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 |
|---|---|---|---|---|---|---|---|---|
google-research/batch-ppo | in_graph_batch_env.py | InGraphBatchEnv.action | action | Access the variable holding the last received action. | [
"Access",
"the",
"variable",
"holding",
"the",
"last",
"received",
"action."
] | def action(self):
return self._action | ['def', 'action(self):', 'return', 'self._action'] | 94,962 |
nicknochnack/RealTimeSignLanguageTFJS | autoaugment_utils.py | translate_bbox | translate_bbox | Equivalent of PIL Translate in X/Y dimension that shifts image and bbox. | [
"Equivalent",
"of",
"PIL",
"Translate",
"in",
"X/Y",
"dimension",
"that",
"shifts",
"image",
"and",
"bbox."
] | def translate_bbox(image, bboxes, pixels, replace, shift_horizontal):
if shift_horizontal:
image = translate_x(image, pixels, replace)
else:
image = translate_y(image, pixels, replace)
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
wrapped_shift_bbox = lambda bbox... | ['def', 'translate_bbox(image,', 'bboxes,', 'pixels,', 'replace,', 'shift_horizontal):', 'if', 'shift_horizontal:', 'image', '=', 'translate_x(image,', 'pixels,', 'replace)', 'else:', 'image', '=', 'translate_y(image,', 'pixels,', 'replace)', 'image_height', '=', 'tf.shape(image)[0]', 'image_width', '=', 'tf.shape(imag... | 830,816 |
FilipMiscevic/random_walk | rw.py | irt_self_longterm_avg | irt_self_longterm_avg | Determine IRTs for each patch entry positions using fluid categories normalized to the long-term average IRT accross all trials in the experiment. | [
"Determine",
"IRTs",
"for",
"each",
"patch",
"entry",
"positions",
"using",
"fluid",
"categories",
"normalized",
"to",
"the",
"long-term",
"average",
"IRT",
"accross",
"all",
"trials",
"in",
"the",
"experiment."
] | def irt_self_longterm_avg(b, cat, multi=True):
orders = []
n = []
p = []
irts = []
if multi == True:
size = len(cat)
for (q, w) in enumerate(cat):
neg_order = []
pos_order = []
for (j, kk) in enumerate(w):
if kk >= max(w):
... | ['def', 'irt_self_longterm_avg(b,', 'cat,', 'multi=True):', 'orders', '=', '[]', 'n', '=', '[]', 'p', '=', '[]', 'irts', '=', '[]', 'if', 'multi', '==', 'True:', 'size', '=', 'len(cat)', 'for', '(q,', 'w)', 'in', 'enumerate(cat):', 'neg_order', '=', '[]', 'pos_order', '=', '[]', 'for', '(j,', 'kk)', 'in', 'enumerate(w)... | 304,284 |
nkthiebaut/zeugma | test_texttransformers.py | test_item_selector | test_item_selector | Test selecting items in a mappable from previous pipeline step. | [
"Test",
"selecting",
"items",
"in",
"a",
"mappable",
"from",
"previous",
"pipeline",
"step."
] | def test_item_selector():
test_case = {'a': 1, 'b': 2}
item_selector = ItemSelector('a')
out = item_selector.fit_transform(test_case)
assert out == test_case['a'] | ['def', 'test_item_selector():', 'test_case', '=', "{'a':", '1,', "'b':", '2}', 'item_selector', '=', "ItemSelector('a')", 'out', '=', 'item_selector.fit_transform(test_case)', 'assert', 'out', '==', "test_case['a']"] | 971,818 |
facebookresearch/CompilerGym | gcc_env.py | GccEnv.obj | obj | Get the object code. | [
"Get",
"the",
"object",
"code."
] | def obj(self) -> bytes:
return self.observation['obj'] | ['def', 'obj(self)', '->', 'bytes:', 'return', "self.observation['obj']"] | 125,463 |
amazon-science/gluonmm | image_classification_config.py | get_cfg_defaults | get_cfg_defaults | Get a yacs CfgNode object with default values for your project. | [
"Get",
"a",
"yacs",
"CfgNode",
"object",
"with",
"default",
"values",
"for",
"your",
"project."
] | def get_cfg_defaults():
return _C.clone() | ['def', 'get_cfg_defaults():', 'return', '_C.clone()'] | 578,291 |
YuYaoYang2333/SyntaLinker | misc.py | set_random_seed | set_random_seed | Sets the random seed. | [
"Sets",
"the",
"random",
"seed."
] | def set_random_seed(seed, is_cuda):
if seed > 0:
torch.manual_seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
if is_cuda and seed > 0:
torch.cuda.manual_seed(seed) | ['def', 'set_random_seed(seed,', 'is_cuda):', 'if', 'seed', '>', '0:', 'torch.manual_seed(seed)', 'random.seed(seed)', 'torch.backends.cudnn.deterministic', '=', 'True', 'if', 'is_cuda', 'and', 'seed', '>', '0:', 'torch.cuda.manual_seed(seed)'] | 905,975 |
enlite-ai/maze | test_export_gif_wrapper.py | assert_gif_export | assert_gif_export | Checks if gif got exported correctly. | [
"Checks",
"if",
"gif",
"got",
"exported",
"correctly."
] | def assert_gif_export(env: MazeEnv) -> None:
env.reset()
for _ in range(3):
env.step(env.action_space.sample())
env.close()
gif_files = glob.glob('*.gif')
assert len(gif_files) == 1 | ['def', 'assert_gif_export(env:', 'MazeEnv)', '->', 'None:', 'env.reset()', 'for', '_', 'in', 'range(3):', 'env.step(env.action_space.sample())', 'env.close()', 'gif_files', '=', "glob.glob('*.gif')", 'assert', 'len(gif_files)', '==', '1'] | 647,176 |
rudranil723/mini-main | runner.py | partition_suite_by_case | partition_suite_by_case | Partition a test suite by test case, preserving the order of tests. | [
"Partition",
"a",
"test",
"suite",
"by",
"test",
"case,",
"preserving",
"the",
"order",
"of",
"tests."
] | def partition_suite_by_case(suite):
groups = []
suite_class = type(suite)
for (test_type, test_group) in itertools.groupby(suite, type):
if issubclass(test_type, unittest.TestCase):
groups.append(suite_class(test_group))
else:
for item in test_group:
g... | ['def', 'partition_suite_by_case(suite):', 'groups', '=', '[]', 'suite_class', '=', 'type(suite)', 'for', '(test_type,', 'test_group)', 'in', 'itertools.groupby(suite,', 'type):', 'if', 'issubclass(test_type,', 'unittest.TestCase):', 'groups.append(suite_class(test_group))', 'else:', 'for', 'item', 'in', 'test_group:',... | 316,553 |
rlgraph/rlgraph | openai_gym.py | OpenAIGymEnv.translate_space | translate_space | Translates openAI spaces into RLGraph Space classes. | [
"Translates",
"openAI",
"spaces",
"into",
"RLGraph",
"Space",
"classes."
] | def translate_space(space, dtype=None, force_float32=False):
if isinstance(space, gym.spaces.Discrete):
return IntBox(space.n)
elif isinstance(space, gym.spaces.MultiBinary):
return BoolBox(shape=(space.n,))
elif isinstance(space, gym.spaces.MultiDiscrete):
return IntBox(low=np.zeros... | ['def', 'translate_space(space,', 'dtype=None,', 'force_float32=False):', 'if', 'isinstance(space,', 'gym.spaces.Discrete):', 'return', 'IntBox(space.n)', 'elif', 'isinstance(space,', 'gym.spaces.MultiBinary):', 'return', 'BoolBox(shape=(space.n,))', 'elif', 'isinstance(space,', 'gym.spaces.MultiDiscrete):', 'return', ... | 862,542 |
rlworkgroup/garage | _environment.py | EnvStep.last | last | bool: Whether this `TimeStep` is the last of a sequence. | [
"bool:",
"Whether",
"this",
"`TimeStep`",
"is",
"the",
"last",
"of",
"a",
"sequence."
] | def last(self):
return self.step_type is StepType.TERMINAL or self.step_type is StepType.TIMEOUT | ['def', 'last(self):', 'return', 'self.step_type', 'is', 'StepType.TERMINAL', 'or', 'self.step_type', 'is', 'StepType.TIMEOUT'] | 200,163 |
Deeplite/deeplite-torch-zoo | utils.py | verify_image_label | verify_image_label | Verify one image-label pair. | [
"Verify",
"one",
"image-label",
"pair."
] | def verify_image_label(args):
(im_file, lb_file, prefix, keypoint, num_cls, nkpt, ndim) = args
(nm, nf, ne, nc, msg, segments, keypoints) = (0, 0, 0, 0, '', [], None)
try:
im = Image.open(im_file)
im.verify()
shape = exif_size(im)
shape = (shape[1], shape[0])
assert (... | ['def', 'verify_image_label(args):', '(im_file,', 'lb_file,', 'prefix,', 'keypoint,', 'num_cls,', 'nkpt,', 'ndim)', '=', 'args', '(nm,', 'nf,', 'ne,', 'nc,', 'msg,', 'segments,', 'keypoints)', '=', '(0,', '0,', '0,', '0,', "'',", '[],', 'None)', 'try:', 'im', '=', 'Image.open(im_file)', 'im.verify()', 'shape', '=', 'ex... | 538,843 |
Speech-Lab-IITM/CCC-wav2vec-2.0 | online_backtranslation.py | OnlineBackTranslationTask.load_train_dataset | load_train_dataset | The training dataset is made of backtranslation dataset and denoising dataset. | [
"The",
"training",
"dataset",
"is",
"made",
"of",
"backtranslation",
"dataset",
"and",
"denoising",
"dataset."
