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 |
|---|---|---|---|---|---|---|---|---|
tensorflow/agents | system_multiprocessing.py | get_context | get_context | Get a context: an object with the same API as multiprocessing module. | [
"Get",
"a",
"context:",
"an",
"object",
"with",
"the",
"same",
"API",
"as",
"multiprocessing",
"module."
] | def get_context(method: Text=None) -> _multiprocessing.context.BaseContext:
if not multiprocessing_core.initialized():
raise RuntimeError(_NOT_INITIALIZED_ERROR)
return _rewrite_target_with_state(multiprocessing_core.get_context(method)) | ['def', 'get_context(method:', 'Text=None)', '->', '_multiprocessing.context.BaseContext:', 'if', 'not', 'multiprocessing_core.initialized():', 'raise', 'RuntimeError(_NOT_INITIALIZED_ERROR)', 'return', '_rewrite_target_with_state(multiprocessing_core.get_context(method))'] | 23,713 |
Megvii-BaseDetection/cvpods | history_buffer.py | HistoryBuffer.avg | avg | Return the mean of the latest `window_size` values in the buffer. | [
"Return",
"the",
"mean",
"of",
"the",
"latest",
"`window_size`",
"values",
"in",
"the",
"buffer."
] | def avg(self, window_size: int):
return np.mean([x[0] for x in self._data[-window_size:]]) | ['def', 'avg(self,', 'window_size:', 'int):', 'return', 'np.mean([x[0]', 'for', 'x', 'in', 'self._data[-window_size:]])'] | 523,187 |
voxel51/fiftyone | database.py | sync_database | sync_database | Syncs all pending database writes to disk. | [
"Syncs",
"all",
"pending",
"database",
"writes",
"to",
"disk."
] | def sync_database():
if _client is not None:
_client.admin.command('fsync') | ['def', 'sync_database():', 'if', '_client', 'is', 'not', 'None:', "_client.admin.command('fsync')"] | 583,526 |
ldkong1205/LaserMix | mink_resnet.py | MinkResNet.forward | forward | Forward pass of ResNet. | [
"Forward",
"pass",
"of",
"ResNet."
] | def forward(self, x: SparseTensor) -> List[SparseTensor]:
x = self.conv1(x)
x = self.norm1(x)
x = self.relu(x)
if self.pool:
x = self.maxpool(x)
outs = []
for i in range(self.num_stages):
x = getattr(self, f'layer{i + 1}')(x)
outs.append(x)
return outs | ['def', 'forward(self,', 'x:', 'SparseTensor)', '->', 'List[SparseTensor]:', 'x', '=', 'self.conv1(x)', 'x', '=', 'self.norm1(x)', 'x', '=', 'self.relu(x)', 'if', 'self.pool:', 'x', '=', 'self.maxpool(x)', 'outs', '=', '[]', 'for', 'i', 'in', 'range(self.num_stages):', 'x', '=', 'getattr(self,', "f'layer{i", '+', "1}')... | 623,925 |
LiWentomng/OrientedRepPoints | hooks.py | patch_forward_method | patch_forward_method | Patch the forward method of a module. | [
"Patch",
"the",
"forward",
"method",
"of",
"a",
"module."
] | def patch_forward_method(func, src_type, dst_type, convert_output=True):
def new_forward(*args, **kwargs):
output = func(*cast_tensor_type(args, src_type, dst_type), **cast_tensor_type(kwargs, src_type, dst_type))
if convert_output:
output = cast_tensor_type(output, dst_type, src_type)
... | ['def', 'patch_forward_method(func,', 'src_type,', 'dst_type,', 'convert_output=True):', 'def', 'new_forward(*args,', '**kwargs):', 'output', '=', 'func(*cast_tensor_type(args,', 'src_type,', 'dst_type),', '**cast_tensor_type(kwargs,', 'src_type,', 'dst_type))', 'if', 'convert_output:', 'output', '=', 'cast_tensor_type... | 776,541 |
JayantGoel001/Artificial- | search.py | OnlineSearchProblem.c | c | Returns a cost estimate for an agent to move from state 's' to state 's1'. | [
"Returns",
"a",
"cost",
"estimate",
"for",
"an",
"agent",
"to",
"move",
"from",
"state",
"'s'",
"to",
"state",
"'s1'."
] | def c(self, s, a, s1):
return 1 | ['def', 'c(self,', 's,', 'a,', 's1):', 'return', '1'] | 117,533 |
triaquae/triaquae | six.py | iterkeys | iterkeys | Return an iterator over the keys of a dictionary. | [
"Return",
"an",
"iterator",
"over",
"the",
"keys",
"of",
"a",
"dictionary."
