimpleSourcedString.docid | docid | An identifier (such as a filename) that specifies the document where the string was found. | [
"An",
"identifier",
"(such",
"as",
"a",
"filename)",
"that",
"specifies",
"the",
"document",
"where",
"the",
"string",
"was",
"found."
] | def docid(self):
return self.source.docid | ['def', 'docid(self):', 'return', 'self.source.docid'] | 485,121 |
AboudyKreidieh/h-baselines | humanoid_env.py | mass_center | mass_center | Compute the position of the agent's center of mass. | [
"Compute",
"the",
"position",
"of",
"the",
"agent's",
"center",
"of",
"mass."
] | def mass_center(model, sim):
mass = np.expand_dims(model.body_mass, 1)
xpos = sim.data.xipos
return (np.sum(mass * xpos, 0) / np.sum(mass))[0] | ['def', 'mass_center(model,', 'sim):', 'mass', '=', 'np.expand_dims(model.body_mass,', '1)', 'xpos', '=', 'sim.data.xipos', 'return', '(np.sum(mass', '*', 'xpos,', '0)', '/', 'np.sum(mass))[0]'] | 573,912 |
AiIsBetter/computer_vision | sast_process.py | SASTProcessTrain.gen_quad_tbo | gen_quad_tbo | Generate tbo_map for give quad. | [
"Generate",
"tbo_map",
"for",
"give",
"quad."
] | def gen_quad_tbo(self, quad, tcl_mask, tbo_map):
up_line = self.line_cross_two_point(quad[0], quad[1])
lower_line = self.line_cross_two_point(quad[3], quad[2])
quad_h = 0.5 * (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2]))
quad_w = 0.5 * (np.linalg.norm(quad[0] - quad[1]) + np.li... | ['def', 'gen_quad_tbo(self,', 'quad,', 'tcl_mask,', 'tbo_map):', 'up_line', '=', 'self.line_cross_two_point(quad[0],', 'quad[1])', 'lower_line', '=', 'self.line_cross_two_point(quad[3],', 'quad[2])', 'quad_h', '=', '0.5', '*', '(np.linalg.norm(quad[0]', '-', 'quad[3])', '+', 'np.linalg.norm(quad[1]', '-', 'quad[2]))', ... | 502,212 |
ifzhang/ByteTrack | metric.py | occupy_mem | occupy_mem | pre-allocate gpu memory for training to avoid memory Fragmentation. | [
"pre-allocate",
"gpu",
"memory",
"for",
"training",
"to",
"avoid",
"memory",
"Fragmentation."
] | def occupy_mem(cuda_device, mem_ratio=0.95):
(total, used) = get_total_and_free_memory_in_Mb(cuda_device)
max_mem = int(total * mem_ratio)
block_mem = max_mem - used
x = torch.cuda.FloatTensor(256, 1024, block_mem)
del x
time.sleep(5) | ['def', 'occupy_mem(cuda_device,', 'mem_ratio=0.95):', '(total,', 'used)', '=', 'get_total_and_free_memory_in_Mb(cuda_device)', 'max_mem', '=', 'int(total', '*', 'mem_ratio)', 'block_mem', '=', 'max_mem', '-', 'used', 'x', '=', 'torch.cuda.FloatTensor(256,', '1024,', 'block_mem)', 'del', 'x', 'time.sleep(5)'] | 410,720 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | traitlets.py | Type.info | info | Returns a description of the trait. | [
"Returns",
"a",
"description",
"of",
"the",
"trait."