] | def load_train_dataset(self, data_path: str) -> FairseqDataset:
data = []
for lang in self.mono_langs:
train_path = os.path.join(data_path, lang, 'train')
data.append((f'{lang}-BT', self.load_bt_dataset(train_path, lang)))
data.append((f'{lang}-DENOISE', self.load_denoise_dataset(train_p... | ['def', 'load_train_dataset(self,', 'data_path:', 'str)', '->', 'FairseqDataset:', 'data', '=', '[]', 'for', 'lang', 'in', 'self.mono_langs:', 'train_path', '=', 'os.path.join(data_path,', 'lang,', "'train')", "data.append((f'{lang}-BT',", 'self.load_bt_dataset(train_path,', 'lang)))', "data.append((f'{lang}-DENOISE',"... | 104,150 |
openvinotoolkit/training_extensions | f_measure.py | FMeasure.f_measure_per_confidence | f_measure_per_confidence | Returns the curve for f-measure per confidence as CurveMetric if exists. | [
"Returns",
"the",
"curve",
"for",
"f-measure",
"per",
"confidence",
"as",
"CurveMetric",
"if",
"exists."
] | def f_measure_per_confidence(self) -> Optional[CurveMetric]:
return self._f_measure_per_confidence | ['def', 'f_measure_per_confidence(self)', '->', 'Optional[CurveMetric]:', 'return', 'self._f_measure_per_confidence'] | 918,762 |
lektor/lektor-archive | dash.py | generic_endpoint | generic_endpoint | This function is invoked by all dash endpoints. | [
"This",
"function",
"is",
"invoked",
"by",
"all",
"dash",
"endpoints."
] | def generic_endpoint(**kwargs):
return render_template('dash.html') | ['def', 'generic_endpoint(**kwargs):', 'return', "render_template('dash.html')"] | 216,516 |
open-mmlab/mmrotate | gmm.py | GaussianMixture.check_size | check_size | Make sure that the shape of x is (T, n, 1, d). | [
"Make",
"sure",
"that",
"the",
"shape",
"of",
"x",
"is",
"(T,",
"n,",
"1,",
"d)."
] | def check_size(self, x):
if len(x.size()) == 3:
x = x.unsqueeze(2)
return x | ['def', 'check_size(self,', 'x):', 'if', 'len(x.size())', '==', '3:', 'x', '=', 'x.unsqueeze(2)', 'return', 'x'] | 625,055 |
tryolabs/luminoth | fasterrcnn.py | FasterRCNN.summary | summary | Generate merged summary of all the sub-summaries used inside the Faster R-CNN network. | [
"Generate",
"merged",
"summary",
"of",
"all",
"the",
"sub-summaries",
"used",
"inside",
"the",
"Faster",
"R-CNN",
"network."
] | def summary(self):
summaries = [tf.summary.merge_all(key='rpn')]
summaries.append(tf.summary.merge_all(key=self._losses_collections[0]))
if self._with_rcnn:
summaries.append(tf.summary.merge_all(key='rcnn'))
return tf.summary.merge(summaries) | ['def', 'summary(self):', 'summaries', '=', "[tf.summary.merge_all(key='rpn')]", 'summaries.append(tf.summary.merge_all(key=self._losses_collections[0]))', 'if', 'self._with_rcnn:', "summaries.append(tf.summary.merge_all(key='rcnn'))", 'return', 'tf.summary.merge(summaries)'] | 617,470 |
ahottung/CVAE-Opt | tsp.py | update_mask | update_mask | Marks the visited city, so it can't be selected a second time. | [
"Marks",
"the",
"visited",
"city,",
"so",
"it",
"can't",
"be",
"selected",
"a",
"second",
"time."
] | def update_mask(mask, dynamic, chosen_idx):
mask.scatter_(1, chosen_idx.unsqueeze(1), 0)
return mask | ['def', 'update_mask(mask,', 'dynamic,', 'chosen_idx):', 'mask.scatter_(1,', 'chosen_idx.unsqueeze(1),', '0)', 'return', 'mask'] | 509,425 |
ifwe/digsby | imwin_ctrl.py | ImWinCtrl.on_send_message_im | on_send_message_im | Invoked when enter is pressed in the message input box during IM mode. | [
"Invoked",
"when",
"enter",
"is",
"pressed",
"in",
"the",
"message",
"input",
"box",
"during",
"IM",
"mode."
] | def on_send_message_im(self):
val = self.input_area.GetFormattedValue()
if not val.format_as('plaintext'):
return
self.history.commit(val.format_as('plaintext'))
if self.set_conversation_from_combos():
self.convo.send_message(val)
self.ClearAndFocus()
return True | ['def', 'on_send_message_im(self):', 'val', '=', 'self.input_area.GetFormattedValue()', 'if', 'not', "val.format_as('plaintext'):", 'return', "self.history.commit(val.format_as('plaintext'))", 'if', 'self.set_conversation_from_combos():', 'self.convo.send_message(val)', 'self.ClearAndFocus()', 'return', 'True'] | 185,382 |
43Carrig/recurrent_neural_networks_practice | gbdt_batch.py | GradientBoostedDecisionTreeModel.update_stats | update_stats | Update the accumulators with stats from this batch. | [
"Update",
"the",
"accumulators",
"with",
"stats",
"from",
"this",
"batch."
] | def update_stats(self, loss, predictions_dict):
input_deps = self._dense_floats + self._sparse_float_indices + self._sparse_int_indices
worker_device = input_deps[0].device
predictions = predictions_dict[PREDICTIONS]
partition_ids = predictions_dict[PARTITION_IDS]
ensemble_stamp = predictions_dict[E... | ['def', 'update_stats(self,', 'loss,', 'predictions_dict):', 'input_deps', '=', 'self._dense_floats', '+', 'self._sparse_float_indices', '+', 'self._sparse_int_indices', 'worker_device', '=', 'input_deps[0].device', 'predictions', '=', 'predictions_dict[PREDICTIONS]', 'partition_ids', '=', 'predictions_dict[PARTITION_I... | 312,593 |
rudranil723/mini-main | builder.py | PairPosBuilder.addClassPair | addClassPair | Add a class pair positioning rule to the current lookup. | [
"Add",
"a",
"class",
"pair",
"positioning",
"rule",
"to",
"the",
"current",
"lookup."
] | def addClassPair(self, location, glyphclass1, value1, glyphclass2, value2):
self.pairs.append((glyphclass1, value1, glyphclass2, value2)) | ['def', 'addClassPair(self,', 'location,', 'glyphclass1,', 'value1,', 'glyphclass2,', 'value2):', 'self.pairs.append((glyphclass1,', 'value1,', 'glyphclass2,', 'value2))'] | 317,315 |
arshpreetsingh/quantopian-machinelearning | frontend_widget.py | FrontendWidget.append_stream | append_stream | Appends text to the output stream. | [
"Appends",
"text",
"to",
"the",
"output",
"stream."
] | def append_stream(self, text):
text = text.expandtabs(8)
self._append_plain_text(text, before_prompt=True) | ['def', 'append_stream(self,', 'text):', 'text', '=', 'text.expandtabs(8)', 'self._append_plain_text(text,', 'before_prompt=True)'] | 892,880 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | AutoExpand.py | AutoExpand.getprevword | getprevword | Return the word prefix before the cursor. | [
"Return",
"the",
"word",
"prefix",
"before",
"the",
"cursor."
] | def getprevword(self):
line = self.text.get('insert linestart', 'insert')
i = len(line)
while i > 0 and line[i - 1] in self.wordchars:
i = i - 1
return line[i:] | ['def', 'getprevword(self):', 'line', '=', "self.text.get('insert", "linestart',", "'insert')", 'i', '=', 'len(line)', 'while', 'i', '>', '0', 'and', 'line[i', '-', '1]', 'in', 'self.wordchars:', 'i', '=', 'i', '-', '1', 'return', 'line[i:]'] | 430,763 |
scikit-learn-contrib/imbalanced-learn | test_weight_boosting.py | test_rus_boost_classifier_base_estimator | test_rus_boost_classifier_base_estimator | Check that we raise a FutureWarning when accessing `base_estimator_`. | [
"Check",
"that",
"we",
"raise",
"a",
"FutureWarning",
"when",
"accessing",
"`base_estimator_`."
] | def test_rus_boost_classifier_base_estimator():
(X, y) = load_iris(return_X_y=True)
estimator = RUSBoostClassifier().fit(X, y)
with pytest.warns(FutureWarning, match='`base_estimator_` was deprecated'):
estimator.base_estimator_ | ['def', 'test_rus_boost_classifier_base_estimator():', '(X,', 'y)', '=', 'load_iris(return_X_y=True)', 'estimator', '=', 'RUSBoostClassifier().fit(X,', 'y)', 'with', 'pytest.warns(FutureWarning,', "match='`base_estimator_`", 'was', "deprecated'):", 'estimator.base_estimator_'] | 610,652 |
Novartis/ChemBioMultimodalAutoencoders | joint_trainer.py | JointTrainer.translate | translate | Utility function allowing to translate a numpy array between any two registered modalities/models. | [
"Utility",
"function",
"allowing",
"to",
"translate",
"a",
"numpy",
"array",
"between",
"any",
"two",
"registered",
"modalities/models."
] | def translate(self, from_key: str, to_key: str, from_X: np.array, batch_size: int=256, use_gpu: bool=False) -> np.array:
dataloader = self._dataloader_from_numpy(from_X, batch_size, False)
from_model = self.model_dict[from_key]
to_model = self.model_dict[to_key]
from_model.eval()
to_model.eval()
... | ['def', 'translate(self,', 'from_key:', 'str,', 'to_key:', 'str,', 'from_X:', 'np.array,', 'batch_size:', 'int=256,', 'use_gpu:', 'bool=False)', '->', 'np.array:', 'dataloader', '=', 'self._dataloader_from_numpy(from_X,', 'batch_size,', 'False)', 'from_model', '=', 'self.model_dict[from_key]', 'to_model', '=', 'self.mo... | 486,116 |
aws/sagemaker-python-sdk | estimator.py | TensorFlow.hyperparameters | hyperparameters | Return hyperparameters used by your custom TensorFlow code during model training. | [
"Return",
"hyperparameters",
"used",
"by",
"your",
"custom",
"TensorFlow",
"code",
"during",
"model",
"training."