] | def iterkeys(d):
return iter(getattr(d, _iterkeys)()) | ['def', 'iterkeys(d):', 'return', 'iter(getattr(d,', '_iterkeys)())'] | 356,745 |
rlgraph/rlgraph | component_test.py | ComponentTest.read_variable_values | read_variable_values | Executes a session to retrieve the values of the provided variables. | [
"Executes",
"a",
"session",
"to",
"retrieve",
"the",
"values",
"of",
"the",
"provided",
"variables."
] | def read_variable_values(self, *variables):
if len(variables) == 0:
variables = self.component.variable_registry
ret = self.graph_executor.read_variable_values(variables)
if len(variables) == 1:
return ret[0]
return ret | ['def', 'read_variable_values(self,', '*variables):', 'if', 'len(variables)', '==', '0:', 'variables', '=', 'self.component.variable_registry', 'ret', '=', 'self.graph_executor.read_variable_values(variables)', 'if', 'len(variables)', '==', '1:', 'return', 'ret[0]', 'return', 'ret'] | 862,657 |
heartkilla/yolo-v3 | yolo_v3.py | darknet53_residual_block | darknet53_residual_block | Creates a residual block for Darknet. | [
"Creates",
"a",
"residual",
"block",
"for",
"Darknet."
] | def darknet53_residual_block(inputs, filters, training, data_format, strides=1):
shortcut = inputs
inputs = conv2d_fixed_padding(inputs, filters=filters, kernel_size=1, strides=strides, data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_... | ['def', 'darknet53_residual_block(inputs,', 'filters,', 'training,', 'data_format,', 'strides=1):', 'shortcut', '=', 'inputs', 'inputs', '=', 'conv2d_fixed_padding(inputs,', 'filters=filters,', 'kernel_size=1,', 'strides=strides,', 'data_format=data_format)', 'inputs', '=', 'batch_norm(inputs,', 'training=training,', '... | 969,159 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | nb_004b.py | MixedPrecision.on_backward_end | on_backward_end | Convert the gradients back to FP32 and divide them by the scale. | [
"Convert",
"the",
"gradients",
"back",
"to",
"FP32",
"and",
"divide",
"them",
"by",
"the",
"scale."
] | def on_backward_end(self, **kwargs: Any):
model_g2master_g(self.model_params, self.master_params, self.flat_master)
for group in self.master_params:
for param in group:
param.grad.div_(self.loss_scale) | ['def', 'on_backward_end(self,', '**kwargs:', 'Any):', 'model_g2master_g(self.model_params,', 'self.master_params,', 'self.flat_master)', 'for', 'group', 'in', 'self.master_params:', 'for', 'param', 'in', 'group:', 'param.grad.div_(self.loss_scale)'] | 81,237 |
jimtin/Stock_Comparison | parser.py | Parser.parse_if | parse_if | Parse an if construct. | [
"Parse",
"an",
"if",
"construct."
] | def parse_if(self):
node = result = nodes.If(lineno=self.stream.expect('name:if').lineno)
while 1:
node.test = self.parse_tuple(with_condexpr=False)
node.body = self.parse_statements(('name:elif', 'name:else', 'name:endif'))
token = next(self.stream)
if token.test('name:elif'):
... | ['def', 'parse_if(self):', 'node', '=', 'result', '=', "nodes.If(lineno=self.stream.expect('name:if').lineno)", 'while', '1:', 'node.test', '=', 'self.parse_tuple(with_condexpr=False)', 'node.body', '=', "self.parse_statements(('name:elif',", "'name:else',", "'name:endif'))", 'token', '=', 'next(self.stream)', 'if', "t... | 385,878 |
rishab-sharma/object_detection | c2.py | const_fill | const_fill | Constant fill helper to reduce verbosity. | [
"Constant",
"fill",
"helper",
"to",
"reduce",
"verbosity."
] | def const_fill(value):
return ('ConstantFill', {'value': value}) | ['def', 'const_fill(value):', 'return', "('ConstantFill',", "{'value':", 'value})'] | 773,218 |
scikit-learn/scikit-learn | test_glm.py | test_newton_solver_verbosity | test_newton_solver_verbosity | Test the std output of verbose newton solvers. | [
"Test",
"the",
"std",
"output",
"of",
"verbose",
"newton",
"solvers."
