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 |
|---|---|---|---|---|---|---|---|---|
bhrnjica/ObjectDetection | fp16util.py | model_grads_to_master_grads | model_grads_to_master_grads | Copy model gradients to master gradients. | [
"Copy",
"model",
"gradients",
"to",
"master",
"gradients."
] | def model_grads_to_master_grads(model_params, master_params, flat_master=False):
if flat_master:
master_params[0].grad.data.copy_(_flatten_dense_tensors([p.grad.data for p in model_params]))
else:
for (model, master) in zip(model_params, master_params):
if model.grad is not None:
... | ['def', 'model_grads_to_master_grads(model_params,', 'master_params,', 'flat_master=False):', 'if', 'flat_master:', 'master_params[0].grad.data.copy_(_flatten_dense_tensors([p.grad.data', 'for', 'p', 'in', 'model_params]))', 'else:', 'for', '(model,', 'master)', 'in', 'zip(model_params,', 'master_params):', 'if', 'mode... | 744,392 |
QinganZhao/Deep-Learning-Based-Structural-Damage-Detection | cpp_lint.py | ProcessFileData | ProcessFileData | Performs lint checks and reports any errors to the given error function. | [
"Performs",
"lint",
"checks",
"and",
"reports",
"any",
"errors",
"to",
"the",
"given",
"error",
"function."
] | def ProcessFileData(filename, file_extension, lines, error, extra_check_functions=[]):
lines = ['// marker so line numbers and indices both start at 1'] + lines + ['// marker so line numbers end in a known way']
include_state = _IncludeState()
function_state = _FunctionState()
nesting_state = _NestingSt... | ['def', 'ProcessFileData(filename,', 'file_extension,', 'lines,', 'error,', 'extra_check_functions=[]):', 'lines', '=', "['//", 'marker', 'so', 'line', 'numbers', 'and', 'indices', 'both', 'start', 'at', "1']", '+', 'lines', '+', "['//", 'marker', 'so', 'line', 'numbers', 'end', 'in', 'a', 'known', "way']", 'include_st... | 127,574 |
neokarn/computer_vision | yacs.py | CfgNode.defrost | defrost | Make this CfgNode and all of its children mutable. | [
"Make",
"this",
"CfgNode",
"and",
"all",
"of",
"its",
"children",
"mutable."
] | def defrost(self):
self._immutable(False) | ['def', 'defrost(self):', 'self._immutable(False)'] | 475,639 |
rlworkgroup/garage | test_tanh_gaussian_mlp_policy.py | TestTanhGaussianMLPPolicy.test_get_action_np | test_get_action_np | Test Policy get action function with numpy inputs. | [
"Test",
"Policy",
"get",
"action",
"function",
"with",
"numpy",
"inputs."
] | def test_get_action_np(self, hidden_sizes):
env_spec = GymEnv(DummyBoxEnv())
obs_dim = env_spec.observation_space.flat_dim
act_dim = env_spec.action_space.flat_dim
obs = np.ones((obs_dim,), dtype=np.float32)
init_std = 2.0
policy = TanhGaussianMLPPolicy(env_spec=env_spec, hidden_sizes=hidden_siz... | ['def', 'test_get_action_np(self,', 'hidden_sizes):', 'env_spec', '=', 'GymEnv(DummyBoxEnv())', 'obs_dim', '=', 'env_spec.observation_space.flat_dim', 'act_dim', '=', 'env_spec.action_space.flat_dim', 'obs', '=', 'np.ones((obs_dim,),', 'dtype=np.float32)', 'init_std', '=', '2.0', 'policy', '=', 'TanhGaussianMLPPolicy(e... | 201,064 |
xiaoaleiBLUE/computer_vision | util.py | get_keypoints | get_keypoints | Get the COCO keypoints and their left/right flip coorespondence map. | [
"Get",
"the",
"COCO",
"keypoints",
"and",
"their",
"left/right",
"flip",
"coorespondence",
"map."
] | def get_keypoints():
keypoints = ['nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear', 'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow', 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', 'left_knee', 'right_knee', 'left_ankle', 'right_ankle']
keypoint_flip_map = {'left_eye': 'right_eye', ... | ['def', 'get_keypoints():', 'keypoints', '=', "['nose',", "'left_eye',", "'right_eye',", "'left_ear',", "'right_ear',", "'left_shoulder',", "'right_shoulder',", "'left_elbow',", "'right_elbow',", "'left_wrist',", "'right_wrist',", "'left_hip',", "'right_hip',", "'left_knee',", "'right_knee',", "'left_ankle',", "'right_... | 501,705 |
Shark-NLP/DiffuSeq | nn.py | mean_flat | mean_flat | Take the mean over all non-batch dimensions. | [
"Take",
"the",
"mean",
"over",
"all",
"non-batch",
"dimensions."
] | def mean_flat(tensor):
return tensor.mean(dim=list(range(1, len(tensor.shape)))) | ['def', 'mean_flat(tensor):', 'return', 'tensor.mean(dim=list(range(1,', 'len(tensor.shape))))'] | 551,683 |
YiSyuanChen/MTL-ABS | data_builder.py | load_json | load_json | Construct sentences from tokenized json file. | [
"Construct",
"sentences",
"from",
"tokenized",
"json",
"file."
] | def load_json(p, lower):
source = []
tgt = []
flag = False
for sent in json.load(open(p))['sentences']:
tokens = [t['word'] for t in sent['tokens']]
if lower:
tokens = [t.lower() for t in tokens]
if tokens[0] == '@highlight':
flag = True
tgt.ap... | ['def', 'load_json(p,', 'lower):', 'source', '=', '[]', 'tgt', '=', '[]', 'flag', '=', 'False', 'for', 'sent', 'in', "json.load(open(p))['sentences']:", 'tokens', '=', "[t['word']", 'for', 't', 'in', "sent['tokens']]", 'if', 'lower:', 'tokens', '=', '[t.lower()', 'for', 't', 'in', 'tokens]', 'if', 'tokens[0]', '==', "'... | 642,860 |
AEProgrammer/object_detection | train.py | create_model | create_model | Build the model and look for saved model checkpoints in case we can resume from one. | [
"Build",
"the",
"model",
"and",
"look",
"for",
"saved",
"model",
"checkpoints",
"in",
"case",
"we",
"can",
"resume",
"from",
"one."
] | def create_model():
logger = logging.getLogger(__name__)
start_iter = 0
checkpoints = {}
output_dir = get_output_dir(cfg.TRAIN.DATASETS, training=True)
weights_file = cfg.TRAIN.WEIGHTS
if cfg.TRAIN.AUTO_RESUME:
final_path = os.path.join(output_dir, 'model_final.pkl')
if os.path.e... | ['def', 'create_model():', 'logger', '=', 'logging.getLogger(__name__)', 'start_iter', '=', '0', 'checkpoints', '=', '{}', 'output_dir', '=', 'get_output_dir(cfg.TRAIN.DATASETS,', 'training=True)', 'weights_file', '=', 'cfg.TRAIN.WEIGHTS', 'if', 'cfg.TRAIN.AUTO_RESUME:', 'final_path', '=', 'os.path.join(output_dir,', "... | 773,660 |
imoscovitz/wittgenstein | discretize.py | BinTransformer.find_continuous_feats | find_continuous_feats | Return names of df features that seem to be continuous. | [
"Return",
"names",
"of",
"df",
"features",
"that",
"seem",
"to",
"be",
"continuous."
] | def find_continuous_feats(self, df, ignore_feats=[]):
if not self.n_discretize_bins:
return []
cont_feats = df.select_dtypes(np.number).columns
cont_feats = [f for f in cont_feats if len(df[f].unique()) > self.n_discretize_bins]
cont_feats = [f for f in cont_feats if f not in ignore_feats]
r... | ['def', 'find_continuous_feats(self,', 'df,', 'ignore_feats=[]):', 'if', 'not', 'self.n_discretize_bins:', 'return', '[]', 'cont_feats', '=', 'df.select_dtypes(np.number).columns', 'cont_feats', '=', '[f', 'for', 'f', 'in', 'cont_feats', 'if', 'len(df[f].unique())', '>', 'self.n_discretize_bins]', 'cont_feats', '=', '[... | 959,890 |
sek788432/Waymo-2D-Object-Detection | common.py | define_clustering_flags | define_clustering_flags | Define flags for clustering methods. | [
"Define",
"flags",
"for",
"clustering",
"methods."
] | def define_clustering_flags():
flags.DEFINE_string('clustering_method', None, 'None (no clustering) or selective_clustering (cluster last three Conv2D layers of the model).') | ['def', 'define_clustering_flags():', "flags.DEFINE_string('clustering_method',", 'None,', "'None", '(no', 'clustering)', 'or', 'selective_clustering', '(cluster', 'last', 'three', 'Conv2D', 'layers', 'of', 'the', "model).')"] | 973,796 |
clvrai/spirl | train.py | RLTrainer.generate_rollouts | generate_rollouts | Generate rollouts and save to hdf5 files. | [
"Generate",
"rollouts",
"and",
"save",
"to",
"hdf5",
"files."
] | def generate_rollouts(self):
print('Saving {} rollouts to directory {}...'.format(self.args.n_val_samples, self.args.save_dir))
saver = RolloutSaver(self.args.save_dir)
n_success = 0
n_total = 0
with self.agent.val_mode():
with torch.no_grad():
for _ in tqdm(range(self.args.n_val... | ['def', 'generate_rollouts(self):', "print('Saving", '{}', 'rollouts', 'to', 'directory', "{}...'.format(self.args.n_val_samples,", 'self.args.save_dir))', 'saver', '=', 'RolloutSaver(self.args.save_dir)', 'n_success', '=', '0', 'n_total', '=', '0', 'with', 'self.agent.val_mode():', 'with', 'torch.no_grad():', 'for', '... | 896,983 |
bhateharsh/computer_vision | detection_inference.py | build_input | build_input | Builds the graph's input. | [
"Builds",
"the",
"graph's",
"input."
] | def build_input(tfrecord_paths):
filename_queue = tf.train.string_input_producer(tfrecord_paths, shuffle=False, num_epochs=1)
tf_record_reader = tf.TFRecordReader()
(_, serialized_example_tensor) = tf_record_reader.read(filename_queue)
features = tf.parse_single_example(serialized_example_tensor, featur... | ['def', 'build_input(tfrecord_paths):', 'filename_queue', '=', 'tf.train.string_input_producer(tfrecord_paths,', 'shuffle=False,', 'num_epochs=1)', 'tf_record_reader', '=', 'tf.TFRecordReader()', '(_,', 'serialized_example_tensor)', '=', 'tf_record_reader.read(filename_queue)', 'features', '=', 'tf.parse_single_example... | 506,069 |
flightstar/Natural-Language-Processing | a2_test.py | TestA2.test_hmm_viterbi | test_hmm_viterbi | Test viterbi algorithm on 'time flies like an arrow' The given model should predict N,V,P,D,N tags. | [
"Test",
"viterbi",
"algorithm",
"on",
"'time",
"flies",
"like",
"an",
"arrow'",
"The",
"given",
"model",
"should",
"predict",
"N,V,P,D,N",
"tags."
