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 |
|---|---|---|---|---|---|---|---|---|
deepmind/dm_control | renderer.py | SceneCamera.is_initialized | is_initialized | Returns True if camera is properly initialized. | [
"Returns",
"True",
"if",
"camera",
"is",
"properly",
"initialized."
] | def is_initialized(self):
if not self._scene:
return False
frustum_near = self._scene.camera[0].frustum_near
frustum_far = self._scene.camera[0].frustum_far
return frustum_near > 0 and frustum_near < frustum_far | ['def', 'is_initialized(self):', 'if', 'not', 'self._scene:', 'return', 'False', 'frustum_near', '=', 'self._scene.camera[0].frustum_near', 'frustum_far', '=', 'self._scene.camera[0].frustum_far', 'return', 'frustum_near', '>', '0', 'and', 'frustum_near', '<', 'frustum_far'] | 166,582 |
rifqind/Agent-Programs-3KS1 | test_application.py | test_unicode_cwd | test_unicode_cwd | Check that IPython starts with non-ascii characters in the path. | [
"Check",
"that",
"IPython",
"starts",
"with",
"non-ascii",
"characters",
"in",
"the",
"path."
] | def test_unicode_cwd():
wd = tempfile.mkdtemp(suffix=u'âÂ\x82¬')
old_wd = os.getcwd()
os.chdir(wd)
try:
app = BaseIPythonApplication()
app.init_profile_dir()
app.init_config_files()
app.load_config_file(suppress_errors=False)
finally:
os.chdir(old_wd) | ['def', 'test_unicode_cwd():', 'wd', '=', "tempfile.mkdtemp(suffix=u'âÂ\\x82¬')", 'old_wd', '=', 'os.getcwd()', 'os.chdir(wd)', 'try:', 'app', '=', 'BaseIPythonApplication()', 'app.init_profile_dir()', 'app.init_config_files()', 'app.load_config_file(suppress_errors=False)', 'finally:', 'os.chdir(old_wd)'] | 41,358 |
cbaziotis/seq3 | helpers.py | sequence_mask | sequence_mask | Creates a boolean mask from sequence lengths. | [
"Creates",
"a",
"boolean",
"mask",
"from",
"sequence",
"lengths."
] | def sequence_mask(lengths, max_len=None):
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len, device=lengths.device).type_as(lengths).unsqueeze(0).expand(batch_size, max_len).lt(lengths.unsqueeze(1)) | ['def', 'sequence_mask(lengths,', 'max_len=None):', 'batch_size', '=', 'lengths.numel()', 'max_len', '=', 'max_len', 'or', 'lengths.max()', 'return', 'torch.arange(0,', 'max_len,', 'device=lengths.device).type_as(lengths).unsqueeze(0).expand(batch_size,', 'max_len).lt(lengths.unsqueeze(1))'] | 876,498 |
matsu0228/nlp-jp | routing.py | Matcher.reverse | reverse | Reconstructs full url from matcher instance and additional arguments. | [
"Reconstructs",
"full",
"url",
"from",
"matcher",
"instance",
"and",
"additional",
"arguments."
] | def reverse(self, *args):
return None | ['def', 'reverse(self,', '*args):', 'return', 'None'] | 807,347 |
weimin17/Object-Detection_HelmetDetection | bulk_component.py | extract_fixed_feature_ids | extract_fixed_feature_ids | Extracts fixed feature IDs. | [
"Extracts",
"fixed",
"feature",
"IDs."
] | def extract_fixed_feature_ids(comp, state, stride):
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return (state.handle, [])
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_... | ['def', 'extract_fixed_feature_ids(comp,', 'state,', 'stride):', 'num_channels', '=', 'len(comp.spec.fixed_feature)', 'if', 'not', 'num_channels:', 'return', '(state.handle,', '[])', 'for', 'feature_spec', 'in', 'comp.spec.fixed_feature:', 'check.Eq(feature_spec.size,', '1,', "'All", 'features', 'must', 'have', "size=1... | 760,055 |
lvwerra/trl | test_ppo_trainer.py | PPOTrainerTester.test_ppo_step_rewards_shape | test_ppo_step_rewards_shape | Test if the rewards shape is correct by asserting that if a wrong reward shape is passed, we get a value error. | [
"Test",
"if",
"the",
"rewards",
"shape",
"is",
"correct",
"by",
"asserting",
"that",
"if",
"a",
"wrong",
"reward",
"shape",
"is",
"passed,",
"we",
"get",
"a",
"value",
"error."
] | def test_ppo_step_rewards_shape(self):
dummy_dataset = self._init_dummy_dataset()
ppo_trainer = PPOTrainer(config=self.ppo_config, model=self.gpt2_model, ref_model=None, tokenizer=self.gpt2_tokenizer, dataset=dummy_dataset)
dummy_dataloader = ppo_trainer.dataloader
for (query_tensor, response_tensor) in... | ['def', 'test_ppo_step_rewards_shape(self):', 'dummy_dataset', '=', 'self._init_dummy_dataset()', 'ppo_trainer', '=', 'PPOTrainer(config=self.ppo_config,', 'model=self.gpt2_model,', 'ref_model=None,', 'tokenizer=self.gpt2_tokenizer,', 'dataset=dummy_dataset)', 'dummy_dataloader', '=', 'ppo_trainer.dataloader', 'for', '... | 425,842 |
jimtin/Stock_Comparison | demo.py | ClearMixin.marquee | marquee | Blank marquee that returns '' no matter what the input. | [
"Blank",
"marquee",
"that",
"returns",
"''",
"no",
"matter",
"what",
"the",
"input."
] | def marquee(self, txt='', width=78, mark='*'):
return '' | ['def', 'marquee(self,', "txt='',", 'width=78,', "mark='*'):", 'return', "''"] | 385,227 |
QData/deepWordBug | math2html.py | Label.process | process | Process a label container. | [
"Process",
"a",
"label",
"container."
] | def process(self):
key = self.getparameter('name')
self.create(' ', key)
self.lastnumbered = Label.lastlayout | ['def', 'process(self):', 'key', '=', "self.getparameter('name')", "self.create('", "',", 'key)', 'self.lastnumbered', '=', 'Label.lastlayout'] | 542,560 |
cheng052/BRNet | builder.py | build_middle_encoder | build_middle_encoder | Build middle level encoder. | [
"Build",
"middle",
"level",
"encoder."
] | def build_middle_encoder(cfg):
return build(cfg, MIDDLE_ENCODERS) | ['def', 'build_middle_encoder(cfg):', 'return', 'build(cfg,', 'MIDDLE_ENCODERS)'] | 409,855 |
open-mmlab/mmtracking | flow.py | flow_warp_feats | flow_warp_feats | Use flow to warp feature map. | [
"Use",
"flow",
"to",
"warp",
"feature",
"map."
] | def flow_warp_feats(x, flow):
assert len(x.shape) == 4
assert len(flow.shape) == 4 and flow.shape[1] == 2
scale_factor = float(x.shape[-1]) / flow.shape[-1]
flow = torch.nn.functional.interpolate(flow, scale_factor=scale_factor, mode='bilinear', align_corners=False)
flow = flow * scale_factor
(H... | ['def', 'flow_warp_feats(x,', 'flow):', 'assert', 'len(x.shape)', '==', '4', 'assert', 'len(flow.shape)', '==', '4', 'and', 'flow.shape[1]', '==', '2', 'scale_factor', '=', 'float(x.shape[-1])', '/', 'flow.shape[-1]', 'flow', '=', 'torch.nn.functional.interpolate(flow,', 'scale_factor=scale_factor,', "mode='bilinear',"... | 625,695 |
sktime/sktime | test_window_summarizer.py | test_wrong_column | test_wrong_column | Test mismatch between X column names and target_cols. | [
"Test",
"mismatch",
"between",
"X",
"column",
"names",
"and",
"target_cols."
] | def test_wrong_column():
transformer = WindowSummarizer(target_cols=['dummy'])
Xt = transformer.fit_transform(X_ll_train)
return Xt | ['def', 'test_wrong_column():', 'transformer', '=', "WindowSummarizer(target_cols=['dummy'])", 'Xt', '=', 'transformer.fit_transform(X_ll_train)', 'return', 'Xt'] | 877,928 |
arshpreetsingh/quantopian-machinelearning | test_iplib.py | test_reset | test_reset | reset must clear most namespaces. | [
"reset",
"must",
"clear",
"most",
"namespaces."
] | def test_reset():
ip.reset()
nvars_user_ns = len(ip.user_ns)
nvars_hidden = len(ip.user_ns_hidden)
ip.user_ns['x'] = 1
ip.user_ns['y'] = 1
ip.reset()
nt.assert_equal(len(ip.user_ns), nvars_user_ns)
nt.assert_equal(len(ip.user_ns_hidden), nvars_hidden) | ['def', 'test_reset():', 'ip.reset()', 'nvars_user_ns', '=', 'len(ip.user_ns)', 'nvars_hidden', '=', 'len(ip.user_ns_hidden)', "ip.user_ns['x']", '=', '1', "ip.user_ns['y']", '=', '1', 'ip.reset()', 'nt.assert_equal(len(ip.user_ns),', 'nvars_user_ns)', 'nt.assert_equal(len(ip.user_ns_hidden),', 'nvars_hidden)'] | 886,658 |
devashish-patel/webcam-motion-detector | test_process.py | test_arg_split | test_arg_split | Ensure that argument lines are correctly split like in a shell. | [
"Ensure",
"that",
"argument",
"lines",
"are",
"correctly",
"split",
"like",
"in",
"a",
"shell."
] | def test_arg_split():
tests = [['hi', ['hi']], [u'hi', [u'hi']], ['hello there', ['hello', 'there']], [u'hǎllo', [u'hǎllo']], ['something "with quotes"', ['something', '"with quotes"']]]
for (argstr, argv) in tests:
nt.assert_equal(arg_split(argstr), argv) | ['def', 'test_arg_split():', 'tests', '=', "[['hi',", "['hi']],", "[u'hi',", "[u'hi']],", "['hello", "there',", "['hello',", "'there']],", "[u'hǎllo',", "[u'hǎllo']],", "['something", '"with', 'quotes"\',', "['something',", '\'"with', 'quotes"\']]]', 'for', '(argstr,', 'argv)', 'in', 'tests:', 'nt.assert_equal(arg_spli... | 979,544 |
ago109/predictive-forward-forward | sim_train.py | plot_img_grid | plot_img_grid | Visualizes a matrix of vector patterns in the form of an image grid plot. | [
"Visualizes",
"a",
"matrix",
"of",
"vector",
"patterns",
"in",
"the",
"form",
"of",
"an",
"image",
"grid",
"plot."