] | def info(self):
if isinstance(self.klass, str):
klass = self.klass
else:
klass = self.klass.__module__ + '.' + self.klass.__name__
result = "a subclass of '%s'" % klass
if self.allow_none:
return result + ' or None'
return result | ['def', 'info(self):', 'if', 'isinstance(self.klass,', 'str):', 'klass', '=', 'self.klass', 'else:', 'klass', '=', 'self.klass.__module__', '+', "'.'", '+', 'self.klass.__name__', 'result', '=', '"a', 'subclass', 'of', '\'%s\'"', '%', 'klass', 'if', 'self.allow_none:', 'return', 'result', '+', "'", 'or', "None'", 'retu... | 437,861 |
weimin17/Object-Detection_HelmetDetection | graphs.py | make_restore_average_vars_dict | make_restore_average_vars_dict | Returns dict mapping moving average names to variables. | [
"Returns",
"dict",
"mapping",
"moving",
"average",
"names",
"to",
"variables."
] | def make_restore_average_vars_dict():
var_restore_dict = {}
variable_averages = tf.train.ExponentialMovingAverage(0.999)
for v in tf.global_variables():
if v in tf.trainable_variables():
name = variable_averages.average_name(v)
else:
name = v.op.name
var_resto... | ['def', 'make_restore_average_vars_dict():', 'var_restore_dict', '=', '{}', 'variable_averages', '=', 'tf.train.ExponentialMovingAverage(0.999)', 'for', 'v', 'in', 'tf.global_variables():', 'if', 'v', 'in', 'tf.trainable_variables():', 'name', '=', 'variable_averages.average_name(v)', 'else:', 'name', '=', 'v.op.name',... | 761,453 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | model_ptn.py | model_PTN.get_transform_matrix | get_transform_matrix | Get the 4x4 Perspective Transfromation matrix used for PTN. | [
"Get",
"the",
"4x4",
"Perspective",
"Transfromation",
"matrix",
"used",
"for",
"PTN."
] | def get_transform_matrix(self, view_out):
num_views = self._params.num_views
focal_length = self._params.focal_length
focal_range = self._params.focal_range
phi = 30
theta_interval = 360.0 / num_views
theta = theta_interval * view_out
camera_matrix = np.zeros((4, 4), dtype=np.float32)
in... | ['def', 'get_transform_matrix(self,', 'view_out):', 'num_views', '=', 'self._params.num_views', 'focal_length', '=', 'self._params.focal_length', 'focal_range', '=', 'self._params.focal_range', 'phi', '=', '30', 'theta_interval', '=', '360.0', '/', 'num_views', 'theta', '=', 'theta_interval', '*', 'view_out', 'camera_m... | 109,190 |
thaines/helit | params_sets.py | ParamsRange.getP2List | getP2List | returns the list of kernel parameters 2, not always used. | [
"returns",
"the",
"list",
"of",
"kernel",
"parameters",
"2,",
"not",
"always",
"used."
] | def getP2List(self):
return self.p2 | ['def', 'getP2List(self):', 'return', 'self.p2'] | 592,563 |
pipermerriam/flex | common.py | generate_max_length_validator | generate_max_length_validator | Generates a validator for enforcing the maxLength of a string. | [
"Generates",
"a",
"validator",
"for",
"enforcing",
"the",
"maxLength",
"of",
"a",
"string."
] | def generate_max_length_validator(maxLength, **kwargs):
return functools.partial(validate_max_length, maxLength=maxLength) | ['def', 'generate_max_length_validator(maxLength,', '**kwargs):', 'return', 'functools.partial(validate_max_length,', 'maxLength=maxLength)'] | 211,302 |
BWGZK/ShapePU | inference.py | keep_largest_connected_components | keep_largest_connected_components | Keeps only the largest connected components of each label for a segmentation mask. | [
"Keeps",
"only",
"the",
"largest",
"connected",
"components",
"of",
"each",
"label",
"for",
"a",
"segmentation",
"mask."