] | def test_hmm_viterbi(self):
model = HMM()
model.states = ['D', 'N', 'P', 'V']
model.start_probas = {'D': 0.3, 'N': 0.4, 'P': 0.1, 'V': 0.2}
model.emission_probas = {'D': {'time': 0.0, 'flies': 0.0, 'like': 0.0, 'an': 1.0, 'arrow': 0.0}, 'V': {'time': 0.0, 'flies': 0.5, 'like': 0.5, 'an': 0.0, 'arrow': 0... | ['def', 'test_hmm_viterbi(self):', 'model', '=', 'HMM()', 'model.states', '=', "['D',", "'N',", "'P',", "'V']", 'model.start_probas', '=', "{'D':", '0.3,', "'N':", '0.4,', "'P':", '0.1,', "'V':", '0.2}', 'model.emission_probas', '=', "{'D':", "{'time':", '0.0,', "'flies':", '0.0,', "'like':", '0.0,', "'an':", '1.0,', "... | 703,612 |
kornia/kornia | renderer.py | RegularRenderer.forward | forward | Renders 3D regularly sampled points along rays. | [
"Renders",
"3D",
"regularly",
"sampled",
"points",
"along",
"rays."
] | def forward(self, rgbs: Tensor, densities: Tensor, points_3d: Tensor) -> Tensor:
num_ray_points = points_3d.shape[-2]
delta_3d = points_3d.reshape(-1, num_ray_points, 3)[0, 1, :] - points_3d.reshape(-1, num_ray_points, 3)[0, 0, :]
delta = torch.linalg.norm(delta_3d, dim=-1)
alpha = 1 - torch.exp(-1.0 * ... | ['def', 'forward(self,', 'rgbs:', 'Tensor,', 'densities:', 'Tensor,', 'points_3d:', 'Tensor)', '->', 'Tensor:', 'num_ray_points', '=', 'points_3d.shape[-2]', 'delta_3d', '=', 'points_3d.reshape(-1,', 'num_ray_points,', '3)[0,', '1,', ':]', '-', 'points_3d.reshape(-1,', 'num_ray_points,', '3)[0,', '0,', ':]', 'delta', '... | 622,263 |
ripl-org/sockit | __init__.py | parse_job_posting | parse_job_posting | Parse a job posting description. | [
"Parse",
"a",
"job",
"posting",
"description."
] | def parse_job_posting(filename, extension=None, prediction=False):
nonskills_trie = get_trie('nonskills')
skills_trie = get_trie('skills')
results = {'NonSkills': [], 'Skills': {}}
lines = _extract(filename, extension)
if len(lines) == 1:
lines = _split_sentences(lines[0])
for line in li... | ['def', 'parse_job_posting(filename,', 'extension=None,', 'prediction=False):', 'nonskills_trie', '=', "get_trie('nonskills')", 'skills_trie', '=', "get_trie('skills')", 'results', '=', "{'NonSkills':", '[],', "'Skills':", '{}}', 'lines', '=', '_extract(filename,', 'extension)', 'if', 'len(lines)', '==', '1:', 'lines',... | 879,236 |
ds4dm/learn2branch | model.py | PreNormLayer.stop_updates | stop_updates | Ends pre-training for that layer, and fixes the layers's parameters. | [
"Ends",
"pre-training",
"for",
"that",
"layer,",
"and",
"fixes",
"the",
"layers's",
"parameters."
] | def stop_updates(self):
assert self.count > 0
if self.shift is not None:
self.shift.assign(-self.avg)
if self.scale is not None:
self.var = tf.where(tf.equal(self.var, 0), tf.ones_like(self.var), self.var)
self.scale.assign(1 / np.sqrt(self.var))
del self.avg, self.var, self.m2, ... | ['def', 'stop_updates(self):', 'assert', 'self.count', '>', '0', 'if', 'self.shift', 'is', 'not', 'None:', 'self.shift.assign(-self.avg)', 'if', 'self.scale', 'is', 'not', 'None:', 'self.var', '=', 'tf.where(tf.equal(self.var,', '0),', 'tf.ones_like(self.var),', 'self.var)', 'self.scale.assign(1', '/', 'np.sqrt(self.va... | 262,079 |
Victor-Martinez-Pozos/stacked_capsule_autoencoders | math_ops.py | geometric_transform | geometric_transform | Convers paramer tensor into an affine or similarity transform. | [
"Convers",
"paramer",
"tensor",
"into",
"an",
"affine",
"or",
"similarity",
"transform."
] | def geometric_transform(pose_tensor, similarity=False, nonlinear=True, as_matrix=False):
(scale_x, scale_y, theta, shear, trans_x, trans_y) = tf.split(pose_tensor, 6, -1)
if nonlinear:
(scale_x, scale_y) = (tf.nn.sigmoid(i) + 0.01 for i in (scale_x, scale_y))
(trans_x, trans_y, shear) = (tf.nn.t... | ['def', 'geometric_transform(pose_tensor,', 'similarity=False,', 'nonlinear=True,', 'as_matrix=False):', '(scale_x,', 'scale_y,', 'theta,', 'shear,', 'trans_x,', 'trans_y)', '=', 'tf.split(pose_tensor,', '6,', '-1)', 'if', 'nonlinear:', '(scale_x,', 'scale_y)', '=', '(tf.nn.sigmoid(i)', '+', '0.01', 'for', 'i', 'in', '... | 873,316 |
google-research/batch-ppo | wrappers.py | ExternalProcess.call | call | Asynchronously call a method of the external environment. | [
"Asynchronously",
"call",
"a",
"method",
"of",
"the",
"external",
"environment."
] | def call(self, name, *args, **kwargs):
payload = (name, args, kwargs)
self._conn.send((self._CALL, payload))
return self._receive | ['def', 'call(self,', 'name,', '*args,', '**kwargs):', 'payload', '=', '(name,', 'args,', 'kwargs)', 'self._conn.send((self._CALL,', 'payload))', 'return', 'self._receive'] | 95,059 |
yongchi1992/NaturalLanguageProcessing | run_pretraining.py | get_next_sentence_output | get_next_sentence_output | Get loss and log probs for the next sentence prediction. | [
"Get",
"loss",
"and",
"log",
"probs",
"for",
"the",
"next",
"sentence",
"prediction."
] | def get_next_sentence_output(bert_config, input_tensor, labels):
with tf.variable_scope('cls/seq_relationship'):
output_weights = tf.get_variable('output_weights', shape=[2, bert_config.hidden_size], initializer=modeling.create_initializer(bert_config.initializer_range))
output_bias = tf.get_variabl... | ['def', 'get_next_sentence_output(bert_config,', 'input_tensor,', 'labels):', 'with', "tf.variable_scope('cls/seq_relationship'):", 'output_weights', '=', "tf.get_variable('output_weights',", 'shape=[2,', 'bert_config.hidden_size],', 'initializer=modeling.create_initializer(bert_config.initializer_range))', 'output_bia... | 798,732 |
suarez12138/AI-Reversi_IMP_TextDichotomy | colorbar.py | ColorbarBase.set_label_text | set_label_text | Label the long axis of the colorbar. | [
"Label",
"the",
"long",
"axis",
"of",
"the",
"colorbar."
] | def set_label_text(self, label, **kw):
self._label = label
self._labelkw = kw
self._set_label_text() | ['def', 'set_label_text(self,', 'label,', '**kw):', 'self._label', '=', 'label', 'self._labelkw', '=', 'kw', 'self._set_label_text()'] | 97,490 |
aeon-toolkit/aeon | test_rocket.py | test_rocket | test_rocket | Test correct rocket variant is selected. | [
"Test",
"correct",
"rocket",
"variant",
"is",
"selected."
] | def test_rocket():
(X_train, y_train) = make_2d_test_data(n_cases=20, n_timepoints=50)
rocket = RocketClassifier(num_kernels=20)
rocket.fit(X_train, y_train)
assert isinstance(rocket._transformer, Rocket)
rocket = RocketClassifier(num_kernels=100, rocket_transform='minirocket', max_dilations_per_ker... | ['def', 'test_rocket():', '(X_train,', 'y_train)', '=', 'make_2d_test_data(n_cases=20,', 'n_timepoints=50)', 'rocket', '=', 'RocketClassifier(num_kernels=20)', 'rocket.fit(X_train,', 'y_train)', 'assert', 'isinstance(rocket._transformer,', 'Rocket)', 'rocket', '=', 'RocketClassifier(num_kernels=100,', "rocket_transform... | 399,217 |
Farama-Foundation/Minari | setup.py | get_version | get_version | Gets the Minari version. | [
"Gets",
"the",
"Minari",
"version."
] | def get_version():
path = CWD / 'minari' / '__init__.py'
content = path.read_text()
for line in content.splitlines():
if line.startswith('__version__'):
return line.strip().split()[-1].strip().strip('"')
raise RuntimeError('bad version data in __init__.py') | ['def', 'get_version():', 'path', '=', 'CWD', '/', "'minari'", '/', "'__init__.py'", 'content', '=', 'path.read_text()', 'for', 'line', 'in', 'content.splitlines():', 'if', "line.startswith('__version__'):", 'return', 'line.strip().split()[-1].strip().strip(\'"\')', 'raise', "RuntimeError('bad", 'version', 'data', 'in'... | 670,477 |
intra2net/guibot | test_region_expect.py | RegionTest.test_find_guess_target_steps | test_find_guess_target_steps | Test target guess from data file extension (target has no match config). | [
"Test",
"target",
"guess",
"from",
"data",
"file",
"extension",
"(target",
"has",
"no",
"match",
"config)."
] | def test_find_guess_target_steps(self):
self.show_image('all_shapes')
imgroot = os.path.join(common_test.unittest_dir, 'images')
self.assertFalse(os.path.exists(os.path.join(imgroot, 'circle.match')))
self.assertTrue(os.path.exists(os.path.join(imgroot, 'circle.steps')))
self.region.find('circle')
... | ['def', 'test_find_guess_target_steps(self):', "self.show_image('all_shapes')", 'imgroot', '=', 'os.path.join(common_test.unittest_dir,', "'images')", 'self.assertFalse(os.path.exists(os.path.join(imgroot,', "'circle.match')))", 'self.assertTrue(os.path.exists(os.path.join(imgroot,', "'circle.steps')))", "self.region.f... | 572,682 |
devashish-patel/webcam-motion-detector | libpython.py | PythonCodeExecutor.xdecref | xdecref | Decrement the reference count of a Python object in the inferior. | [
"Decrement",
"the",
"reference",
"count",
"of",
"a",
"Python",
"object",
"in",
"the",
"inferior."
] | def xdecref(self, pointer):
gdb.parse_and_eval('Py_DecRef((PyObject *) %d)' % pointer) | ['def', 'xdecref(self,', 'pointer):', "gdb.parse_and_eval('Py_DecRef((PyObject", '*)', "%d)'", '%', 'pointer)'] | 977,614 |
deepmind/acme | distributional.py | categorical | categorical | Implements the Categorical Distributional TD(0)-learning loss. | [
"Implements",
"the",
"Categorical",
"Distributional",
"TD(0)-learning",
"loss."