] | def hyperparameters(self):
hyperparameters = super(TensorFlow, self).hyperparameters()
additional_hyperparameters = self._distribution_configuration(self.distribution)
if self.model_dir is not False:
self.model_dir = self.model_dir or self._default_s3_path('model', mpi=additional_hyperparameters.get... | ['def', 'hyperparameters(self):', 'hyperparameters', '=', 'super(TensorFlow,', 'self).hyperparameters()', 'additional_hyperparameters', '=', 'self._distribution_configuration(self.distribution)', 'if', 'self.model_dir', 'is', 'not', 'False:', 'self.model_dir', '=', 'self.model_dir', 'or', "self._default_s3_path('model'... | 830,553 |
dibyaghosh/gcsl | mjpy_renderer.py | MjPyRenderer.render_offscreen | render_offscreen | Renders the camera view as a numpy array of pixels. | [
"Renders",
"the",
"camera",
"view",
"as",
"a",
"numpy",
"array",
"of",
"pixels."
] | def render_offscreen(self, width: int, height: int, mode: RenderMode=RenderMode.RGB, camera_id: int=-1) -> np.ndarray:
assert width > 0 and height > 0
if not self._offscreen_renderer:
self._offscreen_renderer = mujoco_py.MjRenderContextOffscreen(self._sim, device_id=-1)
if camera_id == -1:
s... | ['def', 'render_offscreen(self,', 'width:', 'int,', 'height:', 'int,', 'mode:', 'RenderMode=RenderMode.RGB,', 'camera_id:', 'int=-1)', '->', 'np.ndarray:', 'assert', 'width', '>', '0', 'and', 'height', '>', '0', 'if', 'not', 'self._offscreen_renderer:', 'self._offscreen_renderer', '=', 'mujoco_py.MjRenderContextOffscre... | 202,019 |
tanshen/SubCNN | layer.py | GtDataLayer.set_roidb | set_roidb | Set the roidb to be used by this layer during training. | [
"Set",
"the",
"roidb",
"to",
"be",
"used",
"by",
"this",
"layer",
"during",
"training."
] | def set_roidb(self, roidb):
self._roidb = roidb
self._shuffle_roidb_inds() | ['def', 'set_roidb(self,', 'roidb):', 'self._roidb', '=', 'roidb', 'self._shuffle_roidb_inds()'] | 359,959 |
enuguru/artificial_intelligence_and_machine_ | debug.py | dump_stack_frames | dump_stack_frames | Print a summary of the stack to stdout, or some place else. | [
"Print",
"a",
"summary",
"of",
"the",
"stack",
"to",
"stdout,",
"or",
"some",
"place",
"else."
] | def dump_stack_frames(out=None):
out = out or sys.stdout
out.write(short_stack())
out.write('\n') | ['def', 'dump_stack_frames(out=None):', 'out', '=', 'out', 'or', 'sys.stdout', 'out.write(short_stack())', "out.write('\\n')"] | 157,367 |
microsoft/InnerEye-DeepLearning | lightning_loggers.py | StoringLogger.epochs | epochs | Gets the epochs for which the present object holds any results. | [
"Gets",
"the",
"epochs",
"for",
"which",
"the",
"present",
"object",
"holds",
"any",
"results."
] | def epochs(self) -> Iterable[int]:
return self.results_per_epoch.keys() | ['def', 'epochs(self)', '->', 'Iterable[int]:', 'return', 'self.results_per_epoch.keys()'] | 612,916 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | __init__.py | Menu.tk_popup | tk_popup | Post the menu at position X,Y with entry ENTRY. | [
"Post",
"the",
"menu",
"at",
"position",
"X,Y",
"with",
"entry",
"ENTRY."
] | def tk_popup(self, x, y, entry=''):
self.tk.call('tk_popup', self._w, x, y, entry) | ['def', 'tk_popup(self,', 'x,', 'y,', "entry=''):", "self.tk.call('tk_popup',", 'self._w,', 'x,', 'y,', 'entry)'] | 377,010 |
rifqind/Agent-Programs-3KS1 | builtin_trap.py | BuiltinTrap.deactivate | deactivate | Remove any builtins which might have been added by add_builtins, or restore overwritten ones to their previous values. | [
"Remove",
"any",
"builtins",
"which",
"might",
"have",
"been",
"added",
"by",
"add_builtins,",
"or",
"restore",
"overwritten",
"ones",
"to",
"their",
"previous",
"values."
] | def deactivate(self):
remove_builtin = self.remove_builtin
for (key, val) in self._orig_builtins.items():
remove_builtin(key, val)
self._orig_builtins.clear()
self._builtins_added = False | ['def', 'deactivate(self):', 'remove_builtin', '=', 'self.remove_builtin', 'for', '(key,', 'val)', 'in', 'self._orig_builtins.items():', 'remove_builtin(key,', 'val)', 'self._orig_builtins.clear()', 'self._builtins_added', '=', 'False'] | 40,892 |
open-mmlab/mmcv | iou3d.py | boxes_overlap_bev | boxes_overlap_bev | Calculate boxes BEV overlap. | [
"Calculate",
"boxes",
"BEV",
"overlap."
] | def boxes_overlap_bev(boxes_a: Tensor, boxes_b: Tensor) -> Tensor:
ans_overlap = boxes_a.new_zeros(torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
ext_module.iou3d_boxes_overlap_bev_forward(boxes_a.contiguous(), boxes_b.contiguous(), ans_overlap)
return ans_overlap | ['def', 'boxes_overlap_bev(boxes_a:', 'Tensor,', 'boxes_b:', 'Tensor)', '->', 'Tensor:', 'ans_overlap', '=', 'boxes_a.new_zeros(torch.Size((boxes_a.shape[0],', 'boxes_b.shape[0])))', 'ext_module.iou3d_boxes_overlap_bev_forward(boxes_a.contiguous(),', 'boxes_b.contiguous(),', 'ans_overlap)', 'return', 'ans_overlap'] | 631,527 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | trainer_model_based.py | rl_modelrl_xentcutoff_range | rl_modelrl_xentcutoff_range | Cross entropy cutoff tuning grid. | [
"Cross",
"entropy",
"cutoff",
"tuning",
"grid."
] | def rl_modelrl_xentcutoff_range(rhp):
rhp.set_float('model.video_modality_loss_cutoff', 0.01, 0.05) | ['def', 'rl_modelrl_xentcutoff_range(rhp):', "rhp.set_float('model.video_modality_loss_cutoff',", '0.01,', '0.05)'] | 966,012 |
jimtin/Stock_Comparison | inputtransformer.py | InputTransformer.wrap | wrap | Can be used by subclasses as a decorator, to return a factory that will allow instantiation with the decorated object. | [
"Can",
"be",
"used",
"by",
"subclasses",
"as",
"a",
"decorator,",
"to",
"return",
"a",
"factory",
"that",
"will",
"allow",
"instantiation",
"with",
"the",
"decorated",
"object."
] | def wrap(cls, func):
@functools.wraps(func)
def transformer_factory(**kwargs):
return cls(func, **kwargs)
return transformer_factory | ['def', 'wrap(cls,', 'func):', '@functools.wraps(func)', 'def', 'transformer_factory(**kwargs):', 'return', 'cls(func,', '**kwargs)', 'return', 'transformer_factory'] | 384,725 |
aws/sagemaker-python-sdk | session.py | Session.delete_endpoint | delete_endpoint | Delete an Amazon SageMaker ``Endpoint``. | [
"Delete",
"an",
"Amazon",
"SageMaker",
"``Endpoint``."
] | def delete_endpoint(self, endpoint_name):
LOGGER.info('Deleting endpoint with name: %s', endpoint_name)
self.sagemaker_client.delete_endpoint(EndpointName=endpoint_name) | ['def', 'delete_endpoint(self,', 'endpoint_name):', "LOGGER.info('Deleting", 'endpoint', 'with', 'name:', "%s',", 'endpoint_name)', 'self.sagemaker_client.delete_endpoint(EndpointName=endpoint_name)'] | 829,633 |
LiDan456/MAD-GANs | plotting.py | save_mnist_plot_sample | save_mnist_plot_sample | Generates a grid showing mnist digits. | [
"Generates",
"a",
"grid",
"showing",
"mnist",
"digits."
] | def save_mnist_plot_sample(samples, idx, identifier, n_samples, labels=None):
assert n_samples <= samples.shape[0]
if not labels is None:
assert n_samples <= len(labels)
if len(labels.shape) > 1 and (not labels.shape[1] == 1):
label_titles = np.argmax(labels, axis=1)
else:
... | ['def', 'save_mnist_plot_sample(samples,', 'idx,', 'identifier,', 'n_samples,', 'labels=None):', 'assert', 'n_samples', '<=', 'samples.shape[0]', 'if', 'not', 'labels', 'is', 'None:', 'assert', 'n_samples', '<=', 'len(labels)', 'if', 'len(labels.shape)', '>', '1', 'and', '(not', 'labels.shape[1]', '==', '1):', 'label_t... | 626,811 |
HighnessAtharva/VocabCLI | vocabCLI.py | unmaster | unmaster | Removes a word from the mastered list. | [
"Removes",
"a",
"word",
"from",
"the",
"mastered",
"list."
] | def unmaster(words: List[str]=typer.Argument(..., help='ðÂ\x9f¤Â\x94Word to remove from [bold blue]mastered[/bold blue]')):
from modules.Utils import set_unmastered
for word in words:
set_unmastered(word) | ['def', 'unmaster(words:', 'List[str]=typer.Argument(...,', "help='ðÂ\\x9f¤Â\\x94Word", 'to', 'remove', 'from', '[bold', 'blue]mastered[/bold', "blue]')):", 'from', 'modules.Utils', 'import', 'set_unmastered', 'for', 'word', 'in', 'words:', 'set_unmastered(word)'] | 946,219 |
NoGameNoLife00/mybolg | wrappers.py | DynamicCharsetRequestMixin.charset | charset | The charset from the content type. | [
"The",
"charset",
"from",
"the",
"content",
"type."