] | def keep_largest_connected_components(mask):
mask_shape = mask.shape
heart_slice = np.where(mask > 0, 1, 0)
out_heart = np.zeros(heart_slice.shape, dtype=np.uint8)
for struc_id in [1]:
binary_img = heart_slice == struc_id
blobs = measure.label(binary_img, connectivity=1)
props = ... | ['def', 'keep_largest_connected_components(mask):', 'mask_shape', '=', 'mask.shape', 'heart_slice', '=', 'np.where(mask', '>', '0,', '1,', '0)', 'out_heart', '=', 'np.zeros(heart_slice.shape,', 'dtype=np.uint8)', 'for', 'struc_id', 'in', '[1]:', 'binary_img', '=', 'heart_slice', '==', 'struc_id', 'blobs', '=', 'measure... | 350,226 |
junliangma/generativeSSL | half_moon_loader.py | load_semi_supervised | load_semi_supervised | Load the half moon dataset with 6 fixed labeled data points. | [
"Load",
"the",
"half",
"moon",
"dataset",
"with",
"6",
"fixed",
"labeled",
"data",
"points."
] | def load_semi_supervised():
(train_set, test_set, valid_set) = _download()
train_x_l = np.zeros((6, 2))
train_t_l = np.array([0, 0, 0, 1, 1, 1])
train_x_l[0] = [0.7, 1.7]
train_x_l[1] = [1.6, 2.6]
train_x_l[2] = [2.7, 1.7]
train_x_l[3] = [1.6, 2.0]
train_x_l[4] = [2.7, 1.1]
train_x_l... | ['def', 'load_semi_supervised():', '(train_set,', 'test_set,', 'valid_set)', '=', '_download()', 'train_x_l', '=', 'np.zeros((6,', '2))', 'train_t_l', '=', 'np.array([0,', '0,', '0,', '1,', '1,', '1])', 'train_x_l[0]', '=', '[0.7,', '1.7]', 'train_x_l[1]', '=', '[1.6,', '2.6]', 'train_x_l[2]', '=', '[2.7,', '1.7]', 'tr... | 202,314 |
43Carrig/recurrent_neural_networks_practice | inception_v2.py | inception_v2_arg_scope | inception_v2_arg_scope | Defines the default InceptionV2 arg scope. | [
"Defines",
"the",
"default",
"InceptionV2",
"arg",
"scope."
] | def inception_v2_arg_scope(weight_decay=4e-05, batch_norm_var_collection='moving_vars'):
batch_norm_params = {'decay': 0.9997, 'epsilon': 0.001, 'updates_collections': ops.GraphKeys.UPDATE_OPS, 'variables_collections': {'beta': None, 'gamma': None, 'moving_mean': [batch_norm_var_collection], 'moving_variance': [bat... | ['def', 'inception_v2_arg_scope(weight_decay=4e-05,', "batch_norm_var_collection='moving_vars'):", 'batch_norm_params', '=', "{'decay':", '0.9997,', "'epsilon':", '0.001,', "'updates_collections':", 'ops.GraphKeys.UPDATE_OPS,', "'variables_collections':", "{'beta':", 'None,', "'gamma':", 'None,', "'moving_mean':", '[ba... | 335,233 |
eora-ai/torchok | resnet.py | resnet101d | resnet101d | Constructs a ResNet-101-D model. | [
"Constructs",
"a",
"ResNet-101-D",
"model."
] | def resnet101d(pretrained=False, **kwargs):
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnet101d', pretrained, **model_args) | ['def', 'resnet101d(pretrained=False,', '**kwargs):', 'model_args', '=', 'dict(block=Bottleneck,', 'layers=[3,', '4,', '23,', '3],', 'stem_width=32,', "stem_type='deep',", 'avg_down=True,', '**kwargs)', 'return', "_create_resnet('resnet101d',", 'pretrained,', '**model_args)'] | 903,192 |
voxel51/fiftyone | utils.py | ensure_tf | ensure_tf | Verifies that ``tensorflow`` is installed and importable. | [
"Verifies",
"that",
"``tensorflow``",
"is",
"installed",
"and",
"importable."