] | def categorical(q_tm1: networks.DiscreteValuedDistribution, r_t: tf.Tensor, d_t: tf.Tensor, q_t: networks.DiscreteValuedDistribution) -> tf.Tensor:
z_t = tf.reshape(r_t, (-1, 1)) + tf.reshape(d_t, (-1, 1)) * q_t.values
p_t = tf.nn.softmax(q_t.logits)
target = tf.stop_gradient(l2_project(z_t, p_t, q_t.values... | ['def', 'categorical(q_tm1:', 'networks.DiscreteValuedDistribution,', 'r_t:', 'tf.Tensor,', 'd_t:', 'tf.Tensor,', 'q_t:', 'networks.DiscreteValuedDistribution)', '->', 'tf.Tensor:', 'z_t', '=', 'tf.reshape(r_t,', '(-1,', '1))', '+', 'tf.reshape(d_t,', '(-1,', '1))', '*', 'q_t.values', 'p_t', '=', 'tf.nn.softmax(q_t.log... | 8,404 |
sek788432/Waymo-2D-Object-Detection | target_assigner_test.py | CenterNetBoxTargetAssignerTest.test_max_distance_for_overlap_centernet | test_max_distance_for_overlap_centernet | Test the version of the function used in the CenterNet paper. | [
"Test",
"the",
"version",
"of",
"the",
"function",
"used",
"in",
"the",
"CenterNet",
"paper."
] | def test_max_distance_for_overlap_centernet(self):
def graph_fn():
distance = targetassigner.max_distance_for_overlap(10, 5, 0.5)
return distance
distance = self.execute(graph_fn, [])
self.assertAlmostEqual(2.807764064, distance) | ['def', 'test_max_distance_for_overlap_centernet(self):', 'def', 'graph_fn():', 'distance', '=', 'targetassigner.max_distance_for_overlap(10,', '5,', '0.5)', 'return', 'distance', 'distance', '=', 'self.execute(graph_fn,', '[])', 'self.assertAlmostEqual(2.807764064,', 'distance)'] | 974,927 |
rishab-sharma/object_detection | np_box_list_ops.py | scale | scale | Scale box coordinates in x and y dimensions. | [
"Scale",
"box",
"coordinates",
"in",
"x",
"and",
"y",
"dimensions."
] | def scale(boxlist, y_scale, x_scale):
(y_min, x_min, y_max, x_max) = np.array_split(boxlist.get(), 4, axis=1)
y_min = y_scale * y_min
y_max = y_scale * y_max
x_min = x_scale * x_min
x_max = x_scale * x_max
scaled_boxlist = np_box_list.BoxList(np.hstack([y_min, x_min, y_max, x_max]))
fields =... | ['def', 'scale(boxlist,', 'y_scale,', 'x_scale):', '(y_min,', 'x_min,', 'y_max,', 'x_max)', '=', 'np.array_split(boxlist.get(),', '4,', 'axis=1)', 'y_min', '=', 'y_scale', '*', 'y_min', 'y_max', '=', 'y_scale', '*', 'y_max', 'x_min', '=', 'x_scale', '*', 'x_min', 'x_max', '=', 'x_scale', '*', 'x_max', 'scaled_boxlist',... | 793,252 |
google-research/scenic | pretrain.py | get_config | get_config | Returns the ViT experiment configuration for ImageNet. | [
"Returns",
"the",
"ViT",
"experiment",
"configuration",
"for",
"ImageNet."
] | def get_config(runlocal=''):
runlocal = bool(runlocal)
config = ml_collections.ConfigDict()
config.experiment_name = 'imagenet-mae-vit'
config.dataset_name = 'bit'
config.data_dtype_str = 'float32'
config.dataset_configs = ml_collections.ConfigDict()
config.dataset_configs.dataset = 'imagene... | ['def', "get_config(runlocal=''):", 'runlocal', '=', 'bool(runlocal)', 'config', '=', 'ml_collections.ConfigDict()', 'config.experiment_name', '=', "'imagenet-mae-vit'", 'config.dataset_name', '=', "'bit'", 'config.data_dtype_str', '=', "'float32'", 'config.dataset_configs', '=', 'ml_collections.ConfigDict()', 'config.... | 846,476 |
shengwenliang/lpcvc2020_water | efficientnet_condconv_builder.py | build_model | build_model | A helper functiion to creates a model and returns predicted logits. | [
"A",
"helper",
"functiion",
"to",
"creates",
"a",
"model",
"and",
"returns",
"predicted",
"logits."
] | def build_model(images, model_name, training, override_params=None, model_dir=None, fine_tuning=False):
assert isinstance(images, tf.Tensor)
if not training or fine_tuning:
if not override_params:
override_params = {}
override_params['batch_norm'] = utils.BatchNormalization
(bloc... | ['def', 'build_model(images,', 'model_name,', 'training,', 'override_params=None,', 'model_dir=None,', 'fine_tuning=False):', 'assert', 'isinstance(images,', 'tf.Tensor)', 'if', 'not', 'training', 'or', 'fine_tuning:', 'if', 'not', 'override_params:', 'override_params', '=', '{}', "override_params['batch_norm']", '=', ... | 615,886 |
PacktPublishing/Hands-On-Artificial--for-Banking | test_arraypad.py | test_memory_layout_persistence | test_memory_layout_persistence | Test if C and F order is preserved for all pad modes. | [
"Test",
"if",
"C",
"and",
"F",
"order",
"is",
"preserved",
"for",
"all",
"pad",
"modes."
] | def test_memory_layout_persistence(mode):
x = np.ones((5, 10), order='C')
assert np.pad(x, 5, mode).flags['C_CONTIGUOUS']
x = np.ones((5, 10), order='F')
assert np.pad(x, 5, mode).flags['F_CONTIGUOUS'] | ['def', 'test_memory_layout_persistence(mode):', 'x', '=', 'np.ones((5,', '10),', "order='C')", 'assert', 'np.pad(x,', '5,', "mode).flags['C_CONTIGUOUS']", 'x', '=', 'np.ones((5,', '10),', "order='F')", 'assert', 'np.pad(x,', '5,', "mode).flags['F_CONTIGUOUS']"] | 235,674 |
lhotse-speech/lhotse | tar.py | parse_tarinfo | parse_tarinfo | Parse a tarinfo object and return the data it points to as well as the internal path. | [
"Parse",
"a",
"tarinfo",
"object",
"and",
"return",
"the",
"data",
"it",
"points",
"to",
"as",
"well",
"as",
"the",
"internal",
"path."
] | def parse_tarinfo(tarinfo: tarfile.TarInfo, tar_file: tarfile.TarFile) -> Tuple[Optional[bytes], Path]:
path = Path(tarinfo.path)
if path.suffix == '.nodata' or path.suffix == '.nometa':
return (None, path)
data = tar_file.extractfile(tarinfo).read()
return (data, path) | ['def', 'parse_tarinfo(tarinfo:', 'tarfile.TarInfo,', 'tar_file:', 'tarfile.TarFile)', '->', 'Tuple[Optional[bytes],', 'Path]:', 'path', '=', 'Path(tarinfo.path)', 'if', 'path.suffix', '==', "'.nodata'", 'or', 'path.suffix', '==', "'.nometa':", 'return', '(None,', 'path)', 'data', '=', 'tar_file.extractfile(tarinfo).re... | 601,032 |
PaddlePaddle/PaddleSpeech | fastspeech2midi.py | FastSpeech2MIDI.inference | inference | Generate the sequence of features given the sequences of characters. | [
"Generate",
"the",
"sequence",
"of",
"features",
"given",
"the",
"sequences",
"of",
"characters."
] | def inference(self, text: paddle.Tensor, note: paddle.Tensor, note_dur: paddle.Tensor, is_slur: paddle.Tensor, durations: paddle.Tensor=None, pitch: paddle.Tensor=None, energy: paddle.Tensor=None, alpha: float=1.0, use_teacher_forcing: bool=False, spk_emb=None, spk_id=None) -> Tuple[paddle.Tensor, paddle.Tensor, paddle... | ['def', 'inference(self,', 'text:', 'paddle.Tensor,', 'note:', 'paddle.Tensor,', 'note_dur:', 'paddle.Tensor,', 'is_slur:', 'paddle.Tensor,', 'durations:', 'paddle.Tensor=None,', 'pitch:', 'paddle.Tensor=None,', 'energy:', 'paddle.Tensor=None,', 'alpha:', 'float=1.0,', 'use_teacher_forcing:', 'bool=False,', 'spk_emb=No... | 277,191 |
Eric3911/OpenAGI | test_wav.py | DeepSpeech2Tester_hub.setup_output_dir | setup_output_dir | Create a directory used for output. | [
"Create",
"a",
"directory",
"used",
"for",
"output."
] | def setup_output_dir(self):
if self.args.output:
output_dir = Path(self.args.output).expanduser()
output_dir.mkdir(parents=True, exist_ok=True)
else:
output_dir = Path(self.args.checkpoint_path).expanduser().parent.parent
output_dir.mkdir(parents=True, exist_ok=True)
self.out... | ['def', 'setup_output_dir(self):', 'if', 'self.args.output:', 'output_dir', '=', 'Path(self.args.output).expanduser()', 'output_dir.mkdir(parents=True,', 'exist_ok=True)', 'else:', 'output_dir', '=', 'Path(self.args.checkpoint_path).expanduser().parent.parent', 'output_dir.mkdir(parents=True,', 'exist_ok=True)', 'self.... | 251,215 |
ryu-ed/SpaceInvaders_Ros | scrap_test.py | ScrapModuleTest.test_get__owned_empty_type | test_get__owned_empty_type | Ensures get works when there is no data of the requested type in the clipboard and the clipboard is owned by the pygame application. | [
"Ensures",
"get",
"works",
"when",
"there",
"is",
"no",
"data",
"of",
"the",
"requested",
"type",
"in",
"the",
"clipboard",
"and",
"the",
"clipboard",
"is",
"owned",
"by",
"the",
"pygame",
"application."
] | def test_get__owned_empty_type(self):
DATA_TYPE = 'test_get__owned_empty_type'
if scrap.lost():
scrap.put(pygame.SCRAP_TEXT, b'text to clipboard')
if scrap.lost():
self.skipTest('requires the pygame application to own the clipboard')
data = scrap.get(DATA_TYPE)
self.assertIsN... | ['def', 'test_get__owned_empty_type(self):', 'DATA_TYPE', '=', "'test_get__owned_empty_type'", 'if', 'scrap.lost():', 'scrap.put(pygame.SCRAP_TEXT,', "b'text", 'to', "clipboard')", 'if', 'scrap.lost():', "self.skipTest('requires", 'the', 'pygame', 'application', 'to', 'own', 'the', "clipboard')", 'data', '=', 'scrap.ge... | 369,145 |
alinlab/ifseg | progress_bar.py | TensorboardProgressBarWrapper.log | log | Log intermediate stats to tensorboard. | [
"Log",
"intermediate",
"stats",
"to",
"tensorboard."
] | def log(self, stats, tag=None, step=None):
self._log_to_tensorboard(stats, tag, step)
self.wrapped_bar.log(stats, tag=tag, step=step) | ['def', 'log(self,', 'stats,', 'tag=None,', 'step=None):', 'self._log_to_tensorboard(stats,', 'tag,', 'step)', 'self.wrapped_bar.log(stats,', 'tag=tag,', 'step=step)'] | 598,113 |
Westlake-AI/openmixup | mvit.py | attention_pool | attention_pool | Pooling the feature tokens. | [
"Pooling",
"the",
"feature",
"tokens."