] | def test_newton_solver_verbosity(capsys, verbose):
y = np.array([1, 2], dtype=float)
X = np.array([[1.0, 0], [0, 1]], dtype=float)
linear_loss = LinearModelLoss(base_loss=HalfPoissonLoss(), fit_intercept=False)
sol = NewtonCholeskySolver(coef=linear_loss.init_zero_coef(X), linear_loss=linear_loss, l2_re... | ['def', 'test_newton_solver_verbosity(capsys,', 'verbose):', 'y', '=', 'np.array([1,', '2],', 'dtype=float)', 'X', '=', 'np.array([[1.0,', '0],', '[0,', '1]],', 'dtype=float)', 'linear_loss', '=', 'LinearModelLoss(base_loss=HalfPoissonLoss(),', 'fit_intercept=False)', 'sol', '=', 'NewtonCholeskySolver(coef=linear_loss.... | 853,637 |
weimin17/Object-Detection_HelmetDetection | convolutional.py | data_type | data_type | Return the type of the activations, weights, and placeholder variables. | [
"Return",
"the",
"type",
"of",
"the",
"activations,",
"weights,",
"and",
"placeholder",
"variables."
] | def data_type():
if FLAGS.use_fp16:
return tf.float16
else:
return tf.float32 | ['def', 'data_type():', 'if', 'FLAGS.use_fp16:', 'return', 'tf.float16', 'else:', 'return', 'tf.float32'] | 754,222 |
ShuLiu1993/PANet | training_stats.py | TrainingStats.LogIterStats | LogIterStats | Log the tracked statistics. | [
"Log",
"the",
"tracked",
"statistics."
] | def LogIterStats(self, cur_iter, lr):
if cur_iter % self.LOG_PERIOD == 0 or cur_iter == cfg.SOLVER.MAX_ITER - 1:
stats = self.GetStats(cur_iter, lr)
log_stats(stats, self.misc_args)
if self.tblogger:
self.tb_log_stats(stats, cur_iter) | ['def', 'LogIterStats(self,', 'cur_iter,', 'lr):', 'if', 'cur_iter', '%', 'self.LOG_PERIOD', '==', '0', 'or', 'cur_iter', '==', 'cfg.SOLVER.MAX_ITER', '-', '1:', 'stats', '=', 'self.GetStats(cur_iter,', 'lr)', 'log_stats(stats,', 'self.misc_args)', 'if', 'self.tblogger:', 'self.tb_log_stats(stats,', 'cur_iter)'] | 778,952 |
omarmhaimdat/twitter_nlp_native_swift | ipaddress.py | v4_int_to_packed | v4_int_to_packed | Represent an address as 4 packed bytes in network (big-endian) order. | [
"Represent",
"an",
"address",
"as",
"4",
"packed",
"bytes",
"in",
"network",
"(big-endian)",
"order."
] | def v4_int_to_packed(address):
try:
return _compat_to_bytes(address, 4, 'big')
except (struct.error, OverflowError):
raise ValueError('Address negative or too large for IPv4') | ['def', 'v4_int_to_packed(address):', 'try:', 'return', '_compat_to_bytes(address,', '4,', "'big')", 'except', '(struct.error,', 'OverflowError):', 'raise', "ValueError('Address", 'negative', 'or', 'too', 'large', 'for', "IPv4')"] | 954,467 |
rohanpsingh/LearningHumanoidWalking | robot_interface.py | RobotInterface.step | step | Increment simulation by one step. | [
"Increment",
"simulation",
"by",
"one",
"step."
] | def step(self):
mujoco.mj_step(self.model, self.data) | ['def', 'step(self):', 'mujoco.mj_step(self.model,', 'self.data)'] | 588,277 |
google/deepvariant | model_train_test.py | ModelTrainTest.test_end2end_inception_v3_warm_up_allow_different_num_channels | test_end2end_inception_v3_warm_up_allow_different_num_channels | End-to-end test of model_train script. | [
"End-to-end",
"test",
"of",
"model_train",
"script."
] | def test_end2end_inception_v3_warm_up_allow_different_num_channels(self):
FLAGS.allow_warmstart_from_different_num_channels = True
checkpoint_dir = tf_test_utils.test_tmpdir('inception_v3_warm_up_allow_different_num_channels')
tf_test_utils.write_fake_checkpoint('inception_v3', self.test_session(), checkpoi... | ['def', 'test_end2end_inception_v3_warm_up_allow_different_num_channels(self):', 'FLAGS.allow_warmstart_from_different_num_channels', '=', 'True', 'checkpoint_dir', '=', "tf_test_utils.test_tmpdir('inception_v3_warm_up_allow_different_num_channels')", "tf_test_utils.write_fake_checkpoint('inception_v3',", 'self.test_se... | 540,380 |
jaywalnut310/Vector-Quantized-Autoencoders | transformer_vq.py | residual_conv | residual_conv | A stack of convolution blocks with residual connections. | [
"A",
"stack",
"of",
"convolution",
"blocks",
"with",
"residual",
"connections."
