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 |
|---|---|---|---|---|---|---|---|---|
rudranil723/mini-main | autopep8.py | ReformattedLines.previous_item | previous_item | Return the previous non-whitespace item. | [
"Return",
"the",
"previous",
"non-whitespace",
"item."
] | def previous_item(self):
return self._prev_item | ['def', 'previous_item(self):', 'return', 'self._prev_item'] | 313,929 |
zplizzi/fusedprop | tf_fid_score.py | check_or_download_inception | check_or_download_inception | Checks if the path to the inception file is valid, or downloads the file if it is not present. | [
"Checks",
"if",
"the",
"path",
"to",
"the",
"inception",
"file",
"is",
"valid,",
"or",
"downloads",
"the",
"file",
"if",
"it",
"is",
"not",
"present."
] | def check_or_download_inception(inception_path):
INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
if inception_path is None:
inception_path = '/tmp'
inception_path = pathlib.Path(inception_path)
model_file = inception_path / 'classify_image_graph_de... | ['def', 'check_or_download_inception(inception_path):', 'INCEPTION_URL', '=', "'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'", 'if', 'inception_path', 'is', 'None:', 'inception_path', '=', "'/tmp'", 'inception_path', '=', 'pathlib.Path(inception_path)', 'model_file', '=', 'inception_pa... | 565,690 |
weimin17/Object-Detection_HelmetDetection | helper.py | recursive_length | recursive_length | Recursively determine the total number of elements in nested list. | [
"Recursively",
"determine",
"the",
"total",
"number",
"of",
"elements",
"in",
"nested",
"list."
] | def recursive_length(item):
if type(item) == list:
return sum((recursive_length(subitem) for subitem in item))
else:
return 1.0 | ['def', 'recursive_length(item):', 'if', 'type(item)', '==', 'list:', 'return', 'sum((recursive_length(subitem)', 'for', 'subitem', 'in', 'item))', 'else:', 'return', '1.0'] | 763,740 |
aws/sagemaker-python-sdk | utils.py | construct_container_object | construct_container_object | Function to construct container object. | [
"Function",
"to",
"construct",
"container",
"object."
] | def construct_container_object(obj, data_input_configuration, framework, framework_version, nearest_model_name):
if framework is not None:
obj.update({'Framework': framework})
if framework_version is not None:
obj.update({'FrameworkVersion': framework_version})
if nearest_model_name is not N... | ['def', 'construct_container_object(obj,', 'data_input_configuration,', 'framework,', 'framework_version,', 'nearest_model_name):', 'if', 'framework', 'is', 'not', 'None:', "obj.update({'Framework':", 'framework})', 'if', 'framework_version', 'is', 'not', 'None:', "obj.update({'FrameworkVersion':", 'framework_version})... | 829,733 |
facebookresearch/salina | ant.py | Ant.step | step | Run one timestep of the environment's dynamics. | [
"Run",
"one",
"timestep",
"of",
"the",
"environment's",
"dynamics."
] | def step(self, state: _env.State, action: jp.ndarray) -> _env.State:
action = action * self.action_mask
(qp, info) = self.sys.step(state.qp, action)
velocity = (qp.pos[0] - state.qp.pos[0]) / self.sys.config.dt
forward_reward = velocity[0]
(min_z, max_z) = self._healthy_z_range
is_healthy = jp.w... | ['def', 'step(self,', 'state:', '_env.State,', 'action:', 'jp.ndarray)', '->', '_env.State:', 'action', '=', 'action', '*', 'self.action_mask', '(qp,', 'info)', '=', 'self.sys.step(state.qp,', 'action)', 'velocity', '=', '(qp.pos[0]', '-', 'state.qp.pos[0])', '/', 'self.sys.config.dt', 'forward_reward', '=', 'velocity[... | 328,601 |
jonathanking/sidechainnet | errors.py | ProteinErrors.write_summary_files | write_summary_files | For all counted errors, writes the list of pnids with each error to the errors/ directory. | [
"For",
"all",
"counted",
"errors,",
"writes",
"the",
"list",
"of",
"pnids",
"with",
"each",
"error",
"to",
"the",
"errors/",
"directory."
] | def write_summary_files(self):
os.makedirs('errors/', exist_ok=True)
for e in self.get_error_names():
if len(self.get_pnids_with_error_name(e)) > 0:
with open(f'errors/{e}.txt', 'w') as f:
f.write('\n'.join(self.get_pnids_with_error_name(e)) + '\n') | ['def', 'write_summary_files(self):', "os.makedirs('errors/',", 'exist_ok=True)', 'for', 'e', 'in', 'self.get_error_names():', 'if', 'len(self.get_pnids_with_error_name(e))', '>', '0:', 'with', "open(f'errors/{e}.txt',", "'w')", 'as', 'f:', "f.write('\\n'.join(self.get_pnids_with_error_name(e))", '+', "'\\n')"] | 934,110 |
clovaai/assembled-cnn | hooks.py | ExamplesPerSecondHook.after_run | after_run | Called after each call to run(). | [
"Called",
"after",
"each",
"call",
"to",
"run()."
] | def after_run(self, run_context, run_values):
global_step = run_values.results
if self._timer.should_trigger_for_step(global_step) and global_step > self._warm_steps:
(elapsed_time, elapsed_steps) = self._timer.update_last_triggered_step(global_step)
if elapsed_time is not None:
self... | ['def', 'after_run(self,', 'run_context,', 'run_values):', 'global_step', '=', 'run_values.results', 'if', 'self._timer.should_trigger_for_step(global_step)', 'and', 'global_step', '>', 'self._warm_steps:', '(elapsed_time,', 'elapsed_steps)', '=', 'self._timer.update_last_triggered_step(global_step)', 'if', 'elapsed_ti... | 92,445 |
facebookresearch/CompilerGym | compiler_env_state.py | CompilerEnvState.has_reward | has_reward | Return whether the state has a reward value. | [
"Return",
"whether",
"the",
"state",
"has",
"a",
"reward",
"value."
] | def has_reward(self) -> bool:
return self.reward is not None | ['def', 'has_reward(self)', '->', 'bool:', 'return', 'self.reward', 'is', 'not', 'None'] | 125,335 |
TheCurryMan/MedicAI | tests.py | test_string | test_string | Return true if the object is a string. | [
"Return",
"true",
"if",
"the",
"object",
"is",
"a",
"string."
] | def test_string(value):
return isinstance(value, string_types) | ['def', 'test_string(value):', 'return', 'isinstance(value,', 'string_types)'] | 648,503 |
augmentedstartups/AS-One | distance.py | euclidean_squared_distance | euclidean_squared_distance | Computes euclidean squared distance. | [
"Computes",
"euclidean",
"squared",
"distance."
] | def euclidean_squared_distance(input1, input2):
(m, n) = (input1.size(0), input2.size(0))
mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)
mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat = mat1 + mat2
distmat.addmm_(input1, input2.t(), beta=1, alpha=-2)... | ['def', 'euclidean_squared_distance(input1,', 'input2):', '(m,', 'n)', '=', '(input1.size(0),', 'input2.size(0))', 'mat1', '=', 'torch.pow(input1,', '2).sum(dim=1,', 'keepdim=True).expand(m,', 'n)', 'mat2', '=', 'torch.pow(input2,', '2).sum(dim=1,', 'keepdim=True).expand(n,', 'm).t()', 'distmat', '=', 'mat1', '+', 'mat... | 402,429 |
rifqind/Agent-Programs-3KS1 | transform_test.py | TransformModuleTest.test_average_surfaces__subclassed_surfaces | test_average_surfaces__subclassed_surfaces | Ensure average_surfaces accepts subclassed surfaces. | [
"Ensure",
"average_surfaces",
"accepts",
"subclassed",
"surfaces."
] | def test_average_surfaces__subclassed_surfaces(self):
expected_size = (23, 17)
expected_flags = 0
expected_depth = 32
expected_color = (50, 50, 50, 255)
surfaces = []
for color in ((40, 60, 40), (60, 40, 60)):
s = test_utils.SurfaceSubclass(expected_size, expected_flags, expected_depth)
... | ['def', 'test_average_surfaces__subclassed_surfaces(self):', 'expected_size', '=', '(23,', '17)', 'expected_flags', '=', '0', 'expected_depth', '=', '32', 'expected_color', '=', '(50,', '50,', '50,', '255)', 'surfaces', '=', '[]', 'for', 'color', 'in', '((40,', '60,', '40),', '(60,', '40,', '60)):', 's', '=', 'test_uti... | 46,009 |
sek788432/Waymo-2D-Object-Detection | replay_buffer.py | PrioritizedReplayBuffer.get_batch | get_batch | Get batch of episodes to train on. | [
"Get",
"batch",
"of",
"episodes",
"to",
"train",
"on."
] | def get_batch(self, n):
p = self.sampling_distribution()
idxs = np.random.choice(self.cur_size, size=int(n), replace=False, p=p)
self.last_batch = idxs
return ([self.buffer[idx] for idx in idxs], p[idxs]) | ['def', 'get_batch(self,', 'n):', 'p', '=', 'self.sampling_distribution()', 'idxs', '=', 'np.random.choice(self.cur_size,', 'size=int(n),', 'replace=False,', 'p=p)', 'self.last_batch', '=', 'idxs', 'return', '([self.buffer[idx]', 'for', 'idx', 'in', 'idxs],', 'p[idxs])'] | 975,674 |
zzndream/ShipRSImageNet | auto_augment.py | enhance_level_to_value | enhance_level_to_value | Map from level to values. | [
"Map",
"from",
"level",
"to",
"values."
] | def enhance_level_to_value(level, a=1.8, b=0.1):
return level / _MAX_LEVEL * a + b | ['def', 'enhance_level_to_value(level,', 'a=1.8,', 'b=0.1):', 'return', 'level', '/', '_MAX_LEVEL', '*', 'a', '+', 'b'] | 901,258 |
myothida/Supervised-Machine-Learning | test_polynomial.py | test_spline_transformer_periodic_splines_smoothness | test_spline_transformer_periodic_splines_smoothness | Test that spline transformation is smooth at first / last knot. | [
"Test",
"that",
"spline",
"transformation",
"is",
"smooth",
"at",
"first",
"/",
"last",
"knot."
] | def test_spline_transformer_periodic_splines_smoothness(degree):
X = np.linspace(-2, 10, 10000)[:, None]
transformer = SplineTransformer(degree=degree, extrapolation='periodic', knots=[[0.0], [1.0], [3.0], [4.0], [5.0], [8.0]])
Xt = transformer.fit_transform(X)
delta = (X.max() - X.min()) / len(X)
t... | ['def', 'test_spline_transformer_periodic_splines_smoothness(degree):', 'X', '=', 'np.linspace(-2,', '10,', '10000)[:,', 'None]', 'transformer', '=', 'SplineTransformer(degree=degree,', "extrapolation='periodic',", 'knots=[[0.0],', '[1.0],', '[3.0],', '[4.0],', '[5.0],', '[8.0]])', 'Xt', '=', 'transformer.fit_transform... | 364,576 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | info.py | TableBuilderAbstract.memory_usage_string | memory_usage_string | Memory usage string with proper size qualifier. | [
"Memory",
"usage",
"string",
"with",
"proper",
"size",
"qualifier."