] | def plot_img_grid(samples, fname, nx, ny, px, py, plt, rotNeg90=False):
px_dim = px
py_dim = py
canvas = np.empty((px_dim * nx, py_dim * ny))
ptr = 0
for i in range(0, nx, 1):
for j in range(0, ny, 1):
xs = np.expand_dims(samples[ptr, :], axis=0)
xs = xs[0].reshape(px... | ['def', 'plot_img_grid(samples,', 'fname,', 'nx,', 'ny,', 'px,', 'py,', 'plt,', 'rotNeg90=False):', 'px_dim', '=', 'px', 'py_dim', '=', 'py', 'canvas', '=', 'np.empty((px_dim', '*', 'nx,', 'py_dim', '*', 'ny))', 'ptr', '=', '0', 'for', 'i', 'in', 'range(0,', 'nx,', '1):', 'for', 'j', 'in', 'range(0,', 'ny,', '1):', 'xs... | 305,930 |
intel/neural-compressor | transform.py | read_squad_examples | read_squad_examples | Read a SQuAD json file into a list of SquadExample. | [
"Read",
"a",
"SQuAD",
"json",
"file",
"into",
"a",
"list",
"of",
"SquadExample."
] | def read_squad_examples(input_file):
import json
with tf.io.gfile.GFile(input_file, 'r') as reader:
input_data = json.load(reader)['data']
def is_whitespace(c):
if c == ' ' or c == '\t' or c == '\r' or (c == '\n') or (ord(c) == 8239):
return True
return False
example... | ['def', 'read_squad_examples(input_file):', 'import', 'json', 'with', 'tf.io.gfile.GFile(input_file,', "'r')", 'as', 'reader:', 'input_data', '=', "json.load(reader)['data']", 'def', 'is_whitespace(c):', 'if', 'c', '==', "'", "'", 'or', 'c', '==', "'\\t'", 'or', 'c', '==', "'\\r'", 'or', '(c', '==', "'\\n')", 'or', '(o... | 738,496 |
ludwig-ai/ludwig | strings_utils.py | values_are_pandas_numbers | values_are_pandas_numbers | Returns True if values would be read by pandas as dtype float or int. | [
"Returns",
"True",
"if",
"values",
"would",
"be",
"read",
"by",
"pandas",
"as",
"dtype",
"float",
"or",
"int."
] | def values_are_pandas_numbers(values: List[str]):
for v in values:
try:
float(v)
except ValueError:
return False
return True | ['def', 'values_are_pandas_numbers(values:', 'List[str]):', 'for', 'v', 'in', 'values:', 'try:', 'float(v)', 'except', 'ValueError:', 'return', 'False', 'return', 'True'] | 617,150 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | build_src.py | subst_vars | subst_vars | Substitute any occurrence of @foo@ by d['foo'] from source file into target. | [
"Substitute",
"any",
"occurrence",
"of",
"@foo@",
"by",
"d['foo']",
"from",
"source",
"file",
"into",
"target."
] | def subst_vars(target, source, d):
var = re.compile('@([a-zA-Z_]+)@')
fs = open(source, 'r')
try:
ft = open(target, 'w')
try:
for l in fs:
m = var.search(l)
if m:
ft.write(l.replace('@%s@' % m.group(1), d[m.group(1)]))
... | ['def', 'subst_vars(target,', 'source,', 'd):', 'var', '=', "re.compile('@([a-zA-Z_]+)@')", 'fs', '=', 'open(source,', "'r')", 'try:', 'ft', '=', 'open(target,', "'w')", 'try:', 'for', 'l', 'in', 'fs:', 'm', '=', 'var.search(l)', 'if', 'm:', "ft.write(l.replace('@%s@'", '%', 'm.group(1),', 'd[m.group(1)]))', 'else:', '... | 966,639 |
Levantespot/UDA_for_RS | uda_decorator.py | UDADecorator.simple_test | simple_test | Simple test with single image. | [
"Simple",
"test",
"with",
"single",
"image."
] | def simple_test(self, img, img_meta, rescale=True):
return self.get_model().simple_test(img, img_meta, rescale) | ['def', 'simple_test(self,', 'img,', 'img_meta,', 'rescale=True):', 'return', 'self.get_model().simple_test(img,', 'img_meta,', 'rescale)'] | 947,413 |
bhateharsh/computer_vision | model_lib_test.py | ModelLibTest.test_model_fn_in_predict_mode | test_model_fn_in_predict_mode | Tests the model function in PREDICT mode. | [
"Tests",
"the",
"model",
"function",
"in",
"PREDICT",
"mode."
] | def test_model_fn_in_predict_mode(self):
configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
self._assert_model_fn_for_predict(configs) | ['def', 'test_model_fn_in_predict_mode(self):', 'configs', '=', '_get_configs_for_model(MODEL_NAME_FOR_TEST)', 'self._assert_model_fn_for_predict(configs)'] | 503,717 |
danamyu/hedgehog_detector | pg_train.py | AsyncTrainer.maybe_save_best_model | maybe_save_best_model | Check if this model got the highest reward and save to disk if so. | [
"Check",
"if",
"this",
"model",
"got",
"the",
"highest",
"reward",
"and",
"save",
"to",
"disk",
"if",
"so."
] | def maybe_save_best_model(self, session, saver, checkpoint_file):
if self.is_chief and session.run(self.is_best_model):
logging.info('Saving best model to "%s"', checkpoint_file)
saver.save(session, checkpoint_file)
session.run(self.reset_is_best_model) | ['def', 'maybe_save_best_model(self,', 'session,', 'saver,', 'checkpoint_file):', 'if', 'self.is_chief', 'and', 'session.run(self.is_best_model):', "logging.info('Saving", 'best', 'model', 'to', '"%s"\',', 'checkpoint_file)', 'saver.save(session,', 'checkpoint_file)', 'session.run(self.reset_is_best_model)'] | 589,379 |
caiiiac/Machine-Learning-with-Python | dviread.py | Dvi.close | close | Close the underlying file if it is open. | [
"Close",
"the",
"underlying",
"file",
"if",
"it",
"is",
"open."
] | def close(self):
if not self.file.closed:
self.file.close() | ['def', 'close(self):', 'if', 'not', 'self.file.closed:', 'self.file.close()'] | 715,442 |
KleinYuan/tf-object-detection | coco_evaluation_test.py | CocoDetectionEvaluationTest.testRejectionOnDuplicateGroundtruth | testRejectionOnDuplicateGroundtruth | Tests that groundtruth cannot be added more than once for an image. | [
"Tests",
"that",
"groundtruth",
"cannot",
"be",
"added",
"more",
"than",
"once",
"for",
"an",
"image."
] | def testRejectionOnDuplicateGroundtruth(self):
categories = [{'id': 1, 'name': 'cat'}, {'id': 2, 'name': 'dog'}, {'id': 3, 'name': 'elephant'}]
coco_evaluator = coco_evaluation.CocoDetectionEvaluator(categories)
image_key1 = 'img1'
groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]]... | ['def', 'testRejectionOnDuplicateGroundtruth(self):', 'categories', '=', "[{'id':", '1,', "'name':", "'cat'},", "{'id':", '2,', "'name':", "'dog'},", "{'id':", '3,', "'name':", "'elephant'}]", 'coco_evaluator', '=', 'coco_evaluation.CocoDetectionEvaluator(categories)', 'image_key1', '=', "'img1'", 'groundtruth_boxes1',... | 914,874 |
sanujkul/Artificial-Intelligence | search.py | Graph.connect1 | connect1 | Add a link from A to B of given distance, in one direction only. | [
"Add",
"a",
"link",
"from",
"A",
"to",
"B",
"of",
"given",
"distance,",
"in",
"one",
"direction",
"only."
] | def connect1(self, A, B, distance):
self.graph_dict.setdefault(A, {})[B] = distance | ['def', 'connect1(self,', 'A,', 'B,', 'distance):', 'self.graph_dict.setdefault(A,', '{})[B]', '=', 'distance'] | 118,317 |
calico/basenji | seqnn.py | SeqNN.build_slice | build_slice | Slice and/or sum across tasks, in graph. | [
"Slice",
"and/or",
"sum",
"across",
"tasks,",
"in",
"graph."
] | def build_slice(self, target_slice=None, target_sum=False):
if target_slice is not None or target_sum:
sequence = tf.keras.Input(shape=(self.seq_length, 4), name='sequence')
predictions = self.model(sequence)
if target_slice is None:
predictions_slice = predictions
else:
... | ['def', 'build_slice(self,', 'target_slice=None,', 'target_sum=False):', 'if', 'target_slice', 'is', 'not', 'None', 'or', 'target_sum:', 'sequence', '=', 'tf.keras.Input(shape=(self.seq_length,', '4),', "name='sequence')", 'predictions', '=', 'self.model(sequence)', 'if', 'target_slice', 'is', 'None:', 'predictions_sli... | 94,597 |
vishalprabha/Image-Classification-Transfer-Learning-with-Inception-v3 | retrain.py | should_distort_images | should_distort_images | Whether any distortions are enabled, from the input flags. | [
"Whether",
"any",
"distortions",
"are",
"enabled,",
"from",
"the",
"input",
"flags."
] | def should_distort_images(flip_left_right, random_crop, random_scale, random_brightness):
return flip_left_right or random_crop != 0 or random_scale != 0 or (random_brightness != 0) | ['def', 'should_distort_images(flip_left_right,', 'random_crop,', 'random_scale,', 'random_brightness):', 'return', 'flip_left_right', 'or', 'random_crop', '!=', '0', 'or', 'random_scale', '!=', '0', 'or', '(random_brightness', '!=', '0)'] | 599,092 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | nb_102a.py | create_grid | create_grid | Create a grid of a given `size`. | [
"Create",
"a",
"grid",
"of",
"a",
"given",
"`size`."