] | def residual_conv(x, repeat, k, hparams, name, reuse=None):
with tf.variable_scope(name, reuse=reuse):
dilations_and_kernels = [(1, k) for _ in range(3)]
for i in range(repeat):
with tf.variable_scope('repeat_%d' % i):
y = commons.conv_block(commons.layer_norm(x, name='ln... | ['def', 'residual_conv(x,', 'repeat,', 'k,', 'hparams,', 'name,', 'reuse=None):', 'with', 'tf.variable_scope(name,', 'reuse=reuse):', 'dilations_and_kernels', '=', '[(1,', 'k)', 'for', '_', 'in', 'range(3)]', 'for', 'i', 'in', 'range(repeat):', 'with', "tf.variable_scope('repeat_%d'", '%', 'i):', 'y', '=', 'commons.con... | 931,053 |
okfn-brasil/serenata-de-amor | fetch_receipts.py | Receipts.all | all | List generator with Receipt objects containing the path of the receipt image (to be used when saving it, for example) and the URL of the receipt at the Lower House servers. | [
"List",
"generator",
"with",
"Receipt",
"objects",
"containing",
"the",
"path",
"of",
"the",
"receipt",
"image",
"(to",
"be",
"used",
"when",
"saving",
"it,",
"for",
"example)",
"and",
"the",
"URL",
"of",
"the",
"receipt",
"at",
"the",
"Lower",
"House",
"s... | def all(self):
dtype = {'document_id': np.str, 'congressperson_id': np.str, 'congressperson_document': np.str, 'term_id': np.str, 'cnpj_cpf': np.str, 'reimbursement_number': np.str}
for dataset in self.datasets:
df = pd.read_csv(dataset, parse_dates=[16], dtype=dtype)
rows = filter(self.is_valid... | ['def', 'all(self):', 'dtype', '=', "{'document_id':", 'np.str,', "'congressperson_id':", 'np.str,', "'congressperson_document':", 'np.str,', "'term_id':", 'np.str,', "'cnpj_cpf':", 'np.str,', "'reimbursement_number':", 'np.str}', 'for', 'dataset', 'in', 'self.datasets:', 'df', '=', 'pd.read_csv(dataset,', 'parse_dates... | 349,801 |
Akash671/AI | inference.py | JointParticleFilter.initialize | initialize | Store information about the game, then initialize particles. | [
"Store",
"information",
"about",
"the",
"game,",
"then",
"initialize",
"particles."
] | def initialize(self, gameState, legalPositions):
self.numGhosts = gameState.getNumAgents() - 1
self.ghostAgents = []
self.legalPositions = legalPositions
self.initializeUniformly(gameState) | ['def', 'initialize(self,', 'gameState,', 'legalPositions):', 'self.numGhosts', '=', 'gameState.getNumAgents()', '-', '1', 'self.ghostAgents', '=', '[]', 'self.legalPositions', '=', 'legalPositions', 'self.initializeUniformly(gameState)'] | 67,403 |
weimin17/Object-Detection_HelmetDetection | graph_rewriter_builder.py | build | build | Returns a function that modifies default graph based on options. | [
"Returns",
"a",
"function",
"that",
"modifies",
"default",
"graph",
"based",
"on",
"options."
] | def build(graph_rewriter_config, is_training):
def graph_rewrite_fn():
if graph_rewriter_config.quantization.weight_bits != 8 or graph_rewriter_config.quantization.activation_bits != 8:
raise ValueError('Only 8bit quantization is supported')
if is_training:
tf.contrib.quanti... | ['def', 'build(graph_rewriter_config,', 'is_training):', 'def', 'graph_rewrite_fn():', 'if', 'graph_rewriter_config.quantization.weight_bits', '!=', '8', 'or', 'graph_rewriter_config.quantization.activation_bits', '!=', '8:', 'raise', "ValueError('Only", '8bit', 'quantization', 'is', "supported')", 'if', 'is_training:'... | 758,503 |
sktime/sktime | base.py | BaseDistribution.shape | shape | Shape of self, a pair (2-tuple). | [
"Shape",
"of",
"self,",
"a",
"pair",
"(2-tuple)."
] | def shape(self):
return (len(self.index), len(self.columns)) | ['def', 'shape(self):', 'return', '(len(self.index),', 'len(self.columns))'] | 877,455 |
suarez12138/AI-Reversi_IMP_TextDichotomy | backend_tools.py | ToolViewsPositions.forward | forward | Forward one step in the stack of views and positions. | [
"Forward",
"one",
"step",
"in",
"the",
"stack",
"of",
"views",
"and",
"positions."