] | def ensure_tf(eager=False, error_level=None, error_msg=None):
if error_level is None:
error_level = fo.config.requirement_error_level
success = ensure_import('tensorflow', error_level=error_level, error_msg=error_msg)
if not success or not eager:
return success
try:
import tensor... | ['def', 'ensure_tf(eager=False,', 'error_level=None,', 'error_msg=None):', 'if', 'error_level', 'is', 'None:', 'error_level', '=', 'fo.config.requirement_error_level', 'success', '=', "ensure_import('tensorflow',", 'error_level=error_level,', 'error_msg=error_msg)', 'if', 'not', 'success', 'or', 'not', 'eager:', 'retur... | 583,441 |
microsoft/nni | mnist.py | max_pool | max_pool | max_pool downsamples a feature map by 2X. | [
"max_pool",
"downsamples",
"a",
"feature",
"map",
"by",
"2X."
] | def max_pool(x_input, pool_size):
return tf.nn.max_pool(x_input, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME') | ['def', 'max_pool(x_input,', 'pool_size):', 'return', 'tf.nn.max_pool(x_input,', 'ksize=[1,', 'pool_size,', 'pool_size,', '1],', 'strides=[1,', 'pool_size,', 'pool_size,', '1],', "padding='SAME')"] | 728,081 |
pathak22/noreward-rl | inference.py | inference | inference | It only restores LSTMPolicy architecture, and does inference using that. | [
"It",
"only",
"restores",
"LSTMPolicy",
"architecture,",
"and",
"does",
"inference",
"using",
"that."
] | def inference(args):
indir = os.path.join(args.log_dir, 'train')
outdir = os.path.join(args.log_dir, 'inference') if args.out_dir is None else args.out_dir
with open(indir + '/checkpoint', 'r') as f:
first_line = f.readline().strip()
ckpt = first_line.split(' ')[-1].split('/')[-1][:-1]
ckpt ... | ['def', 'inference(args):', 'indir', '=', 'os.path.join(args.log_dir,', "'train')", 'outdir', '=', 'os.path.join(args.log_dir,', "'inference')", 'if', 'args.out_dir', 'is', 'None', 'else', 'args.out_dir', 'with', 'open(indir', '+', "'/checkpoint',", "'r')", 'as', 'f:', 'first_line', '=', 'f.readline().strip()', 'ckpt',... | 249,508 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | track_perplexity.py | run_once | run_once | Evaluates the latest model checkpoint. | [
"Evaluates",
"the",
"latest",
"model",
"checkpoint."
] | def run_once(model, losses, weights, saver, summary_writer, summary_op):
model_path = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if not model_path:
tf.logging.info('Skipping evaluation. No checkpoint found in: %s', FLAGS.checkpoint_dir)
return
with tf.Session() as sess:
tf.logg... | ['def', 'run_once(model,', 'losses,', 'weights,', 'saver,', 'summary_writer,', 'summary_op):', 'model_path', '=', 'tf.train.latest_checkpoint(FLAGS.checkpoint_dir)', 'if', 'not', 'model_path:', "tf.logging.info('Skipping", 'evaluation.', 'No', 'checkpoint', 'found', 'in:', "%s',", 'FLAGS.checkpoint_dir)', 'return', 'wi... | 26,836 |
JoyHuYY1412/Class_Imbalanced_Semi_Supervised_Learning | data.py | DataSet.memoize | memoize | Call before parsing, since it calls for parse inside. | [
"Call",
"before",
"parsing,",
"since",
"it",
"calls",
"for",
"parse",
"inside."
] | def memoize(self):
data = []
with tf.Session(config=utils.get_config()) as session:
it = self.parse().prefetch(16).make_one_shot_iterator().get_next()
try:
while 1:
data.append(session.run(it))
except tf.errors.OutOfRangeError:
pass
images = np... | ['def', 'memoize(self):', 'data', '=', '[]', 'with', 'tf.Session(config=utils.get_config())', 'as', 'session:', 'it', '=', 'self.parse().prefetch(16).make_one_shot_iterator().get_next()', 'try:', 'while', '1:', 'data.append(session.run(it))', 'except', 'tf.errors.OutOfRangeError:', 'pass', 'images', '=', "np.stack([x['... | 122,258 |
43Carrig/recurrent_neural_networks_practice | symbol_database.py | SymbolDatabase.RegisterServiceDescriptor | RegisterServiceDescriptor | Registers the given service descriptor in the local database. | [
"Registers",
"the",
"given",
"service",
"descriptor",
"in",
"the",
"local",
"database."