] | def charset(self):
header = self.environ.get('CONTENT_TYPE')
if header:
(ct, options) = parse_options_header(header)
charset = options.get('charset')
if charset:
if is_known_charset(charset):
return charset
return self.unknown_charset(charset)
... | ['def', 'charset(self):', 'header', '=', "self.environ.get('CONTENT_TYPE')", 'if', 'header:', '(ct,', 'options)', '=', 'parse_options_header(header)', 'charset', '=', "options.get('charset')", 'if', 'charset:', 'if', 'is_known_charset(charset):', 'return', 'charset', 'return', 'self.unknown_charset(charset)', 'return',... | 290,083 |
ryu-ed/SpaceInvaders_Ros | math2html.py | BigBracket.getpiece4 | getpiece4 | Get the nth piece for a 4-piece bracket: curly bracket. | [
"Get",
"the",
"nth",
"piece",
"for",
"a",
"4-piece",
"bracket:",
"curly",
"bracket."
] | def getpiece4(self, index):
if index == 0:
return self.pieces[0]
if index == self.size - 1:
return self.pieces[3]
if index == (self.size - 1) / 2:
return self.pieces[2]
return self.pieces[1] | ['def', 'getpiece4(self,', 'index):', 'if', 'index', '==', '0:', 'return', 'self.pieces[0]', 'if', 'index', '==', 'self.size', '-', '1:', 'return', 'self.pieces[3]', 'if', 'index', '==', '(self.size', '-', '1)', '/', '2:', 'return', 'self.pieces[2]', 'return', 'self.pieces[1]'] | 395,313 |
ZumoLabs/zpy | saver_video.py | VideoSaver.add_annotation | add_annotation | Add a new annotation to the Saver object. | [
"Add",
"a",
"new",
"annotation",
"to",
"the",
"Saver",
"object."
] | def add_annotation(self, *args, video: str='default video', **kwargs) -> Dict:
annotation = super().add_annotation(*args, **kwargs)
video_id = self.video_name_to_id.get(video, None)
assert video_id is not None, f'Could not find id for video {video}'
annotation['video_id'] = video_id
annotation.updat... | ['def', 'add_annotation(self,', '*args,', 'video:', "str='default", "video',", '**kwargs)', '->', 'Dict:', 'annotation', '=', 'super().add_annotation(*args,', '**kwargs)', 'video_id', '=', 'self.video_name_to_id.get(video,', 'None)', 'assert', 'video_id', 'is', 'not', 'None,', "f'Could", 'not', 'find', 'id', 'for', 'vi... | 972,122 |
QData/deepWordBug | states.py | Body.line_block_line | line_block_line | Return one line element of a line_block. | [
"Return",
"one",
"line",
"element",
"of",
"a",
"line_block."
] | def line_block_line(self, match, lineno):
(indented, indent, line_offset, blank_finish) = self.state_machine.get_first_known_indented(match.end(), until_blank=True)
text = '\n'.join(indented)
(text_nodes, messages) = self.inline_text(text, lineno)
line = nodes.line(text, '', *text_nodes)
if match.st... | ['def', 'line_block_line(self,', 'match,', 'lineno):', '(indented,', 'indent,', 'line_offset,', 'blank_finish)', '=', 'self.state_machine.get_first_known_indented(match.end(),', 'until_blank=True)', 'text', '=', "'\\n'.join(indented)", '(text_nodes,', 'messages)', '=', 'self.inline_text(text,', 'lineno)', 'line', '=', ... | 542,169 |
johny-c/incremental-label-propagation | data_flow.py | gen_data_stream | gen_data_stream | Generates a sequence of all inputs and targets, optionally shuffled. | [
"Generates",
"a",
"sequence",
"of",
"all",
"inputs",
"and",
"targets,",
"optionally",
"shuffled."
] | def gen_data_stream(inputs, targets, shuffle=False, seed=None):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
random_state = check_random_state(seed)
random_state.shuffle(indices)
for i in indices:
yield (inputs[i], targets[i])
el... | ['def', 'gen_data_stream(inputs,', 'targets,', 'shuffle=False,', 'seed=None):', 'assert', 'len(inputs)', '==', 'len(targets)', 'if', 'shuffle:', 'indices', '=', 'np.arange(len(inputs))', 'random_state', '=', 'check_random_state(seed)', 'random_state.shuffle(indices)', 'for', 'i', 'in', 'indices:', 'yield', '(inputs[i],... | 229,488 |
rifqind/Agent-Programs-3KS1 | agents.py | GraphicEnvironment.get_world | get_world | Returns all the items in the world in a format understandable by the ipythonblocks BlockGrid. | [
"Returns",
"all",
"the",
"items",
"in",
"the",
"world",
"in",
"a",
"format",
"understandable",
"by",
"the",
"ipythonblocks",
"BlockGrid."
] | def get_world(self):
result = []
(x_start, y_start) = (0, 0)
(x_end, y_end) = (self.width, self.height)
for x in range(x_start, x_end):
row = []
for y in range(y_start, y_end):
row.append(self.list_things_at([x, y]))
result.append(row)
return result | ['def', 'get_world(self):', 'result', '=', '[]', '(x_start,', 'y_start)', '=', '(0,', '0)', '(x_end,', 'y_end)', '=', '(self.width,', 'self.height)', 'for', 'x', 'in', 'range(x_start,', 'x_end):', 'row', '=', '[]', 'for', 'y', 'in', 'range(y_start,', 'y_end):', 'row.append(self.list_things_at([x,', 'y]))', 'result.appe... | 40,387 |
facebookresearch/CompilerGym | benchmark_test.py | test_benchmark_immutable | test_benchmark_immutable | Test that benchmark properties are immutable. | [
"Test",
"that",
"benchmark",
"properties",
"are",
"immutable."
] | def test_benchmark_immutable():
benchmark = Benchmark(BenchmarkProto(uri='benchmark://example-compiler-v0/foobar'))
with pytest.raises(AttributeError):
benchmark.uri = 123
with pytest.raises(AttributeError):
benchmark.proto = 123 | ['def', 'test_benchmark_immutable():', 'benchmark', '=', "Benchmark(BenchmarkProto(uri='benchmark://example-compiler-v0/foobar'))", 'with', 'pytest.raises(AttributeError):', 'benchmark.uri', '=', '123', 'with', 'pytest.raises(AttributeError):', 'benchmark.proto', '=', '123'] | 125,849 |
kubeflow/pipelines | metrics_utils.py | ConfusionMatrix.log_row | log_row | Logs a confusion matrix row. | [
"Logs",
"a",
"confusion",
"matrix",
"row."
] | def log_row(self, row_category: str, row: List[int]):
if row_category not in self._categories:
raise ValueError('Invalid category: {} passed. Expected one of: {}'.format(row_category, self._categories))
if len(row) != len(self._categories):
raise ValueError('Invalid row. Expected size: {} got: {... | ['def', 'log_row(self,', 'row_category:', 'str,', 'row:', 'List[int]):', 'if', 'row_category', 'not', 'in', 'self._categories:', 'raise', "ValueError('Invalid", 'category:', '{}', 'passed.', 'Expected', 'one', 'of:', "{}'.format(row_category,", 'self._categories))', 'if', 'len(row)', '!=', 'len(self._categories):', 'ra... | 780,094 |
xuannianz/SAPD | efficientnet.py | round_filters | round_filters | Round number of filters based on width multiplier. | [
"Round",
"number",
"of",
"filters",
"based",
"on",
"width",
"multiplier."
] | def round_filters(filters, width_coefficient, depth_divisor):
filters *= width_coefficient
new_filters = int(filters + depth_divisor / 2) // depth_divisor * depth_divisor
new_filters = max(depth_divisor, new_filters)
if new_filters < 0.9 * filters:
new_filters += depth_divisor
return int(new... | ['def', 'round_filters(filters,', 'width_coefficient,', 'depth_divisor):', 'filters', '*=', 'width_coefficient', 'new_filters', '=', 'int(filters', '+', 'depth_divisor', '/', '2)', '//', 'depth_divisor', '*', 'depth_divisor', 'new_filters', '=', 'max(depth_divisor,', 'new_filters)', 'if', 'new_filters', '<', '0.9', '*'... | 845,370 |
TonyLianLong/VAI-ReinforcementLearning | namescope.py | NameScope.remove | remove | Removes an identifier from this name scope. | [
"Removes",
"an",
"identifier",
"from",
"this",
"name",
"scope."
] | def remove(self, namespace, identifier):
del self._namespaces[namespace][identifier]
self.increment_revision() | ['def', 'remove(self,', 'namespace,', 'identifier):', 'del', 'self._namespaces[namespace][identifier]', 'self.increment_revision()'] | 440,032 |
vturrisi/solo-learn | mae.py | MAE.forward | forward | Performs forward pass of the online backbone, projector and predictor. | [
"Performs",
"forward",
"pass",
"of",
"the",
"online",
"backbone,",
"projector",
"and",
"predictor."
] | def forward(self, X: torch.Tensor) -> Dict[str, Any]:
if not self.no_channel_last:
X = X.to(memory_format=torch.channels_last)
out = {}
if self.training:
(feats, patch_feats, mask, ids_restore) = self.backbone(X, self.mask_ratio)
pred = self.decoder(patch_feats, ids_restore)
... | ['def', 'forward(self,', 'X:', 'torch.Tensor)', '->', 'Dict[str,', 'Any]:', 'if', 'not', 'self.no_channel_last:', 'X', '=', 'X.to(memory_format=torch.channels_last)', 'out', '=', '{}', 'if', 'self.training:', '(feats,', 'patch_feats,', 'mask,', 'ids_restore)', '=', 'self.backbone(X,', 'self.mask_ratio)', 'pred', '=', '... | 393,635 |
opendilab/DI-star | renderer_human.py | RendererHuman.run | run | Run loop that gets observations, renders them, and sends back actions. | [
"Run",
"loop",
"that",
"gets",
"observations,",
"renders",
"them,",
"and",
"sends",
"back",
"actions."
] | def run(self, run_config, controller, max_game_steps=0, max_episodes=0, game_steps_per_episode=0, save_replay=False):
is_replay = controller.status == remote_controller.Status.in_replay
total_game_steps = 0
start_time = time.time()
num_episodes = 0
try:
while True:
self.init(cont... | ['def', 'run(self,', 'run_config,', 'controller,', 'max_game_steps=0,', 'max_episodes=0,', 'game_steps_per_episode=0,', 'save_replay=False):', 'is_replay', '=', 'controller.status', '==', 'remote_controller.Status.in_replay', 'total_game_steps', '=', '0', 'start_time', '=', 'time.time()', 'num_episodes', '=', '0', 'try... | 184,802 |
microsoft/nni | serializer.py | Traceable.trace_kwargs | trace_kwargs | Dict of keyword arguments. | [
"Dict",
"of",
"keyword",
"arguments."