] | def forward(self):
self.views[self.figure].forward()
self.positions[self.figure].forward() | ['def', 'forward(self):', 'self.views[self.figure].forward()', 'self.positions[self.figure].forward()'] | 96,290 |
ryu-ed/SpaceInvaders_Ros | system_info.py | combine_paths | combine_paths | Return a list of existing paths composed by all combinations of items from arguments. | [
"Return",
"a",
"list",
"of",
"existing",
"paths",
"composed",
"by",
"all",
"combinations",
"of",
"items",
"from",
"arguments."
] | def combine_paths(*args, **kws):
r = []
for a in args:
if not a:
continue
if is_string(a):
a = [a]
r.append(a)
args = r
if not args:
return []
if len(args) == 1:
result = reduce(lambda a, b: a + b, map(glob, args[0]), [])
elif len(a... | ['def', 'combine_paths(*args,', '**kws):', 'r', '=', '[]', 'for', 'a', 'in', 'args:', 'if', 'not', 'a:', 'continue', 'if', 'is_string(a):', 'a', '=', '[a]', 'r.append(a)', 'args', '=', 'r', 'if', 'not', 'args:', 'return', '[]', 'if', 'len(args)', '==', '1:', 'result', '=', 'reduce(lambda', 'a,', 'b:', 'a', '+', 'b,', '... | 396,507 |
mkusner/grammarVAE | test_blocksparse.py | BlockSparse_Gemv_and_Outer.test_sparseblockdot | test_sparseblockdot | Compares the numpy version of sparseblockgemv to sparse_block_dot. | [
"Compares",
"the",
"numpy",
"version",
"of",
"sparseblockgemv",
"to",
"sparse_block_dot."
] | def test_sparseblockdot(self):
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
o = sparse_block_dot(W, h, iIdx, b, oIdx)
f = theano.function([W, h, iIdx, b, oIdx], o, mode=self.mode)
(W_val, h_val, iIdx_val, b_val, oIdx_val... | ['def', 'test_sparseblockdot(self):', 'b', '=', 'tensor.fmatrix()', 'W', '=', 'tensor.ftensor4()', 'h', '=', 'tensor.ftensor3()', 'iIdx', '=', 'tensor.imatrix()', 'oIdx', '=', 'tensor.imatrix()', 'o', '=', 'sparse_block_dot(W,', 'h,', 'iIdx,', 'b,', 'oIdx)', 'f', '=', 'theano.function([W,', 'h,', 'iIdx,', 'b,', 'oIdx],... | 580,069 |
rudranil723/mini-main | options.py | ModelAdmin.response_post_save_change | response_post_save_change | Figure out where to redirect after the 'Save' button has been pressed when editing an existing object. | [
"Figure",
"out",
"where",
"to",
"redirect",
"after",
"the",
"'Save'",
"button",
"has",
"been",
"pressed",
"when",
"editing",
"an",
"existing",
"object."
] | def response_post_save_change(self, request, obj):
opts = self.model._meta
if self.has_change_permission(request, None):
post_url = reverse('admin:%s_%s_changelist' % (opts.app_label, opts.model_name), current_app=self.admin_site.name)
preserved_filters = self.get_preserved_filters(request)
... | ['def', 'response_post_save_change(self,', 'request,', 'obj):', 'opts', '=', 'self.model._meta', 'if', 'self.has_change_permission(request,', 'None):', 'post_url', '=', "reverse('admin:%s_%s_changelist'", '%', '(opts.app_label,', 'opts.model_name),', 'current_app=self.admin_site.name)', 'preserved_filters', '=', 'self.... | 314,782 |
PacktPublishing/Hands-On-Generative-Adversarial--with-PyTorch-1.x | utils.py | create_folder | create_folder | Create a folder if it does not exist. | [
"Create",
"a",
"folder",
"if",
"it",
"does",
"not",
"exist."
] | def create_folder(folder_path):
try:
os.makedirs(folder_path)
except OSError as _e:
if _e.errno != errno.EEXIST:
raise | ['def', 'create_folder(folder_path):', 'try:', 'os.makedirs(folder_path)', 'except', 'OSError', 'as', '_e:', 'if', '_e.errno', '!=', 'errno.EEXIST:', 'raise'] | 575,632 |
kubeflow/pipelines | bigquery_util.py | back_quoted_if_needed | back_quoted_if_needed | Enclose resource name with ` if it's not yet. | [
"Enclose",
"resource",
"name",
"with",
"`",
"if",
"it's",
"not",
"yet."