] | def RegisterServiceDescriptor(self, service_descriptor):
self.pool.AddServiceDescriptor(service_descriptor) | ['def', 'RegisterServiceDescriptor(self,', 'service_descriptor):', 'self.pool.AddServiceDescriptor(service_descriptor)'] | 309,865 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | search.py | Node.path | path | Return a list of nodes forming the path from the root to this node. | [
"Return",
"a",
"list",
"of",
"nodes",
"forming",
"the",
"path",
"from",
"the",
"root",
"to",
"this",
"node."
] | def path(self):
(node, path_back) = (self, [])
while node:
path_back.append(node)
node = node.parent
return list(reversed(path_back)) | ['def', 'path(self):', '(node,', 'path_back)', '=', '(self,', '[])', 'while', 'node:', 'path_back.append(node)', 'node', '=', 'node.parent', 'return', 'list(reversed(path_back))'] | 428,100 |
neardws/Game-Theoretic-Deep-Reinforcement-Learning | agent.py | MAD3PGAgent.make_actor | make_actor | Create an actor instance. | [
"Create",
"an",
"actor",
"instance."
] | def make_actor(self, policy_one_networks: snt.Module, policy_two_networks: snt.Module, adder: Optional[adders.Adder]=None, variable_source: Optional[core.VariableSource]=None):
if variable_source:
variables = dict()
variables['policy_one_network'] = policy_one_networks.variables
variables['p... | ['def', 'make_actor(self,', 'policy_one_networks:', 'snt.Module,', 'policy_two_networks:', 'snt.Module,', 'adder:', 'Optional[adders.Adder]=None,', 'variable_source:', 'Optional[core.VariableSource]=None):', 'if', 'variable_source:', 'variables', '=', 'dict()', "variables['policy_one_network']", '=', 'policy_one_networ... | 199,713 |
Kvatsx/Artificial-Intelligence-Assignments | nonlin.py | asjacobian | asjacobian | Convert given object to one suitable for use as a Jacobian. | [
"Convert",
"given",
"object",
"to",
"one",
"suitable",
"for",
"use",
"as",
"a",
"Jacobian."
] | def asjacobian(J):
spsolve = scipy.sparse.linalg.spsolve
if isinstance(J, Jacobian):
return J
elif inspect.isclass(J) and issubclass(J, Jacobian):
return J()
elif isinstance(J, np.ndarray):
if J.ndim > 2:
raise ValueError('array must have rank <= 2')
J = np.at... | ['def', 'asjacobian(J):', 'spsolve', '=', 'scipy.sparse.linalg.spsolve', 'if', 'isinstance(J,', 'Jacobian):', 'return', 'J', 'elif', 'inspect.isclass(J)', 'and', 'issubclass(J,', 'Jacobian):', 'return', 'J()', 'elif', 'isinstance(J,', 'np.ndarray):', 'if', 'J.ndim', '>', '2:', 'raise', "ValueError('array", 'must', 'hav... | 77,710 |
sek788432/Waymo-2D-Object-Detection | context_rcnn_lib.py | project_features | project_features | Projects features to another feature space. | [
"Projects",
"features",
"to",
"another",
"feature",
"space."
] | def project_features(features, projection_dimension, is_training, normalize):
batch_norm_params = {'is_training': is_training, 'decay': 0.97, 'epsilon': 0.001, 'center': True, 'scale': True}
(batch_size, _, num_features) = features.shape
features = tf.reshape(features, [-1, num_features])
projected_feat... | ['def', 'project_features(features,', 'projection_dimension,', 'is_training,', 'normalize):', 'batch_norm_params', '=', "{'is_training':", 'is_training,', "'decay':", '0.97,', "'epsilon':", '0.001,', "'center':", 'True,', "'scale':", 'True}', '(batch_size,', '_,', 'num_features)', '=', 'features.shape', 'features', '='... | 975,045 |
tobegit3hub/deep_image_model | resource_variable_ops.py | ResourceVariable.op | op | The op which reads the value of this variable. | [
"The",
"op",
"which",
"reads",
"the",
"value",
"of",
"this",
"variable."