] | def attention_pool(x: torch.Tensor, pool: nn.Module, in_size: tuple, norm: Optional[nn.Module]=None):
ndim = x.ndim
if ndim == 4:
(B, num_heads, L, C) = x.shape
elif ndim == 3:
num_heads = 1
(B, L, C) = x.shape
else:
raise RuntimeError(f'Unsupported input dimension {x.sha... | ['def', 'attention_pool(x:', 'torch.Tensor,', 'pool:', 'nn.Module,', 'in_size:', 'tuple,', 'norm:', 'Optional[nn.Module]=None):', 'ndim', '=', 'x.ndim', 'if', 'ndim', '==', '4:', '(B,', 'num_heads,', 'L,', 'C)', '=', 'x.shape', 'elif', 'ndim', '==', '3:', 'num_heads', '=', '1', '(B,', 'L,', 'C)', '=', 'x.shape', 'else:... | 252,407 |
JonasLandman/QCNN | ipaddress.py | _BaseNetwork.overlaps | overlaps | Tell if self is partly contained in other. | [
"Tell",
"if",
"self",
"is",
"partly",
"contained",
"in",
"other."
] | def overlaps(self, other):
return self.network_address in other or (self.broadcast_address in other or (other.network_address in self or other.broadcast_address in self)) | ['def', 'overlaps(self,', 'other):', 'return', 'self.network_address', 'in', 'other', 'or', '(self.broadcast_address', 'in', 'other', 'or', '(other.network_address', 'in', 'self', 'or', 'other.broadcast_address', 'in', 'self))'] | 303,053 |
ADLab3Ds/TiG-BEV | cam_box3d.py | CameraInstance3DBoxes.rotate | rotate | Rotate boxes with points (optional) with the given angle or rotation matrix. | [
"Rotate",
"boxes",
"with",
"points",
"(optional)",
"with",
"the",
"given",
"angle",
"or",
"rotation",
"matrix."
] | def rotate(self, angle, points=None):
if not isinstance(angle, torch.Tensor):
angle = self.tensor.new_tensor(angle)
assert angle.shape == torch.Size([3, 3]) or angle.numel() == 1, f'invalid rotation angle shape {angle.shape}'
if angle.numel() == 1:
rot_sin = torch.sin(angle)
rot_cos ... | ['def', 'rotate(self,', 'angle,', 'points=None):', 'if', 'not', 'isinstance(angle,', 'torch.Tensor):', 'angle', '=', 'self.tensor.new_tensor(angle)', 'assert', 'angle.shape', '==', 'torch.Size([3,', '3])', 'or', 'angle.numel()', '==', '1,', "f'invalid", 'rotation', 'angle', 'shape', "{angle.shape}'", 'if', 'angle.numel... | 916,763 |
danamyu/hedgehog_detector | sequence_layers.py | SequenceLayerBase.get_input | get_input | A wrapper for get_train_input and get_eval_input. | [
"A",
"wrapper",
"for",
"get_train_input",
"and",
"get_eval_input."
] | def get_input(self, prev, i):
if self.is_training():
return self.get_train_input(prev, i)
else:
return self.get_eval_input(prev, i) | ['def', 'get_input(self,', 'prev,', 'i):', 'if', 'self.is_training():', 'return', 'self.get_train_input(prev,', 'i)', 'else:', 'return', 'self.get_eval_input(prev,', 'i)'] | 589,268 |
clvrai/spirl | environment.py | BaseEnvironment.reset | reset | Resets all internal variables of the environment. | [
"Resets",
"all",
"internal",
"variables",
"of",
"the",
"environment."
] | def reset(self):
raise NotImplementedError | ['def', 'reset(self):', 'raise', 'NotImplementedError'] | 897,009 |
yinyunie/ScenePriors | test_points_to_volumes.py | TestRawFunction.single_point | single_point | Check the outcome of _points_to_volumes where a single point exists which lines up with a single voxel. | [
"Check",
"the",
"outcome",
"of",
"_points_to_volumes",
"where",
"a",
"single",
"point",
"exists",
"which",
"lines",
"up",
"with",
"a",
"single",
"voxel."
] | def single_point(self, device, splat: bool, align_corners: bool):
(D, H, W) = (6, 6, 11) if align_corners else (5, 5, 10)
(N, C, P) = (1, 1, 1)
if align_corners:
points_3d = torch.tensor([[[-0.2, 0.2, -0.2]]], device=device)
else:
points_3d = torch.tensor([[[-0.3, 0.4, -0.4]]], device=de... | ['def', 'single_point(self,', 'device,', 'splat:', 'bool,', 'align_corners:', 'bool):', '(D,', 'H,', 'W)', '=', '(6,', '6,', '11)', 'if', 'align_corners', 'else', '(5,', '5,', '10)', '(N,', 'C,', 'P)', '=', '(1,', '1,', '1)', 'if', 'align_corners:', 'points_3d', '=', 'torch.tensor([[[-0.2,', '0.2,', '-0.2]]],', 'device... | 330,076 |
wuzheng-sjtu/FastFPN | gprof2dot.py | AXEParser.translate | translate | Extract a structure from a match object, while translating the types in the process. | [
"Extract",
"a",
"structure",
"from",
"a",
"match",
"object,",
"while",
"translating",
"the",
"types",
"in",
"the",
"process."
] | def translate(self, mo):
attrs = {}
groupdict = mo.groupdict()
for (name, value) in compat_iteritems(groupdict):
if value is None:
value = None
elif self._int_re.match(value):
value = int(value)
elif self._float_re.match(value):
value = float(value... | ['def', 'translate(self,', 'mo):', 'attrs', '=', '{}', 'groupdict', '=', 'mo.groupdict()', 'for', '(name,', 'value)', 'in', 'compat_iteritems(groupdict):', 'if', 'value', 'is', 'None:', 'value', '=', 'None', 'elif', 'self._int_re.match(value):', 'value', '=', 'int(value)', 'elif', 'self._float_re.match(value):', 'value... | 559,730 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | thinkstats2.py | Hist.Freqs | Freqs | Gets frequencies for a sequence of values. | [
"Gets",
"frequencies",
"for",
"a",
"sequence",
"of",
"values."
] | def Freqs(self, xs):
return [self.Freq(x) for x in xs] | ['def', 'Freqs(self,', 'xs):', 'return', '[self.Freq(x)', 'for', 'x', 'in', 'xs]'] | 13,452 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | input_data.py | DataSet.next_batch | next_batch | Return the next `batch_size` examples from this data set. | [
"Return",
"the",
"next",
"`batch_size`",
"examples",
"from",
"this",
"data",
"set."
] | def next_batch(self, batch_size, fake_data=False):
if fake_data:
fake_image = [1.0 for _ in xrange(784)]
fake_label = 0
return ([fake_image for _ in xrange(batch_size)], [fake_label for _ in xrange(batch_size)])
start = self._index_in_epoch
self._index_in_epoch += batch_size
if s... | ['def', 'next_batch(self,', 'batch_size,', 'fake_data=False):', 'if', 'fake_data:', 'fake_image', '=', '[1.0', 'for', '_', 'in', 'xrange(784)]', 'fake_label', '=', '0', 'return', '([fake_image', 'for', '_', 'in', 'xrange(batch_size)],', '[fake_label', 'for', '_', 'in', 'xrange(batch_size)])', 'start', '=', 'self._index... | 9,020 |
mseg-dataset/mseg-semantic | inference_task.py | InferenceTask.render_single_img_pred | render_single_img_pred | Since overlaid class text is difficult to read below 1080p, we upsample predictions. | [
"Since",
"overlaid",
"class",
"text",
"is",
"difficult",
"to",
"read",
"below",
"1080p,",
"we",
"upsample",
"predictions."
] | def render_single_img_pred(self, min_resolution: int=1080) -> None:
in_fname_stem = Path(self.input_file).stem
output_gray_fpath = f'{in_fname_stem}_gray.jpg'
output_demo_fpath = f'{in_fname_stem}_overlaid_classes.jpg'
logger.info(f'Write image prediction to {output_demo_fpath}')
rgb_img = imread_rg... | ['def', 'render_single_img_pred(self,', 'min_resolution:', 'int=1080)', '->', 'None:', 'in_fname_stem', '=', 'Path(self.input_file).stem', 'output_gray_fpath', '=', "f'{in_fname_stem}_gray.jpg'", 'output_demo_fpath', '=', "f'{in_fname_stem}_overlaid_classes.jpg'", "logger.info(f'Write", 'image', 'prediction', 'to', "{o... | 642,445 |
tensorly/quantum | tfq_ps_util_ops_test.py | PSWeightsFromSymbolTest.test_many_values | test_many_values | Ensure that padding with few symbols and many values works. | [
"Ensure",
"that",
"padding",
"with",
"few",
"symbols",
"and",
"many",
"values",
"works."
] | def test_many_values(self):
bit = cirq.LineQubit(1)
circuits = [cirq.Circuit(cirq.X(bit) ** (sympy.Symbol('alpha') * 2.0), cirq.Y(bit) ** (sympy.Symbol('alpha') * 3.0), cirq.Z(bit) ** sympy.Symbol('alpha'), cirq.X(bit) ** (sympy.Symbol('alpha') * 4.0)), cirq.Circuit(cirq.X(bit) ** (sympy.Symbol('alpha') * 9.0))... | ['def', 'test_many_values(self):', 'bit', '=', 'cirq.LineQubit(1)', 'circuits', '=', '[cirq.Circuit(cirq.X(bit)', '**', "(sympy.Symbol('alpha')", '*', '2.0),', 'cirq.Y(bit)', '**', "(sympy.Symbol('alpha')", '*', '3.0),', 'cirq.Z(bit)', '**', "sympy.Symbol('alpha'),", 'cirq.X(bit)', '**', "(sympy.Symbol('alpha')", '*', ... | 834,694 |
Trusted-AI/AIF360 | classification_metric.py | ClassificationMetric.true_positive_rate | true_positive_rate | Return the ratio of true positives to positive examples in the dataset, :math:`TPR = TP/P`, optionally conditioned on protected attributes. | [
"Return",
"the",
"ratio",
"of",
"true",
"positives",
"to",
"positive",
"examples",
"in",
"the",
"dataset,",
":math:`TPR",
"=",
"TP/P`,",
"optionally",
"conditioned",
"on",
"protected",
"attributes."
] | def true_positive_rate(self, privileged=None):
return self.performance_measures(privileged=privileged)['TPR'] | ['def', 'true_positive_rate(self,', 'privileged=None):', 'return', "self.performance_measures(privileged=privileged)['TPR']"] | 412,325 |
sarnsdev/social-alignment-data-mining | test_iforest.py | test_iforest_sparse | test_iforest_sparse | Check IForest for various parameter settings on sparse input. | [
"Check",
"IForest",
"for",
"various",
"parameter",
"settings",
"on",
"sparse",
"input."
] | def test_iforest_sparse():
rng = check_random_state(0)
(X_train, X_test, y_train, y_test) = train_test_split(boston.data[:50], boston.target[:50], random_state=rng)
grid = ParameterGrid({'max_samples': [0.5, 1.0], 'bootstrap': [True, False]})
for sparse_format in [csc_matrix, csr_matrix]:
X_trai... | ['def', 'test_iforest_sparse():', 'rng', '=', 'check_random_state(0)', '(X_train,', 'X_test,', 'y_train,', 'y_test)', '=', 'train_test_split(boston.data[:50],', 'boston.target[:50],', 'random_state=rng)', 'grid', '=', "ParameterGrid({'max_samples':", '[0.5,', '1.0],', "'bootstrap':", '[True,', 'False]})', 'for', 'spars... | 391,934 |
Trusted-AI/AIF360 | mdss_classification_metric.py | MDSSClassificationMetric.score_groups | score_groups | Compute the bias score for a prespecified group of records. | [
"Compute",
"the",
"bias",
"score",
"for",
"a",
"prespecified",
"group",
"of",
"records."