] | def create_grid(size):
(H, W) = size if is_tuple(size) else (size, size)
grid = FloatTensor(H, W, 2)
linear_points = torch.linspace(-1 + 1 / W, 1 - 1 / W, W) if W > 1 else tensor([0.0])
grid[:, :, 1] = torch.ger(torch.ones(H), linear_points).expand_as(grid[:, :, 0])
linear_points = torch.linspace(-1... | ['def', 'create_grid(size):', '(H,', 'W)', '=', 'size', 'if', 'is_tuple(size)', 'else', '(size,', 'size)', 'grid', '=', 'FloatTensor(H,', 'W,', '2)', 'linear_points', '=', 'torch.linspace(-1', '+', '1', '/', 'W,', '1', '-', '1', '/', 'W,', 'W)', 'if', 'W', '>', '1', 'else', 'tensor([0.0])', 'grid[:,', ':,', '1]', '=', ... | 81,895 |
AdroitAnandAI/Computer-Vision-Math-Magic-vs-AI | searchImgObject.py | match | match | Here we are using correlation diff because we are searching objects. | [
"Here",
"we",
"are",
"using",
"correlation",
"diff",
"because",
"we",
"are",
"searching",
"objects."
] | def match(base, current):
res = cdist(base, current, metric='correlation')
return np.nansum(res) | ['def', 'match(base,', 'current):', 'res', '=', 'cdist(base,', 'current,', "metric='correlation')", 'return', 'np.nansum(res)'] | 470,302 |
rlpy/rlpy | IndependentDiscretizationCompactBinary.py | IndependentDiscretizationCompactBinary.getDimNumber | getDimNumber | Returns the dimension number corresponding to feature ``f``. | [
"Returns",
"the",
"dimension",
"number",
"corresponding",
"to",
"feature",
"``f``."
] | def getDimNumber(self, f):
dim = np.searchsorted(self.maxFeatureIDperDimension, f)
return dim | ['def', 'getDimNumber(self,', 'f):', 'dim', '=', 'np.searchsorted(self.maxFeatureIDperDimension,', 'f)', 'return', 'dim'] | 333,878 |
sek788432/Waymo-2D-Object-Detection | image_classification.py | image_classification_imagenet_resnetrs | image_classification_imagenet_resnetrs | Image classification on imagenet with resnet-rs. | [
"Image",
"classification",
"on",
"imagenet",
"with",
"resnet-rs."
] | def image_classification_imagenet_resnetrs() -> cfg.ExperimentConfig:
train_batch_size = 4096
eval_batch_size = 4096
steps_per_epoch = IMAGENET_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(task=ImageClassificationTask(model=ImageClassificationModel(num_classes=1001, input_size=[160, ... | ['def', 'image_classification_imagenet_resnetrs()', '->', 'cfg.ExperimentConfig:', 'train_batch_size', '=', '4096', 'eval_batch_size', '=', '4096', 'steps_per_epoch', '=', 'IMAGENET_TRAIN_EXAMPLES', '//', 'train_batch_size', 'config', '=', 'cfg.ExperimentConfig(task=ImageClassificationTask(model=ImageClassificationMode... | 973,018 |
ArdaGunay99/Key_Detection_Unsupervised_Learning | scale.py | ScaleBase.set_default_locators_and_formatters | set_default_locators_and_formatters | Set the locators and formatters of *axis* to instances suitable for this scale. | [
"Set",
"the",
"locators",
"and",
"formatters",
"of",
"*axis*",
"to",
"instances",
"suitable",
"for",
"this",
"scale."
] | def set_default_locators_and_formatters(self, axis):
raise NotImplementedError() | ['def', 'set_default_locators_and_formatters(self,', 'axis):', 'raise', 'NotImplementedError()'] | 257,255 |
open-mmlab/mmdetection3d | kitti_metric.py | KittiMetric.bbox2result_kitti | bbox2result_kitti | Convert 3D detection results to kitti format for evaluation and test submission. | [
"Convert",
"3D",
"detection",
"results",
"to",
"kitti",
"format",
"for",
"evaluation",
"and",
"test",
"submission."
] | def bbox2result_kitti(self, net_outputs: List[dict], sample_idx_list: List[int], class_names: List[str], pklfile_prefix: Optional[str]=None, submission_prefix: Optional[str]=None) -> List[dict]:
assert len(net_outputs) == len(self.data_infos), 'invalid list length of network outputs'
if submission_prefix is not... | ['def', 'bbox2result_kitti(self,', 'net_outputs:', 'List[dict],', 'sample_idx_list:', 'List[int],', 'class_names:', 'List[str],', 'pklfile_prefix:', 'Optional[str]=None,', 'submission_prefix:', 'Optional[str]=None)', '->', 'List[dict]:', 'assert', 'len(net_outputs)', '==', 'len(self.data_infos),', "'invalid", 'list', '... | 631,802 |
nicknochnack/RealTimeSignLanguageTFJS | anchor_generator.py | maybe_map_structure_for_anchor | maybe_map_structure_for_anchor | broadcast the params to match anchor_sizes. | [
"broadcast",
"the",
"params",
"to",
"match",
"anchor_sizes."
] | def maybe_map_structure_for_anchor(params, anchor_sizes):
if all((isinstance(param, (int, float)) for param in params)):
if isinstance(anchor_sizes, (tuple, list)):
return [params] * len(anchor_sizes)
elif isinstance(anchor_sizes, dict):
return tf.nest.map_structure(lambda _:... | ['def', 'maybe_map_structure_for_anchor(params,', 'anchor_sizes):', 'if', 'all((isinstance(param,', '(int,', 'float))', 'for', 'param', 'in', 'params)):', 'if', 'isinstance(anchor_sizes,', '(tuple,', 'list)):', 'return', '[params]', '*', 'len(anchor_sizes)', 'elif', 'isinstance(anchor_sizes,', 'dict):', 'return', 'tf.n... | 851,244 |
SapienzaNLP/xl-amr | predictor.py | Predictor.dump_line | dump_line | If you don't want your outputs in JSON-lines format you can override this function to output them differently. | [
"If",
"you",
"don't",
"want",
"your",
"outputs",
"in",
"JSON-lines",
"format",
"you",
"can",
"override",
"this",
"function",
"to",
"output",
"them",
"differently."
] | def dump_line(self, outputs: JsonDict) -> str:
return json.dumps(outputs) + '\n' | ['def', 'dump_line(self,', 'outputs:', 'JsonDict)', '->', 'str:', 'return', 'json.dumps(outputs)', '+', "'\\n'"] | 968,623 |
ITZ-ZAID/AI | cnf_transformation.py | move_not_inward | move_not_inward | Moves the 'ì' operator inward and returns the given formula transformed. | [
"Moves",
"the",
"'ì'",
"operator",
"inward",
"and",
"returns",
"the",
"given",
"formula",
"transformed."
] | def move_not_inward(f):
inside = f.child
if isinstance(inside, Atom) or isinstance(inside, Not):
return ~inside
inside.lchild = ~inside.lchild
inside.rchild = ~inside.rchild
if inside.op == 'âÂ\x88§':
inside.op = 'âÂ\x88¨'
elif inside.op == 'âÂ\x88¨':
inside.op = 'Ã... | ['def', 'move_not_inward(f):', 'inside', '=', 'f.child', 'if', 'isinstance(inside,', 'Atom)', 'or', 'isinstance(inside,', 'Not):', 'return', '~inside', 'inside.lchild', '=', '~inside.lchild', 'inside.rchild', '=', '~inside.rchild', 'if', 'inside.op', '==', "'âÂ\\x88§':", 'inside.op', '=', "'âÂ\\x88¨'", 'elif', 'ins... | 69,414 |
fcjian/TOOD | cascade_rcnn.py | CascadeRCNN.show_result | show_result | Show prediction results of the detector. | [
"Show",
"prediction",
"results",
"of",
"the",
"detector."
] | def show_result(self, data, result, **kwargs):
if self.with_mask:
(ms_bbox_result, ms_segm_result) = result
if isinstance(ms_bbox_result, dict):
result = (ms_bbox_result['ensemble'], ms_segm_result['ensemble'])
elif isinstance(result, dict):
result = result['ensemble']
re... | ['def', 'show_result(self,', 'data,', 'result,', '**kwargs):', 'if', 'self.with_mask:', '(ms_bbox_result,', 'ms_segm_result)', '=', 'result', 'if', 'isinstance(ms_bbox_result,', 'dict):', 'result', '=', "(ms_bbox_result['ensemble'],", "ms_segm_result['ensemble'])", 'elif', 'isinstance(result,', 'dict):', 'result', '=',... | 902,150 |
ldkong1205/LaserMix | vis_utils.py | to_depth_mode | to_depth_mode | Convert points and bboxes to Depth Coord and Depth Box mode. | [
"Convert",
"points",
"and",
"bboxes",
"to",
"Depth",
"Coord",
"and",
"Depth",
"Box",
"mode."
] | def to_depth_mode(points: np.ndarray, bboxes: BaseInstance3DBoxes) -> Tuple[np.ndarray, BaseInstance3DBoxes]:
if points is not None:
points = Coord3DMode.convert_point(points.copy(), Coord3DMode.LIDAR, Coord3DMode.DEPTH)
if bboxes is not None:
bboxes = Box3DMode.convert(bboxes.clone(), Box3DMode... | ['def', 'to_depth_mode(points:', 'np.ndarray,', 'bboxes:', 'BaseInstance3DBoxes)', '->', 'Tuple[np.ndarray,', 'BaseInstance3DBoxes]:', 'if', 'points', 'is', 'not', 'None:', 'points', '=', 'Coord3DMode.convert_point(points.copy(),', 'Coord3DMode.LIDAR,', 'Coord3DMode.DEPTH)', 'if', 'bboxes', 'is', 'not', 'None:', 'bboxe... | 624,478 |
thaines/helit | multiclass.py | MultiModel.getLabels | getLabels | Returns a list of the labels supported. | [
"Returns",
"a",
"list",
"of",
"the",
"labels",
"supported."