] | def memory_usage_string(self) -> str:
return self.info.memory_usage_string | ['def', 'memory_usage_string(self)', '->', 'str:', 'return', 'self.info.memory_usage_string'] | 453,544 |
avisingh599/reward-learning-rl | gym_adapter.py | GymAdapter.active_observation_shape | active_observation_shape | Shape for the active observation based on observation_keys. | [
"Shape",
"for",
"the",
"active",
"observation",
"based",
"on",
"observation_keys."
] | def active_observation_shape(self):
if not isinstance(self._env.observation_space, spaces.Dict):
return super(GymAdapter, self).active_observation_shape
active_size = sum((np.prod(self._env.observation_space.spaces[key].shape) for key in self.observation_keys))
active_observation_shape = (active_siz... | ['def', 'active_observation_shape(self):', 'if', 'not', 'isinstance(self._env.observation_space,', 'spaces.Dict):', 'return', 'super(GymAdapter,', 'self).active_observation_shape', 'active_size', '=', 'sum((np.prod(self._env.observation_space.spaces[key].shape)', 'for', 'key', 'in', 'self.observation_keys))', 'active_o... | 348,734 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | lstm.py | lstm_seq2seq_internal_attention_bid_encoder | lstm_seq2seq_internal_attention_bid_encoder | LSTM seq2seq model with attention, main step used for training. | [
"LSTM",
"seq2seq",
"model",
"with",
"attention,",
"main",
"step",
"used",
"for",
"training."
] | def lstm_seq2seq_internal_attention_bid_encoder(inputs, targets, hparams, train):
with tf.variable_scope('lstm_seq2seq_attention_bid_encoder'):
inputs_length = common_layers.length_from_embedding(inputs)
inputs = common_layers.flatten4d3d(inputs)
(encoder_outputs, final_encoder_state) = lstm... | ['def', 'lstm_seq2seq_internal_attention_bid_encoder(inputs,', 'targets,', 'hparams,', 'train):', 'with', "tf.variable_scope('lstm_seq2seq_attention_bid_encoder'):", 'inputs_length', '=', 'common_layers.length_from_embedding(inputs)', 'inputs', '=', 'common_layers.flatten4d3d(inputs)', '(encoder_outputs,', 'final_encod... | 965,646 |
caiiiac/Machine-Learning-with-Python | axis_artist.py | Ticks.get_ticksize | get_ticksize | Return length of the ticks in points. | [
"Return",
"length",
"of",
"the",
"ticks",
"in",
"points."
] | def get_ticksize(self):
return self._ticksize | ['def', 'get_ticksize(self):', 'return', 'self._ticksize'] | 716,802 |
ludwig-ai/ludwig | mnist.py | MNISTLoader.load_unprocessed_dataframe | load_unprocessed_dataframe | Load dataset files into a dataframe. | [
"Load",
"dataset",
"files",
"into",
"a",
"dataframe."
] | def load_unprocessed_dataframe(self, file_paths: List[str]) -> pd.DataFrame:
return self.output_training_and_test_data() | ['def', 'load_unprocessed_dataframe(self,', 'file_paths:', 'List[str])', '->', 'pd.DataFrame:', 'return', 'self.output_training_and_test_data()'] | 616,701 |
ashwanitanwar/nmt-transfer-learning-xlm-r | test_noising.py | TestDataNoising.assert_word_shuffle_matches_expected | assert_word_shuffle_matches_expected | This verifies that with a given x, x_len, max_shuffle_distance, and vocab, we get the expected shuffle result. | [
"This",
"verifies",
"that",
"with",
"a",
"given",
"x,",
"x_len,",
"max_shuffle_distance,",
"and",
"vocab,",
"we",
"get",
"the",
"expected",
"shuffle",
"result."
] | def assert_word_shuffle_matches_expected(self, x, x_len, max_shuffle_distance: int, vocab: Dictionary, expected_shufle_maps: List[Dict[int, int]], expect_eos_at_end: bool, bpe_end_marker=None):
bpe_cont_marker = None
if bpe_end_marker is None:
bpe_cont_marker = '@@'
with data_utils.numpy_seed(1234):... | ['def', 'assert_word_shuffle_matches_expected(self,', 'x,', 'x_len,', 'max_shuffle_distance:', 'int,', 'vocab:', 'Dictionary,', 'expected_shufle_maps:', 'List[Dict[int,', 'int]],', 'expect_eos_at_end:', 'bool,', 'bpe_end_marker=None):', 'bpe_cont_marker', '=', 'None', 'if', 'bpe_end_marker', 'is', 'None:', 'bpe_cont_ma... | 732,809 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | __init__.py | PhotoImage.cget | cget | Return the value of OPTION. | [
"Return",
"the",
"value",
"of",
"OPTION."
] | def cget(self, option):
return self.tk.call(self.name, 'cget', '-' + option) | ['def', 'cget(self,', 'option):', 'return', 'self.tk.call(self.name,', "'cget',", "'-'", '+', 'option)'] | 377,099 |
clips/pattern | metrics.py | cdf | cdf | Returns the cumulative distribution function at x. | [
"Returns",
"the",
"cumulative",
"distribution",
"function",
"at",
"x."
] | def cdf(x, mean=0.0, stdev=1.0):
return min(1.0, 0.5 * erfc((-x + mean) / (stdev * 2 ** 0.5))) | ['def', 'cdf(x,', 'mean=0.0,', 'stdev=1.0):', 'return', 'min(1.0,', '0.5', '*', 'erfc((-x', '+', 'mean)', '/', '(stdev', '*', '2', '**', '0.5)))'] | 764,533 |
ryu-ed/SpaceInvaders_Ros | test_print.py | test_complex_inf_nan | test_complex_inf_nan | Check inf/nan formatting of complex types. | [
"Check",
"inf/nan",
"formatting",
"of",
"complex",
"types."
] | def test_complex_inf_nan(dtype):
TESTS = {complex(np.inf, 0): '(inf+0j)', complex(0, np.inf): 'infj', complex(-np.inf, 0): '(-inf+0j)', complex(0, -np.inf): '-infj', complex(np.inf, 1): '(inf+1j)', complex(1, np.inf): '(1+infj)', complex(-np.inf, 1): '(-inf+1j)', complex(1, -np.inf): '(1-infj)', complex(np.nan, 0):... | ['def', 'test_complex_inf_nan(dtype):', 'TESTS', '=', '{complex(np.inf,', '0):', "'(inf+0j)',", 'complex(0,', 'np.inf):', "'infj',", 'complex(-np.inf,', '0):', "'(-inf+0j)',", 'complex(0,', '-np.inf):', "'-infj',", 'complex(np.inf,', '1):', "'(inf+1j)',", 'complex(1,', 'np.inf):', "'(1+infj)',", 'complex(-np.inf,', '1)... | 396,397 |
zihuitang/medical_AI_platform | test_main.py | TestMain.setup_test_source_trees | setup_test_source_trees | Setup a test source tree and output destination tree. | [
"Setup",
"a",
"test",
"source",
"tree",
"and",
"output",
"destination",
"tree."
] | def setup_test_source_trees(self):
self.temp_dir = tempfile.mkdtemp()
self.py2_src_dir = os.path.join(self.temp_dir, 'python2_project')
self.py3_dest_dir = os.path.join(self.temp_dir, 'python3_project')
os.mkdir(self.py2_src_dir)
os.mkdir(self.py3_dest_dir)
self.setup_files = []
open(os.path... | ['def', 'setup_test_source_trees(self):', 'self.temp_dir', '=', 'tempfile.mkdtemp()', 'self.py2_src_dir', '=', 'os.path.join(self.temp_dir,', "'python2_project')", 'self.py3_dest_dir', '=', 'os.path.join(self.temp_dir,', "'python3_project')", 'os.mkdir(self.py2_src_dir)', 'os.mkdir(self.py3_dest_dir)', 'self.setup_file... | 283,019 |
thu-ml/tianshou | base.py | BasePolicy.soft_update | soft_update | Softly update the parameters of target module towards the parameters of source module. | [
"Softly",
"update",
"the",
"parameters",
"of",
"target",
"module",
"towards",
"the",
"parameters",
"of",
"source",
"module."
] | def soft_update(self, tgt: nn.Module, src: nn.Module, tau: float) -> None:
for (tgt_param, src_param) in zip(tgt.parameters(), src.parameters()):
tgt_param.data.copy_(tau * src_param.data + (1 - tau) * tgt_param.data) | ['def', 'soft_update(self,', 'tgt:', 'nn.Module,', 'src:', 'nn.Module,', 'tau:', 'float)', '->', 'None:', 'for', '(tgt_param,', 'src_param)', 'in', 'zip(tgt.parameters(),', 'src.parameters()):', 'tgt_param.data.copy_(tau', '*', 'src_param.data', '+', '(1', '-', 'tau)', '*', 'tgt_param.data)'] | 355,246 |
intra2net/guibot | test_calibrator.py | CalibratorTest.test_benchmark_text | test_benchmark_text | Check that benchmarking of OCR backends produces correct results. | [
"Check",
"that",
"benchmarking",
"of",
"OCR",
"backends",
"produces",
"correct",
"results."
] | def test_benchmark_text(self):
self.benchmark_setUp()
calibrator = Calibrator(Text('Text'), Image('all_shapes'))
for (calibration, random_starts) in [(False, 0), (False, 1), (True, 0), (True, 1)]:
finder = TextFinder()
finder.algorithms['threshold_filters2'] = ('adaptive',)
finder.al... | ['def', 'test_benchmark_text(self):', 'self.benchmark_setUp()', 'calibrator', '=', "Calibrator(Text('Text'),", "Image('all_shapes'))", 'for', '(calibration,', 'random_starts)', 'in', '[(False,', '0),', '(False,', '1),', '(True,', '0),', '(True,', '1)]:', 'finder', '=', 'TextFinder()', "finder.algorithms['threshold_filt... | 572,607 |
paniabhisek/AlexNet | model.py | AlexNet.get_summary_writer | get_summary_writer | Get summary writer for training and validation Responsible for creating summary writer so it can write summaries to a file so it can be read by tensorboard later. | [
"Get",
"summary",
"writer",
"for",
"training",
"and",
"validation",
"Responsible",
"for",
"creating",
"summary",
"writer",
"so",
"it",
"can",
"write",
"summaries",
"to",
"a",
"file",
"so",
"it",
"can",
"be",
"read",
"by",
"tensorboard",
"later."