] | def score_groups(self, privileged=True, penalty=1e-17):
groups = self.privileged_groups if privileged else self.unprivileged_groups
subset = dict()
for g in groups:
for (k, v) in g.items():
if k in subset.keys():
subset[k].append(v)
else:
subse... | ['def', 'score_groups(self,', 'privileged=True,', 'penalty=1e-17):', 'groups', '=', 'self.privileged_groups', 'if', 'privileged', 'else', 'self.unprivileged_groups', 'subset', '=', 'dict()', 'for', 'g', 'in', 'groups:', 'for', '(k,', 'v)', 'in', 'g.items():', 'if', 'k', 'in', 'subset.keys():', 'subset[k].append(v)', 'e... | 412,364 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | shor.py | run_shor | run_shor | Runs the quantum subroutine of Shor's algorithm for factoring. | [
"Runs",
"the",
"quantum",
"subroutine",
"of",
"Shor's",
"algorithm",
"for",
"factoring."
] | def run_shor(eng, N, a, verbose=False):
n = int(math.ceil(math.log(N, 2)))
x = eng.allocate_qureg(n)
X | x[0]
measurements = [0] * (2 * n)
ctrl_qubit = eng.allocate_qubit()
for k in range(2 * n):
current_a = pow(a, 1 << 2 * n - 1 - k, N)
H | ctrl_qubit
with Control(eng, c... | ['def', 'run_shor(eng,', 'N,', 'a,', 'verbose=False):', 'n', '=', 'int(math.ceil(math.log(N,', '2)))', 'x', '=', 'eng.allocate_qureg(n)', 'X', '|', 'x[0]', 'measurements', '=', '[0]', '*', '(2', '*', 'n)', 'ctrl_qubit', '=', 'eng.allocate_qubit()', 'for', 'k', 'in', 'range(2', '*', 'n):', 'current_a', '=', 'pow(a,', '1... | 20,022 |
facebookresearch/CompilerGym | gcc_env.py | GccEnv.source | source | Get the source code. | [
"Get",
"the",
"source",
"code."
] | def source(self) -> str:
return self.observation['source'] | ['def', 'source(self)', '->', 'str:', 'return', "self.observation['source']"] | 125,457 |
clw5180/remote_sensing_object_detection_2019 | polygon_wrapper.py | area_of_intersection | area_of_intersection | This helper calculates the area of intersection. | [
"This",
"helper",
"calculates",
"the",
"area",
"of",
"intersection."
] | def area_of_intersection(det_x, det_y, gt_x, gt_y):
if approx_area_of_intersection(det_x, det_y, gt_x, gt_y) > 1:
ymax = np.maximum(np.max(det_y), np.max(gt_y)) + 1
xmax = np.maximum(np.max(det_x), np.max(gt_x)) + 1
bin_mask = np.zeros((ymax, xmax))
det_bin_mask = np.zeros_like(bin_m... | ['def', 'area_of_intersection(det_x,', 'det_y,', 'gt_x,', 'gt_y):', 'if', 'approx_area_of_intersection(det_x,', 'det_y,', 'gt_x,', 'gt_y)', '>', '1:', 'ymax', '=', 'np.maximum(np.max(det_y),', 'np.max(gt_y))', '+', '1', 'xmax', '=', 'np.maximum(np.max(det_x),', 'np.max(gt_x))', '+', '1', 'bin_mask', '=', 'np.zeros((yma... | 840,057 |
weimin17/Object-Detection_HelmetDetection | neural_gpu_trainer.py | print_vectors | print_vectors | Print vectors from the given variable. | [
"Print",
"vectors",
"from",
"the",
"given",
"variable."
] | def print_vectors(embedding_key, vocab_path, word_vector_file):
(_, rev_vocab) = wmt.initialize_vocabulary(vocab_path)
vectors_variable = [v for v in tf.trainable_variables() if embedding_key == v.name]
if len(vectors_variable) != 1:
data.print_out('Word vector variable not found or too many.')
... | ['def', 'print_vectors(embedding_key,', 'vocab_path,', 'word_vector_file):', '(_,', 'rev_vocab)', '=', 'wmt.initialize_vocabulary(vocab_path)', 'vectors_variable', '=', '[v', 'for', 'v', 'in', 'tf.trainable_variables()', 'if', 'embedding_key', '==', 'v.name]', 'if', 'len(vectors_variable)', '!=', '1:', "data.print_out(... | 751,388 |
keras-team/keras-cv | preprocessing.py | ensure_tensor | ensure_tensor | Ensures the input is a Tensor, SparseTensor or RaggedTensor. | [
"Ensures",
"the",
"input",
"is",
"a",
"Tensor,",
"SparseTensor",
"or",
"RaggedTensor."
] | def ensure_tensor(inputs, dtype=None):
if not ops.is_tensor(inputs):
inputs = ops.convert_to_tensor(inputs, dtype)
if dtype is not None and inputs.dtype != dtype:
inputs = ops.cast(inputs, dtype)
return inputs | ['def', 'ensure_tensor(inputs,', 'dtype=None):', 'if', 'not', 'ops.is_tensor(inputs):', 'inputs', '=', 'ops.convert_to_tensor(inputs,', 'dtype)', 'if', 'dtype', 'is', 'not', 'None', 'and', 'inputs.dtype', '!=', 'dtype:', 'inputs', '=', 'ops.cast(inputs,', 'dtype)', 'return', 'inputs'] | 595,383 |
muhanzhang/D-VAE | test_debugmode.py | test_badoptimization_opt_err | test_badoptimization_opt_err | This variant of test_badoptimization() replace the working code with a new apply node that will raise an error. | [
"This",
"variant",
"of",
"test_badoptimization()",
"replace",
"the",
"working",
"code",
"with",
"a",
"new",
"apply",
"node",
"that",
"will",
"raise",
"an",
"error."
] | def test_badoptimization_opt_err():
@gof.local_optimizer([theano.tensor.add])
def insert_bigger_b_add(node):
if node.op == theano.tensor.add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
inputs[-1] = theano.tensor.concatenate((inputs[-1], inputs[-1]))
... | ['def', 'test_badoptimization_opt_err():', '@gof.local_optimizer([theano.tensor.add])', 'def', 'insert_bigger_b_add(node):', 'if', 'node.op', '==', 'theano.tensor.add:', 'inputs', '=', 'list(node.inputs)', 'if', 'inputs[-1].owner', 'is', 'None:', 'inputs[-1]', '=', 'theano.tensor.concatenate((inputs[-1],', 'inputs[-1])... | 524,820 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | networks.py | generator | generator | Thin wrapper around CycleGAN generator to conform to the TFGAN API. | [
"Thin",
"wrapper",
"around",
"CycleGAN",
"generator",
"to",
"conform",
"to",
"the",
"TFGAN",
"API."
] | def generator(input_images):
input_images.shape.assert_has_rank(4)
with tf.contrib.framework.arg_scope(cyclegan.cyclegan_arg_scope()):
(output_images, _) = cyclegan.cyclegan_generator_resnet(input_images)
return output_images | ['def', 'generator(input_images):', 'input_images.shape.assert_has_rank(4)', 'with', 'tf.contrib.framework.arg_scope(cyclegan.cyclegan_arg_scope()):', '(output_images,', '_)', '=', 'cyclegan.cyclegan_generator_resnet(input_images)', 'return', 'output_images'] | 54,907 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | vecs.py | Vecs.neighbors | neighbors | Returns the nearest neighbors to the query (a word or vector). | [
"Returns",
"the",
"nearest",
"neighbors",
"to",
"the",
"query",
"(a",
"word",
"or",
"vector)."
] | def neighbors(self, query):
if isinstance(query, string_types):
idx = self.word_to_idx.get(query)
if idx is None:
return None
query = self.vecs[idx]
neighbors = self.vecs * query.transpose()
return sorted(zip(self.vocab, neighbors.flat), key=lambda kv: kv[1], reverse=True... | ['def', 'neighbors(self,', 'query):', 'if', 'isinstance(query,', 'string_types):', 'idx', '=', 'self.word_to_idx.get(query)', 'if', 'idx', 'is', 'None:', 'return', 'None', 'query', '=', 'self.vecs[idx]', 'neighbors', '=', 'self.vecs', '*', 'query.transpose()', 'return', 'sorted(zip(self.vocab,', 'neighbors.flat),', 'ke... | 27,964 |
avalonstrel/SketchBERT | utils.py | DataLoader.calculate_normalizing_scale_factor | calculate_normalizing_scale_factor | Calculate the normalizing factor explained in appendix of sketch-rnn. | [
"Calculate",
"the",
"normalizing",
"factor",
"explained",
"in",
"appendix",
"of",
"sketch-rnn."
] | def calculate_normalizing_scale_factor(self):
data = []
for i in range(len(self.strokes)):
if len(self.strokes[i]) > self.max_seq_length:
continue
for j in range(len(self.strokes[i])):
data.append(self.strokes[i][j, 0])
data.append(self.strokes[i][j, 1])
d... | ['def', 'calculate_normalizing_scale_factor(self):', 'data', '=', '[]', 'for', 'i', 'in', 'range(len(self.strokes)):', 'if', 'len(self.strokes[i])', '>', 'self.max_seq_length:', 'continue', 'for', 'j', 'in', 'range(len(self.strokes[i])):', 'data.append(self.strokes[i][j,', '0])', 'data.append(self.strokes[i][j,', '1])'... | 350,939 |
IINemo/isanlp | processor_lemmatizer_nltk_en.py | get_wordnet_pos | get_wordnet_pos | Converts Penn treebank postag into WordNet postag. | [
"Converts",
"Penn",
"treebank",
"postag",
"into",
"WordNet",
"postag."
] | def get_wordnet_pos(treebank_tag):
if treebank_tag.startswith('J'):
return wordnet.ADJ
elif treebank_tag.startswith('V'):
return wordnet.VERB
elif treebank_tag.startswith('N'):
return wordnet.NOUN
elif treebank_tag.startswith('R'):
return wordnet.ADV
else:
ret... | ['def', 'get_wordnet_pos(treebank_tag):', 'if', "treebank_tag.startswith('J'):", 'return', 'wordnet.ADJ', 'elif', "treebank_tag.startswith('V'):", 'return', 'wordnet.VERB', 'elif', "treebank_tag.startswith('N'):", 'return', 'wordnet.NOUN', 'elif', "treebank_tag.startswith('R'):", 'return', 'wordnet.ADV', 'else:', 'retu... | 577,241 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | feature_io.py | ArraysToDelfFeatures | ArraysToDelfFeatures | Converts DELF features to DelfFeatures proto. | [
"Converts",
"DELF",
"features",
"to",
"DelfFeatures",
"proto."