] | def back_quoted_if_needed(resource_name) -> str:
if not resource_name or resource_name.startswith('`'):
return resource_name
return '`{}`'.format(resource_name) | ['def', 'back_quoted_if_needed(resource_name)', '->', 'str:', 'if', 'not', 'resource_name', 'or', "resource_name.startswith('`'):", 'return', 'resource_name', 'return', "'`{}`'.format(resource_name)"] | 770,782 |
dawdleryang/object_detection | detector.py | DetectionModelHelper.ConvAffine | ConvAffine | ConvAffine adds a Conv op followed by a AffineChannel op (which replaces BN during fine tuning). | [
"ConvAffine",
"adds",
"a",
"Conv",
"op",
"followed",
"by",
"a",
"AffineChannel",
"op",
"(which",
"replaces",
"BN",
"during",
"fine",
"tuning)."
] | def ConvAffine(self, blob_in, prefix, dim_in, dim_out, kernel, stride, pad, group=1, dilation=1, weight_init=None, bias_init=None, suffix='_bn', inplace=False):
conv_blob = self.Conv(blob_in, prefix, dim_in, dim_out, kernel, stride=stride, pad=pad, group=group, dilation=dilation, weight_init=weight_init, bias_init=... | ['def', 'ConvAffine(self,', 'blob_in,', 'prefix,', 'dim_in,', 'dim_out,', 'kernel,', 'stride,', 'pad,', 'group=1,', 'dilation=1,', 'weight_init=None,', 'bias_init=None,', "suffix='_bn',", 'inplace=False):', 'conv_blob', '=', 'self.Conv(blob_in,', 'prefix,', 'dim_in,', 'dim_out,', 'kernel,', 'stride=stride,', 'pad=pad,'... | 772,597 |
ludwig-ai/ludwig | base.py | BaseModel.build_single_input | build_single_input | Builds a single input feature from the input feature definition. | [
"Builds",
"a",
"single",
"input",
"feature",
"from",
"the",
"input",
"feature",
"definition."
] | def build_single_input(feature_config: BaseInputFeatureConfig, other_input_features: Optional[Dict[str, InputFeature]]) -> InputFeature:
logger.debug(f'Input {feature_config.type} feature {feature_config.name}')
encoder_obj = None
if feature_config.tied is not None:
tied_input_feature_name = feature... | ['def', 'build_single_input(feature_config:', 'BaseInputFeatureConfig,', 'other_input_features:', 'Optional[Dict[str,', 'InputFeature]])', '->', 'InputFeature:', "logger.debug(f'Input", '{feature_config.type}', 'feature', "{feature_config.name}')", 'encoder_obj', '=', 'None', 'if', 'feature_config.tied', 'is', 'not', '... | 616,823 |
QData/deepWordBug | proxy.py | ProxyConfig.get_environment | get_environment | Return a dictionary representing the environment variables used to set the proxy settings. | [
"Return",
"a",
"dictionary",
"representing",
"the",
"environment",
"variables",
"used",
"to",
"set",
"the",
"proxy",
"settings."
] | def get_environment(self):
env = {}
if self.http:
env['http_proxy'] = env['HTTP_PROXY'] = self.http
if self.https:
env['https_proxy'] = env['HTTPS_PROXY'] = self.https
if self.ftp:
env['ftp_proxy'] = env['FTP_PROXY'] = self.ftp
if self.no_proxy:
env['no_proxy'] = env[... | ['def', 'get_environment(self):', 'env', '=', '{}', 'if', 'self.http:', "env['http_proxy']", '=', "env['HTTP_PROXY']", '=', 'self.http', 'if', 'self.https:', "env['https_proxy']", '=', "env['HTTPS_PROXY']", '=', 'self.https', 'if', 'self.ftp:', "env['ftp_proxy']", '=', "env['FTP_PROXY']", '=', 'self.ftp', 'if', 'self.n... | 541,945 |
huawei-noah/xingtian | register.py | path_to_module_format | path_to_module_format | Transform a python/file/path to module format match to the importlib. | [
"Transform",
"a",
"python/file/path",
"to",
"module",
"format",
"match",
"to",
"the",
"importlib."
] | def path_to_module_format(py_path):
return os.path.splitext(py_path)[0].replace('/', '.') | ['def', 'path_to_module_format(py_path):', 'return', "os.path.splitext(py_path)[0].replace('/',", "'.')"] | 962,457 |
beancount/smart_importer | pipelines.py | txn_attr_getter | txn_attr_getter | Return attribute getter for a transaction that also handles metadata. | [
"Return",
"attribute",
"getter",
"for",
"a",
"transaction",
"that",
"also",
"handles",
"metadata."