] | def get_summary_writer(self, sess):
if not os.path.exists(os.path.join('summary', 'train')):
os.makedirs(os.path.join('summary', 'train'))
if not os.path.exists(os.path.join('summary', 'val')):
os.makedirs(os.path.join('summary', 'val'))
return (tf.summary.FileWriter(os.path.join(os.getcwd()... | ['def', 'get_summary_writer(self,', 'sess):', 'if', 'not', "os.path.exists(os.path.join('summary',", "'train')):", "os.makedirs(os.path.join('summary',", "'train'))", 'if', 'not', "os.path.exists(os.path.join('summary',", "'val')):", "os.makedirs(os.path.join('summary',", "'val'))", 'return', '(tf.summary.FileWriter(os... | 32,902 |
43Carrig/recurrent_neural_networks_practice | distribute_coordinator.py | _Barrier.wait | wait | Waits until all other callers reach the same wait call. | [
"Waits",
"until",
"all",
"other",
"callers",
"reach",
"the",
"same",
"wait",
"call."
] | def wait(self):
if not hasattr(self._local_sense, 'value'):
self._local_sense.value = False
self._local_sense.value = not self._flag
with self._lock:
self._counter += 1
if self._counter == self._num_participants:
self._counter = 0
self._flag = self._local_sens... | ['def', 'wait(self):', 'if', 'not', 'hasattr(self._local_sense,', "'value'):", 'self._local_sense.value', '=', 'False', 'self._local_sense.value', '=', 'not', 'self._flag', 'with', 'self._lock:', 'self._counter', '+=', '1', 'if', 'self._counter', '==', 'self._num_participants:', 'self._counter', '=', '0', 'self._flag',... | 336,051 |
ArtificialIntelligenceToolkit/aitk.robots | compass.py | Compass.from_json | from_json | Set the settings from a device config. | [
"Set",
"the",
"settings",
"from",
"a",
"device",
"config."
] | def from_json(self, config):
valid_keys = set(['position', 'name', 'class'])
self.verify_config(valid_keys, config)
if 'name' in config:
self.name = config['name']
if 'position' in config:
self.position = config['position']
self.dist_from_center = distance(0, 0, self.position[0],... | ['def', 'from_json(self,', 'config):', 'valid_keys', '=', "set(['position',", "'name',", "'class'])", 'self.verify_config(valid_keys,', 'config)', 'if', "'name'", 'in', 'config:', 'self.name', '=', "config['name']", 'if', "'position'", 'in', 'config:', 'self.position', '=', "config['position']", 'self.dist_from_center'... | 86,744 |
muhanzhang/D-VAE | test_basic.py | copymod | copymod | Return dct but with the keys named by args removed, and with kwargs added. | [
"Return",
"dct",
"but",
"with",
"the",
"keys",
"named",
"by",
"args",
"removed,",
"and",
"with",
"kwargs",
"added."
] | def copymod(dct, without=None, **kwargs):
if without is None:
without = []
rval = copy(dct)
for a in without:
if a in rval:
del rval[a]
for (kw, val) in iteritems(kwargs):
rval[kw] = val
return rval | ['def', 'copymod(dct,', 'without=None,', '**kwargs):', 'if', 'without', 'is', 'None:', 'without', '=', '[]', 'rval', '=', 'copy(dct)', 'for', 'a', 'in', 'without:', 'if', 'a', 'in', 'rval:', 'del', 'rval[a]', 'for', '(kw,', 'val)', 'in', 'iteritems(kwargs):', 'rval[kw]', '=', 'val', 'return', 'rval'] | 525,785 |
Farama-Foundation/Gymnasium | test_order_enforcing.py | test_order_enforcing | test_order_enforcing | Checks that the order enforcing works as expected, raising an error before reset is called and not after. | [
"Checks",
"that",
"the",
"order",
"enforcing",
"works",
"as",
"expected,",
"raising",
"an",
"error",
"before",
"reset",
"is",
"called",
"and",
"not",
"after."
] | def test_order_enforcing():
env = CartPoleEnv(render_mode='rgb_array_list')
assert not has_wrapper(env, OrderEnforcing)
order_enforced_env = OrderEnforcing(env)
assert order_enforced_env.has_reset is False
with pytest.raises(ResetNeeded):
order_enforced_env.step(0)
with pytest.raises(Res... | ['def', 'test_order_enforcing():', 'env', '=', "CartPoleEnv(render_mode='rgb_array_list')", 'assert', 'not', 'has_wrapper(env,', 'OrderEnforcing)', 'order_enforced_env', '=', 'OrderEnforcing(env)', 'assert', 'order_enforced_env.has_reset', 'is', 'False', 'with', 'pytest.raises(ResetNeeded):', 'order_enforced_env.step(0... | 573,679 |
deepmind/meltingpot | chemistry__two_metabolic_cycles_with_distractors.py | make_graph | make_graph | User defined graph construction function using networkx. | [
"User",
"defined",
"graph",
"construction",
"function",
"using",
"networkx."
] | def make_graph():
g = nx.MultiDiGraph()
graph_utils.add_system_nodes(g)
cycle(g, 'R', intermediates=['ax', 'bx', 'cx'], product='x', secondary_product='iy', food='food1')
cycle(g, 'R', intermediates=['ay', 'by', 'cy'], product='y', secondary_product='ix', food='food2')
null(g, 'Holding', 'distractor... | ['def', 'make_graph():', 'g', '=', 'nx.MultiDiGraph()', 'graph_utils.add_system_nodes(g)', 'cycle(g,', "'R',", "intermediates=['ax',", "'bx',", "'cx'],", "product='x',", "secondary_product='iy',", "food='food1')", 'cycle(g,', "'R',", "intermediates=['ay',", "'by',", "'cy'],", "product='y',", "secondary_product='ix',", ... | 285,282 |
ludwig-ai/ludwig | llm.py | LLM.get_target_ids | get_target_ids | Returns the output ids for the text feature output. | [
"Returns",
"the",
"output",
"ids",
"for",
"the",
"text",
"feature",
"output."
] | def get_target_ids(self, outputs: Dict[str, torch.Tensor]) -> torch.Tensor:
return outputs[self.config_obj.output_features[0].name].type(torch.int32) | ['def', 'get_target_ids(self,', 'outputs:', 'Dict[str,', 'torch.Tensor])', '->', 'torch.Tensor:', 'return', 'outputs[self.config_obj.output_features[0].name].type(torch.int32)'] | 616,873 |
RasaHQ/rasa | test_common.py | test_cli_missing_log_level_env_var_used | test_cli_missing_log_level_env_var_used | Test CLI without log level uses env var for both rasa and libraries. | [
"Test",
"CLI",
"without",
"log",
"level",
"uses",
"env",
"var",
"for",
"both",
"rasa",
"and",
"libraries."
] | def test_cli_missing_log_level_env_var_used():
configure_logging_and_warnings()
rasa_logger = logging.getLogger('rasa')
assert rasa_logger.level == logging.WARNING
matplotlib_logger = logging.getLogger('matplotlib')
assert matplotlib_logger.level == logging.INFO | ['def', 'test_cli_missing_log_level_env_var_used():', 'configure_logging_and_warnings()', 'rasa_logger', '=', "logging.getLogger('rasa')", 'assert', 'rasa_logger.level', '==', 'logging.WARNING', 'matplotlib_logger', '=', "logging.getLogger('matplotlib')", 'assert', 'matplotlib_logger.level', '==', 'logging.INFO'] | 838,106 |
tobegit3hub/deep_image_model | svm.py | SVM.predict | predict | Runs inference to determine the predicted class. | [
"Runs",
"inference",
"to",
"determine",
"the",
"predicted",
"class."
] | def predict(self, x=None, input_fn=None, batch_size=None, as_iterable=True):
key = prediction_key.PredictionKey.CLASSES
preds = self._estimator.predict(x=x, input_fn=input_fn, batch_size=batch_size, outputs=[key], as_iterable=as_iterable)
if as_iterable:
return _as_iterable(preds, output=key)
re... | ['def', 'predict(self,', 'x=None,', 'input_fn=None,', 'batch_size=None,', 'as_iterable=True):', 'key', '=', 'prediction_key.PredictionKey.CLASSES', 'preds', '=', 'self._estimator.predict(x=x,', 'input_fn=input_fn,', 'batch_size=batch_size,', 'outputs=[key],', 'as_iterable=as_iterable)', 'if', 'as_iterable:', 'return', ... | 181,804 |
instadeepai/jumanji | conftest.py | cvrp_sparse_reward | cvrp_sparse_reward | Instantiates a CVRP environment with sparse rewards and 5 nodes, maximum capacity of 3 and maximum demand of 2. | [
"Instantiates",
"a",
"CVRP",
"environment",
"with",
"sparse",
"rewards",
"and",
"5",
"nodes,",
"maximum",
"capacity",
"of",
"3",
"and",
"maximum",
"demand",
"of",
"2."
] | def cvrp_sparse_reward(sparse_reward: SparseReward) -> CVRP:
return CVRP(generator=UniformGenerator(num_nodes=5, max_capacity=3, max_demand=2), reward_fn=sparse_reward) | ['def', 'cvrp_sparse_reward(sparse_reward:', 'SparseReward)', '->', 'CVRP:', 'return', 'CVRP(generator=UniformGenerator(num_nodes=5,', 'max_capacity=3,', 'max_demand=2),', 'reward_fn=sparse_reward)'] | 594,345 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | mesh_tensorflow.py | parallel | parallel | Call a function once on each device. | [
"Call",
"a",
"function",
"once",
"on",
"each",
"device."
] | def parallel(devices, fn, *args, **kwargs):
if not isinstance(devices, list):
raise ValueError('devices must be a list')
for x in list(args) + list(six.itervalues(kwargs)):
if not isinstance(x, list) or len(x) != len(devices):
raise ValueError('Argument not a list with same length as... | ['def', 'parallel(devices,', 'fn,', '*args,', '**kwargs):', 'if', 'not', 'isinstance(devices,', 'list):', 'raise', "ValueError('devices", 'must', 'be', 'a', "list')", 'for', 'x', 'in', 'list(args)', '+', 'list(six.itervalues(kwargs)):', 'if', 'not', 'isinstance(x,', 'list)', 'or', 'len(x)', '!=', 'len(devices):', 'rais... | 965,481 |
open-mmlab/mmsegmentation | clip_model.py | ResidualAttentionBlock.forward_dense | forward_dense | Reinplementation of forward function for dense prediction of image encoder in CLIP model. | [
"Reinplementation",
"of",
"forward",
"function",
"for",
"dense",
"prediction",
"of",
"image",
"encoder",
"in",
"CLIP",
"model."