] | def trace_kwargs(self) -> Dict[str, Any]:
raise NotImplementedError() | ['def', 'trace_kwargs(self)', '->', 'Dict[str,', 'Any]:', 'raise', 'NotImplementedError()'] | 728,460 |
BigDataBiology/SemiBin | atomicwrite.py | AtomicWriter.commit | commit | Move the temporary file to the target location. | [
"Move",
"the",
"temporary",
"file",
"to",
"the",
"target",
"location."
] | def commit(self, f):
if self._overwrite:
replace_atomic(f.name, self._path)
else:
move_atomic(f.name, self._path) | ['def', 'commit(self,', 'f):', 'if', 'self._overwrite:', 'replace_atomic(f.name,', 'self._path)', 'else:', 'move_atomic(f.name,', 'self._path)'] | 343,536 |
Eric3911/OpenAGI | analyze_errors.py | ErrorCase.get_spans | get_spans | This method extracts the list of spans. | [
"This",
"method",
"extracts",
"the",
"list",
"of",
"spans."
] | def get_spans(self, tokens_hightlight):
(spans, nb_tokens) = ([], len(tokens_hightlight))
(cur_start_idx, cur_bool_val) = (0, tokens_hightlight[0])
for idx in range(nb_tokens):
if idx == nb_tokens - 1:
if tokens_hightlight[idx] != cur_bool_val:
spans.append((cur_start_idx... | ['def', 'get_spans(self,', 'tokens_hightlight):', '(spans,', 'nb_tokens)', '=', '([],', 'len(tokens_hightlight))', '(cur_start_idx,', 'cur_bool_val)', '=', '(0,', 'tokens_hightlight[0])', 'for', 'idx', 'in', 'range(nb_tokens):', 'if', 'idx', '==', 'nb_tokens', '-', '1:', 'if', 'tokens_hightlight[idx]', '!=', 'cur_bool_... | 272,142 |
aleju/self-driving-truck | batching.py | to_rgb | to_rgb | Convert an image from (h, w) to (h, w, 3). | [
"Convert",
"an",
"image",
"from",
"(h,",
"w)",
"to",
"(h,",
"w,",
"3)."
] | def to_rgb(im):
if im.ndim == 3:
if im.shape[2] == 3:
return im
else:
return np.tile(im, (1, 1, 3))
else:
return np.tile(im[:, :, np.newaxis], (1, 1, 3)) | ['def', 'to_rgb(im):', 'if', 'im.ndim', '==', '3:', 'if', 'im.shape[2]', '==', '3:', 'return', 'im', 'else:', 'return', 'np.tile(im,', '(1,', '1,', '3))', 'else:', 'return', 'np.tile(im[:,', ':,', 'np.newaxis],', '(1,', '1,', '3))'] | 843,258 |
mrdvince/autoencoders | autoencoders.py | TFVariationalAutoencoder.reconstruct | reconstruct | Use VAE to reconstruct given data. | [
"Use",
"VAE",
"to",
"reconstruct",
"given",
"data."
] | def reconstruct(self, X):
return self.sess.run(self.x_reconstr_mean, feed_dict={self.x: X}) | ['def', 'reconstruct(self,', 'X):', 'return', 'self.sess.run(self.x_reconstr_mean,', 'feed_dict={self.x:', 'X})'] | 419,529 |
charlesCXK/TorchSemiSeg | parallel_apply.py | parallel_apply | parallel_apply | Applies each `module` in :attr:`modules` in parallel on arguments contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword) on each of :attr:`devices`. | [
"Applies",
"each",
"`module`",
"in",
":attr:`modules`",
"in",
"parallel",
"on",
"arguments",
"contained",
"in",
":attr:`inputs`",
"(positional)",
"and",
":attr:`kwargs_tup`",
"(keyword)",
"on",
"each",
"of",
":attr:`devices`."
] | def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):
assert len(modules) == len(inputs)
if kwargs_tup is not None:
assert len(modules) == len(kwargs_tup)
else:
kwargs_tup = ({},) * len(modules)
if devices is not None:
assert len(modules) == len(devices)
else:
... | ['def', 'parallel_apply(modules,', 'inputs,', 'kwargs_tup=None,', 'devices=None):', 'assert', 'len(modules)', '==', 'len(inputs)', 'if', 'kwargs_tup', 'is', 'not', 'None:', 'assert', 'len(modules)', '==', 'len(kwargs_tup)', 'else:', 'kwargs_tup', '=', '({},)', '*', 'len(modules)', 'if', 'devices', 'is', 'not', 'None:',... | 903,438 |
tensorflow/quantum | serializable_gate_set_test.py | SerializableGateSetTest.test_serialize_deserialize_empty_circuit | test_serialize_deserialize_empty_circuit | Verify empty case serialize deserialize works. | [
"Verify",
"empty",
"case",
"serialize",
"deserialize",
"works."
] | def test_serialize_deserialize_empty_circuit(self):
circuit = cirq.Circuit()
proto = program_pb2.Program(language=program_pb2.Language(arg_function_language='', gate_set='my_gate_set'), circuit=program_pb2.Circuit(scheduling_strategy=program_pb2.Circuit.MOMENT_BY_MOMENT, moments=[]))
self.assertEqual(proto,... | ['def', 'test_serialize_deserialize_empty_circuit(self):', 'circuit', '=', 'cirq.Circuit()', 'proto', '=', "program_pb2.Program(language=program_pb2.Language(arg_function_language='',", "gate_set='my_gate_set'),", 'circuit=program_pb2.Circuit(scheduling_strategy=program_pb2.Circuit.MOMENT_BY_MOMENT,', 'moments=[]))', '... | 834,967 |
weimin17/Object-Detection_HelmetDetection | model_lib.py | filter_trainable_variables | filter_trainable_variables | Keep only trainable variables which are prefixed with given scopes. | [
"Keep",
"only",
"trainable",
"variables",
"which",
"are",
"prefixed",
"with",
"given",
"scopes."
] | def filter_trainable_variables(trainable_scopes):
if not trainable_scopes:
return
if isinstance(trainable_scopes, six.string_types):
trainable_scopes = [scope.strip() for scope in trainable_scopes.split(',')]
trainable_scopes = {scope for scope in trainable_scopes if scope}
if not traina... | ['def', 'filter_trainable_variables(trainable_scopes):', 'if', 'not', 'trainable_scopes:', 'return', 'if', 'isinstance(trainable_scopes,', 'six.string_types):', 'trainable_scopes', '=', '[scope.strip()', 'for', 'scope', 'in', "trainable_scopes.split(',')]", 'trainable_scopes', '=', '{scope', 'for', 'scope', 'in', 'trai... | 761,405 |
AnuragAnalog/Code-for-learn-machinelearning | scaling.py | scaling.normalization | normalization | The values of the data are scaled to the interval [-1, 1] with a mean of zero. | [
"The",
"values",
"of",
"the",
"data",
"are",
"scaled",
"to",
"the",
"interval",
"[-1,",
"1]",
"with",
"a",
"mean",
"of",
"zero."
] | def normalization(self, data: [np.array, list]) -> np.array:
data = np.array(data)
self.checkna(data)
shape = np.shape(data)
if len(shape) == 1:
data = (data - np.mean(data)) / (max(data) - min(data))
elif len(shape) == 2:
for i in range(shape[-1]):
mu = np.mean(data[:, i... | ['def', 'normalization(self,', 'data:', '[np.array,', 'list])', '->', 'np.array:', 'data', '=', 'np.array(data)', 'self.checkna(data)', 'shape', '=', 'np.shape(data)', 'if', 'len(shape)', '==', '1:', 'data', '=', '(data', '-', 'np.mean(data))', '/', '(max(data)', '-', 'min(data))', 'elif', 'len(shape)', '==', '2:', 'fo... | 493,579 |
MANGA-UOFA/NAUS | utils.py | get_data_parallel_world_size | get_data_parallel_world_size | Return world size for the data parallel group. | [
"Return",
"world",
"size",
"for",
"the",
"data",
"parallel",
"group."
] | def get_data_parallel_world_size():
return get_world_size(get_data_parallel_group()) | ['def', 'get_data_parallel_world_size():', 'return', 'get_world_size(get_data_parallel_group())'] | 291,444 |
loicmarie/hands-detection | utils.py | save_image | save_image | Function that dumps the image to disk. | [
"Function",
"that",
"dumps",
"the",
"image",
"to",
"disk."
] | def save_image(inp_array, image_file):
inp_array = np.clip(inp_array, 0, 255).astype(np.uint8)
image = Image.fromarray(inp_array)
buf = StringIO.StringIO()
image.save(buf, format='JPEG')
with open(image_file, 'w') as f:
f.write(buf.getvalue())
return None | ['def', 'save_image(inp_array,', 'image_file):', 'inp_array', '=', 'np.clip(inp_array,', '0,', '255).astype(np.uint8)', 'image', '=', 'Image.fromarray(inp_array)', 'buf', '=', 'StringIO.StringIO()', 'image.save(buf,', "format='JPEG')", 'with', 'open(image_file,', "'w')", 'as', 'f:', 'f.write(buf.getvalue())', 'return',... | 575,156 |
PaddlePaddle/Paddle3D | bevdet4d.py | BEVDet4D.aug_test | aug_test | Test function without augmentation. | [
"Test",
"function",
"without",
"augmentation."
] | def aug_test(self, points, img_metas, img=None, rescale=False):
assert False | ['def', 'aug_test(self,', 'points,', 'img_metas,', 'img=None,', 'rescale=False):', 'assert', 'False'] | 777,387 |
bnpy/bnpy | ProposalViz.py | plotELBOtermsForProposal | plotELBOtermsForProposal | Create trace plot of ELBO gain/loss relative to current model. | [
"Create",
"trace",
"plot",
"of",
"ELBO",
"gain/loss",
"relative",
"to",
"current",
"model."