] | def ArraysToDelfFeatures(locations, scales, descriptors, attention, orientations=None):
num_features = len(attention)
assert num_features == locations.shape[0]
assert num_features == len(scales)
assert num_features == descriptors.shape[0]
if orientations is None:
orientations = np.zeros([num... | ['def', 'ArraysToDelfFeatures(locations,', 'scales,', 'descriptors,', 'attention,', 'orientations=None):', 'num_features', '=', 'len(attention)', 'assert', 'num_features', '==', 'locations.shape[0]', 'assert', 'num_features', '==', 'len(scales)', 'assert', 'num_features', '==', 'descriptors.shape[0]', 'if', 'orientatio... | 53,759 |
mfbx9da4/neuron-astrocyte-networks | table.py | Table.getValue | getValue | return the value at a certain location in the table. | [
"return",
"the",
"value",
"at",
"a",
"certain",
"location",
"in",
"the",
"table."
] | def getValue(self, row, column):
return self.params.reshape(self.numRows, self.numColumns)[row, column] | ['def', 'getValue(self,', 'row,', 'column):', 'return', 'self.params.reshape(self.numRows,', 'self.numColumns)[row,', 'column]'] | 722,682 |
sek788432/Waymo-2D-Object-Detection | input_pipeline.py | decode_record | decode_record | Decodes a record to a TensorFlow example. | [
"Decodes",
"a",
"record",
"to",
"a",
"TensorFlow",
"example."
] | def decode_record(record, name_to_features):
example = tf.io.parse_single_example(record, name_to_features)
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example | ['def', 'decode_record(record,', 'name_to_features):', 'example', '=', 'tf.io.parse_single_example(record,', 'name_to_features)', 'for', 'name', 'in', 'list(example.keys()):', 't', '=', 'example[name]', 'if', 't.dtype', '==', 'tf.int64:', 't', '=', 'tf.cast(t,', 'tf.int32)', 'example[name]', '=', 't', 'return', 'exampl... | 972,425 |
sktime/sktime | test_fourier.py | test_fourier_list_length_missmatch | test_fourier_list_length_missmatch | Tests exception raised when sp_list & fourier_terms_list lengths don't match. | [
"Tests",
"exception",
"raised",
"when",
"sp_list",
"&",
"fourier_terms_list",
"lengths",
"don't",
"match."
] | def test_fourier_list_length_missmatch():
with pytest.raises(ValueError) as ex:
FourierFeatures(sp_list=[365, 52], fourier_terms_list=[1])
assert ex.value == 'In FourierFeatures the length of the sp_list needs to be equal to the length of fourier_terms_list.' | ['def', 'test_fourier_list_length_missmatch():', 'with', 'pytest.raises(ValueError)', 'as', 'ex:', 'FourierFeatures(sp_list=[365,', '52],', 'fourier_terms_list=[1])', 'assert', 'ex.value', '==', "'In", 'FourierFeatures', 'the', 'length', 'of', 'the', 'sp_list', 'needs', 'to', 'be', 'equal', 'to', 'the', 'length', 'of',... | 877,872 |
yanqi1811/transfer-learning | tf_dataset.py | TFDataset.shuffle_split | shuffle_split | Randomly split the dataset into train, validation, and test subsets with a pseudo-random seed option. | [
"Randomly",
"split",
"the",
"dataset",
"into",
"train,",
"validation,",
"and",
"test",
"subsets",
"with",
"a",
"pseudo-random",
"seed",
"option."
] | def shuffle_split(self, train_pct=0.75, val_pct=0.25, test_pct=0.0, shuffle_files=True, seed=None):
if not (isinstance(train_pct, float) and isinstance(val_pct, float) and isinstance(test_pct, float)):
raise ValueError('Percentage arguments must be floats.')
if train_pct + val_pct + test_pct > 1.0:
... | ['def', 'shuffle_split(self,', 'train_pct=0.75,', 'val_pct=0.25,', 'test_pct=0.0,', 'shuffle_files=True,', 'seed=None):', 'if', 'not', '(isinstance(train_pct,', 'float)', 'and', 'isinstance(val_pct,', 'float)', 'and', 'isinstance(test_pct,', 'float)):', 'raise', "ValueError('Percentage", 'arguments', 'must', 'be', "flo... | 927,688 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | data_utils.py | prepare_data | prepare_data | Preapre all necessary files that are required for the training. | [
"Preapre",
"all",
"necessary",
"files",
"that",
"are",
"required",
"for",
"the",
"training."
] | def prepare_data(data_dir, from_train_path, to_train_path, from_dev_path, to_dev_path, from_vocabulary_size, to_vocabulary_size, tokenizer=None):
to_vocab_path = os.path.join(data_dir, 'vocab%d.to' % to_vocabulary_size)
from_vocab_path = os.path.join(data_dir, 'vocab%d.from' % from_vocabulary_size)
create_v... | ['def', 'prepare_data(data_dir,', 'from_train_path,', 'to_train_path,', 'from_dev_path,', 'to_dev_path,', 'from_vocabulary_size,', 'to_vocabulary_size,', 'tokenizer=None):', 'to_vocab_path', '=', 'os.path.join(data_dir,', "'vocab%d.to'", '%', 'to_vocabulary_size)', 'from_vocab_path', '=', 'os.path.join(data_dir,', "'vo... | 30,487 |
liuzuxin/safe-mbrl | mpi_tf.py | MpiAdamOptimizer.apply_gradients | apply_gradients | Same as normal apply_gradients, except sync params after update. | [
"Same",
"as",
"normal",
"apply_gradients,",
"except",
"sync",
"params",
"after",
"update."
] | def apply_gradients(self, grads_and_vars, global_step=None, name=None):
opt = super().apply_gradients(grads_and_vars, global_step, name)
with tf.control_dependencies([opt]):
sync = sync_params([v for (g, v) in grads_and_vars])
return tf.group([opt, sync]) | ['def', 'apply_gradients(self,', 'grads_and_vars,', 'global_step=None,', 'name=None):', 'opt', '=', 'super().apply_gradients(grads_and_vars,', 'global_step,', 'name)', 'with', 'tf.control_dependencies([opt]):', 'sync', '=', 'sync_params([v', 'for', '(g,', 'v)', 'in', 'grads_and_vars])', 'return', 'tf.group([opt,', 'syn... | 828,862 |
matsu0228/nlp-jp | __init__.py | FCompiler.get_flags_fix | get_flags_fix | List of Fortran 90 fixed format specific flags. | [
"List",
"of",
"Fortran",
"90",
"fixed",
"format",
"specific",
"flags."
] | def get_flags_fix(self):
return self._get_command_flags('compiler_fix') | ['def', 'get_flags_fix(self):', 'return', "self._get_command_flags('compiler_fix')"] | 791,061 |
Jittor/JDet | coco.py | COCODataset.evaluate | evaluate | Evaluation in COCO protocol. | [
"Evaluation",
"in",
"COCO",
"protocol."
] | def evaluate(self, results, work_dir, epoch, metric='bbox', logger=None, classwise=False, proposal_nums=(100, 300, 1000), iou_thrs=None, metric_items=None):
save_file = build_file(work_dir, prefix=f'detections/val_{epoch}.json')
self.save_results(results, save_file)
metrics = metric if isinstance(metric, li... | ['def', 'evaluate(self,', 'results,', 'work_dir,', 'epoch,', "metric='bbox',", 'logger=None,', 'classwise=False,', 'proposal_nums=(100,', '300,', '1000),', 'iou_thrs=None,', 'metric_items=None):', 'save_file', '=', 'build_file(work_dir,', "prefix=f'detections/val_{epoch}.json')", 'self.save_results(results,', 'save_fil... | 577,643 |
YuriyGuts/snake-ai-reinforcement | environment.py | Environment.get_observation | get_observation | Observe the state of the environment. | [
"Observe",
"the",
"state",
"of",
"the",
"environment."
] | def get_observation(self):
return np.copy(self.field._cells) | ['def', 'get_observation(self):', 'return', 'np.copy(self.field._cells)'] | 352,113 |
tobegit3hub/deep_image_model | server_test.py | TensorboardServerTest.testSampleScalars | testSampleScalars | Test the sample_count parameter of /data/scalars. | [
"Test",
"the",
"sample_count",
"parameter",
"of",
"/data/scalars."
] | def testSampleScalars(self):
for i in xrange(10, self._SCALAR_COUNT, 10):
samples = self._getJson('/data/scalars?sample_count=%d' % i)
values = samples['run1']['simple_values']
self.assertEqual(len(values), i)
self.assertEqual(values[0], [100, 10, 1])
self.assertEqual(values[... | ['def', 'testSampleScalars(self):', 'for', 'i', 'in', 'xrange(10,', 'self._SCALAR_COUNT,', '10):', 'samples', '=', "self._getJson('/data/scalars?sample_count=%d'", '%', 'i)', 'values', '=', "samples['run1']['simple_values']", 'self.assertEqual(len(values),', 'i)', 'self.assertEqual(values[0],', '[100,', '10,', '1])', '... | 183,480 |
Ruturaj123/Flowchart-Detection | model_rotator.py | get_init_fn | get_init_fn | Initialization assignment operator function used while training. | [
"Initialization",
"assignment",
"operator",
"function",
"used",
"while",
"training."
] | def get_init_fn(scopes, params):
if not params.init_model:
return None
is_trainable = lambda x: x in tf.trainable_variables()
var_list = []
for scope in scopes:
var_list.extend(filter(is_trainable, tf.contrib.framework.get_model_variables(scope)))
(init_assign_op, init_feed_dict) = s... | ['def', 'get_init_fn(scopes,', 'params):', 'if', 'not', 'params.init_model:', 'return', 'None', 'is_trainable', '=', 'lambda', 'x:', 'x', 'in', 'tf.trainable_variables()', 'var_list', '=', '[]', 'for', 'scope', 'in', 'scopes:', 'var_list.extend(filter(is_trainable,', 'tf.contrib.framework.get_model_variables(scope)))',... | 586,302 |
RasaHQ/rasa | precomputation.py | MessageContainerForCoreFeaturization.all_messages | all_messages | Returns a list containing all messages. | [
"Returns",
"a",
"list",
"containing",
"all",
"messages."
] | def all_messages(self) -> List[Message]:
return [message for key_attribute_table in self._table.values() for message in key_attribute_table.values()] | ['def', 'all_messages(self)', '->', 'List[Message]:', 'return', '[message', 'for', 'key_attribute_table', 'in', 'self._table.values()', 'for', 'message', 'in', 'key_attribute_table.values()]'] | 836,885 |
kwai/DouZero | env.py | DummyAgent.act | act | Simply return the action that is set previously. | [
"Simply",
"return",
"the",
"action",
"that",
"is",
"set",
"previously."
] | def act(self, infoset):
assert self.action in infoset.legal_actions
return self.action | ['def', 'act(self,', 'infoset):', 'assert', 'self.action', 'in', 'infoset.legal_actions', 'return', 'self.action'] | 166,841 |
deepmind/dm_control | control.py | Environment.step_spec | step_spec | May return a specification for the values returned by `step`. | [
"May",
"return",
"a",
"specification",
"for",
"the",
"values",
"returned",
"by",
"`step`."
] | def step_spec(self):
return self._task.step_spec(self._physics) | ['def', 'step_spec(self):', 'return', 'self._task.step_spec(self._physics)'] | 165,347 |
janluke/cs188 | util.py | arrayInvert | arrayInvert | Inverts a matrix stored as a list of lists. | [
"Inverts",
"a",
"matrix",
"stored",
"as",
"a",
"list",
"of",
"lists."