] | def op(self):
return self._value.op | ['def', 'op(self):', 'return', 'self._value.op'] | 183,038 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | network_units.py | NetworkUnitInterface.get_l2_regularized_weights | get_l2_regularized_weights | Gets the weights that need to be regularized. | [
"Gets",
"the",
"weights",
"that",
"need",
"to",
"be",
"regularized."
] | def get_l2_regularized_weights(self):
return self.regularized_weights | ['def', 'get_l2_regularized_weights(self):', 'return', 'self.regularized_weights'] | 111,348 |
tensorflow/data-validation | dashboard_util.py | generate_stats_dashboard_link | generate_stats_dashboard_link | Generate link for stats dashboard. | [
"Generate",
"link",
"for",
"stats",
"dashboard."
] | def generate_stats_dashboard_link():
return dashboard_util_impl.generate_stats_dashboard_link() | ['def', 'generate_stats_dashboard_link():', 'return', 'dashboard_util_impl.generate_stats_dashboard_link()'] | 497,597 |
weimin17/Object-Detection_HelmetDetection | cifar10_input.py | distorted_inputs | distorted_inputs | Construct distorted input for CIFAR training using the Reader ops. | [
"Construct",
"distorted",
"input",
"for",
"CIFAR",
"training",
"using",
"the",
"Reader",
"ops."
] | def distorted_inputs(data_dir, batch_size):
filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)]
for f in filenames:
if not tf.gfile.Exists(f):
raise ValueError('Failed to find file: ' + f)
filename_queue = tf.train.string_input_producer(filenames)
with... | ['def', 'distorted_inputs(data_dir,', 'batch_size):', 'filenames', '=', '[os.path.join(data_dir,', "'data_batch_%d.bin'", '%', 'i)', 'for', 'i', 'in', 'xrange(1,', '6)]', 'for', 'f', 'in', 'filenames:', 'if', 'not', 'tf.gfile.Exists(f):', 'raise', "ValueError('Failed", 'to', 'find', 'file:', "'", '+', 'f)', 'filename_q... | 754,180 |
SamsungLabs/fcaf3d | sparse_unet.py | SparseUNet.make_encoder_layers | make_encoder_layers | make encoder layers using sparse convs. | [
"make",
"encoder",
"layers",
"using",
"sparse",
"convs."
] | def make_encoder_layers(self, make_block, norm_cfg, in_channels):
self.encoder_layers = spconv.SparseSequential()
for (i, blocks) in enumerate(self.encoder_channels):
blocks_list = []
for (j, out_channels) in enumerate(tuple(blocks)):
padding = tuple(self.encoder_paddings[i])[j]
... | ['def', 'make_encoder_layers(self,', 'make_block,', 'norm_cfg,', 'in_channels):', 'self.encoder_layers', '=', 'spconv.SparseSequential()', 'for', '(i,', 'blocks)', 'in', 'enumerate(self.encoder_channels):', 'blocks_list', '=', '[]', 'for', '(j,', 'out_channels)', 'in', 'enumerate(tuple(blocks)):', 'padding', '=', 'tupl... | 560,500 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | trainer_model_based.py | rl_modelrl_tiny_sv2p | rl_modelrl_tiny_sv2p | Tiny setting with a sv2p model. | [
"Tiny",
"setting",
"with",
"a",
"sv2p",
"model."