] | def plotELBOtermsForProposal(curLdict, propLdictList, xs=None, ymin=-0.5, ymax=0.5, savefilename=None, **kwargs):
pylab.figure()
L = len(propLdictList)
if xs is None:
xs = np.arange(0, L)
legendKeys = []
for key in curLdict:
if key.count('_') == 0:
legendKeys.append(key)
... | ['def', 'plotELBOtermsForProposal(curLdict,', 'propLdictList,', 'xs=None,', 'ymin=-0.5,', 'ymax=0.5,', 'savefilename=None,', '**kwargs):', 'pylab.figure()', 'L', '=', 'len(propLdictList)', 'if', 'xs', 'is', 'None:', 'xs', '=', 'np.arange(0,', 'L)', 'legendKeys', '=', '[]', 'for', 'key', 'in', 'curLdict:', 'if', "key.co... | 465,284 |
rudranil723/mini-main | coordseq.py | GEOSCoordSeq.tuple | tuple | Return a tuple version of this coordinate sequence. | [
"Return",
"a",
"tuple",
"version",
"of",
"this",
"coordinate",
"sequence."
] | def tuple(self):
n = self.size
get_point = self._point_getter
if n == 1:
return get_point(0)
return tuple((get_point(i) for i in range(n))) | ['def', 'tuple(self):', 'n', '=', 'self.size', 'get_point', '=', 'self._point_getter', 'if', 'n', '==', '1:', 'return', 'get_point(0)', 'return', 'tuple((get_point(i)', 'for', 'i', 'in', 'range(n)))'] | 315,273 |
wutong8023/CoLL | config.py | OnnxConfig.flatten_output_collection_property | flatten_output_collection_property | Flatten any potential nested structure expanding the name of the field with the index of the element within the structure. | [
"Flatten",
"any",
"potential",
"nested",
"structure",
"expanding",
"the",
"name",
"of",
"the",
"field",
"with",
"the",
"index",
"of",
"the",
"element",
"within",
"the",
"structure."
] | def flatten_output_collection_property(name: str, field: Iterable[Any]) -> Dict[str, Any]:
from itertools import chain
return {f'{name}.{idx}': item for (idx, item) in enumerate(chain.from_iterable(field))} | ['def', 'flatten_output_collection_property(name:', 'str,', 'field:', 'Iterable[Any])', '->', 'Dict[str,', 'Any]:', 'from', 'itertools', 'import', 'chain', 'return', "{f'{name}.{idx}':", 'item', 'for', '(idx,', 'item)', 'in', 'enumerate(chain.from_iterable(field))}'] | 466,997 |
tobegit3hub/deep_image_model | dataframe_test.py | setup_test_df | setup_test_df | Create a dataframe populated with some test columns. | [
"Create",
"a",
"dataframe",
"populated",
"with",
"some",
"test",
"columns."
] | def setup_test_df():
df = learn.DataFrame()
df['a'] = learn.TransformedSeries([mocks.MockSeries('foobar', mocks.MockTensor('Tensor a', tf.int32))], mocks.MockTwoOutputTransform('iue', 'eui', 'snt'), 'out1')
df['b'] = learn.TransformedSeries([mocks.MockSeries('foobar', mocks.MockTensor('Tensor b', tf.int32))... | ['def', 'setup_test_df():', 'df', '=', 'learn.DataFrame()', "df['a']", '=', "learn.TransformedSeries([mocks.MockSeries('foobar',", "mocks.MockTensor('Tensor", "a',", 'tf.int32))],', "mocks.MockTwoOutputTransform('iue',", "'eui',", "'snt'),", "'out1')", "df['b']", '=', "learn.TransformedSeries([mocks.MockSeries('foobar'... | 181,868 |
open-mmlab/mmselfsup | utils.py | _DummyAxis.get_minpos | get_minpos | Return the minimum positive value for the axis. | [
"Return",
"the",
"minimum",
"positive",
"value",
"for",
"the",
"axis."
] | def get_minpos(self) -> float:
return self._minpos | ['def', 'get_minpos(self)', '->', 'float:', 'return', 'self._minpos'] | 240,501 |
hamza-murad/AALU | discovery_v1.py | SourceStatus.from_dict | from_dict | Initialize a SourceStatus object from a json dictionary. | [
"Initialize",
"a",
"SourceStatus",
"object",
"from",
"a",
"json",
"dictionary."
] | def from_dict(cls, _dict: Dict) -> 'SourceStatus':
args = {}
valid_keys = ['status', 'next_crawl']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError('Unrecognized keys detected in dictionary for class SourceStatus: ' + ', '.join(bad_keys))
if 'status' in _dict:
... | ['def', 'from_dict(cls,', '_dict:', 'Dict)', '->', "'SourceStatus':", 'args', '=', '{}', 'valid_keys', '=', "['status',", "'next_crawl']", 'bad_keys', '=', 'set(_dict.keys())', '-', 'set(valid_keys)', 'if', 'bad_keys:', 'raise', "ValueError('Unrecognized", 'keys', 'detected', 'in', 'dictionary', 'for', 'class', 'Source... | 5,680 |
Kvatsx/Artificial-Intelligence-Assignments | test_run.py | TestMagicRunSimple.test_unicode | test_unicode | Check that files in odd encodings are accepted. | [
"Check",
"that",
"files",
"in",
"odd",
"encodings",
"are",
"accepted."
] | def test_unicode(self):
mydir = os.path.dirname(__file__)
na = os.path.join(mydir, 'nonascii.py')
_ip.magic('run "%s"' % na)
nt.assert_equal(_ip.user_ns['u'], u'Ã\x90Â\x8eÃ\x91Â\x82âÂ\x84Â\x96Ã\x90¤') | ['def', 'test_unicode(self):', 'mydir', '=', 'os.path.dirname(__file__)', 'na', '=', 'os.path.join(mydir,', "'nonascii.py')", "_ip.magic('run", '"%s"\'', '%', 'na)', "nt.assert_equal(_ip.user_ns['u'],", "u'Ã\\x90Â\\x8eÃ\\x91Â\\x82âÂ\\x84Â\\x96Ã\\x90¤')"] | 38,510 |
zihuitang/medical_AI_platform | __init__.py | Menu.add_command | add_command | Add command menu item. | [
"Add",
"command",
"menu",
"item."
] | def add_command(self, cnf={}, **kw):
self.add('command', cnf or kw) | ['def', 'add_command(self,', 'cnf={},', '**kw):', "self.add('command',", 'cnf', 'or', 'kw)'] | 284,289 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjModelWrapper.pair_margin | pair_margin | detect contact if dist<margin (npair x 1). | [
"detect",
"contact",
"if",
"dist<margin",
"(npair",
"x",
"1)."
] | def pair_margin(self):
return util.buf_to_npy(self._ptr.contents.pair_margin, (self.npair,)) | ['def', 'pair_margin(self):', 'return', 'util.buf_to_npy(self._ptr.contents.pair_margin,', '(self.npair,))'] | 440,397 |
huawei-noah/xingtian | share_by_redis.py | ShareByRedis.send | send | Send data to redis server. | [
"Send",
"data",
"to",
"redis",
"server."
] | def send(self, data, name=None, block=True):
data_buffer = pyarrow.serialize(data).to_buffer()
self.redis.set(name, data_buffer) | ['def', 'send(self,', 'data,', 'name=None,', 'block=True):', 'data_buffer', '=', 'pyarrow.serialize(data).to_buffer()', 'self.redis.set(name,', 'data_buffer)'] | 962,382 |
pokaxpoka/sunrise | fisher_factors.py | set_global_constants | set_global_constants | Sets various global constants used by the classes in this module. | [
"Sets",
"various",
"global",
"constants",
"used",
"by",
"the",
"classes",
"in",
"this",
"module."
] | def set_global_constants(init_covariances_at_zero=None, zero_debias=None, eigenvalue_decomposition_threshold=None, eigenvalue_clipping_threshold=None):
global INIT_COVARIANCES_AT_ZERO
global ZERO_DEBIAS
global EIGENVALUE_DECOMPOSITION_THRESHOLD
global EIGENVALUE_CLIPPING_THRESHOLD
if init_covariance... | ['def', 'set_global_constants(init_covariances_at_zero=None,', 'zero_debias=None,', 'eigenvalue_decomposition_threshold=None,', 'eigenvalue_clipping_threshold=None):', 'global', 'INIT_COVARIANCES_AT_ZERO', 'global', 'ZERO_DEBIAS', 'global', 'EIGENVALUE_DECOMPOSITION_THRESHOLD', 'global', 'EIGENVALUE_CLIPPING_THRESHOLD'... | 911,809 |
Ruturaj123/Flowchart-Detection | metric_ops_test.py | StreamingAUCTest.np_auc | np_auc | Computes the AUC explicitly using Numpy. | [
"Computes",
"the",
"AUC",
"explicitly",
"using",
"Numpy."
] | def np_auc(self, predictions, labels, weights):
if weights is None:
weights = np.ones(np.size(predictions))
is_positive = labels > 0
num_positives = np.sum(weights[is_positive])
num_negatives = np.sum(weights[~is_positive])
inds = np.argsort(-predictions)
sorted_labels = labels[inds]
... | ['def', 'np_auc(self,', 'predictions,', 'labels,', 'weights):', 'if', 'weights', 'is', 'None:', 'weights', '=', 'np.ones(np.size(predictions))', 'is_positive', '=', 'labels', '>', '0', 'num_positives', '=', 'np.sum(weights[is_positive])', 'num_negatives', '=', 'np.sum(weights[~is_positive])', 'inds', '=', 'np.argsort(-... | 604,333 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | cifar_input.py | build_input | build_input | Build CIFAR image and labels. | [
"Build",
"CIFAR",
"image",
"and",
"labels."