] | def forward_dense(self, x: torch.Tensor):
y = self.ln_1(x)
y = F.linear(y, self.attn.in_proj_weight, self.attn.in_proj_bias)
(L, N, D) = y.shape
y = y.reshape(L, N, 3, D // 3).permute(2, 1, 0, 3).reshape(3 * N, L, D // 3)
y = F.linear(y, self.attn.out_proj.weight, self.attn.out_proj.bias)
(q, k,... | ['def', 'forward_dense(self,', 'x:', 'torch.Tensor):', 'y', '=', 'self.ln_1(x)', 'y', '=', 'F.linear(y,', 'self.attn.in_proj_weight,', 'self.attn.in_proj_bias)', '(L,', 'N,', 'D)', '=', 'y.shape', 'y', '=', 'y.reshape(L,', 'N,', '3,', 'D', '//', '3).permute(2,', '1,', '0,', '3).reshape(3', '*', 'N,', 'L,', 'D', '//', '... | 625,538 |
myothida/Supervised-Machine-Learning | backend_bases.py | GraphicsContextBase.copy_properties | copy_properties | Copy properties from *gc* to self. | [
"Copy",
"properties",
"from",
"*gc*",
"to",
"self."
] | def copy_properties(self, gc):
self._alpha = gc._alpha
self._forced_alpha = gc._forced_alpha
self._antialiased = gc._antialiased
self._capstyle = gc._capstyle
self._cliprect = gc._cliprect
self._clippath = gc._clippath
self._dashes = gc._dashes
self._joinstyle = gc._joinstyle
self._l... | ['def', 'copy_properties(self,', 'gc):', 'self._alpha', '=', 'gc._alpha', 'self._forced_alpha', '=', 'gc._forced_alpha', 'self._antialiased', '=', 'gc._antialiased', 'self._capstyle', '=', 'gc._capstyle', 'self._cliprect', '=', 'gc._cliprect', 'self._clippath', '=', 'gc._clippath', 'self._dashes', '=', 'gc._dashes', 's... | 361,704 |
yuantn/MI-AOD | mask_scoring_roi_head.py | MaskScoringRoIHead.simple_test_mask | simple_test_mask | Obtain mask prediction without augmentation. | [
"Obtain",
"mask",
"prediction",
"without",
"augmentation."
] | def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False):
ori_shape = img_metas[0]['ori_shape']
scale_factor = img_metas[0]['scale_factor']
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes)]
mask_scores = [[] for _ in range(self.... | ['def', 'simple_test_mask(self,', 'x,', 'img_metas,', 'det_bboxes,', 'det_labels,', 'rescale=False):', 'ori_shape', '=', "img_metas[0]['ori_shape']", 'scale_factor', '=', "img_metas[0]['scale_factor']", 'if', 'det_bboxes.shape[0]', '==', '0:', 'segm_result', '=', '[[]', 'for', '_', 'in', 'range(self.mask_head.num_class... | 635,313 |
eddylau328/fyp-artificial-intelligence-ac-control-device | face.py | StreamStreamMultiCallable.event | event | Asynchronously invokes the underlying RPC. | [
"Asynchronously",
"invokes",
"the",
"underlying",
"RPC."
] | def event(self, receiver, abortion_callback, timeout, metadata=None, protocol_options=None):
raise NotImplementedError() | ['def', 'event(self,', 'receiver,', 'abortion_callback,', 'timeout,', 'metadata=None,', 'protocol_options=None):', 'raise', 'NotImplementedError()'] | 215,699 |
instadeepai/jumanji | utils_test.py | test_board_size_6 | test_board_size_6 | Validate that various actions can be performed on a 6x6 game board. | [
"Validate",
"that",
"various",
"actions",
"can",
"be",
"performed",
"on",
"a",
"6x6",
"game",
"board."
] | def test_board_size_6(board6x6: Board) -> None:
(board_up, reward) = move_up(board6x6)
expected_board = jnp.array([[3, 1, 2, 3, 1, 2], [3, 4, 1, 0, 4, 6], [3, 4, 0, 0, 1, 1], [0, 3, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]])
assert jnp.array_equal(expected_board, board_up)
assert reward == 14... | ['def', 'test_board_size_6(board6x6:', 'Board)', '->', 'None:', '(board_up,', 'reward)', '=', 'move_up(board6x6)', 'expected_board', '=', 'jnp.array([[3,', '1,', '2,', '3,', '1,', '2],', '[3,', '4,', '1,', '0,', '4,', '6],', '[3,', '4,', '0,', '0,', '1,', '1],', '[0,', '3,', '0,', '0,', '0,', '2],', '[0,', '0,', '0,', ... | 594,039 |
megvii-research/MSCL | download.py | create_video_folders | create_video_folders | Creates a directory for each label name in the dataset. | [
"Creates",
"a",
"directory",
"for",
"each",
"label",
"name",
"in",
"the",
"dataset."
] | def create_video_folders(dataset, output_dir, tmp_dir):
if 'label-name' not in dataset.columns:
this_dir = os.path.join(output_dir, 'test')
if not os.path.exists(this_dir):
os.makedirs(this_dir)
return this_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir... | ['def', 'create_video_folders(dataset,', 'output_dir,', 'tmp_dir):', 'if', "'label-name'", 'not', 'in', 'dataset.columns:', 'this_dir', '=', 'os.path.join(output_dir,', "'test')", 'if', 'not', 'os.path.exists(this_dir):', 'os.makedirs(this_dir)', 'return', 'this_dir', 'if', 'not', 'os.path.exists(output_dir):', 'os.mak... | 265,049 |
deepmind/meltingpot | paintball__capture_the_flag.py | build | build | Build substrate definition given player roles. | [
"Build",
"substrate",
"definition",
"given",
"player",
"roles."
] | def build(roles: Sequence[str], config: config_dict.ConfigDict) -> Mapping[str, Any]:
num_players = len(roles)
substrate_definition = dict(levelName='paintball__capture_the_flag', levelDirectory='meltingpot/lua/levels', numPlayers=num_players, maxEpisodeLengthFrames=1000, spriteSize=8, topology='BOUNDED', simul... | ['def', 'build(roles:', 'Sequence[str],', 'config:', 'config_dict.ConfigDict)', '->', 'Mapping[str,', 'Any]:', 'num_players', '=', 'len(roles)', 'substrate_definition', '=', "dict(levelName='paintball__capture_the_flag',", "levelDirectory='meltingpot/lua/levels',", 'numPlayers=num_players,', 'maxEpisodeLengthFrames=100... | 285,770 |
yihui-he/KL-Loss | config.py | cache_cfg_urls | cache_cfg_urls | Download URLs in the config, cache them locally, and rewrite cfg to make use of the locally cached file. | [
"Download",
"URLs",
"in",
"the",
"config,",
"cache",
"them",
"locally,",
"and",
"rewrite",
"cfg",
"to",
"make",
"use",
"of",
"the",
"locally",
"cached",
"file."
] | def cache_cfg_urls():
__C.TRAIN.WEIGHTS = cache_url(__C.TRAIN.WEIGHTS, __C.DOWNLOAD_CACHE)
__C.TEST.WEIGHTS = cache_url(__C.TEST.WEIGHTS, __C.DOWNLOAD_CACHE)
__C.TRAIN.PROPOSAL_FILES = tuple((cache_url(f, __C.DOWNLOAD_CACHE) for f in __C.TRAIN.PROPOSAL_FILES))
__C.TEST.PROPOSAL_FILES = tuple((cache_url(... | ['def', 'cache_cfg_urls():', '__C.TRAIN.WEIGHTS', '=', 'cache_url(__C.TRAIN.WEIGHTS,', '__C.DOWNLOAD_CACHE)', '__C.TEST.WEIGHTS', '=', 'cache_url(__C.TEST.WEIGHTS,', '__C.DOWNLOAD_CACHE)', '__C.TRAIN.PROPOSAL_FILES', '=', 'tuple((cache_url(f,', '__C.DOWNLOAD_CACHE)', 'for', 'f', 'in', '__C.TRAIN.PROPOSAL_FILES))', '__C... | 596,437 |
sony/nnabla-rl | test_icml2015_trpo.py | TestICML2015TRPO.test_run_online_training | test_run_online_training | Check that no error occurs when calling online training. | [
"Check",
"that",
"no",
"error",
"occurs",
"when",
"calling",
"online",
"training."
] | def test_run_online_training(self):
dummy_env = E.DummyDiscreteImg()
dummy_env = EpisodicEnv(dummy_env, min_episode_length=3)
config = A.ICML2015TRPOConfig(batch_size=5, gpu_batch_size=2, num_steps_per_iteration=5, sigma_kl_divergence_constraint=10.0, maximum_backtrack_numbers=2)
trpo = A.ICML2015TRPO(d... | ['def', 'test_run_online_training(self):', 'dummy_env', '=', 'E.DummyDiscreteImg()', 'dummy_env', '=', 'EpisodicEnv(dummy_env,', 'min_episode_length=3)', 'config', '=', 'A.ICML2015TRPOConfig(batch_size=5,', 'gpu_batch_size=2,', 'num_steps_per_iteration=5,', 'sigma_kl_divergence_constraint=10.0,', 'maximum_backtrack_num... | 727,378 |
Ruturaj123/Flowchart-Detection | vgslspecs_test.py | VgslspecsTest.testScalingOps | testScalingOps | Test a heterogeneous series with scaling. | [
"Test",
"a",
"heterogeneous",
"series",
"with",
"scaling."
] | def testScalingOps(self):
self.ExpectScaledSize('[Cs5,5,16 Mp{MyPool}2,2 Ct3,3,32 Mp3,3 Lfx32 Lry64]', (self.batch_size, self.max_height / 6, self.max_width / 6, 64), 6) | ['def', 'testScalingOps(self):', "self.ExpectScaledSize('[Cs5,5,16", 'Mp{MyPool}2,2', 'Ct3,3,32', 'Mp3,3', 'Lfx32', "Lry64]',", '(self.batch_size,', 'self.max_height', '/', '6,', 'self.max_width', '/', '6,', '64),', '6)'] | 586,530 |
ryu-ed/SpaceInvaders_Ros | _constraints.py | strict_bounds | strict_bounds | Remove bounds which are not asked to be kept feasible. | [
"Remove",
"bounds",
"which",
"are",
"not",
"asked",
"to",
"be",
"kept",
"feasible."