] | def txn_attr_getter(attribute_name: str):
if attribute_name.startswith('meta.'):
meta_attr = attribute_name[5:]
def getter(txn):
return txn.meta.get(meta_attr)
return getter
return operator.attrgetter(attribute_name) | ['def', 'txn_attr_getter(attribute_name:', 'str):', 'if', "attribute_name.startswith('meta.'):", 'meta_attr', '=', 'attribute_name[5:]', 'def', 'getter(txn):', 'return', 'txn.meta.get(meta_attr)', 'return', 'getter', 'return', 'operator.attrgetter(attribute_name)'] | 878,701 |
tensorflow/data-validation | stats_util.py | load_statistics | load_statistics | Loads data statistics proto from file. | [
"Loads",
"data",
"statistics",
"proto",
"from",
"file."
] | def load_statistics(input_path: Text) -> statistics_pb2.DatasetFeatureStatisticsList:
if not tf.io.gfile.exists(input_path):
raise IOError('Invalid input path {}.'.format(input_path))
try:
return load_stats_tfrecord(input_path)
except Exception:
logging.info('File %s did not look lik... | ['def', 'load_statistics(input_path:', 'Text)', '->', 'statistics_pb2.DatasetFeatureStatisticsList:', 'if', 'not', 'tf.io.gfile.exists(input_path):', 'raise', "IOError('Invalid", 'input', 'path', "{}.'.format(input_path))", 'try:', 'return', 'load_stats_tfrecord(input_path)', 'except', 'Exception:', "logging.info('File... | 497,658 |
weimin17/Object-Detection_HelmetDetection | model_callbacks.py | ExamplesPerSecondCallback.on_batch_end | on_batch_end | Log the examples_per_sec metric every_n_steps. | [
"Log",
"the",
"examples_per_sec",
"metric",
"every_n_steps."
] | def on_batch_end(self, batch, logs=None):
self._global_step += 1
current_time = time.time()
if self._global_step % self._every_n_steps == 0:
average_examples_per_sec = self._batch_size * (self._global_step / (current_time - self._train_start_time))
self._logger.log_metric('average_examples_p... | ['def', 'on_batch_end(self,', 'batch,', 'logs=None):', 'self._global_step', '+=', '1', 'current_time', '=', 'time.time()', 'if', 'self._global_step', '%', 'self._every_n_steps', '==', '0:', 'average_examples_per_sec', '=', 'self._batch_size', '*', '(self._global_step', '/', '(current_time', '-', 'self._train_start_time... | 761,026 |
deepmind/meltingpot | paintball__capture_the_flag.py | create_ground_prefab | create_ground_prefab | Return a prefab for a colorable ground prefab. | [
"Return",
"a",
"prefab",
"for",
"a",
"colorable",
"ground",
"prefab."
] | def create_ground_prefab():
sprite_names = ['RedGround', 'BlueGround']
sprite_colors = [DARKEST_RED_COLOR, DARKEST_BLUE_COLOR]
prefab = {'name': 'ground', 'components': [{'component': 'StateManager', 'kwargs': {'initialState': 'clean', 'stateConfigs': [{'state': 'clean', 'layer': 'alternateLogic'}, {'state'... | ['def', 'create_ground_prefab():', 'sprite_names', '=', "['RedGround',", "'BlueGround']", 'sprite_colors', '=', '[DARKEST_RED_COLOR,', 'DARKEST_BLUE_COLOR]', 'prefab', '=', "{'name':", "'ground',", "'components':", "[{'component':", "'StateManager',", "'kwargs':", "{'initialState':", "'clean',", "'stateConfigs':", "[{'... | 285,385 |
myothida/Supervised-Machine-Learning | test_array_api.py | test_get_namespace_ndarray | test_get_namespace_ndarray | Test get_namespace on NumPy ndarrays. | [
"Test",
"get_namespace",
"on",
"NumPy",
"ndarrays."
] | def test_get_namespace_ndarray():
pytest.importorskip('numpy.array_api')
X_np = numpy.asarray([[1, 2, 3]])
for array_api_dispatch in [True, False]:
with config_context(array_api_dispatch=array_api_dispatch):
(xp_out, is_array_api) = get_namespace(X_np)
assert not is_array_api... | ['def', 'test_get_namespace_ndarray():', "pytest.importorskip('numpy.array_api')", 'X_np', '=', 'numpy.asarray([[1,', '2,', '3]])', 'for', 'array_api_dispatch', 'in', '[True,', 'False]:', 'with', 'config_context(array_api_dispatch=array_api_dispatch):', '(xp_out,', 'is_array_api)', '=', 'get_namespace(X_np)', 'assert',... | 364,734 |
SurakshaRV/AI-Lab1BM17CS108 | lab8-forward-reasoning.py | parse_definite_clause | parse_definite_clause | Return the antecedents and the consequent of a definite clause. | [
"Return",
"the",
"antecedents",
"and",
"the",
"consequent",
"of",
"a",
"definite",
"clause."