] | def build_input(dataset, data_path, batch_size, mode):
image_size = 32
if dataset == 'cifar10':
label_bytes = 1
label_offset = 0
num_classes = 10
elif dataset == 'cifar100':
label_bytes = 1
label_offset = 1
num_classes = 100
else:
raise ValueError(... | ['def', 'build_input(dataset,', 'data_path,', 'batch_size,', 'mode):', 'image_size', '=', '32', 'if', 'dataset', '==', "'cifar10':", 'label_bytes', '=', '1', 'label_offset', '=', '0', 'num_classes', '=', '10', 'elif', 'dataset', '==', "'cifar100':", 'label_bytes', '=', '1', 'label_offset', '=', '1', 'num_classes', '=',... | 26,728 |
FedML-AI/FedML | envs.py | get_envs | get_envs | Get PyTorch needed environments from system envirionments. | [
"Get",
"PyTorch",
"needed",
"environments",
"from",
"system",
"envirionments."
] | def get_envs():
local_rank = int(os.getenv('LOCAL_RANK', -1))
rank = int(os.getenv('RANK', -1))
world_size = int(os.getenv('WORLD_SIZE', 1))
return (local_rank, rank, world_size) | ['def', 'get_envs():', 'local_rank', '=', "int(os.getenv('LOCAL_RANK',", '-1))', 'rank', '=', "int(os.getenv('RANK',", '-1))', 'world_size', '=', "int(os.getenv('WORLD_SIZE',", '1))', 'return', '(local_rank,', 'rank,', 'world_size)'] | 545,085 |
triaquae/triaquae | operations.py | PostGISOperations.postgis_geos_version | postgis_geos_version | Returns the version of the GEOS library used with PostGIS. | [
"Returns",
"the",
"version",
"of",
"the",
"GEOS",
"library",
"used",
"with",
"PostGIS."
] | def postgis_geos_version(self):
return self._get_postgis_func('postgis_geos_version') | ['def', 'postgis_geos_version(self):', 'return', "self._get_postgis_func('postgis_geos_version')"] | 357,452 |
matsu0228/nlp-jp | image.py | _ImageBase.get_resample | get_resample | Return the image resample boolean. | [
"Return",
"the",
"image",
"resample",
"boolean."
] | def get_resample(self):
return self._resample | ['def', 'get_resample(self):', 'return', 'self._resample'] | 788,834 |
pedrojrv/nucml | plot.py | ml_results_plotly | ml_results_plotly | Plot the machine learning predictions from the dictionary generated by the. | [
"Plot",
"the",
"machine",
"learning",
"predictions",
"from",
"the",
"dictionary",
"generated",
"by",
"the."
] | def ml_results_plotly(results_dict, order_dict={}, save=False, render_browser=False, show=False):
fig = go.Figure()
if len(order_dict) == 0:
order_dict = {'1': 'endf', '4': 'exfor_ml_original', '3': 'exfor_ml', '2': 'exfor_new'}
exfor_original_trace = go.Scattergl(x=results_dict['exfor_ml_original']... | ['def', 'ml_results_plotly(results_dict,', 'order_dict={},', 'save=False,', 'render_browser=False,', 'show=False):', 'fig', '=', 'go.Figure()', 'if', 'len(order_dict)', '==', '0:', 'order_dict', '=', "{'1':", "'endf',", "'4':", "'exfor_ml_original',", "'3':", "'exfor_ml',", "'2':", "'exfor_new'}", 'exfor_original_trace... | 249,734 |
vals/SdA | utils.py | load_data | load_data | Loads the dataset :type dataset: string :param dataset: the path to the dataset. | [
"Loads",
"the",
"dataset",
":type",
"dataset:",
"string",
":param",
"dataset:",
"the",
"path",
"to",
"the",
"dataset."
] | def load_data(dataset):
df = pd.read_table(dataset)
data = df.ix[:, 1:].as_matrix()
index = list(df.ix[:, 0])
def shared_dataset(data, borrow=True):
data = data
shared_data = theano.shared(numpy.asarray(data, dtype=theano.config.floatX), borrow=borrow)
return shared_data
dat... | ['def', 'load_data(dataset):', 'df', '=', 'pd.read_table(dataset)', 'data', '=', 'df.ix[:,', '1:].as_matrix()', 'index', '=', 'list(df.ix[:,', '0])', 'def', 'shared_dataset(data,', 'borrow=True):', 'data', '=', 'data', 'shared_data', '=', 'theano.shared(numpy.asarray(data,', 'dtype=theano.config.floatX),', 'borrow=borr... | 855,129 |
rudranil723/mini-main | options.py | ModelAdmin.save_formset | save_formset | Given an inline formset save it to the database. | [
"Given",
"an",
"inline",
"formset",
"save",
"it",
"to",
"the",
"database."
] | def save_formset(self, request, form, formset, change):
formset.save() | ['def', 'save_formset(self,', 'request,', 'form,', 'formset,', 'change):', 'formset.save()'] | 314,777 |
RasaHQ/rasa | trackers.py | DialogueStateTracker.reject_action | reject_action | Notify active loop that it was rejected. | [
"Notify",
"active",
"loop",
"that",
"it",
"was",
"rejected."
] | def reject_action(self, action_name: Text) -> None:
if self.active_loop is not None and action_name == self.active_loop_name:
self.active_loop.rejected = True | ['def', 'reject_action(self,', 'action_name:', 'Text)', '->', 'None:', 'if', 'self.active_loop', 'is', 'not', 'None', 'and', 'action_name', '==', 'self.active_loop_name:', 'self.active_loop.rejected', '=', 'True'] | 837,529 |
google-research/batch_rl | fixed_replay_buffer.py | FixedReplayBuffer.load_single_buffer | load_single_buffer | Load a single replay buffer. | [
"Load",
"a",
"single",
"replay",
"buffer."
] | def load_single_buffer(self, suffix):
replay_buffer = self._load_buffer(suffix)
if replay_buffer is not None:
self._replay_buffers = [replay_buffer]
self.add_count = replay_buffer.add_count
self._num_replay_buffers = 1
self._loaded_buffers = True | ['def', 'load_single_buffer(self,', 'suffix):', 'replay_buffer', '=', 'self._load_buffer(suffix)', 'if', 'replay_buffer', 'is', 'not', 'None:', 'self._replay_buffers', '=', '[replay_buffer]', 'self.add_count', '=', 'replay_buffer.add_count', 'self._num_replay_buffers', '=', '1', 'self._loaded_buffers', '=', 'True'] | 105,912 |
gyh75520/Relational_DRL | dataset.py | ExpertDataset.log_info | log_info | Log the information of the dataset. | [
"Log",
"the",
"information",
"of",
"the",
"dataset."
] | def log_info(self):
logger.log('Total trajectories: {}'.format(self.num_traj))
logger.log('Total transitions: {}'.format(self.num_transition))
logger.log('Average returns: {}'.format(self.avg_ret))
logger.log('Std for returns: {}'.format(self.std_ret)) | ['def', 'log_info(self):', "logger.log('Total", 'trajectories:', "{}'.format(self.num_traj))", "logger.log('Total", 'transitions:', "{}'.format(self.num_transition))", "logger.log('Average", 'returns:', "{}'.format(self.avg_ret))", "logger.log('Std", 'for', 'returns:', "{}'.format(self.std_ret))"] | 839,421 |
airbus/scikit-decide | scheduling_domains.py | SchedulingDomain.update_complete_dummy_tasks_simulation | update_complete_dummy_tasks_simulation | In a simulated scheduling environment, update the status of newly started tasks whose duration is 0 from ongoing to complete. | [
"In",
"a",
"simulated",
"scheduling",
"environment,",
"update",
"the",
"status",
"of",
"newly",
"started",
"tasks",
"whose",
"duration",
"is",
"0",
"from",
"ongoing",
"to",
"complete."
] | def update_complete_dummy_tasks_simulation(self, state: State, action: SchedulingAction):
return self.update_complete_dummy_tasks(state, action) | ['def', 'update_complete_dummy_tasks_simulation(self,', 'state:', 'State,', 'action:', 'SchedulingAction):', 'return', 'self.update_complete_dummy_tasks(state,', 'action)'] | 847,865 |
google-research/scenic | dataset_utils.py | crop_and_resize_image_tong | crop_and_resize_image_tong | Crops and resizes the images in the given sequence of images. | [
"Crops",
"and",
"resizes",
"the",
"images",
"in",
"the",
"given",
"sequence",
"of",
"images."
] | def crop_and_resize_image_tong(frames: tf.Tensor, resized_size: tuple[int, int]=(224, 224), scales: tf.Tensor=tf.constant([1, 0.875, 0.75, 0.66])) -> tf.Tensor:
shape = tf.shape(input=frames)
timesteps = shape[0]
image_h = shape[1]
image_w = shape[2]
channels = shape[3]
(crop_h, crop_w, offset_h... | ['def', 'crop_and_resize_image_tong(frames:', 'tf.Tensor,', 'resized_size:', 'tuple[int,', 'int]=(224,', '224),', 'scales:', 'tf.Tensor=tf.constant([1,', '0.875,', '0.75,', '0.66]))', '->', 'tf.Tensor:', 'shape', '=', 'tf.shape(input=frames)', 'timesteps', '=', 'shape[0]', 'image_h', '=', 'shape[1]', 'image_w', '=', 's... | 847,118 |
rifqind/Agent-Programs-3KS1 | document.py | Document.empty_line_count_at_the_end | empty_line_count_at_the_end | Return number of empty lines at the end of the document. | [
"Return",
"number",
"of",
"empty",
"lines",
"at",
"the",
"end",
"of",
"the",
"document."