] | def getLabels(self):
return self.labels | ['def', 'getLabels(self):', 'return', 'self.labels'] | 592,501 |
enuguru/artificial_intelligence_and_machine_ | mcore.py | Matcher.children | children | Returns an (possibly empty) list of the submatchers of this matcher. | [
"Returns",
"an",
"(possibly",
"empty)",
"list",
"of",
"the",
"submatchers",
"of",
"this",
"matcher."
] | def children(self):
return [] | ['def', 'children(self):', 'return', '[]'] | 133,482 |
guanyuelee/midrae | utils.py | to_png | to_png | Convert a 3D tensor to png. | [
"Convert",
"a",
"3D",
"tensor",
"to",
"png."
] | def to_png(x):
with tf.Graph().as_default():
with tf.Session() as sess_temp:
x = tf.constant(x)
y = tf.image.encode_png(tf.cast(tf.clip_by_value(tf.round(127.5 + 127.5 * x), 0, 255), tf.uint8), compression=9)
return sess_temp.run(y) | ['def', 'to_png(x):', 'with', 'tf.Graph().as_default():', 'with', 'tf.Session()', 'as', 'sess_temp:', 'x', '=', 'tf.constant(x)', 'y', '=', 'tf.image.encode_png(tf.cast(tf.clip_by_value(tf.round(127.5', '+', '127.5', '*', 'x),', '0,', '255),', 'tf.uint8),', 'compression=9)', 'return', 'sess_temp.run(y)'] | 670,318 |
enuguru/artificial_intelligence_and_machine_learning | lexer.py | Lexer.tokenize | tokenize | Calls tokeniter + tokenize and wraps it in a token stream. | [
"Calls",
"tokeniter",
"+",
"tokenize",
"and",
"wraps",
"it",
"in",
"a",
"token",
"stream."
] | def tokenize(self, source, name=None, filename=None, state=None):
stream = self.tokeniter(source, name, filename, state)
return TokenStream(self.wrap(stream, name, filename), name, filename) | ['def', 'tokenize(self,', 'source,', 'name=None,', 'filename=None,', 'state=None):', 'stream', '=', 'self.tokeniter(source,', 'name,', 'filename,', 'state)', 'return', 'TokenStream(self.wrap(stream,', 'name,', 'filename),', 'name,', 'filename)'] | 129,303 |
santhoshkolloju/Abstractive-Summarization-With-Transfer- | agent_utils.py | Space.dtype | dtype | Data type of the element. | [
"Data",
"type",
"of",
"the",
"element."
] | def dtype(self):
return self._dtype | ['def', 'dtype(self):', 'return', 'self._dtype'] | 405,953 |
rainer85ah/ComputerVision | camera.py | Camera.center | center | Compute and return the camera center. | [
"Compute",
"and",
"return",
"the",
"camera",
"center."
] | def center(self):
if self.c is not None:
return self.c
else:
self.factor()
self.c = -dot(self.R.T, self.t)
return self.c | ['def', 'center(self):', 'if', 'self.c', 'is', 'not', 'None:', 'return', 'self.c', 'else:', 'self.factor()', 'self.c', '=', '-dot(self.R.T,', 'self.t)', 'return', 'self.c'] | 471,476 |
NoaCahan/WavenetAutoEncoder | generate.py | decode | decode | Synthesize audio from an array of embeddings. | [
"Synthesize",
"audio",
"from",
"an",
"array",
"of",
"embeddings."
] | def decode(model_path, model_name, encoding, decoder_path, decoder_name, sr=16000, duration=10):
if os.path.exists(decoder_path) is False:
os.makedirs(decoder_path)
with open('./params/model_params.json') as f:
model_params = json.load(f)
f.close()
net = WavenetAutoencoder(**model_params... | ['def', 'decode(model_path,', 'model_name,', 'encoding,', 'decoder_path,', 'decoder_name,', 'sr=16000,', 'duration=10):', 'if', 'os.path.exists(decoder_path)', 'is', 'False:', 'os.makedirs(decoder_path)', 'with', "open('./params/model_params.json')", 'as', 'f:', 'model_params', '=', 'json.load(f)', 'f.close()', 'net', ... | 972,268 |
akash-agni/Real-Time-Object-Detection | object_detection_evaluation.py | ObjectDetectionEvaluation.add_single_detected_image_info | add_single_detected_image_info | Add detected result of a single image into the evaluation database. | [
"Add",
"detected",
"result",
"of",
"a",
"single",
"image",
"into",
"the",
"evaluation",
"database."
] | def add_single_detected_image_info(self, image_key, detected_boxes, detected_scores, detected_class_labels):
if len(detected_boxes) != len(detected_scores) or len(detected_boxes) != len(detected_class_labels):
raise ValueError('detected_boxes, detected_scores and detected_class_labels should all have same l... | ['def', 'add_single_detected_image_info(self,', 'image_key,', 'detected_boxes,', 'detected_scores,', 'detected_class_labels):', 'if', 'len(detected_boxes)', '!=', 'len(detected_scores)', 'or', 'len(detected_boxes)', '!=', 'len(detected_class_labels):', 'raise', "ValueError('detected_boxes,", 'detected_scores', 'and', '... | 850,007 |
openvinotoolkit/training_extensions | random_augment.py | auto_contrast | auto_contrast | Applies auto contrast to an image. | [
"Applies",
"auto",
"contrast",
"to",
"an",
"image."
] | def auto_contrast(img, **kwargs):
return (PIL.ImageOps.autocontrast(img), None) | ['def', 'auto_contrast(img,', '**kwargs):', 'return', '(PIL.ImageOps.autocontrast(img),', 'None)'] | 903,986 |
Kvatsx/Artificial-Intelligence-Assignments | image_test.py | ImageModuleTest.testLoadIcon | testLoadIcon | see if we can load the pygame icon. | [
"see",
"if",
"we",
"can",
"load",
"the",
"pygame",
"icon."
] | def testLoadIcon(self):
f = pygame.pkgdata.getResource('pygame_icon.bmp')
self.assertEqual(f.mode, 'rb')
surf = pygame.image.load_basic(f)
self.assertEqual(surf.get_at((0, 0)), (5, 4, 5, 255))
self.assertEqual(surf.get_height(), 32)
self.assertEqual(surf.get_width(), 32) | ['def', 'testLoadIcon(self):', 'f', '=', "pygame.pkgdata.getResource('pygame_icon.bmp')", 'self.assertEqual(f.mode,', "'rb')", 'surf', '=', 'pygame.image.load_basic(f)', 'self.assertEqual(surf.get_at((0,', '0)),', '(5,', '4,', '5,', '255))', 'self.assertEqual(surf.get_height(),', '32)', 'self.assertEqual(surf.get_width... | 76,411 |
MinRegret/deluca | _gpc.py | GPC.update | update | Description: update agent internal state. | [
"Description:",
"update",
"agent",
"internal",
"state."
] | def update(self, state: jnp.ndarray, u: jnp.ndarray) -> None:
noise = state - self.A @ self.state - self.B @ u
self.noise_history = jax.ops.index_update(self.noise_history, 0, noise)
self.noise_history = jnp.roll(self.noise_history, -1, axis=0)
(delta_M, delta_bias) = self.grad(self.M, self.noise_histor... | ['def', 'update(self,', 'state:', 'jnp.ndarray,', 'u:', 'jnp.ndarray)', '->', 'None:', 'noise', '=', 'state', '-', 'self.A', '@', 'self.state', '-', 'self.B', '@', 'u', 'self.noise_history', '=', 'jax.ops.index_update(self.noise_history,', '0,', 'noise)', 'self.noise_history', '=', 'jnp.roll(self.noise_history,', '-1,'... | 537,851 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | lfads.py | LFADS.train_epoch | train_epoch | Train the model through the entire dataset once. | [
"Train",
"the",
"model",
"through",
"the",
"entire",
"dataset",
"once."
] | def train_epoch(self, datasets, batch_size=None, do_save_ckpt=True):
ops_to_eval = [self.cost, self.recon_cost, self.kl_cost, self.kl_weight, self.l2_cost, self.l2_weight, self.train_op]
collected_op_values = self.run_epoch(datasets, ops_to_eval, kind='train')
total_cost = total_recon_cost = total_kl_cost =... | ['def', 'train_epoch(self,', 'datasets,', 'batch_size=None,', 'do_save_ckpt=True):', 'ops_to_eval', '=', '[self.cost,', 'self.recon_cost,', 'self.kl_cost,', 'self.kl_weight,', 'self.l2_cost,', 'self.l2_weight,', 'self.train_op]', 'collected_op_values', '=', 'self.run_epoch(datasets,', 'ops_to_eval,', "kind='train')", '... | 49,701 |
tonysy/Deep-Feature-Flow-Segmentation | module.py | Module.output_names | output_names | A list of names for the outputs of this module. | [
"A",
"list",
"of",
"names",
"for",
"the",
"outputs",
"of",
"this",
"module."
] | def output_names(self):
return self._output_names | ['def', 'output_names(self):', 'return', 'self._output_names'] | 517,109 |
fpaupier/tensorflow-serving_sidecar | oid_od_challenge_evaluation_utils.py | build_groundtruth_boxes_dictionary | build_groundtruth_boxes_dictionary | Builds a groundtruth dictionary from groundtruth data in CSV file. | [
"Builds",
"a",
"groundtruth",
"dictionary",
"from",
"groundtruth",
"data",
"in",
"CSV",
"file."