] | def strict_bounds(lb, ub, keep_feasible, n_vars):
strict_lb = np.resize(lb, n_vars).astype(float)
strict_ub = np.resize(ub, n_vars).astype(float)
keep_feasible = np.resize(keep_feasible, n_vars)
strict_lb[~keep_feasible] = -np.inf
strict_ub[~keep_feasible] = np.inf
return (strict_lb, strict_ub) | ['def', 'strict_bounds(lb,', 'ub,', 'keep_feasible,', 'n_vars):', 'strict_lb', '=', 'np.resize(lb,', 'n_vars).astype(float)', 'strict_ub', '=', 'np.resize(ub,', 'n_vars).astype(float)', 'keep_feasible', '=', 'np.resize(keep_feasible,', 'n_vars)', 'strict_lb[~keep_feasible]', '=', '-np.inf', 'strict_ub[~keep_feasible]',... | 370,615 |
enuguru/artificial_intelligence_and_machine_learning | searching.py | Searcher.doc_count | doc_count | Returns the number of UNDELETED documents in the index. | [
"Returns",
"the",
"number",
"of",
"UNDELETED",
"documents",
"in",
"the",
"index."
] | def doc_count(self):
return self.ixreader.doc_count() | ['def', 'doc_count(self):', 'return', 'self.ixreader.doc_count()'] | 162,198 |
suarez12138/AI-Reversi_IMP_TextDichotomy | canonical_constraint.py | CanonicalConstraint.from_PreparedConstraint | from_PreparedConstraint | Create an instance from `PreparedConstrained` object. | [
"Create",
"an",
"instance",
"from",
"`PreparedConstrained`",
"object."
] | def from_PreparedConstraint(cls, constraint):
(lb, ub) = constraint.bounds
cfun = constraint.fun
keep_feasible = constraint.keep_feasible
if np.all(lb == -np.inf) and np.all(ub == np.inf):
return cls.empty(cfun.n)
if np.all(lb == -np.inf) and np.all(ub == np.inf):
return cls.empty(cf... | ['def', 'from_PreparedConstraint(cls,', 'constraint):', '(lb,', 'ub)', '=', 'constraint.bounds', 'cfun', '=', 'constraint.fun', 'keep_feasible', '=', 'constraint.keep_feasible', 'if', 'np.all(lb', '==', '-np.inf)', 'and', 'np.all(ub', '==', 'np.inf):', 'return', 'cls.empty(cfun.n)', 'if', 'np.all(lb', '==', '-np.inf)',... | 99,940 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | sysconfig_cpython.py | get_makefile_filename | get_makefile_filename | Return full pathname of installed Makefile from the Python build. | [
"Return",
"full",
"pathname",
"of",
"installed",
"Makefile",
"from",
"the",
"Python",
"build."
] | def get_makefile_filename():
if python_build:
return os.path.join(_sys_home or project_base, 'Makefile')
lib_dir = get_python_lib(plat_specific=0, standard_lib=1)
config_file = 'config-{}{}'.format(get_python_version(), build_flags)
return os.path.join(lib_dir, config_file, 'Makefile') | ['def', 'get_makefile_filename():', 'if', 'python_build:', 'return', 'os.path.join(_sys_home', 'or', 'project_base,', "'Makefile')", 'lib_dir', '=', 'get_python_lib(plat_specific=0,', 'standard_lib=1)', 'config_file', '=', "'config-{}{}'.format(get_python_version(),", 'build_flags)', 'return', 'os.path.join(lib_dir,', ... | 430,361 |
devashish-patel/webcam-motion-detector | surrogateescape.py | replace_surrogate_encode | replace_surrogate_encode | Returns a (unicode) string, not the more logical bytes, because the codecs register_error functionality expects this. | [
"Returns",
"a",
"(unicode)",
"string,",
"not",
"the",
"more",
"logical",
"bytes,",
"because",
"the",
"codecs",
"register_error",
"functionality",
"expects",
"this."
] | def replace_surrogate_encode(mystring):
decoded = []
for ch in mystring:
code = ord(ch)
if not 55296 <= code <= 56575:
raise exc
if 56320 <= code <= 56447:
decoded.append(_unichr(code - 56320))
elif code <= 56575:
decoded.append(_unichr(code - ... | ['def', 'replace_surrogate_encode(mystring):', 'decoded', '=', '[]', 'for', 'ch', 'in', 'mystring:', 'code', '=', 'ord(ch)', 'if', 'not', '55296', '<=', 'code', '<=', '56575:', 'raise', 'exc', 'if', '56320', '<=', 'code', '<=', '56447:', 'decoded.append(_unichr(code', '-', '56320))', 'elif', 'code', '<=', '56575:', 'de... | 978,253 |
RasaHQ/rasa | common.py | clean_duplicates | clean_duplicates | Removes keys for empty values. | [
"Removes",
"keys",
"for",
"empty",
"values."
] | def clean_duplicates(dupes: Dict[Text, Any]) -> Dict[Text, Any]:
duplicates = dupes.copy()
for k in dupes:
if not dupes[k]:
duplicates.pop(k)
return duplicates | ['def', 'clean_duplicates(dupes:', 'Dict[Text,', 'Any])', '->', 'Dict[Text,', 'Any]:', 'duplicates', '=', 'dupes.copy()', 'for', 'k', 'in', 'dupes:', 'if', 'not', 'dupes[k]:', 'duplicates.pop(k)', 'return', 'duplicates'] | 837,786 |
tensorflow/quantum | serializer_test.py | SerializerTest.test_serialize_deserialize_circuit_consistency | test_serialize_deserialize_circuit_consistency | Ensure that serializing followed by deserializing works. | [
"Ensure",
"that",
"serializing",
"followed",
"by",
"deserializing",
"works."
] | def test_serialize_deserialize_circuit_consistency(self, circ_proto_pair):
self.assertProtoEquals(serializer.serialize_circuit(serializer.deserialize_circuit(circ_proto_pair[1])), circ_proto_pair[1])
self.assertEqual(serializer.deserialize_circuit(serializer.serialize_circuit(circ_proto_pair[0])), circ_proto_pa... | ['def', 'test_serialize_deserialize_circuit_consistency(self,', 'circ_proto_pair):', 'self.assertProtoEquals(serializer.serialize_circuit(serializer.deserialize_circuit(circ_proto_pair[1])),', 'circ_proto_pair[1])', 'self.assertEqual(serializer.deserialize_circuit(serializer.serialize_circuit(circ_proto_pair[0])),', 'c... | 835,020 |
weimin17/Object-Detection_HelmetDetection | data_download.py | compile_files | compile_files | Compile raw files into a single file for each language. | [
"Compile",
"raw",
"files",
"into",
"a",
"single",
"file",
"for",
"each",
"language."
] | def compile_files(raw_dir, raw_files, tag):
tf.logging.info('Compiling files with tag %s.' % tag)
filename = '%s-%s' % (_PREFIX, tag)
input_compiled_file = os.path.join(raw_dir, filename + '.lang1')
target_compiled_file = os.path.join(raw_dir, filename + '.lang2')
with tf.gfile.Open(input_compiled_f... | ['def', 'compile_files(raw_dir,', 'raw_files,', 'tag):', "tf.logging.info('Compiling", 'files', 'with', 'tag', "%s.'", '%', 'tag)', 'filename', '=', "'%s-%s'", '%', '(_PREFIX,', 'tag)', 'input_compiled_file', '=', 'os.path.join(raw_dir,', 'filename', '+', "'.lang1')", 'target_compiled_file', '=', 'os.path.join(raw_dir,... | 761,149 |
apeterswu/RL4NMT | common_layers.py | smoothing_cross_entropy | smoothing_cross_entropy | Cross entropy with label smoothing to limit over-confidence. | [
"Cross",
"entropy",
"with",
"label",
"smoothing",
"to",
"limit",
"over-confidence."
] | def smoothing_cross_entropy(logits, labels, vocab_size, confidence, use_focal_loss=False, focal_loss_gamma=0.0, gaussian=False):
with tf.name_scope('smoothing_cross_entropy', [logits, labels]):
low_confidence = (1.0 - confidence) / tf.to_float(vocab_size - 1)
normalizing = -(confidence * tf.log(conf... | ['def', 'smoothing_cross_entropy(logits,', 'labels,', 'vocab_size,', 'confidence,', 'use_focal_loss=False,', 'focal_loss_gamma=0.0,', 'gaussian=False):', 'with', "tf.name_scope('smoothing_cross_entropy',", '[logits,', 'labels]):', 'low_confidence', '=', '(1.0', '-', 'confidence)', '/', 'tf.to_float(vocab_size', '-', '1... | 331,068 |
AgnostiqHQ/covalent | transport_test.py | test_transport_graph_get_dependencies | test_transport_graph_get_dependencies | Test the graph node retrieval method in the transport graph. | [
"Test",
"the",
"graph",
"node",
"retrieval",
"method",
"in",
"the",
"transport",
"graph."
] | def test_transport_graph_get_dependencies(workflow_transport_graph):
wtg = workflow_transport_graph
assert not list(wtg.get_dependencies(node_key=0))
assert not list(wtg.get_dependencies(node_key=1))
wtg.add_edge(x=0, y=1, edge_name='apples')
assert not list(wtg.get_dependencies(node_key=0))
ass... | ['def', 'test_transport_graph_get_dependencies(workflow_transport_graph):', 'wtg', '=', 'workflow_transport_graph', 'assert', 'not', 'list(wtg.get_dependencies(node_key=0))', 'assert', 'not', 'list(wtg.get_dependencies(node_key=1))', 'wtg.add_edge(x=0,', 'y=1,', "edge_name='apples')", 'assert', 'not', 'list(wtg.get_dep... | 489,949 |
edsonbollis/Weakly-Supervised-Learning-Citrus-Pest-Benchmark | instance-database-generator.py | guided_backprop | guided_backprop | Guided Backpropagation method for visualizing input saliency. | [
"Guided",
"Backpropagation",
"method",
"for",
"visualizing",
"input",
"saliency."
] | def guided_backprop(input_model, images):
input_imgs = input_model.input
layer_output = input_model.get_layer(layer_name).output
grads = K.gradients(layer_output, input_imgs)[0]
backprop_fn = K.function([input_imgs, K.learning_phase()], [grads])
grads_val = backprop_fn([images, 0])[0]
return gra... | ['def', 'guided_backprop(input_model,', 'images):', 'input_imgs', '=', 'input_model.input', 'layer_output', '=', 'input_model.get_layer(layer_name).output', 'grads', '=', 'K.gradients(layer_output,', 'input_imgs)[0]', 'backprop_fn', '=', 'K.function([input_imgs,', 'K.learning_phase()],', '[grads])', 'grads_val', '=', '... | 373,321 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjrContextWrapper.rangeFont | rangeFont | all characters in font. | [
"all",
"characters",
"in",
"font."
] | def rangeFont(self):
return self._ptr.contents.rangeFont | ['def', 'rangeFont(self):', 'return', 'self._ptr.contents.rangeFont'] | 440,652 |
sunishsheth2009/ChatterBot | schema.py | ChangesetColumn.alter | alter | Makes a call to :func:`alter_column` for the column this method is called on. | [
"Makes",
"a",
"call",
"to",
":func:`alter_column`",
"for",
"the",
"column",
"this",
"method",
"is",
"called",
"on."