] | def rl_modelrl_tiny_sv2p():
hparams = rl_modelrl_tiny()
hparams.generative_model = 'next_frame_sv2p'
hparams.generative_model_params = 'next_frame_sv2p_tiny'
return hparams | ['def', 'rl_modelrl_tiny_sv2p():', 'hparams', '=', 'rl_modelrl_tiny()', 'hparams.generative_model', '=', "'next_frame_sv2p'", 'hparams.generative_model_params', '=', "'next_frame_sv2p_tiny'", 'return', 'hparams'] | 965,995 |
zhaocq-nlp/NJUNMT-tf | bridges.py | ZeroBridge.default_params | default_params | Returns a dictionary of default parameters of this bridge. | [
"Returns",
"a",
"dictionary",
"of",
"default",
"parameters",
"of",
"this",
"bridge."
] | def default_params():
return {} | ['def', 'default_params():', 'return', '{}'] | 782,947 |
scotthuang1989/object_detection_with_tensorflow | graph_builder_test.py | GraphBuilderTest.testWarmupGetsAndReleasesSession | testWarmupGetsAndReleasesSession | Checks that create_warmup_graph creates Get and ReleaseSession. | [
"Checks",
"that",
"create_warmup_graph",
"creates",
"Get",
"and",
"ReleaseSession."
] | def testWarmupGetsAndReleasesSession(self):
test_name = 'warmup-graph-structure'
with tf.Graph().as_default():
(builder, _) = self.getBuilderAndTarget(test_name)
warmup = builder.build_warmup_graph('foo')
self.checkOpOrder('annotations', warmup, ['SetAssetDirectory', 'GetSession', 'Relea... | ['def', 'testWarmupGetsAndReleasesSession(self):', 'test_name', '=', "'warmup-graph-structure'", 'with', 'tf.Graph().as_default():', '(builder,', '_)', '=', 'self.getBuilderAndTarget(test_name)', 'warmup', '=', "builder.build_warmup_graph('foo')", "self.checkOpOrder('annotations',", 'warmup,', "['SetAssetDirectory',", ... | 739,815 |
xiaoachen98/DDB | ckd.py | CKD.inference | inference | Inference with slide/whole style. | [
"Inference",
"with",
"slide/whole",
"style."
] | def inference(self, img, img_meta, rescale, name='stu', return_seg_logit=False):
return self.get_model(name).inference(img, img_meta, rescale, return_seg_logit) | ['def', 'inference(self,', 'img,', 'img_meta,', 'rescale,', "name='stu',", 'return_seg_logit=False):', 'return', 'self.get_model(name).inference(img,', 'img_meta,', 'rescale,', 'return_seg_logit)'] | 498,908 |
deepmind/dm_control | cartpole.py | Physics.bounded_position | bounded_position | Returns the state, with pole angle split into sin/cos. | [
"Returns",
"the",
"state,",
"with",
"pole",
"angle",
"split",
"into",
"sin/cos."
] | def bounded_position(self):
return np.hstack((self.cart_position(), self.named.data.xmat[2:, ['zz', 'xz']].ravel())) | ['def', 'bounded_position(self):', 'return', 'np.hstack((self.cart_position(),', 'self.named.data.xmat[2:,', "['zz',", "'xz']].ravel()))"] | 166,294 |
rudranil723/mini-main | core.py | enable_diag | enable_diag | Enable a global pyparsing diagnostic flag (see :class:`Diagnostics`). | [
"Enable",
"a",
"global",
"pyparsing",
"diagnostic",
"flag",
"(see",
":class:`Diagnostics`)."
] | def enable_diag(diag_enum: Diagnostics) -> None:
__diag__.enable(diag_enum.name) | ['def', 'enable_diag(diag_enum:', 'Diagnostics)', '->', 'None:', '__diag__.enable(diag_enum.name)'] | 268,657 |
annieyan/PreprocessSatelliteImagery- | get_data_stat.py | parse_args | parse_args | Parse command line arguments passed to script invocation. | [
"Parse",
"command",
"line",
"arguments",
"passed",
"to",
"script",
"invocation."