] | def arrayInvert(array):
result = [[] for i in array]
for outer in array:
for inner in range(len(outer)):
result[inner].append(outer[inner])
return result | ['def', 'arrayInvert(array):', 'result', '=', '[[]', 'for', 'i', 'in', 'array]', 'for', 'outer', 'in', 'array:', 'for', 'inner', 'in', 'range(len(outer)):', 'result[inner].append(outer[inner])', 'return', 'result'] | 224,744 |
nicknochnack/RealTimeSignLanguageTFJS | image_classification.py | ImageClassificationTask.inference_step | inference_step | Performs the forward step. | [
"Performs",
"the",
"forward",
"step."
] | def inference_step(self, inputs, model):
return model(inputs, training=False) | ['def', 'inference_step(self,', 'inputs,', 'model):', 'return', 'model(inputs,', 'training=False)'] | 850,910 |
ifwe/digsby | imagefx.py | rounded_mask | rounded_mask | Returns a grayscale image with the specified size, with alpha values dropping off at the corners. | [
"Returns",
"a",
"grayscale",
"image",
"with",
"the",
"specified",
"size,",
"with",
"alpha",
"values",
"dropping",
"off",
"at",
"the",
"corners."
] | def rounded_mask(size, cornersize=1):
img = Image.new('L', size, 255)
(w, h) = size
(p, r) = (img.paste, rounded_corners(cornersize))
i = r[0]
p(i, (0, 0, i.size[0], i.size[1]))
i = r[1]
p(i, (w - i.size[0], 0, w, i.size[1]))
i = r[2]
p(i, (0, h - i.size[1], i.size[0], h))
i = r[... | ['def', 'rounded_mask(size,', 'cornersize=1):', 'img', '=', "Image.new('L',", 'size,', '255)', '(w,', 'h)', '=', 'size', '(p,', 'r)', '=', '(img.paste,', 'rounded_corners(cornersize))', 'i', '=', 'r[0]', 'p(i,', '(0,', '0,', 'i.size[0],', 'i.size[1]))', 'i', '=', 'r[1]', 'p(i,', '(w', '-', 'i.size[0],', '0,', 'w,', 'i.... | 185,559 |
amazon-science/unified-ept | custom.py | CustomDataset.load_annotations | load_annotations | Load annotation from directory. | [
"Load",
"annotation",
"from",
"directory."
] | def load_annotations(self, img_dir, img_suffix, ann_dir, dt_dir, seg_map_suffix, split):
img_infos = []
if split is not None:
with open(split) as f:
for line in f:
img_name = line.strip()
img_info = dict(filename=img_name + img_suffix)
if ann_d... | ['def', 'load_annotations(self,', 'img_dir,', 'img_suffix,', 'ann_dir,', 'dt_dir,', 'seg_map_suffix,', 'split):', 'img_infos', '=', '[]', 'if', 'split', 'is', 'not', 'None:', 'with', 'open(split)', 'as', 'f:', 'for', 'line', 'in', 'f:', 'img_name', '=', 'line.strip()', 'img_info', '=', 'dict(filename=img_name', '+', 'i... | 947,975 |
greydanus/mr_london | OleFileIO.py | OleFileIO.loadfat | loadfat | Load the FAT table. | [
"Load",
"the",
"FAT",
"table."
] | def loadfat(self, header):
sect = header[76:512]
debug('len(sect)=%d, so %d integers' % (len(sect), len(sect) // 4))
self.fat = array.array(UINT32)
self.loadfat_sect(sect)
if self.csectDif != 0:
if self.csectFat <= 109:
self.raise_defect(DEFECT_INCORRECT, 'incorrect DIFAT, not en... | ['def', 'loadfat(self,', 'header):', 'sect', '=', 'header[76:512]', "debug('len(sect)=%d,", 'so', '%d', "integers'", '%', '(len(sect),', 'len(sect)', '//', '4))', 'self.fat', '=', 'array.array(UINT32)', 'self.loadfat_sect(sect)', 'if', 'self.csectDif', '!=', '0:', 'if', 'self.csectFat', '<=', '109:', 'self.raise_defect... | 263,271 |
intel/neural-compressor | graph_util.py | GraphAnalyzer.remove_node_with_single_input_output | remove_node_with_single_input_output | Remove node with one input and rebuild internal graph data structure. | [
"Remove",
"node",
"with",
"one",
"input",
"and",
"rebuild",
"internal",
"graph",
"data",
"structure."
] | def remove_node_with_single_input_output(self, node_name):
if node_name not in self.node_name_details:
logger.debug('The {} is not a valid node name.'.format(node_name))
return False
non_const_node_count = len([GraphRewriterHelper.node_name_from_input(i) for i in self.node_name_details[node_name... | ['def', 'remove_node_with_single_input_output(self,', 'node_name):', 'if', 'node_name', 'not', 'in', 'self.node_name_details:', "logger.debug('The", '{}', 'is', 'not', 'a', 'valid', 'node', "name.'.format(node_name))", 'return', 'False', 'non_const_node_count', '=', 'len([GraphRewriterHelper.node_name_from_input(i)', '... | 737,590 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | thinkplot.py | Cdf | Cdf | Plots a CDF as a line. | [
"Plots",
"a",
"CDF",
"as",
"a",
"line."
] | def Cdf(cdf, complement=False, transform=None, **options):
(xs, ps) = cdf.Render()
xs = np.asarray(xs)
ps = np.asarray(ps)
scale = dict(xscale='linear', yscale='linear')
for s in ['xscale', 'yscale']:
if s in options:
scale[s] = options.pop(s)
if transform == 'exponential':
... | ['def', 'Cdf(cdf,', 'complement=False,', 'transform=None,', '**options):', '(xs,', 'ps)', '=', 'cdf.Render()', 'xs', '=', 'np.asarray(xs)', 'ps', '=', 'np.asarray(ps)', 'scale', '=', "dict(xscale='linear',", "yscale='linear')", 'for', 's', 'in', "['xscale',", "'yscale']:", 'if', 's', 'in', 'options:', 'scale[s]', '=', ... | 12,739 |
caiiiac/Machine-Learning-with-Python | test_waveforms.py | TestSweepPoly.test_sweep_poly_cubic3 | test_sweep_poly_cubic3 | Use a list of coefficients instead of a poly1d. | [
"Use",
"a",
"list",
"of",
"coefficients",
"instead",
"of",
"a",
"poly1d."
] | def test_sweep_poly_cubic3(self):
p = [2.0, 1.0, 0.0, -2.0]
t = np.linspace(0, 2.0, 10000)
phase = waveforms._sweep_poly_phase(t, p)
(tf, f) = compute_frequency(t, phase)
expected = np.poly1d(p)(tf)
abserr = np.max(np.abs(f - expected))
assert_(abserr < 1e-06) | ['def', 'test_sweep_poly_cubic3(self):', 'p', '=', '[2.0,', '1.0,', '0.0,', '-2.0]', 't', '=', 'np.linspace(0,', '2.0,', '10000)', 'phase', '=', 'waveforms._sweep_poly_phase(t,', 'p)', '(tf,', 'f)', '=', 'compute_frequency(t,', 'phase)', 'expected', '=', 'np.poly1d(p)(tf)', 'abserr', '=', 'np.max(np.abs(f', '-', 'expec... | 719,876 |
voxel51/fiftyone | manager.py | PlotManager.remove | remove | Removes the plot from this manager. | [
"Removes",
"the",
"plot",
"from",
"this",
"manager."
] | def remove(self, name):
self.pop(name) | ['def', 'remove(self,', 'name):', 'self.pop(name)'] | 583,628 |
sek788432/Waymo-2D-Object-Detection | base_config.py | Config.from_args | from_args | Builds a config from the given list of arguments. | [
"Builds",
"a",
"config",
"from",
"the",
"given",
"list",
"of",
"arguments."
] | def from_args(cls, *args, **kwargs):
attributes = list(cls.__annotations__.keys())
default_params = {a: p for (a, p) in zip(attributes, args)}
default_params.update(kwargs)
return cls(default_params) | ['def', 'from_args(cls,', '*args,', '**kwargs):', 'attributes', '=', 'list(cls.__annotations__.keys())', 'default_params', '=', '{a:', 'p', 'for', '(a,', 'p)', 'in', 'zip(attributes,', 'args)}', 'default_params.update(kwargs)', 'return', 'cls(default_params)'] | 972,356 |
CQCL/lambeq | parser.py | Chart.min_score | min_score | Get the lowest score needed to add a tree to the given cell. | [
"Get",
"the",
"lowest",
"score",
"needed",
"to",
"add",
"a",
"tree",
"to",
"the",
"given",
"cell."
] | def min_score(self, start: int, end: int) -> float:
try:
return self.chart[start, end].min_score
except KeyError:
return NEGATIVE_INFINITY | ['def', 'min_score(self,', 'start:', 'int,', 'end:', 'int)', '->', 'float:', 'try:', 'return', 'self.chart[start,', 'end].min_score', 'except', 'KeyError:', 'return', 'NEGATIVE_INFINITY'] | 623,190 |
devashish-patel/webcam-motion-detector | prefilter.py | PrefilterManager.register_handler | register_handler | Register a handler instance by name with esc_strings. | [
"Register",
"a",
"handler",
"instance",
"by",
"name",
"with",
"esc_strings."
] | def register_handler(self, name, handler, esc_strings):
self._handlers[name] = handler
for esc_str in esc_strings:
self._esc_handlers[esc_str] = handler | ['def', 'register_handler(self,', 'name,', 'handler,', 'esc_strings):', 'self._handlers[name]', '=', 'handler', 'for', 'esc_str', 'in', 'esc_strings:', 'self._esc_handlers[esc_str]', '=', 'handler'] | 978,804 |
gunthercox/ChatterBot | schema.py | ChangesetColumn.copy_fixed | copy_fixed | Create a copy of this ``Column``, with all attributes. | [
"Create",
"a",
"copy",
"of",
"this",
"``Column``,",
"with",
"all",
"attributes."
] | def copy_fixed(self, **kw):
return sqlalchemy.Column(self.name, self.type, self.default, *[c.copy(**kw) for c in self.constraints], key=self.key, primary_key=self.primary_key, nullable=self.nullable, quote=self.quote, index=self.index, unique=self.unique, onupdate=self.onupdate, autoincrement=self.autoincrement, se... | ['def', 'copy_fixed(self,', '**kw):', 'return', 'sqlalchemy.Column(self.name,', 'self.type,', 'self.default,', '*[c.copy(**kw)', 'for', 'c', 'in', 'self.constraints],', 'key=self.key,', 'primary_key=self.primary_key,', 'nullable=self.nullable,', 'quote=self.quote,', 'index=self.index,', 'unique=self.unique,', 'onupdate... | 479,485 |
Eric3911/OpenAGI | rnnt_pytorch.py | RNNTLossPytorch.input_types | input_types | Input types definitions for CTCLoss. | [
"Input",
"types",
"definitions",
"for",
"CTCLoss."
] | def input_types(self):
return {'acts': NeuralType(('B', 'T', 'T', 'D'), LogprobsType()), 'labels': NeuralType(('B', 'T'), LabelsType()), 'act_lens': NeuralType(tuple('B'), LengthsType()), 'label_lens': NeuralType(tuple('B'), LengthsType())} | ['def', 'input_types(self):', 'return', "{'acts':", "NeuralType(('B',", "'T',", "'T',", "'D'),", 'LogprobsType()),', "'labels':", "NeuralType(('B',", "'T'),", 'LabelsType()),', "'act_lens':", "NeuralType(tuple('B'),", 'LengthsType()),', "'label_lens':", "NeuralType(tuple('B'),", 'LengthsType())}'] | 272,327 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | beam_reader_ops_test.py | ParsingReaderOpsTest.testPathScoresAgree | testPathScoresAgree | Ensures that path scores computed in the beam are same in the net. | [
"Ensures",
"that",
"path",
"scores",
"computed",
"in",
"the",
"beam",
"are",
"same",
"in",
"the",
"net."