] | def alter(self, *p, **k):
if 'table' not in k:
k['table'] = self.table
if 'engine' not in k:
k['engine'] = k['table'].bind
return alter_column(self, *p, **k) | ['def', 'alter(self,', '*p,', '**k):', 'if', "'table'", 'not', 'in', 'k:', "k['table']", '=', 'self.table', 'if', "'engine'", 'not', 'in', 'k:', "k['engine']", '=', "k['table'].bind", 'return', 'alter_column(self,', '*p,', '**k)'] | 529,650 |
openvinotoolkit/training_extensions | scored_label.py | ScoredLabel.get_label | get_label | Gets the label that the ScoredLabel object was initialized with. | [
"Gets",
"the",
"label",
"that",
"the",
"ScoredLabel",
"object",
"was",
"initialized",
"with."
] | def get_label(self) -> LabelEntity:
return self.label | ['def', 'get_label(self)', '->', 'LabelEntity:', 'return', 'self.label'] | 918,670 |
NifTK/NiftyNet | grid_warper.py | AffineWarpConstraints.shear_2d | shear_2d | Assigns constraints on shear components of affine transform in 2d. | [
"Assigns",
"constraints",
"on",
"shear",
"components",
"of",
"affine",
"transform",
"in",
"2d."
] | def shear_2d(cls, x=None, y=None):
return cls([[None, x, None], [y, None, None]]) | ['def', 'shear_2d(cls,', 'x=None,', 'y=None):', 'return', 'cls([[None,', 'x,', 'None],', '[y,', 'None,', 'None]])'] | 294,172 |
rtlee9/recipe-summarization | vocabulary-embedding.py | build_word_to_glove | build_word_to_glove | Map full vocabulary to glove based on cosine distance. | [
"Map",
"full",
"vocabulary",
"to",
"glove",
"based",
"on",
"cosine",
"distance."
] | def build_word_to_glove(embedding, word2idx, idx2word, glove_index_dict, glove_embedding_weights):
glove_thr = 0.5
word2glove = {}
for w in word2idx:
if w in glove_index_dict:
g = w
elif w.lower() in glove_index_dict:
g = w.lower()
elif w.startswith('#') and w... | ['def', 'build_word_to_glove(embedding,', 'word2idx,', 'idx2word,', 'glove_index_dict,', 'glove_embedding_weights):', 'glove_thr', '=', '0.5', 'word2glove', '=', '{}', 'for', 'w', 'in', 'word2idx:', 'if', 'w', 'in', 'glove_index_dict:', 'g', '=', 'w', 'elif', 'w.lower()', 'in', 'glove_index_dict:', 'g', '=', 'w.lower()... | 309,102 |
replit-archive/empythoned | inspect.py | trace | trace | Return a list of records for the stack below the current exception. | [
"Return",
"a",
"list",
"of",
"records",
"for",
"the",
"stack",
"below",
"the",
"current",
"exception."
] | def trace(context=1):
return getinnerframes(sys.exc_info()[2], context) | ['def', 'trace(context=1):', 'return', 'getinnerframes(sys.exc_info()[2],', 'context)'] | 176,369 |
bhateharsh/computer_vision | tps.py | GridGenerator.forward | forward | Generate the grid for the grid_sampler. | [
"Generate",
"the",
"grid",
"for",
"the",
"grid_sampler."
] | def forward(self, batch_C_prime, I_r_size):
C = self.build_C_paddle()
P = self.build_P_paddle(I_r_size)
inv_delta_C_tensor = self.build_inv_delta_C_paddle(C).astype('float32')
P_hat_tensor = self.build_P_hat_paddle(C, paddle.to_tensor(P)).astype('float32')
inv_delta_C_tensor.stop_gradient = True
... | ['def', 'forward(self,', 'batch_C_prime,', 'I_r_size):', 'C', '=', 'self.build_C_paddle()', 'P', '=', 'self.build_P_paddle(I_r_size)', 'inv_delta_C_tensor', '=', "self.build_inv_delta_C_paddle(C).astype('float32')", 'P_hat_tensor', '=', 'self.build_P_hat_paddle(C,', "paddle.to_tensor(P)).astype('float32')", 'inv_delta_... | 502,307 |
devashish-patel/webcam-motion-detector | test_decorators.py | test_deliberately_broken2 | test_deliberately_broken2 | Another deliberately broken test - we want to skip this one. | [
"Another",
"deliberately",
"broken",
"test",
"-",
"we",
"want",
"to",
"skip",
"this",
"one."
] | def test_deliberately_broken2():
1 / 0 | ['def', 'test_deliberately_broken2():', '1', '/', '0'] | 979,358 |
Oporto/CS4341_Artificial_Inteligence | mask.py | Sprite.collide | collide | Test if the sprites are colliding and resolve the collision in this case. | [
"Test",
"if",
"the",
"sprites",
"are",
"colliding",
"and",
"resolve",
"the",
"collision",
"in",
"this",
"case."
] | def collide(self, s):
offset = [int(x) for x in vsub(s.pos, self.pos)]
overlap = self.mask.overlap_area(s.mask, offset)
if overlap == 0:
return
'Calculate collision normal'
nx = self.mask.overlap_area(s.mask, (offset[0] + 1, offset[1])) - self.mask.overlap_area(s.mask, (offset[0] - 1, offset... | ['def', 'collide(self,', 's):', 'offset', '=', '[int(x)', 'for', 'x', 'in', 'vsub(s.pos,', 'self.pos)]', 'overlap', '=', 'self.mask.overlap_area(s.mask,', 'offset)', 'if', 'overlap', '==', '0:', 'return', "'Calculate", 'collision', "normal'", 'nx', '=', 'self.mask.overlap_area(s.mask,', '(offset[0]', '+', '1,', 'offset... | 191,656 |
DeepGraphLearning/torchdrug | graph.py | Graph.node | node | Context manager for node attributes. | [
"Context",
"manager",
"for",
"node",
"attributes."
] | def node(self):
return self.context('node') | ['def', 'node(self):', 'return', "self.context('node')"] | 902,689 |
suarez12138/AI-Reversi_IMP_TextDichotomy | figure.py | _AxesStack.remove | remove | Remove the axes from the stack. | [
"Remove",
"the",
"axes",
"from",
"the",
"stack."
] | def remove(self, a):
super().remove(self._entry_from_axes(a)) | ['def', 'remove(self,', 'a):', 'super().remove(self._entry_from_axes(a))'] | 96,437 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | scan_ex2_solution.py | set_p_to_zero | set_p_to_zero | Provided utility function: given a symbolic vector of probabilities and an index 'i', set the probability of the i-th element to 0 and renormalize the probabilities so they sum to 1. | [
"Provided",
"utility",
"function:",
"given",
"a",
"symbolic",
"vector",
"of",
"probabilities",
"and",
"an",
"index",
"'i',",
"set",
"the",
"probability",
"of",
"the",
"i-th",
"element",
"to",
"0",
"and",
"renormalize",
"the",
"probabilities",
"so",
"they",
"su... | def set_p_to_zero(pvect, i):
new_pvect = T.set_subtensor(pvect[i], 0.0)
new_pvect = new_pvect / new_pvect.sum()
return new_pvect | ['def', 'set_p_to_zero(pvect,', 'i):', 'new_pvect', '=', 'T.set_subtensor(pvect[i],', '0.0)', 'new_pvect', '=', 'new_pvect', '/', 'new_pvect.sum()', 'return', 'new_pvect'] | 15,225 |
weimin17/Object-Detection_HelmetDetection | seq2seq_vd.py | discriminator | discriminator | Define the Discriminator graph. | [
"Define",
"the",
"Discriminator",
"graph."
] | def discriminator(hparams, inputs, targets_present, sequence, is_training, reuse=None):
if FLAGS.dis_share_embedding:
assert hparams.dis_rnn_size == hparams.gen_rnn_size, 'If you wish to share Discriminator/Generator embeddings, they must be same dimension.'
with tf.variable_scope('gen/decoder/rnn',... | ['def', 'discriminator(hparams,', 'inputs,', 'targets_present,', 'sequence,', 'is_training,', 'reuse=None):', 'if', 'FLAGS.dis_share_embedding:', 'assert', 'hparams.dis_rnn_size', '==', 'hparams.gen_rnn_size,', "'If", 'you', 'wish', 'to', 'share', 'Discriminator/Generator', 'embeddings,', 'they', 'must', 'be', 'same', ... | 763,723 |
CosmiQ/solaris | test_mask.py | TestBoundaryMask.test_make_inner_mask_from_fp | test_make_inner_mask_from_fp | test creating a boundary mask using an existing footprint mask. | [
"test",
"creating",
"a",
"boundary",
"mask",
"using",
"an",
"existing",
"footprint",
"mask."
] | def test_make_inner_mask_from_fp(self):
fp_mask = skimage.io.imread(os.path.join(data_dir, 'sample_fp_mask.tif'))
output_mask = boundary_mask(fp_mask)
truth_mask = skimage.io.imread(os.path.join(data_dir, 'sample_b_mask_inner.tif'))
assert np.array_equal(output_mask, truth_mask) | ['def', 'test_make_inner_mask_from_fp(self):', 'fp_mask', '=', 'skimage.io.imread(os.path.join(data_dir,', "'sample_fp_mask.tif'))", 'output_mask', '=', 'boundary_mask(fp_mask)', 'truth_mask', '=', 'skimage.io.imread(os.path.join(data_dir,', "'sample_b_mask_inner.tif'))", 'assert', 'np.array_equal(output_mask,', 'truth... | 879,455 |
trenton3983/Programming_Computer__with_Python | hcluster.py | ClusterNode.get_depth | get_depth | Return the depth of a node, depth is max of each child plus own distance. | [
"Return",
"the",
"depth",
"of",
"a",
"node,",
"depth",
"is",
"max",
"of",
"each",
"child",
"plus",
"own",
"distance."
] | def get_depth(self):
return max(self.left.get_depth(), self.right.get_depth()) + self.distance | ['def', 'get_depth(self):', 'return', 'max(self.left.get_depth(),', 'self.right.get_depth())', '+', 'self.distance'] | 817,373 |
shengwenliang/lpcvc2020_water | export_model.py | representative_dataset_gen | representative_dataset_gen | Gets a python generator of image numpy arrays for ImageNet. | [
"Gets",
"a",
"python",
"generator",
"of",
"image",
"numpy",
"arrays",
"for",
"ImageNet."