] | def build_groundtruth_boxes_dictionary(data, class_label_map):
data_boxes = data[data.ConfidenceImageLabel.isnull()]
data_labels = data[data.XMin.isnull()]
return {standard_fields.InputDataFields.groundtruth_boxes: data_boxes[['YMin', 'XMin', 'YMax', 'XMax']].as_matrix(), standard_fields.InputDataFields.gro... | ['def', 'build_groundtruth_boxes_dictionary(data,', 'class_label_map):', 'data_boxes', '=', 'data[data.ConfidenceImageLabel.isnull()]', 'data_labels', '=', 'data[data.XMin.isnull()]', 'return', '{standard_fields.InputDataFields.groundtruth_boxes:', "data_boxes[['YMin',", "'XMin',", "'YMax',", "'XMax']].as_matrix(),", '... | 922,083 |
ivanmontero/autobot | utils_summarization.py | encode_for_summarization | encode_for_summarization | Encode the story and summary lines, and join them as specified in [1] by using `[SEP] [CLS]` tokens to separate sentences. | [
"Encode",
"the",
"story",
"and",
"summary",
"lines,",
"and",
"join",
"them",
"as",
"specified",
"in",
"[1]",
"by",
"using",
"`[SEP]",
"[CLS]`",
"tokens",
"to",
"separate",
"sentences."
] | def encode_for_summarization(story_lines, summary_lines, tokenizer):
story_lines_token_ids = [tokenizer.encode(line) for line in story_lines]
story_token_ids = [token for sentence in story_lines_token_ids for token in sentence]
summary_lines_token_ids = [tokenizer.encode(line) for line in summary_lines]
... | ['def', 'encode_for_summarization(story_lines,', 'summary_lines,', 'tokenizer):', 'story_lines_token_ids', '=', '[tokenizer.encode(line)', 'for', 'line', 'in', 'story_lines]', 'story_token_ids', '=', '[token', 'for', 'sentence', 'in', 'story_lines_token_ids', 'for', 'token', 'in', 'sentence]', 'summary_lines_token_ids'... | 417,774 |
wanyao1992/code_summarization_public | Dict.py | Dict.prune | prune | Return a new dictionary with the `size` most frequent entries. | [
"Return",
"a",
"new",
"dictionary",
"with",
"the",
"`size`",
"most",
"frequent",
"entries."
] | def prune(self, size):
if size >= self.size():
return self
freq = torch.Tensor([self.frequencies[i] for i in range(len(self.frequencies))])
(_, idx) = torch.sort(freq, 0, True)
newDict = Dict()
newDict.lower = self.lower
for i in self.special:
newDict.addSpecial(self.idxToLabel[i... | ['def', 'prune(self,', 'size):', 'if', 'size', '>=', 'self.size():', 'return', 'self', 'freq', '=', 'torch.Tensor([self.frequencies[i]', 'for', 'i', 'in', 'range(len(self.frequencies))])', '(_,', 'idx)', '=', 'torch.sort(freq,', '0,', 'True)', 'newDict', '=', 'Dict()', 'newDict.lower', '=', 'self.lower', 'for', 'i', 'i... | 495,844 |
violet-zct/fairseq-detect-hallucination | trainer.py | Trainer.set_num_updates | set_num_updates | Set the number of parameters updates. | [
"Set",
"the",
"number",
"of",
"parameters",
"updates."
] | def set_num_updates(self, num_updates):
self._num_updates = num_updates
self.lr_step_update()
if self.quantizer:
self.quantizer.step_update(self._num_updates)
metrics.log_scalar('num_updates', self._num_updates, weight=0, priority=200) | ['def', 'set_num_updates(self,', 'num_updates):', 'self._num_updates', '=', 'num_updates', 'self.lr_step_update()', 'if', 'self.quantizer:', 'self.quantizer.step_update(self._num_updates)', "metrics.log_scalar('num_updates',", 'self._num_updates,', 'weight=0,', 'priority=200)'] | 558,632 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjModelWrapper.nuser_geom | nuser_geom | number of mjtNums in geom_user. | [
"number",
"of",
"mjtNums",
"in",
"geom_user."
] | def nuser_geom(self):
return self._ptr.contents.nuser_geom | ['def', 'nuser_geom(self):', 'return', 'self._ptr.contents.nuser_geom'] | 440,216 |
kemaloksuz/RankSortLoss | guided_anchor_head.py | GuidedAnchorHead.get_sampled_approxs | get_sampled_approxs | Get sampled approxs and inside flags according to feature map sizes. | [
"Get",
"sampled",
"approxs",
"and",
"inside",
"flags",
"according",
"to",
"feature",
"map",
"sizes."
] | def get_sampled_approxs(self, featmap_sizes, img_metas, device='cuda'):
num_imgs = len(img_metas)
multi_level_approxs = self.approx_anchor_generator.grid_anchors(featmap_sizes, device=device)
approxs_list = [multi_level_approxs for _ in range(num_imgs)]
inside_flag_list = []
for (img_id, img_meta) i... | ['def', 'get_sampled_approxs(self,', 'featmap_sizes,', 'img_metas,', "device='cuda'):", 'num_imgs', '=', 'len(img_metas)', 'multi_level_approxs', '=', 'self.approx_anchor_generator.grid_anchors(featmap_sizes,', 'device=device)', 'approxs_list', '=', '[multi_level_approxs', 'for', '_', 'in', 'range(num_imgs)]', 'inside_... | 836,135 |
tensorly/quantum | noisy_expectation_op_test.py | NoisyExpectationCalculationTest.test_single_channel | test_single_channel | Individually test adding just a single channel type to circuits. | [
"Individually",
"test",
"adding",
"just",
"a",
"single",
"channel",
"type",
"to",
"circuits."
] | def test_single_channel(self, channel):
symbol_names = []
batch_size = 5
n_qubits = 6
qubits = cirq.LineQubit.range(n_qubits)
(circuit_batch, resolver_batch) = util.random_circuit_resolver_batch(qubits, batch_size, include_channels=False)
for i in range(batch_size):
circuit_batch[i] = ci... | ['def', 'test_single_channel(self,', 'channel):', 'symbol_names', '=', '[]', 'batch_size', '=', '5', 'n_qubits', '=', '6', 'qubits', '=', 'cirq.LineQubit.range(n_qubits)', '(circuit_batch,', 'resolver_batch)', '=', 'util.random_circuit_resolver_batch(qubits,', 'batch_size,', 'include_channels=False)', 'for', 'i', 'in',... | 834,846 |
fudan-zvg/SeaFormer | hub.py | get_cache_dir | get_cache_dir | Returns the location of the directory where models are cached (and creates it if necessary). | [
"Returns",
"the",
"location",
"of",
"the",
"directory",
"where",
"models",
"are",
"cached",
"(and",
"creates",
"it",
"if",
"necessary)."
] | def get_cache_dir(child_dir=''):
if os.getenv('TORCH_MODEL_ZOO'):
_logger.warning('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')
hub_dir = get_dir()
child_dir = () if not child_dir else (child_dir,)
model_dir = os.path.join(hub_dir, 'checkpoints', *child_dir)
os.makedirs... | ['def', "get_cache_dir(child_dir=''):", 'if', "os.getenv('TORCH_MODEL_ZOO'):", "_logger.warning('TORCH_MODEL_ZOO", 'is', 'deprecated,', 'please', 'use', 'env', 'TORCH_HOME', "instead')", 'hub_dir', '=', 'get_dir()', 'child_dir', '=', '()', 'if', 'not', 'child_dir', 'else', '(child_dir,)', 'model_dir', '=', 'os.path.joi... | 855,487 |
deepmind/dm_control | rodent.py | Rat.ground_contact_geoms | ground_contact_geoms | Return ground contact geoms. | [
"Return",
"ground",
"contact",
"geoms."
] | def ground_contact_geoms(self):
return tuple(self._mjcf_root.find('body', 'foot_L').find_all('geom') + self._mjcf_root.find('body', 'foot_R').find_all('geom') + self._mjcf_root.find('body', 'hand_L').find_all('geom') + self._mjcf_root.find('body', 'hand_R').find_all('geom') + self._mjcf_root.find('body', 'vertebra_... | ['def', 'ground_contact_geoms(self):', 'return', "tuple(self._mjcf_root.find('body',", "'foot_L').find_all('geom')", '+', "self._mjcf_root.find('body',", "'foot_R').find_all('geom')", '+', "self._mjcf_root.find('body',", "'hand_L').find_all('geom')", '+', "self._mjcf_root.find('body',", "'hand_R').find_all('geom')", '+... | 165,145 |
Levantespot/UDA_for_RS | ohem_pixel_sampler.py | OHEMPixelSampler.sample | sample | Sample pixels that have high loss or with low prediction confidence. | [
"Sample",
"pixels",
"that",
"have",
"high",
"loss",
"or",
"with",
"low",
"prediction",
"confidence."
] | def sample(self, seg_logit, seg_label):
with torch.no_grad():
assert seg_logit.shape[2:] == seg_label.shape[2:]
assert seg_label.shape[1] == 1
seg_label = seg_label.squeeze(1).long()
batch_kept = self.min_kept * seg_label.size(0)
valid_mask = seg_label != self.context.ignore_... | ['def', 'sample(self,', 'seg_logit,', 'seg_label):', 'with', 'torch.no_grad():', 'assert', 'seg_logit.shape[2:]', '==', 'seg_label.shape[2:]', 'assert', 'seg_label.shape[1]', '==', '1', 'seg_label', '=', 'seg_label.squeeze(1).long()', 'batch_kept', '=', 'self.min_kept', '*', 'seg_label.size(0)', 'valid_mask', '=', 'seg... | 947,333 |
ldamewood/renormalization | polyfit.py | polyfit | polyfit | Fit a polynomial using LinearRegression model. | [
"Fit",
"a",
"polynomial",
"using",
"LinearRegression",
"model."
] | def polyfit(xdata, ydata, deg=2, linearMethod=NormalSVD()):
if deg < 1:
raise ValueError('Polynomial degree must be > 1')
return linearMethod.getWeights(_xpoly(xdata, deg), ydata) | ['def', 'polyfit(xdata,', 'ydata,', 'deg=2,', 'linearMethod=NormalSVD()):', 'if', 'deg', '<', '1:', 'raise', "ValueError('Polynomial", 'degree', 'must', 'be', '>', "1')", 'return', 'linearMethod.getWeights(_xpoly(xdata,', 'deg),', 'ydata)'] | 840,220 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | data_utils.py | Vocabulary.encode | encode | Convert a sentence to a list of ids, with special tokens added. | [
"Convert",
"a",
"sentence",
"to",
"a",
"list",
"of",
"ids,",
"with",
"special",
"tokens",
"added."