] | def testPathScoresAgree(self):
(all_path_scores, beam_path_scores) = self.PathScores(iterations=1, beam_size=130, max_steps=5, batch_size=1)
self.assertArrayNear(all_path_scores[0], beam_path_scores[0], 1e-06) | ['def', 'testPathScoresAgree(self):', '(all_path_scores,', 'beam_path_scores)', '=', 'self.PathScores(iterations=1,', 'beam_size=130,', 'max_steps=5,', 'batch_size=1)', 'self.assertArrayNear(all_path_scores[0],', 'beam_path_scores[0],', '1e-06)'] | 28,848 |
WHU-ZQH/E2S2 | polynomial_decay_schedule.py | PolynomialDecayLRSchedule.step_update | step_update | Update the learning rate after each update. | [
"Update",
"the",
"learning",
"rate",
"after",
"each",
"update."
] | def step_update(self, num_updates):
if self.cfg.warmup_updates > 0 and num_updates <= self.cfg.warmup_updates:
self.warmup_factor = num_updates / float(self.cfg.warmup_updates)
lr = self.warmup_factor * self.lr
elif num_updates >= self.total_num_update:
lr = self.end_learning_rate
el... | ['def', 'step_update(self,', 'num_updates):', 'if', 'self.cfg.warmup_updates', '>', '0', 'and', 'num_updates', '<=', 'self.cfg.warmup_updates:', 'self.warmup_factor', '=', 'num_updates', '/', 'float(self.cfg.warmup_updates)', 'lr', '=', 'self.warmup_factor', '*', 'self.lr', 'elif', 'num_updates', '>=', 'self.total_num_... | 556,136 |
ancasag/ensembleObjectDetection | common.py | Generator.get_augmented_data | get_augmented_data | Compute inputs and target outputs for the network. | [
"Compute",
"inputs",
"and",
"target",
"outputs",
"for",
"the",
"network."
] | def get_augmented_data(self, group):
image_group = self.load_image_group(group)
annotations_group = self.load_annotations_group(group)
(image_group, annotations_group) = self.filter_annotations(image_group, annotations_group, group)
(image_group, annotations_group) = self.random_visual_effect_group(imag... | ['def', 'get_augmented_data(self,', 'group):', 'image_group', '=', 'self.load_image_group(group)', 'annotations_group', '=', 'self.load_annotations_group(group)', '(image_group,', 'annotations_group)', '=', 'self.filter_annotations(image_group,', 'annotations_group,', 'group)', '(image_group,', 'annotations_group)', '=... | 562,124 |
deepmind/dm_control | swimmer.py | Physics.body_velocities | body_velocities | Returns local body velocities: x,y linear, z rotational. | [
"Returns",
"local",
"body",
"velocities:",
"x,y",
"linear,",
"z",
"rotational."
] | def body_velocities(self):
xvel_local = self.data.sensordata[12:].reshape((-1, 6))
vx_vy_wz = [0, 1, 5]
return xvel_local[:, vx_vy_wz].ravel() | ['def', 'body_velocities(self):', 'xvel_local', '=', 'self.data.sensordata[12:].reshape((-1,', '6))', 'vx_vy_wz', '=', '[0,', '1,', '5]', 'return', 'xvel_local[:,', 'vx_vy_wz].ravel()'] | 165,587 |
tswsxk/CangJie | bert.py | BertEmbedding.data_loader | data_loader | Load, tokenize and prepare the input sentences. | [
"Load,",
"tokenize",
"and",
"prepare",
"the",
"input",
"sentences."
] | def data_loader(self, sentences, shuffle=False):
dataset = BertEmbeddingDataset(sentences, self.transform)
return DataLoader(dataset=dataset, batch_size=self.batch_size, shuffle=shuffle) | ['def', 'data_loader(self,', 'sentences,', 'shuffle=False):', 'dataset', '=', 'BertEmbeddingDataset(sentences,', 'self.transform)', 'return', 'DataLoader(dataset=dataset,', 'batch_size=self.batch_size,', 'shuffle=shuffle)'] | 454,798 |
ishtiaq1495/Generative_adversarial_networks | CycleGAN.py | to_var | to_var | Converts numpy to variable. | [
"Converts",
"numpy",
"to",
"variable."
] | def to_var(x):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x) | ['def', 'to_var(x):', 'if', 'torch.cuda.is_available():', 'x', '=', 'x.cuda()', 'return', 'Variable(x)'] | 556,598 |
interpretml/DiCE | public_data_interface.py | PublicData.prepare_query_instance | prepare_query_instance | Prepares user defined test input(s) for DiCE. | [
"Prepares",
"user",
"defined",
"test",
"input(s)",
"for",
"DiCE."
] | def prepare_query_instance(self, query_instance):
if isinstance(query_instance, list):
if isinstance(query_instance[0], dict):
test = pd.DataFrame(query_instance, columns=self.feature_names)
else:
query_instance = {'row1': query_instance}
test = pd.DataFrame.from_... | ['def', 'prepare_query_instance(self,', 'query_instance):', 'if', 'isinstance(query_instance,', 'list):', 'if', 'isinstance(query_instance[0],', 'dict):', 'test', '=', 'pd.DataFrame(query_instance,', 'columns=self.feature_names)', 'else:', 'query_instance', '=', "{'row1':", 'query_instance}', 'test', '=', 'pd.DataFrame... | 550,206 |
vanderschaarlab/mlforhealthlabpub | adsgan.py | adsgan | adsgan | Generate synthetic data for ADSGAN framework. | [
"Generate",
"synthetic",
"data",
"for",
"ADSGAN",
"framework."
] | def adsgan(orig_data, params):
tf.reset_default_graph()
x_dim = len(orig_data.columns)
no = len(orig_data)
mb_size = params['mb_size']
z_dim = params['z_dim']
h_dim = params['h_dim']
lamda = params['lamda']
iterations = params['iterations']
lam = 10
lr = 0.0001
orig_data = np... | ['def', 'adsgan(orig_data,', 'params):', 'tf.reset_default_graph()', 'x_dim', '=', 'len(orig_data.columns)', 'no', '=', 'len(orig_data)', 'mb_size', '=', "params['mb_size']", 'z_dim', '=', "params['z_dim']", 'h_dim', '=', "params['h_dim']", 'lamda', '=', "params['lamda']", 'iterations', '=', "params['iterations']", 'la... | 239,956 |
amzn/xfer | metalogger.py | MetaLogger.report | report | Report results at end of epoch/task/metastep using hook function. | [
"Report",
"results",
"at",
"end",
"of",
"epoch/task/metastep",
"using",
"hook",
"function."
] | def report(self, end, hook=None):
if hook is None:
hook = logging.info
reporter = {self.EPOCH: self._report_epoch, self.TASK: self._report_task, self.METASTEP: self._report_metastep}
reporter[end](hook) | ['def', 'report(self,', 'end,', 'hook=None):', 'if', 'hook', 'is', 'None:', 'hook', '=', 'logging.info', 'reporter', '=', '{self.EPOCH:', 'self._report_epoch,', 'self.TASK:', 'self._report_task,', 'self.METASTEP:', 'self._report_metastep}', 'reporter[end](hook)'] | 961,926 |
Stable-Baselines-Team/stable-baselines | run_mujoco.py | train | train | Train PPO2 model for Mujoco environment, for testing purposes :param env_id: (str) the environment id string :param num_timesteps: (int) the number of timesteps to run :param seed: (int) Used to seed the random generator. | [
"Train",
"PPO2",
"model",
"for",
"Mujoco",
"environment,",
"for",
"testing",
"purposes",
":param",
"env_id:",
"(str)",
"the",
"environment",
"id",
"string",
":param",
"num_timesteps:",
"(int)",
"the",
"number",
"of",
"timesteps",
"to",
"run",
":param",
"seed:",
... | def train(env_id, num_timesteps, seed):
def make_env():
env_out = gym.make(env_id)
env_out = bench.Monitor(env_out, logger.get_dir(), allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
env = VecNormalize(env)
set_global_seeds(seed)
policy = MlpPolicy
m... | ['def', 'train(env_id,', 'num_timesteps,', 'seed):', 'def', 'make_env():', 'env_out', '=', 'gym.make(env_id)', 'env_out', '=', 'bench.Monitor(env_out,', 'logger.get_dir(),', 'allow_early_resets=True)', 'return', 'env_out', 'env', '=', 'DummyVecEnv([make_env])', 'env', '=', 'VecNormalize(env)', 'set_global_seeds(seed)',... | 873,204 |
marysia/thesis | augmentation.py | crop_volume | crop_volume | Crops the volume to the desired shape, with translations from the center. | [
"Crops",
"the",
"volume",
"to",
"the",
"desired",
"shape,",
"with",
"translations",
"from",
"the",
"center."
] | def crop_volume(x, shape):
center = {'x': x.shape[2] / 2 + np.random.randint(-3, 4), 'y': x.shape[1] / 2 + np.random.randint(-3, 4), 'z': x.shape[0] / 2 + np.random.randint(0, 1)}
dif = {'x': shape[2] / 2, 'y': shape[1] / 2, 'z': shape[0] / 2}
return x[center['z'] - dif['z']:center['z'] + dif['z'], center['... | ['def', 'crop_volume(x,', 'shape):', 'center', '=', "{'x':", 'x.shape[2]', '/', '2', '+', 'np.random.randint(-3,', '4),', "'y':", 'x.shape[1]', '/', '2', '+', 'np.random.randint(-3,', '4),', "'z':", 'x.shape[0]', '/', '2', '+', 'np.random.randint(0,', '1)}', 'dif', '=', "{'x':", 'shape[2]', '/', '2,', "'y':", 'shape[1]... | 354,672 |
LucasAlegre/morl-baselines | linear_support.py | LinearSupport.remove_obsolete_weights | remove_obsolete_weights | Remove from the queue the weight vectors for which the new value vector is better than previous values. | [
"Remove",
"from",
"the",
"queue",
"the",
"weight",
"vectors",
"for",
"which",
"the",
"new",
"value",
"vector",
"is",
"better",
"than",
"previous",
"values."
] | def remove_obsolete_weights(self, new_value: np.ndarray) -> List[np.ndarray]:
if len(self.ccs) == 0:
return []
W_del = []
inds_remove = []
for (i, (priority, cw)) in enumerate(self.queue):
if np.dot(cw, new_value) > self.max_scalarized_value(cw):
W_del.append(cw)
... | ['def', 'remove_obsolete_weights(self,', 'new_value:', 'np.ndarray)', '->', 'List[np.ndarray]:', 'if', 'len(self.ccs)', '==', '0:', 'return', '[]', 'W_del', '=', '[]', 'inds_remove', '=', '[]', 'for', '(i,', '(priority,', 'cw))', 'in', 'enumerate(self.queue):', 'if', 'np.dot(cw,', 'new_value)', '>', 'self.max_scalarize... | 655,906 |
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