] | def parse_args():
parser = argparse.ArgumentParser(description='Get statistics for training data and test data from geojson and tif images.')
parser.add_argument('src_geojson', help='source geojson')
return parser.parse_args() | ['def', 'parse_args():', 'parser', '=', "argparse.ArgumentParser(description='Get", 'statistics', 'for', 'training', 'data', 'and', 'test', 'data', 'from', 'geojson', 'and', 'tif', "images.')", "parser.add_argument('src_geojson',", "help='source", "geojson')", 'return', 'parser.parse_args()'] | 824,388 |
flavioschneider/rl-transfer- | test_functions.py | TestOptimizerInterface.test_tf_make_optimizer_with_type | test_tf_make_optimizer_with_type | Test make_optimizer function with type as first argument. | [
"Test",
"make_optimizer",
"function",
"with",
"type",
"as",
"first",
"argument."
] | def test_tf_make_optimizer_with_type(self):
optimizer_type = tf.compat.v1.train.AdamOptimizer
lr = 0.123
optimizer = make_optimizer(optimizer_type, learning_rate=lr, name='testOptimizer')
assert isinstance(optimizer, optimizer_type)
self.sess.run(tf.compat.v1.global_variables_initializer())
asse... | ['def', 'test_tf_make_optimizer_with_type(self):', 'optimizer_type', '=', 'tf.compat.v1.train.AdamOptimizer', 'lr', '=', '0.123', 'optimizer', '=', 'make_optimizer(optimizer_type,', 'learning_rate=lr,', "name='testOptimizer')", 'assert', 'isinstance(optimizer,', 'optimizer_type)', 'self.sess.run(tf.compat.v1.global_var... | 861,705 |
zihuitang/medical_AI_platform | cookiejar.py | CookieJar.make_cookies | make_cookies | Return sequence of Cookie objects extracted from response object. | [
"Return",
"sequence",
"of",
"Cookie",
"objects",
"extracted",
"from",
"response",
"object."
] | def make_cookies(self, response, request):
headers = response.info()
rfc2965_hdrs = headers.get_all('Set-Cookie2', [])
ns_hdrs = headers.get_all('Set-Cookie', [])
rfc2965 = self._policy.rfc2965
netscape = self._policy.netscape
if not rfc2965_hdrs and (not ns_hdrs) or (not ns_hdrs and (not rfc296... | ['def', 'make_cookies(self,', 'response,', 'request):', 'headers', '=', 'response.info()', 'rfc2965_hdrs', '=', "headers.get_all('Set-Cookie2',", '[])', 'ns_hdrs', '=', "headers.get_all('Set-Cookie',", '[])', 'rfc2965', '=', 'self._policy.rfc2965', 'netscape', '=', 'self._policy.netscape', 'if', 'not', 'rfc2965_hdrs', ... | 282,623 |
rudranil723/mini-main | axes_rgb.py | make_rgb_axes | make_rgb_axes | Parameters ---------- pad : float Fraction of the axes height. | [
"Parameters",
"----------",
"pad",
":",
"float",
"Fraction",
"of",
"the",
"axes",
"height."
] | def make_rgb_axes(ax, pad=0.01, axes_class=None, **kwargs):
divider = make_axes_locatable(ax)
pad_size = pad * Size.AxesY(ax)
xsize = (1 - 2 * pad) / 3 * Size.AxesX(ax)
ysize = (1 - 2 * pad) / 3 * Size.AxesY(ax)
divider.set_horizontal([Size.AxesX(ax), pad_size, xsize])
divider.set_vertical([ysiz... | ['def', 'make_rgb_axes(ax,', 'pad=0.01,', 'axes_class=None,', '**kwargs):', 'divider', '=', 'make_axes_locatable(ax)', 'pad_size', '=', 'pad', '*', 'Size.AxesY(ax)', 'xsize', '=', '(1', '-', '2', '*', 'pad)', '/', '3', '*', 'Size.AxesX(ax)', 'ysize', '=', '(1', '-', '2', '*', 'pad)', '/', '3', '*', 'Size.AxesY(ax)', 'd... | 320,424 |
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