] | def representative_dataset_gen():
params = dict(batch_size=FLAGS.batch_size)
imagenet_eval = imagenet_input.ImageNetInput(is_training=False, data_dir=FLAGS.data_dir, transpose_input=False, cache=False, image_size=FLAGS.image_size, num_parallel_calls=1, use_bfloat16=False, include_background_label=True)
data... | ['def', 'representative_dataset_gen():', 'params', '=', 'dict(batch_size=FLAGS.batch_size)', 'imagenet_eval', '=', 'imagenet_input.ImageNetInput(is_training=False,', 'data_dir=FLAGS.data_dir,', 'transpose_input=False,', 'cache=False,', 'image_size=FLAGS.image_size,', 'num_parallel_calls=1,', 'use_bfloat16=False,', 'inc... | 615,859 |
tobegit3hub/deep_image_model | exporter.py | regression_signature | regression_signature | Creates a regression signature. | [
"Creates",
"a",
"regression",
"signature."
] | def regression_signature(input_tensor, output_tensor):
signature = manifest_pb2.Signature()
signature.regression_signature.input.tensor_name = input_tensor.name
signature.regression_signature.output.tensor_name = output_tensor.name
return signature | ['def', 'regression_signature(input_tensor,', 'output_tensor):', 'signature', '=', 'manifest_pb2.Signature()', 'signature.regression_signature.input.tensor_name', '=', 'input_tensor.name', 'signature.regression_signature.output.tensor_name', '=', 'output_tensor.name', 'return', 'signature'] | 181,991 |
brijeshiitg/XuNet-Structural-Design-of---Networksfor-Steganalysis | options.py | arguments | arguments | This function returns arguments. | [
"This",
"function",
"returns",
"arguments."
] | def arguments() -> str:
parser = argparse.ArgumentParser()
parser.add_argument('--cover_path', default='D:\\Github\\Toy-Bossbase-dataset\\bossbase_toy_dataset\\train\\cover')
parser.add_argument('--stego_path', default='D:\\Github\\Toy-Bossbase-dataset\\bossbase_toy_dataset\\train\\stego')
parser.add_ar... | ['def', 'arguments()', '->', 'str:', 'parser', '=', 'argparse.ArgumentParser()', "parser.add_argument('--cover_path',", "default='D:\\\\Github\\\\Toy-Bossbase-dataset\\\\bossbase_toy_dataset\\\\train\\\\cover')", "parser.add_argument('--stego_path',", "default='D:\\\\Github\\\\Toy-Bossbase-dataset\\\\bossbase_toy_datas... | 374,640 |
xiaoaleiBLUE/computer_vision | sast_process.py | SASTProcessTrain.shrink_poly_along_width | shrink_poly_along_width | shrink poly with given length. | [
"shrink",
"poly",
"with",
"given",
"length."
] | def shrink_poly_along_width(self, quads, shrink_ratio_of_width, expand_height_ratio=1.0):
upper_edge_list = []
def get_cut_info(edge_len_list, cut_len):
for (idx, edge_len) in enumerate(edge_len_list):
cut_len -= edge_len
if cut_len <= 1e-06:
ratio = (cut_len + e... | ['def', 'shrink_poly_along_width(self,', 'quads,', 'shrink_ratio_of_width,', 'expand_height_ratio=1.0):', 'upper_edge_list', '=', '[]', 'def', 'get_cut_info(edge_len_list,', 'cut_len):', 'for', '(idx,', 'edge_len)', 'in', 'enumerate(edge_len_list):', 'cut_len', '-=', 'edge_len', 'if', 'cut_len', '<=', '1e-06:', 'ratio'... | 502,156 |
ThomasBrouwer/HMF | updates_Gibbs.py | column_tau_individual_mtf | column_tau_individual_mtf | Return the component of the tau update for an individual matrix, for matrix tri-factorisation. | [
"Return",
"the",
"component",
"of",
"the",
"tau",
"update",
"for",
"an",
"individual",
"matrix,",
"for",
"matrix",
"tri-factorisation."
] | def column_tau_individual_mtf(dataset, mask, F, S, G, tau, alpha, k):
return tau * alpha * (mask * numpy.dot(S[k, :], G.T) ** 2).sum(axis=1) | ['def', 'column_tau_individual_mtf(dataset,', 'mask,', 'F,', 'S,', 'G,', 'tau,', 'alpha,', 'k):', 'return', 'tau', '*', 'alpha', '*', '(mask', '*', 'numpy.dot(S[k,', ':],', 'G.T)', '**', '2).sum(axis=1)'] | 206,695 |
rudranil723/mini-main | conftest.py | series | series | Make mocked series as fixture. | [
"Make",
"mocked",
"series",
"as",
"fixture."
] | def series():
arr = np.random.randn(100)
locs = np.arange(20, 40)
arr[locs] = np.NaN
series = Series(arr, index=bdate_range(datetime(2009, 1, 1), periods=100))
return series | ['def', 'series():', 'arr', '=', 'np.random.randn(100)', 'locs', '=', 'np.arange(20,', '40)', 'arr[locs]', '=', 'np.NaN', 'series', '=', 'Series(arr,', 'index=bdate_range(datetime(2009,', '1,', '1),', 'periods=100))', 'return', 'series'] | 267,724 |
ljw-struggle/Bioinfor-DeepATT | utils.py | write_json | write_json | Write dict to json file. | [
"Write",
"dict",
"to",
"json",
"file."
] | def write_json(content, file_path):
with open(file_path, 'wt') as f:
json.dump(content, f, indent=4, sort_keys=False) | ['def', 'write_json(content,', 'file_path):', 'with', 'open(file_path,', "'wt')", 'as', 'f:', 'json.dump(content,', 'f,', 'indent=4,', 'sort_keys=False)'] | 461,042 |
noambassat/SpeechTrainer | cmd.py | Command.ensure_string | ensure_string | Ensure that 'option' is a string; if not defined, set it to 'default'. | [
"Ensure",
"that",
"'option'",
"is",
"a",
"string;",
"if",
"not",
"defined,",
"set",
"it",
"to",
"'default'."
] | def ensure_string(self, option, default=None):
self._ensure_stringlike(option, 'string', default) | ['def', 'ensure_string(self,', 'option,', 'default=None):', 'self._ensure_stringlike(option,', "'string',", 'default)'] | 896,199 |
f-dangel/cockpit | test_bin_adaptation.py | test_grad_hist1d_adapted | test_grad_hist1d_adapted | Compare the 1d histogram with bin adaptation versus autograd. | [
"Compare",
"the",
"1d",
"histogram",
"with",
"bin",
"adaptation",
"versus",
"autograd."
] | def test_grad_hist1d_adapted(problem, q_kwargs):
def adapt_schedule(global_step):
return global_step in [1, 2]
q1 = GradHist1d(**q_kwargs, adapt=GradAbsMax(adapt_schedule, verbose=True))
output1 = run_harness_get_output(problem, [q1])[0]
q2 = AutogradGradHist1d(**q_kwargs, adapt=AutogradGradAbs... | ['def', 'test_grad_hist1d_adapted(problem,', 'q_kwargs):', 'def', 'adapt_schedule(global_step):', 'return', 'global_step', 'in', '[1,', '2]', 'q1', '=', 'GradHist1d(**q_kwargs,', 'adapt=GradAbsMax(adapt_schedule,', 'verbose=True))', 'output1', '=', 'run_harness_get_output(problem,', '[q1])[0]', 'q2', '=', 'AutogradGrad... | 492,783 |
arshpreetsingh/quantopian-machinelearning | document.py | Document.cursor_position | cursor_position | The document cursor position. | [
"The",
"document",
"cursor",
"position."
] | def cursor_position(self):
return self._cursor_position | ['def', 'cursor_position(self):', 'return', 'self._cursor_position'] | 892,015 |
triaquae/triaquae | forms.py | PasswordChangeForm.clean_old_password | clean_old_password | Validates that the old_password field is correct. | [
"Validates",
"that",
"the",
"old_password",
"field",
"is",
"correct."
] | def clean_old_password(self):
old_password = self.cleaned_data['old_password']
if not self.user.check_password(old_password):
raise forms.ValidationError(self.error_messages['password_incorrect'])
return old_password | ['def', 'clean_old_password(self):', 'old_password', '=', "self.cleaned_data['old_password']", 'if', 'not', 'self.user.check_password(old_password):', 'raise', "forms.ValidationError(self.error_messages['password_incorrect'])", 'return', 'old_password'] | 357,070 |
opendilab/DI-star | point.py | Point.scale | scale | Scale the vector to have the target length. | [
"Scale",
"the",
"vector",
"to",
"have",
"the",
"target",
"length."
] | def scale(self, target_len):
return self * (target_len / self.len()) | ['def', 'scale(self,', 'target_len):', 'return', 'self', '*', '(target_len', '/', 'self.len())'] | 184,714 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | registry_test.py | RegistryTest.testCannotCreateNonSubclass | testCannotCreateNonSubclass | Tests that Create fails if the class is not a subclass of Base. | [
"Tests",
"that",
"Create",
"fails",
"if",
"the",
"class",
"is",
"not",
"a",
"subclass",
"of",
"Base."
] | def testCannotCreateNonSubclass(self):
with self.assertRaisesRegexp(ValueError, 'Failed to create'):
registry_test_base.Base.Create(PATH + 'registry_test_impl.NonSubclass', 'hello world') | ['def', 'testCannotCreateNonSubclass(self):', 'with', 'self.assertRaisesRegexp(ValueError,', "'Failed", 'to', "create'):", 'registry_test_base.Base.Create(PATH', '+', "'registry_test_impl.NonSubclass',", "'hello", "world')"] | 111,937 |
43Carrig/recurrent_neural_networks_practice | mvn_linear_operator.py | MultivariateNormalLinearOperator.loc | loc | The `loc` `Tensor` in `Y = scale @ X + loc`. | [
"The",
"`loc`",
"`Tensor`",
"in",
"`Y",
"=",
"scale",
"@",
"X",
"+",
"loc`."
] | def loc(self):
return self.bijector.shift | ['def', 'loc(self):', 'return', 'self.bijector.shift'] | 312,837 |
erfaneshrati/meta-transfer-learning | args.py | argument_parser | argument_parser | Get an argument parser for a training script. | [
"Get",
"an",
"argument",
"parser",
"for",
"a",
"training",
"script."
] | def argument_parser():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--pretrained', help='evaluate a pre-trained model', action='store_true', default=False)
parser.add_argument('--seed', help='random seed', default=0, type=int)
parser.add_a... | ['def', 'argument_parser():', 'parser', '=', 'argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)', "parser.add_argument('--pretrained',", "help='evaluate", 'a', 'pre-trained', "model',", "action='store_true',", 'default=False)', "parser.add_argument('--seed',", "help='random", "seed',", 'de... | 633,373 |
LucasAlegre/morl-baselines | networks.py | NatureCNN.forward | forward | Predicts the features from the observations. | [
"Predicts",
"the",
"features",
"from",
"the",
"observations."