] | def empty_line_count_at_the_end(self):
count = 0
for line in self.lines[::-1]:
if not line or line.isspace():
count += 1
else:
break
return count | ['def', 'empty_line_count_at_the_end(self):', 'count', '=', '0', 'for', 'line', 'in', 'self.lines[::-1]:', 'if', 'not', 'line', 'or', 'line.isspace():', 'count', '+=', '1', 'else:', 'break', 'return', 'count'] | 44,989 |
ShuLiu1993/PANet | json_dataset.py | JsonDataset.valid_cached_keys | valid_cached_keys | Can load following key-ed values from the cached roidb file 'image'(image path) and 'flipped' values are already filled on _prep_roidb_entry, so we don't need to overwrite it again. | [
"Can",
"load",
"following",
"key-ed",
"values",
"from",
"the",
"cached",
"roidb",
"file",
"'image'(image",
"path)",
"and",
"'flipped'",
"values",
"are",
"already",
"filled",
"on",
"_prep_roidb_entry,",
"so",
"we",
"don't",
"need",
"to",
"overwrite",
"it",
"again... | def valid_cached_keys(self):
keys = ['boxes', 'segms', 'gt_classes', 'seg_areas', 'gt_overlaps', 'is_crowd', 'box_to_gt_ind_map']
if self.keypoints is not None:
keys += ['gt_keypoints', 'has_visible_keypoints']
return keys | ['def', 'valid_cached_keys(self):', 'keys', '=', "['boxes',", "'segms',", "'gt_classes',", "'seg_areas',", "'gt_overlaps',", "'is_crowd',", "'box_to_gt_ind_map']", 'if', 'self.keypoints', 'is', 'not', 'None:', 'keys', '+=', "['gt_keypoints',", "'has_visible_keypoints']", 'return', 'keys'] | 778,694 |
dongminlee94/deep_rl | a2c.py | Agent.select_action | select_action | Select an action from the set of available actions. | [
"Select",
"an",
"action",
"from",
"the",
"set",
"of",
"available",
"actions."
] | def select_action(self, obs):
(action, _, log_pi) = self.policy(obs)
v = self.vf(obs)
self.transition.extend([log_pi, v])
return action.detach().cpu().numpy() | ['def', 'select_action(self,', 'obs):', '(action,', '_,', 'log_pi)', '=', 'self.policy(obs)', 'v', '=', 'self.vf(obs)', 'self.transition.extend([log_pi,', 'v])', 'return', 'action.detach().cpu().numpy()'] | 183,588 |
Media-Smart/vedadet | colorspace.py | gray2rgb | gray2rgb | Convert a grayscale image to RGB image. | [
"Convert",
"a",
"grayscale",
"image",
"to",
"RGB",
"image."
] | def gray2rgb(img):
img = img[..., None] if img.ndim == 2 else img
out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
return out_img | ['def', 'gray2rgb(img):', 'img', '=', 'img[...,', 'None]', 'if', 'img.ndim', '==', '2', 'else', 'img', 'out_img', '=', 'cv2.cvtColor(img,', 'cv2.COLOR_GRAY2RGB)', 'return', 'out_img'] | 931,073 |
facebookresearch/detectron2 | rotated_fast_rcnn.py | fast_rcnn_inference_rotated | fast_rcnn_inference_rotated | Call `fast_rcnn_inference_single_image_rotated` for all images. | [
"Call",
"`fast_rcnn_inference_single_image_rotated`",
"for",
"all",
"images."
] | def fast_rcnn_inference_rotated(boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image):
result_per_image = [fast_rcnn_inference_single_image_rotated(boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image) for (scores_per_image, boxes_per_image, image_shape) in zip(s... | ['def', 'fast_rcnn_inference_rotated(boxes,', 'scores,', 'image_shapes,', 'score_thresh,', 'nms_thresh,', 'topk_per_image):', 'result_per_image', '=', '[fast_rcnn_inference_single_image_rotated(boxes_per_image,', 'scores_per_image,', 'image_shape,', 'score_thresh,', 'nms_thresh,', 'topk_per_image)', 'for', '(scores_per... | 549,286 |
atulkum/object_detection | loader.py | RoIDataLoader.enqueue_blobs | enqueue_blobs | Put a mini-batch on a BlobsQueue. | [
"Put",
"a",
"mini-batch",
"on",
"a",
"BlobsQueue."
] | def enqueue_blobs(self, gpu_id, blob_names, blobs):
assert len(blob_names) == len(blobs)
t = time.time()
dev = c2_utils.CudaDevice(gpu_id)
queue_name = 'gpu_{}/{}'.format(gpu_id, self._blobs_queue_name)
blob_names = ['gpu_{}/{}'.format(gpu_id, b) for b in blob_names]
for (blob_name, blob) in zip... | ['def', 'enqueue_blobs(self,', 'gpu_id,', 'blob_names,', 'blobs):', 'assert', 'len(blob_names)', '==', 'len(blobs)', 't', '=', 'time.time()', 'dev', '=', 'c2_utils.CudaDevice(gpu_id)', 'queue_name', '=', "'gpu_{}/{}'.format(gpu_id,", 'self._blobs_queue_name)', 'blob_names', '=', "['gpu_{}/{}'.format(gpu_id,", 'b)', 'fo... | 772,988 |
suarez12138/AI-Reversi_IMP_TextDichotomy | texmanager.py | TexManager.get_custom_preamble | get_custom_preamble | Return a string containing user additions to the tex preamble. | [
"Return",
"a",
"string",
"containing",
"user",
"additions",
"to",
"the",
"tex",
"preamble."
] | def get_custom_preamble(self):
return rcParams['text.latex.preamble'] | ['def', 'get_custom_preamble(self):', 'return', "rcParams['text.latex.preamble']"] | 96,769 |
Shuijing725/CrowdNav_DSRNN | srnn_model.py | EdgeAttention.forward | forward | Forward pass for the model params: h_temporal : Hidden state of the temporal edgeRNN h_spatials : Hidden states of all spatial edgeRNNs connected to the node. | [
"Forward",
"pass",
"for",
"the",
"model",
"params:",
"h_temporal",
":",
"Hidden",
"state",
"of",
"the",
"temporal",
"edgeRNN",
"h_spatials",
":",
"Hidden",
"states",
"of",
"all",
"spatial",
"edgeRNNs",
"connected",
"to",
"the",
"node."
] | def forward(self, h_temporal, h_spatials):
self.human_num = h_spatials.size()[2] // self.agent_num
(weighted_value_list, attn_list) = ([], [])
for i in range(self.num_attention_head):
temporal_embed = self.temporal_edge_layer[i](h_temporal)
spatial_embed = self.spatial_edge_layer[i](h_spatia... | ['def', 'forward(self,', 'h_temporal,', 'h_spatials):', 'self.human_num', '=', 'h_spatials.size()[2]', '//', 'self.agent_num', '(weighted_value_list,', 'attn_list)', '=', '([],', '[])', 'for', 'i', 'in', 'range(self.num_attention_head):', 'temporal_embed', '=', 'self.temporal_edge_layer[i](h_temporal)', 'spatial_embed'... | 506,309 |
apeterswu/RL4NMT | transformer_vae.py | nearest | nearest | Find the nearest means to elements in x. | [
"Find",
"the",
"nearest",
"means",
"to",
"elements",
"in",
"x."
] | def nearest(x, means, hparams):
(x, means) = (tf.stop_gradient(x), tf.stop_gradient(means))
means = tf.nn.l2_normalize(means, dim=1)
x_flat = tf.reshape(x, [-1, hparams.hidden_size])
dist = -tf.matmul(x_flat, means, transpose_b=True)
(_, nearest_idx) = tf.nn.top_k(-dist, k=1)
nearest_hot = tf.on... | ['def', 'nearest(x,', 'means,', 'hparams):', '(x,', 'means)', '=', '(tf.stop_gradient(x),', 'tf.stop_gradient(means))', 'means', '=', 'tf.nn.l2_normalize(means,', 'dim=1)', 'x_flat', '=', 'tf.reshape(x,', '[-1,', 'hparams.hidden_size])', 'dist', '=', '-tf.matmul(x_flat,', 'means,', 'transpose_b=True)', '(_,', 'nearest_... | 331,699 |
openvinotoolkit/training_extensions | annotation.py | AnnotationSceneEntity.append_annotations | append_annotations | Adds a list of annotations to the annotation scene. | [
"Adds",
"a",
"list",
"of",
"annotations",
"to",
"the",
"annotation",
"scene."
] | def append_annotations(self, annotations: List[Annotation]) -> None:
self.annotations.extend(annotations) | ['def', 'append_annotations(self,', 'annotations:', 'List[Annotation])', '->', 'None:', 'self.annotations.extend(annotations)'] | 918,472 |
tobegit3hub/deep_image_model | event_multiplexer.py | EventMultiplexer.RunPaths | RunPaths | Returns a dict mapping run names to event file paths. | [
"Returns",
"a",
"dict",
"mapping",
"run",
"names",
"to",
"event",
"file",
"paths."
] | def RunPaths(self):
return self._paths | ['def', 'RunPaths(self):', 'return', 'self._paths'] | 183,233 |
rudranil723/mini-main | fields.py | Field.widget_attrs | widget_attrs | Given a Widget instance (*not* a Widget class), return a dictionary of any HTML attributes that should be added to the Widget, based on this Field. | [
"Given",
"a",
"Widget",
"instance",
"(*not*",
"a",
"Widget",
"class),",
"return",
"a",
"dictionary",
"of",
"any",
"HTML",
"attributes",
"that",
"should",
"be",
"added",
"to",
"the",
"Widget,",
"based",
"on",
"this",
"Field."
] | def widget_attrs(self, widget):
return {} | ['def', 'widget_attrs(self,', 'widget):', 'return', '{}'] | 316,217 |
bruinxiong/EG3D-pytorch | util.py | Logger.write | write | Write text to stdout (and a file) and optionally flush. | [
"Write",
"text",
"to",
"stdout",
"(and",
"a",
"file)",
"and",
"optionally",
"flush."
] | def write(self, text: Union[str, bytes]) -> None:
if isinstance(text, bytes):
text = text.decode()
if len(text) == 0:
return
if self.file is not None:
self.file.write(text)
self.stdout.write(text)
if self.should_flush:
self.flush() | ['def', 'write(self,', 'text:', 'Union[str,', 'bytes])', '->', 'None:', 'if', 'isinstance(text,', 'bytes):', 'text', '=', 'text.decode()', 'if', 'len(text)', '==', '0:', 'return', 'if', 'self.file', 'is', 'not', 'None:', 'self.file.write(text)', 'self.stdout.write(text)', 'if', 'self.should_flush:', 'self.flush()'] | 561,208 |
ilya16/MultINN | encoder.py | Encoder.num_layers | num_layers | int: The number of layers in the Encoder. | [
"int:",
"The",
"number",
"of",
"layers",
"in",
"the",
"Encoder."
] | def num_layers(self):
return len(self._num_hidden) | ['def', 'num_layers(self):', 'return', 'len(self._num_hidden)'] | 644,225 |
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