] | def encode(self, sentence):
word_ids = [self.word_to_id(cur_word) for cur_word in sentence.split()]
return np.array([self.bos] + word_ids + [self.eos], dtype=np.int32) | ['def', 'encode(self,', 'sentence):', 'word_ids', '=', '[self.word_to_id(cur_word)', 'for', 'cur_word', 'in', 'sentence.split()]', 'return', 'np.array([self.bos]', '+', 'word_ids', '+', '[self.eos],', 'dtype=np.int32)'] | 49,972 |
megvii-research/TreeEnergyLoss | video_helper.py | VideoReader.width | width | int: Width of video frames. | [
"int:",
"Width",
"of",
"video",
"frames."
] | def width(self):
return self._width | ['def', 'width(self):', 'return', 'self._width'] | 951,447 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | req_uninstall.py | StashedUninstallPathSet.commit | commit | Commits the uninstall by removing stashed files. | [
"Commits",
"the",
"uninstall",
"by",
"removing",
"stashed",
"files."
] | def commit(self):
for (_, save_dir) in self._save_dirs.items():
save_dir.cleanup()
self._moves = []
self._save_dirs = {} | ['def', 'commit(self):', 'for', '(_,', 'save_dir)', 'in', 'self._save_dirs.items():', 'save_dir.cleanup()', 'self._moves', '=', '[]', 'self._save_dirs', '=', '{}'] | 950,100 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | _pydecimal.py | Decimal.exp | exp | Returns e ** self. | [
"Returns",
"e",
"**",
"self."
] | def exp(self, context=None):
if context is None:
context = getcontext()
ans = self._check_nans(context=context)
if ans:
return ans
if self._isinfinity() == -1:
return _Zero
if not self:
return _One
if self._isinfinity() == 1:
return Decimal(self)
p = c... | ['def', 'exp(self,', 'context=None):', 'if', 'context', 'is', 'None:', 'context', '=', 'getcontext()', 'ans', '=', 'self._check_nans(context=context)', 'if', 'ans:', 'return', 'ans', 'if', 'self._isinfinity()', '==', '-1:', 'return', '_Zero', 'if', 'not', 'self:', 'return', '_One', 'if', 'self._isinfinity()', '==', '1:... | 429,980 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | pydoc.py | stripid | stripid | Remove the hexadecimal id from a Python object representation. | [
"Remove",
"the",
"hexadecimal",
"id",
"from",
"a",
"Python",
"object",
"representation."
] | def stripid(text):
return _re_stripid.sub('\\1', text) | ['def', 'stripid(text):', 'return', "_re_stripid.sub('\\\\1',", 'text)'] | 429,281 |
43Carrig/recurrent_neural_networks_practice | ops.py | get_all_collection_keys | get_all_collection_keys | Returns a list of collections used in the default graph. | [
"Returns",
"a",
"list",
"of",
"collections",
"used",
"in",
"the",
"default",
"graph."
] | def get_all_collection_keys():
return get_default_graph().get_all_collection_keys() | ['def', 'get_all_collection_keys():', 'return', 'get_default_graph().get_all_collection_keys()'] | 336,362 |
GregorKobsik/Octree-Transformer | kd_tree_test.py | TestQuadtree.test_token_sequence_retrival_short | test_token_sequence_retrival_short | Inserts a sequence representing a diagonal line and tries to retrive the same token sequence. | [
"Inserts",
"a",
"sequence",
"representing",
"a",
"diagonal",
"line",
"and",
"tries",
"to",
"retrive",
"the",
"same",
"token",
"sequence."
] | def test_token_sequence_retrival_short(self):
input = '1221' + '12211221'
qtree = kdTree(spatial_dim=2).insert_token_sequence(input, resolution=32)
output = qtree.get_token_sequence()[0]
self.assertEqual(len(input), len(output))
self.assertSequenceEqual(input, ''.join((str(x) for x in output))) | ['def', 'test_token_sequence_retrival_short(self):', 'input', '=', "'1221'", '+', "'12211221'", 'qtree', '=', 'kdTree(spatial_dim=2).insert_token_sequence(input,', 'resolution=32)', 'output', '=', 'qtree.get_token_sequence()[0]', 'self.assertEqual(len(input),', 'len(output))', 'self.assertSequenceEqual(input,', "''.joi... | 755,113 |
weimin17/Object-Detection_HelmetDetection | evaluation_utils.py | print_formatted | print_formatted | Print and log metrics. | [
"Print",
"and",
"log",
"metrics."
] | def print_formatted(present, id_to_word, log, batch_of_tuples):
num_cols = len(batch_of_tuples[0][0])
repeat_float_format = '{:<12.3f} '
repeat_str_format = '{:<13}'
format_str = ''.join(['[{:<1}] {:<20}', str(repeat_float_format * (num_cols - 1))])
header_format_str = ''.join(['[{:<1}] {:<20}', s... | ['def', 'print_formatted(present,', 'id_to_word,', 'log,', 'batch_of_tuples):', 'num_cols', '=', 'len(batch_of_tuples[0][0])', 'repeat_float_format', '=', "'{:<12.3f}", "'", 'repeat_str_format', '=', "'{:<13}'", 'format_str', '=', "''.join(['[{:<1}]", "{:<20}',", 'str(repeat_float_format', '*', '(num_cols', '-', '1))])... | 763,657 |
tueimage/essential-skills | scrollview.py | ScrollView.slice_index | slice_index | The index of the slice that is currently shown in the plot. | [
"The",
"index",
"of",
"the",
"slice",
"that",
"is",
"currently",
"shown",
"in",
"the",
"plot."
] | def slice_index(self):
return self._slice_index | ['def', 'slice_index(self):', 'return', 'self._slice_index'] | 563,381 |
43Carrig/recurrent_neural_networks_practice | plugin_event_multiplexer.py | EventMultiplexer.SummaryMetadata | SummaryMetadata | Return the summary metadata for the given tag on the given run. | [
"Return",
"the",
"summary",
"metadata",
"for",
"the",
"given",
"tag",
"on",
"the",
"given",
"run."
] | def SummaryMetadata(self, run, tag):
accumulator = self.GetAccumulator(run)
return accumulator.SummaryMetadata(tag) | ['def', 'SummaryMetadata(self,', 'run,', 'tag):', 'accumulator', '=', 'self.GetAccumulator(run)', 'return', 'accumulator.SummaryMetadata(tag)'] | 312,115 |
rudranil723/mini-main | dependencygraph.py | DependencyGraph.right_children | right_children | Returns the number of right children under the node specified by the given address. | [
"Returns",
"the",
"number",
"of",
"right",
"children",
"under",
"the",
"node",
"specified",
"by",
"the",
"given",
"address."
] | def right_children(self, node_index):
children = chain.from_iterable(self.nodes[node_index]['deps'].values())
index = self.nodes[node_index]['address']
return sum((1 for c in children if c > index)) | ['def', 'right_children(self,', 'node_index):', 'children', '=', "chain.from_iterable(self.nodes[node_index]['deps'].values())", 'index', '=', "self.nodes[node_index]['address']", 'return', 'sum((1', 'for', 'c', 'in', 'children', 'if', 'c', '>', 'index))'] | 321,455 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | encoder_manager.py | EncoderManager.load_model | load_model | Loads a skip-thoughts model. | [
"Loads",
"a",
"skip-thoughts",
"model."
] | def load_model(self, model_config, vocabulary_file, embedding_matrix_file, checkpoint_path):
tf.logging.info('Reading vocabulary from %s', vocabulary_file)
with tf.gfile.GFile(vocabulary_file, mode='r') as f:
lines = list(f.readlines())
reverse_vocab = [line.decode('utf-8').strip() for line in lines... | ['def', 'load_model(self,', 'model_config,', 'vocabulary_file,', 'embedding_matrix_file,', 'checkpoint_path):', "tf.logging.info('Reading", 'vocabulary', 'from', "%s',", 'vocabulary_file)', 'with', 'tf.gfile.GFile(vocabulary_file,', "mode='r')", 'as', 'f:', 'lines', '=', 'list(f.readlines())', 'reverse_vocab', '=', "[l... | 109,596 |
Center-of-Diagnostics-and-Telemedicine/ai-testing-platform | multi.py | MultiIndex.levshape | levshape | A tuple with the length of each level. | [
"A",
"tuple",
"with",
"the",
"length",
"of",
"each",
"level."
] | def levshape(self):
return tuple((len(x) for x in self.levels)) | ['def', 'levshape(self):', 'return', 'tuple((len(x)', 'for', 'x', 'in', 'self.levels))'] | 82,953 |
zedom1/nlp | inferer.py | Inferer.start | start | Runs the whole inferring process. | [
"Runs",
"the",
"whole",
"inferring",
"process."
] | def start(self):
self.logger.info('start inferring...')
(is_exist, infer_file) = self.get_infer_file()
if is_exist:
self.logger.info('file {} exists, skipping.'.format(infer_file))
self.model.evaluate(infer_file, from_file=True)
return None
all_res = []
for (i, batch) in enum... | ['def', 'start(self):', "self.logger.info('start", "inferring...')", '(is_exist,', 'infer_file)', '=', 'self.get_infer_file()', 'if', 'is_exist:', "self.logger.info('file", '{}', 'exists,', "skipping.'.format(infer_file))", 'self.model.evaluate(infer_file,', 'from_file=True)', 'return', 'None', 'all_res', '=', '[]', 'f... | 808,448 |
sunishsheth2009/ChatterBot | mcore.py | Matcher.term_matchers | term_matchers | Returns an iterator of term matchers in this tree. | [
"Returns",
"an",
"iterator",
"of",
"term",
"matchers",
"in",
"this",
"tree."
] | def term_matchers(self):
if self.term() is not None:
yield self
else:
for cm in self.children():
for m in cm.term_matchers():
yield m | ['def', 'term_matchers(self):', 'if', 'self.term()', 'is', 'not', 'None:', 'yield', 'self', 'else:', 'for', 'cm', 'in', 'self.children():', 'for', 'm', 'in', 'cm.term_matchers():', 'yield', 'm'] | 484,550 |
43Carrig/recurrent_neural_networks_practice | control_flow_ops.py | CondContext.BuildCondBranch | BuildCondBranch | Add the subgraph defined by fn() to the graph. | [
"Add",
"the",
"subgraph",
"defined",
"by",
"fn()",
"to",
"the",
"graph."