] | def forward(self, observations: th.Tensor) -> th.Tensor:
if observations.dim() == 3:
observations = observations.unsqueeze(0)
return self.linear(self.cnn(observations / 255.0)) | ['def', 'forward(self,', 'observations:', 'th.Tensor)', '->', 'th.Tensor:', 'if', 'observations.dim()', '==', '3:', 'observations', '=', 'observations.unsqueeze(0)', 'return', 'self.linear(self.cnn(observations', '/', '255.0))'] | 655,817 |
hamza-murad/AALU | visual_recognition_v3.py | ClassifierResult.from_dict | from_dict | Initialize a ClassifierResult object from a json dictionary. | [
"Initialize",
"a",
"ClassifierResult",
"object",
"from",
"a",
"json",
"dictionary."
] | def from_dict(cls, _dict: Dict) -> 'ClassifierResult':
args = {}
valid_keys = ['name', 'classifier_id', 'classes']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError('Unrecognized keys detected in dictionary for class ClassifierResult: ' + ', '.join(bad_keys))
if 'n... | ['def', 'from_dict(cls,', '_dict:', 'Dict)', '->', "'ClassifierResult':", 'args', '=', '{}', 'valid_keys', '=', "['name',", "'classifier_id',", "'classes']", 'bad_keys', '=', 'set(_dict.keys())', '-', 'set(valid_keys)', 'if', 'bad_keys:', 'raise', "ValueError('Unrecognized", 'keys', 'detected', 'in', 'dictionary', 'for... | 6,147 |
ashwanitanwar/nmt-transfer-learning-xlm-r | hub_interface.py | RobertaHubInterface.extract_features_aligned_to_words | extract_features_aligned_to_words | Extract RoBERTa features, aligned to spaCy's word-level tokenizer. | [
"Extract",
"RoBERTa",
"features,",
"aligned",
"to",
"spaCy's",
"word-level",
"tokenizer."
] | def extract_features_aligned_to_words(self, sentence: str, return_all_hiddens: bool=False) -> torch.Tensor:
from fairseq.models.roberta import alignment_utils
from spacy.tokens import Doc
nlp = alignment_utils.spacy_nlp()
tokenizer = alignment_utils.spacy_tokenizer()
bpe_toks = self.encode(sentence)... | ['def', 'extract_features_aligned_to_words(self,', 'sentence:', 'str,', 'return_all_hiddens:', 'bool=False)', '->', 'torch.Tensor:', 'from', 'fairseq.models.roberta', 'import', 'alignment_utils', 'from', 'spacy.tokens', 'import', 'Doc', 'nlp', '=', 'alignment_utils.spacy_nlp()', 'tokenizer', '=', 'alignment_utils.spacy... | 732,124 |
ChenhongyiYang/PPAL | test_mask_head.py | test_mask_head_loss | test_mask_head_loss | Test mask head loss when mask target is empty. | [
"Test",
"mask",
"head",
"loss",
"when",
"mask",
"target",
"is",
"empty."
] | def test_mask_head_loss():
self = FCNMaskHead(num_convs=1, roi_feat_size=6, in_channels=8, conv_out_channels=8, num_classes=8)
proposal_list = [torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]])]
gt_bboxes = [torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]])]
gt_labels = [torch.LongTensor([2])]... | ['def', 'test_mask_head_loss():', 'self', '=', 'FCNMaskHead(num_convs=1,', 'roi_feat_size=6,', 'in_channels=8,', 'conv_out_channels=8,', 'num_classes=8)', 'proposal_list', '=', '[torch.Tensor([[23.6667,', '23.8757,', '228.6326,', '153.8874]])]', 'gt_bboxes', '=', '[torch.Tensor([[23.6667,', '23.8757,', '238.6326,', '15... | 821,888 |
Kvatsx/Artificial-Intelligence-Assignments | _tifffile.py | TiffPage.is_tvips | is_tvips | Page contains TVIPS metadata. | [
"Page",
"contains",
"TVIPS",
"metadata."
] | def is_tvips(self):
return 'TVIPS' in self.tags | ['def', 'is_tvips(self):', 'return', "'TVIPS'", 'in', 'self.tags'] | 37,630 |
enuguru/artificial_intelligence_and_machine_ | acore.py | entoken | entoken | Takes a sequence of unicode strings and yields a series of Token objects (actually the same Token object over and over, for performance reasons), with the attributes filled in with reasonable values (for example, if ``positions`` or ``chars`` is True, the function assumes each token was separated by one space). | [
"Takes",
"a",
"sequence",
"of",
"unicode",
"strings",
"and",
"yields",
"a",
"series",
"of",
"Token",
"objects",
"(actually",
"the",
"same",
"Token",
"object",
"over",
"and",
"over,",
"for",
"performance",
"reasons),",
"with",
"the",
"attributes",
"filled",
"in... | def entoken(textstream, positions=False, chars=False, start_pos=0, start_char=0, **kwargs):
pos = start_pos
char = start_char
t = Token(positions=positions, chars=chars, **kwargs)
for text in textstream:
t.text = text
if positions:
t.pos = pos
pos += 1
if ... | ['def', 'entoken(textstream,', 'positions=False,', 'chars=False,', 'start_pos=0,', 'start_char=0,', '**kwargs):', 'pos', '=', 'start_pos', 'char', '=', 'start_char', 't', '=', 'Token(positions=positions,', 'chars=chars,', '**kwargs)', 'for', 'text', 'in', 'textstream:', 't.text', '=', 'text', 'if', 'positions:', 't.pos... | 133,241 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | vqa_attention.py | question_encoder | question_encoder | Question encoder, run LSTM encoder and get the last output as encoding. | [
"Question",
"encoder,",
"run",
"LSTM",
"encoder",
"and",
"get",
"the",
"last",
"output",
"as",
"encoding."
] | def question_encoder(question, hparams, name='encoder'):
with tf.variable_scope(name, 'encoder', values=[question]):
question = common_layers.flatten4d3d(question)
padding = common_attention.embedding_to_padding(question)
length = common_attention.padding_to_length(padding)
max_quest... | ['def', 'question_encoder(question,', 'hparams,', "name='encoder'):", 'with', 'tf.variable_scope(name,', "'encoder',", 'values=[question]):', 'question', '=', 'common_layers.flatten4d3d(question)', 'padding', '=', 'common_attention.embedding_to_padding(question)', 'length', '=', 'common_attention.padding_to_length(padd... | 965,923 |
spollok/magfield-prediction | create_data.py | ProgressBarActor.update | update | Updates the ProgressBar with the incremental number of items that were just completed. | [
"Updates",
"the",
"ProgressBar",
"with",
"the",
"incremental",
"number",
"of",
"items",
"that",
"were",
"just",
"completed."
] | def update(self, num_items_completed: int) -> None:
self.counter += num_items_completed
self.delta += num_items_completed
self.event.set() | ['def', 'update(self,', 'num_items_completed:', 'int)', '->', 'None:', 'self.counter', '+=', 'num_items_completed', 'self.delta', '+=', 'num_items_completed', 'self.event.set()'] | 627,183 |
QData/deepWordBug | __init__.py | LaTeXTranslator.duclass_close | duclass_close | Close a group of class declarations. | [
"Close",
"a",
"group",
"of",
"class",
"declarations."
] | def duclass_close(self, node):
for cls in reversed(node['classes']):
if cls.startswith('language-'):
language = self.babel.language_name(cls[9:])
if language:
self.babel.otherlanguages[language] = True
self.out.append('\\end{selectlanguage}\n')
... | ['def', 'duclass_close(self,', 'node):', 'for', 'cls', 'in', "reversed(node['classes']):", 'if', "cls.startswith('language-'):", 'language', '=', 'self.babel.language_name(cls[9:])', 'if', 'language:', 'self.babel.otherlanguages[language]', '=', 'True', "self.out.append('\\\\end{selectlanguage}\\n')", 'else:', "self.fa... | 542,721 |
linkedin/lambda-learner | functions.py | flatten | flatten | Flatten a list of lists into a shallow list. | [
"Flatten",
"a",
"list",
"of",
"lists",
"into",
"a",
"shallow",
"list."
] | def flatten(list_of_lists: Iterable[Iterable[Any]]) -> Iterable[Any]:
return list(chain.from_iterable(list_of_lists)) | ['def', 'flatten(list_of_lists:', 'Iterable[Iterable[Any]])', '->', 'Iterable[Any]:', 'return', 'list(chain.from_iterable(list_of_lists))'] | 261,841 |
facebookresearch/CompilerGym | observation_test.py | test_observation_when_raw_step_returns_incorrect_no_of_observations | test_observation_when_raw_step_returns_incorrect_no_of_observations | Test that a ServiceError is propagated when raw_step() returns unexpected number of observations. | [
"Test",
"that",
"a",
"ServiceError",
"is",
"propagated",
"when",
"raw_step()",
"returns",
"unexpected",
"number",
"of",
"observations."
] | def test_observation_when_raw_step_returns_incorrect_no_of_observations():
def make_failing_raw_step(n: int):
def failing_raw_step(*args, **kwargs):
del args
del kwargs
return (['ir'] * n, None, False, {})
return failing_raw_step
spaces = [ObservationSpace(n... | ['def', 'test_observation_when_raw_step_returns_incorrect_no_of_observations():', 'def', 'make_failing_raw_step(n:', 'int):', 'def', 'failing_raw_step(*args,', '**kwargs):', 'del', 'args', 'del', 'kwargs', 'return', "(['ir']", '*', 'n,', 'None,', 'False,', '{})', 'return', 'failing_raw_step', 'spaces', '=', "[Observati... | 135,931 |
google-research/rigl | imagenet_train_eval.py | resnet_model_fn_w_pruning | resnet_model_fn_w_pruning | The model_fn for ResNet-50 with pruning. | [
"The",
"model_fn",
"for",
"ResNet-50",
"with",
"pruning."
] | def resnet_model_fn_w_pruning(features, labels, mode, params):
width = 1.0 if FLAGS.width <= 0 else FLAGS.width
if isinstance(features, dict):
features = features['feature']
if FLAGS.data_format == 'channels_first':
assert not FLAGS.transpose_input
features = tf.transpose(features, [... | ['def', 'resnet_model_fn_w_pruning(features,', 'labels,', 'mode,', 'params):', 'width', '=', '1.0', 'if', 'FLAGS.width', '<=', '0', 'else', 'FLAGS.width', 'if', 'isinstance(features,', 'dict):', 'features', '=', "features['feature']", 'if', 'FLAGS.data_format', '==', "'channels_first':", 'assert', 'not', 'FLAGS.transpo... | 841,577 |
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