] | def BuildCondBranch(self, fn):
pre_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION)
original_result = fn()
post_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION)
if len(post_summaries) > len(pre_summaries):
new_summaries = post_summaries[len(pre_summaries):]
... | ['def', 'BuildCondBranch(self,', 'fn):', 'pre_summaries', '=', 'ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION)', 'original_result', '=', 'fn()', 'post_summaries', '=', 'ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION)', 'if', 'len(post_summaries)', '>', 'len(pre_summaries):', 'new_summaries', '=', 'post_sum... | 337,171 |
thaines/helit | dataset.py | Dataset.getLabels | getLabels | Returns a list of all the labels in the data set. | [
"Returns",
"a",
"list",
"of",
"all",
"the",
"labels",
"in",
"the",
"data",
"set."
] | def getLabels(self):
return self.numToLabel | ['def', 'getLabels(self):', 'return', 'self.numToLabel'] | 592,466 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | nb_102a.py | activ_to_bbox | activ_to_bbox | Extrapolate bounding boxes on anchors from the model activations. | [
"Extrapolate",
"bounding",
"boxes",
"on",
"anchors",
"from",
"the",
"model",
"activations."
] | def activ_to_bbox(acts, anchors, flatten=True):
if flatten:
acts.mul_(acts.new_tensor([[0.1, 0.1, 0.2, 0.2]]))
centers = anchors[..., 2:] * acts[..., :2] + anchors[..., :2]
sizes = anchors[..., 2:] * torch.exp(acts[..., :2])
return torch.cat([centers, sizes], -1)
else:
re... | ['def', 'activ_to_bbox(acts,', 'anchors,', 'flatten=True):', 'if', 'flatten:', 'acts.mul_(acts.new_tensor([[0.1,', '0.1,', '0.2,', '0.2]]))', 'centers', '=', 'anchors[...,', '2:]', '*', 'acts[...,', ':2]', '+', 'anchors[...,', ':2]', 'sizes', '=', 'anchors[...,', '2:]', '*', 'torch.exp(acts[...,', ':2])', 'return', 'to... | 32,608 |
Oneflow-Inc/vision | vision_helpers.py | make_grid | make_grid | Make a grid of images. | [
"Make",
"a",
"grid",
"of",
"images."
] | def make_grid(tensor: Union[flow.Tensor, List[flow.Tensor]], nrow: int=8, padding: int=2, normalize: bool=False, range: Optional[Tuple[int, int]]=None, scale_each: bool=False, pad_value: int=0) -> flow.Tensor:
if not (isinstance(tensor, flow.Tensor) or (isinstance(tensor, list) and all((isinstance(t, flow.Tensor) f... | ['def', 'make_grid(tensor:', 'Union[flow.Tensor,', 'List[flow.Tensor]],', 'nrow:', 'int=8,', 'padding:', 'int=2,', 'normalize:', 'bool=False,', 'range:', 'Optional[Tuple[int,', 'int]]=None,', 'scale_each:', 'bool=False,', 'pad_value:', 'int=0)', '->', 'flow.Tensor:', 'if', 'not', '(isinstance(tensor,', 'flow.Tensor)', ... | 957,637 |
researchmm/WSOD2 | lvis.py | LVISV05Dataset.evaluate | evaluate | Evaluation in LVIS protocol. | [
"Evaluation",
"in",
"LVIS",
"protocol."
] | def evaluate(self, results, metric='bbox', logger=None, jsonfile_prefix=None, classwise=False, proposal_nums=(100, 300, 1000), iou_thrs=np.arange(0.5, 0.96, 0.05)):
try:
import lvis
assert lvis.__version__ >= '10.5.3'
from lvis import LVISResults, LVISEval
except AssertionError:
... | ['def', 'evaluate(self,', 'results,', "metric='bbox',", 'logger=None,', 'jsonfile_prefix=None,', 'classwise=False,', 'proposal_nums=(100,', '300,', '1000),', 'iou_thrs=np.arange(0.5,', '0.96,', '0.05)):', 'try:', 'import', 'lvis', 'assert', 'lvis.__version__', '>=', "'10.5.3'", 'from', 'lvis', 'import', 'LVISResults,',... | 374,104 |
43Carrig/recurrent_neural_networks_practice | bijector_impl.py | Bijector.validate_args | validate_args | Returns True if Tensor arguments will be validated. | [
"Returns",
"True",
"if",
"Tensor",
"arguments",
"will",
"be",
"validated."
] | def validate_args(self):
return self._validate_args | ['def', 'validate_args(self):', 'return', 'self._validate_args'] | 339,160 |
tensorflow/agents | array_spec.py | BoundedArraySpec.minimum | minimum | Returns a NumPy array specifying the minimum bounds (inclusive). | [
"Returns",
"a",
"NumPy",
"array",
"specifying",
"the",
"minimum",
"bounds",
"(inclusive)."
] | def minimum(self):
return self._minimum | ['def', 'minimum(self):', 'return', 'self._minimum'] | 23,680 |
43Carrig/recurrent_neural_networks_practice | categorical_split_handler.py | EqualitySplitHandler.update_stats | update_stats | Updates the state for equality split handler. | [
"Updates",
"the",
"state",
"for",
"equality",
"split",
"handler."
] | def update_stats(self, stamp_token, example_partition_ids, gradients, hessians, empty_gradients, empty_hessians, weights, is_active, scheduled_reads):
del scheduled_reads
def not_active_inputs():
return (constant_op.constant([], dtype=dtypes.int32), constant_op.constant([], dtype=dtypes.int64, shape=[1... | ['def', 'update_stats(self,', 'stamp_token,', 'example_partition_ids,', 'gradients,', 'hessians,', 'empty_gradients,', 'empty_hessians,', 'weights,', 'is_active,', 'scheduled_reads):', 'del', 'scheduled_reads', 'def', 'not_active_inputs():', 'return', '(constant_op.constant([],', 'dtype=dtypes.int32),', 'constant_op.co... | 312,479 |
clips/pattern | __init__.py | Graph.edge | edge | Returns the edge between the nodes with given id1 and id2. | [
"Returns",
"the",
"edge",
"between",
"the",
"nodes",
"with",
"given",
"id1",
"and",
"id2."
] | def edge(self, id1, id2):
if isinstance(id1, Node) and id1.graph == self:
id1 = id1.id
if isinstance(id2, Node) and id2.graph == self:
id2 = id2.id
return id1 in self and id2 in self and self[id1].links.edge(id2) or None | ['def', 'edge(self,', 'id1,', 'id2):', 'if', 'isinstance(id1,', 'Node)', 'and', 'id1.graph', '==', 'self:', 'id1', '=', 'id1.id', 'if', 'isinstance(id2,', 'Node)', 'and', 'id2.graph', '==', 'self:', 'id2', '=', 'id2.id', 'return', 'id1', 'in', 'self', 'and', 'id2', 'in', 'self', 'and', 'self[id1].links.edge(id2)', 'or'... | 764,660 |
QData/deepWordBug | tty.py | Terminal.israw | israw | Returns True if the TTY should operate in raw mode. | [
"Returns",
"True",
"if",
"the",
"TTY",
"should",
"operate",
"in",
"raw",
"mode."
] | def israw(self):
return self.raw | ['def', 'israw(self):', 'return', 'self.raw'] | 541,986 |
masterkapilkumar/Unsupervised-Learning | utils.py | generate_images_helper | generate_images_helper | Helper function to visualize generated images from randomly sampled values from the latent space. | [
"Helper",
"function",
"to",
"visualize",
"generated",
"images",
"from",
"randomly",
"sampled",
"values",
"from",
"the",
"latent",
"space."
] | def generate_images_helper(net, epoch, label='After_'):
net.eval()
with torch.no_grad():
z = torch.randn(config.TEST_SAMPLES, config.LATENT_DIM).to(config.device)
(_, test_images) = net(z, encode=False, decode=True)
test_images = test_images.cpu().detach().numpy()
net.train()
... | ['def', 'generate_images_helper(net,', 'epoch,', "label='After_'):", 'net.eval()', 'with', 'torch.no_grad():', 'z', '=', 'torch.randn(config.TEST_SAMPLES,', 'config.LATENT_DIM).to(config.device)', '(_,', 'test_images)', '=', 'net(z,', 'encode=False,', 'decode=True)', 'test_images', '=', 'test_images.cpu().detach().nump... | 353,427 |
ganyeshprasanna/AI | heuristic_search.py | Grid.get_coin_locations | get_coin_locations | Returns a list of the coordinates of all coins. | [
"Returns",
"a",
"list",
"of",
"the",
"coordinates",
"of",
"all",
"coins."
] | def get_coin_locations(self):
coins = []
for x in range(self.width):
for y in range(self.height):
if self.is_coin(x, y):
coins.append((x, y))
return coins | ['def', 'get_coin_locations(self):', 'coins', '=', '[]', 'for', 'x', 'in', 'range(self.width):', 'for', 'y', 'in', 'range(self.height):', 'if', 'self.is_coin(x,', 'y):', 'coins.append((x,', 'y))', 'return', 'coins'] | 69,582 |
myothida/Supervised-Machine-Learning | arrayTools.py | vectorLength | vectorLength | Calculate the length of the given vector. | [
"Calculate",
"the",
"length",
"of",
"the",
"given",
"vector."
] | def vectorLength(vector):
(x, y) = vector
return math.sqrt(x ** 2 + y ** 2) | ['def', 'vectorLength(vector):', '(x,', 'y)', '=', 'vector', 'return', 'math.sqrt(x', '**', '2', '+', 'y', '**', '2)'] | 360,907 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | nb_007b.py | PoolingLinearClassifier.pool | pool | Pools the tensor along the seq_len dimension. | [
"Pools",
"the",
"tensor",
"along",
"the",
"seq_len",
"dimension."
] | def pool(self, x: Tensor, bs: int, is_max: bool):
f = F.adaptive_max_pool1d if is_max else F.adaptive_avg_pool1d
return f(x.permute(1, 2, 0), (1,)).view(bs, -1) | ['def', 'pool(self,', 'x:', 'Tensor,', 'bs:', 'int,', 'is_max:', 'bool):', 'f', '=', 'F.adaptive_max_pool1d', 'if', 'is_max', 'else', 'F.adaptive_avg_pool1d', 'return', 'f(x.permute(1,', '2,', '0),', '(1,)).view(bs,', '-1)'] | 81,801 |
hideyukiinada/transfer-learning | util_functions.py | gen_preds | gen_preds | Generates predictions on a novel data array using a fit classifier clf is a classifier that has already been fit arr is a data array identical in dimension to the array clf was trained on Returns the array of predictions. | [
"Generates",
"predictions",
"on",
"a",
"novel",
"data",
"array",
"using",
"a",
"fit",
"classifier",
"clf",
"is",
"a",
"classifier",
"that",
"has",
"already",
"been",
"fit",
"arr",
"is",
"a",
"data",
"array",
"identical",
"in",
"dimension",
"to",
"the",
"ar... | def gen_preds(clf, arr):
if hasattr(clf, 'predict_proba'):
ret = clf.predict(arr)
else:
ret = clf.predict(arr)
return ret | ['def', 'gen_preds(clf,', 'arr):', 'if', 'hasattr(clf,', "'predict_proba'):", 'ret', '=', 'clf.predict(arr)', 'else:', 'ret', '=', 'clf.predict(arr)', 'return', 'ret'] | 929,443 |
google-research/batch-ppo | batch_env.py | BatchEnv.reset | reset | Reset the environment and convert the resulting observation. | [
"Reset",
"the",
"environment",
"and",
"convert",
"the",
"resulting",
"observation."
] | def reset(self, indices=None):
if indices is None:
indices = np.arange(len(self._envs))
if self._blocking:
observs = [self._envs[index].reset() for index in indices]
else:
observs = [self._envs[index].reset(blocking=False) for index in indices]
observs = [observ() for observ ... | ['def', 'reset(self,', 'indices=None):', 'if', 'indices', 'is', 'None:', 'indices', '=', 'np.arange(len(self._envs))', 'if', 'self._blocking:', 'observs', '=', '[self._envs[index].reset()', 'for', 'index', 'in', 'indices]', 'else:', 'observs', '=', '[self._envs[index].reset(blocking=False)', 'for', 'index', 'in', 'indi... | 95,032 |
kornia/kornia | tiny_vit.py | TinyViT.from_config | from_config | Create a TinyViT model from pre-defined variants. | [
"Create",
"a",
"TinyViT",
"model",
"from",
"pre-defined",
"variants."
] | def from_config(variant: str, pretrained: bool | str=False, **kwargs: Any) -> TinyViT:
KORNIA_CHECK(variant in ('5m', '11m', '21m'), 'Only variant 5m, 11m, and 21m are supported')
return {'5m': _tiny_vit_5m, '11m': _tiny_vit_11m, '21m': _tiny_vit_21m}[variant](pretrained, **kwargs) | ['def', 'from_config(variant:', 'str,', 'pretrained:', 'bool', '|', 'str=False,', '**kwargs:', 'Any)', '->', 'TinyViT:', 'KORNIA_CHECK(variant', 'in', "('5m',", "'11m',", "'21m'),", "'Only", 'variant', '5m,', '11m,', 'and', '21m', 'are', "supported')", 'return', "{'5m':", '_tiny_vit_5m,', "'11m':", '_tiny_vit_11m,', "'... | 621,625 |
Kvatsx/Artificial-Intelligence-Assignments | _deprecated.py | ZMQIOLoop.current | current | Returns the current threadâÂÂs IOLoop. | [
"Returns",
"the",
"current",
"threadâÂÂs",
"IOLoop."
] | def current(cls, *args, **kwargs):
if tornado_version >= (3,):
PollIOLoop.configure(cls)
loop = PollIOLoop.current(*args, **kwargs)
if not isinstance(loop, cls):
warnings.warn('IOLoop.current expected instance of %r, got %r' % (cls, loop), RuntimeWarning, stacklevel=2)
return loop | ['def', 'current(cls,', '*args,', '**kwargs):', 'if', 'tornado_version', '>=', '(3,):', 'PollIOLoop.configure(cls)', 'loop', '=', 'PollIOLoop.current(*args,', '**kwargs)', 'if', 'not', 'isinstance(loop,', 'cls):', "warnings.warn('IOLoop.current", 'expected', 'instance', 'of', '%r,', 'got', "%r'", '%', '(cls,', 'loop),'... | 79,211 |
vivekchoksi/taxi-pickups | plot.py | Plotter.plotNumPickupsByZone | plotNumPickupsByZone | Plot a histogram showing the distribution of true number of pickups by zone. | [
"Plot",
"a",
"histogram",
"showing",
"the",
"distribution",
"of",
"true",
"number",
"of",
"pickups",
"by",
"zone."
] | def plotNumPickupsByZone(self):
num_pickups_by_zone = {}
num_pickups_list = []
for row in self.data:
zone_id = str(row['zone_id'])
num_pickups_by_zone[zone_id] = num_pickups_by_zone.get(zone_id, 0) + row['num_pickups']
for num_pickups in num_pickups_by_zone.values():
num_pickups_... | ['def', 'plotNumPickupsByZone(self):', 'num_pickups_by_zone', '=', '{}', 'num_pickups_list', '=', '[]', 'for', 'row', 'in', 'self.data:', 'zone_id', '=', "str(row['zone_id'])", 'num_pickups_by_zone[zone_id]', '=', 'num_pickups_by_zone.get(zone_id,', '0)', '+', "row['num_pickups']", 'for', 'num_pickups', 'in', 'num_pick... | 365,496 |
danamyu/hedgehog_detector | model.py | Model.sample_step | sample_step | Sample batch of steps from policy. | [
"Sample",
"batch",
"of",
"steps",
"from",
"policy."
] | def sample_step(self, sess, single_observation, internal_state, single_action, greedy=False):
if greedy:
outputs = [self.greedy_next_internal_state, self.greedy_sampled_actions]
else:
outputs = [self.next_internal_state, self.sampled_actions]
feed_dict = {self.internal_state: internal_state}... | ['def', 'sample_step(self,', 'sess,', 'single_observation,', 'internal_state,', 'single_action,', 'greedy=False):', 'if', 'greedy:', 'outputs', '=', '[self.greedy_next_internal_state,', 'self.greedy_sampled_actions]', 'else:', 'outputs', '=', '[self.next_internal_state,', 'self.sampled_actions]', 'feed_dict', '=', '{se... | 590,242 |
tobegit3hub/deep_image_model | dnn_linear_combined_test.py | DNNLinearCombinedClassifierTest.testLossWithWeights | testLossWithWeights | Tests loss calculation with weights. | [
"Tests",
"loss",
"calculation",
"with",
"weights."
] | def testLossWithWeights(self):
def _input_fn_train():
features = {'x': tf.ones(shape=[4, 1], dtype=tf.float32), 'w': tf.constant([[1.0], [1.0], [1.0], [1.0]])}
labels = tf.constant([[1.0], [0.0], [0.0], [0.0]])
return (features, labels)
def _input_fn_eval():
features = {'x': tf... | ['def', 'testLossWithWeights(self):', 'def', '_input_fn_train():', 'features', '=', "{'x':", 'tf.ones(shape=[4,', '1],', 'dtype=tf.float32),', "'w':", 'tf.constant([[1.0],', '[1.0],', '[1.0],', '[1.0]])}', 'labels', '=', 'tf.constant([[1.0],', '[0.0],', '[0.0],', '[0.0]])', 'return', '(features,', 'labels)', 'def', '_i... | 181,664 |
choasup/SIN | config.py | cfg_from_file | cfg_from_file | Load a config file and merge it into the default options. | [
"Load",
"a",
"config",
"file",
"and",
"merge",
"it",
"into",
"the",
"default",
"options."
] | def cfg_from_file(filename):
import yaml
with open(filename, 'r') as f:
yaml_cfg = edict(yaml.load(f))
_merge_a_into_b(yaml_cfg, __C) | ['def', 'cfg_from_file(filename):', 'import', 'yaml', 'with', 'open(filename,', "'r')", 'as', 'f:', 'yaml_cfg', '=', 'edict(yaml.load(f))', '_merge_a_into_b(yaml_cfg,', '__C)'] | 884,330 |
triaquae/triaquae | archive.py | extract | extract | Unpack the tar or zip file at the specified path to the directory specified by to_path. | [
"Unpack",
"the",
"tar",
"or",
"zip",
"file",
"at",
"the",
"specified",
"path",
"to",
"the",
"directory",
"specified",
"by",
"to_path."
] | def extract(path, to_path=''):
with Archive(path) as archive:
archive.extract(to_path) | ['def', 'extract(path,', "to_path=''):", 'with', 'Archive(path)', 'as', 'archive:', 'archive.extract(to_path)'] | 423,998 |
zihuitang/medical_AI_platform | __init__.py | Listbox.selection_set | selection_set | Set the selection from FIRST to LAST (included) without changing the currently selected elements. | [
"Set",
"the",
"selection",
"from",
"FIRST",
"to",
"LAST",
"(included)",
"without",
"changing",
"the",
"currently",
"selected",
"elements."
] | def selection_set(self, first, last=None):
self.tk.call(self._w, 'selection', 'set', first, last) | ['def', 'selection_set(self,', 'first,', 'last=None):', 'self.tk.call(self._w,', "'selection',", "'set',", 'first,', 'last)'] | 284,281 |
sunishsheth2009/ChatterBot | test_example.py | ViewTestCase.test_get_main_page | test_get_main_page | Test that the main page can be loaded. | [
"Test",
"that",
"the",
"main",
"page",
"can",
"be",
"loaded."
] | def test_get_main_page(self):
response = self.client.get(self.url)
self.assertEqual(response.status_code, 200) | ['def', 'test_get_main_page(self):', 'response', '=', 'self.client.get(self.url)', 'self.assertEqual(response.status_code,', '200)'] | 478,201 |
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