] | def parse_definite_clause(s):
assert is_definite_clause(s)
if is_symbol(s.op):
return ([], s)
else:
(antecedent, consequent) = s.args
return (conjuncts(antecedent), consequent) | ['def', 'parse_definite_clause(s):', 'assert', 'is_definite_clause(s)', 'if', 'is_symbol(s.op):', 'return', '([],', 's)', 'else:', '(antecedent,', 'consequent)', '=', 's.args', 'return', '(conjuncts(antecedent),', 'consequent)'] | 24,690 |
rudranil723/mini-main | req_command.py | RequirementCommand.trace_basic_info | trace_basic_info | Trace basic information about the provided objects. | [
"Trace",
"basic",
"information",
"about",
"the",
"provided",
"objects."
] | def trace_basic_info(finder: PackageFinder) -> None:
search_scope = finder.search_scope
locations = search_scope.get_formatted_locations()
if locations:
logger.info(locations) | ['def', 'trace_basic_info(finder:', 'PackageFinder)', '->', 'None:', 'search_scope', '=', 'finder.search_scope', 'locations', '=', 'search_scope.get_formatted_locations()', 'if', 'locations:', 'logger.info(locations)'] | 267,920 |
BlissChapman/ICW-fMRI-GAN | transformations.py | Transformer.add | add | Add a named linear transformation. | [
"Add",
"a",
"named",
"linear",
"transformation."
] | def add(self, name, mat):
self.transformations[name] = mat | ['def', 'add(self,', 'name,', 'mat):', 'self.transformations[name]', '=', 'mat'] | 597,100 |
bachiraoun/fullrmc | Collection.py | RandomIntegerGenerator.upperLimit | upperLimit | Upper limit of the number generation. | [
"Upper",
"limit",
"of",
"the",
"number",
"generation."
] | def upperLimit(self):
return self.__upperLimit | ['def', 'upperLimit(self):', 'return', 'self.__upperLimit'] | 213,732 |
ilya16/MultINN | multinn_jamming.py | MultINNJamming.train_generators | train_generators | Constructs training ops for training per-track MultINN Generators. | [
"Constructs",
"training",
"ops",
"for",
"training",
"per-track",
"MultINN",
"Generators."
] | def train_generators(self, optimizer, lr, separate_losses=False):
(init_ops, update_ops, metrics, metrics_upd, summaries) = self._train_generators(optimizer, lr, pretrain=False, separate_losses=separate_losses)
summaries['metrics'] = tf.summary.merge([self.summaries['metrics'], summaries['metrics']])
metric... | ['def', 'train_generators(self,', 'optimizer,', 'lr,', 'separate_losses=False):', '(init_ops,', 'update_ops,', 'metrics,', 'metrics_upd,', 'summaries)', '=', 'self._train_generators(optimizer,', 'lr,', 'pretrain=False,', 'separate_losses=separate_losses)', "summaries['metrics']", '=', "tf.summary.merge([self.summaries[... | 644,311 |
guxm2021/ALT_SpeechBrain | encoder.py | TextEncoder.limited_labelset_from_iterable | limited_labelset_from_iterable | Change default for sequence_input to True. | [
"Change",
"default",
"for",
"sequence_input",
"to",
"True."
] | def limited_labelset_from_iterable(self, iterable, sequence_input=True, n_most_common=None, min_count=1):
return super().limited_labelset_from_iterable(iterable, sequence_input=True, n_most_common=None, min_count=1) | ['def', 'limited_labelset_from_iterable(self,', 'iterable,', 'sequence_input=True,', 'n_most_common=None,', 'min_count=1):', 'return', 'super().limited_labelset_from_iterable(iterable,', 'sequence_input=True,', 'n_most_common=None,', 'min_count=1)'] | 415,486 |
YanZiQinKevin/object_detection | config_util_test.py | ConfigUtilTest.test_save_pipeline_config | test_save_pipeline_config | Tests that the pipeline config is properly saved to disk. | [
"Tests",
"that",
"the",
"pipeline",
"config",
"is",
"properly",
"saved",
"to",
"disk."
] | def test_save_pipeline_config(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = 'path/to/label_map'
pipeline_config.eval_config.num_exampl... | ['def', 'test_save_pipeline_config(self):', 'pipeline_config', '=', 'pipeline_pb2.TrainEvalPipelineConfig()', 'pipeline_config.model.faster_rcnn.num_classes', '=', '10', 'pipeline_config.train_config.batch_size', '=', '32', 'pipeline_config.train_input_reader.label_map_path', '=', "'path/to/label_map'", 'pipeline_confi... | 792,841 |
zomux/deepy | network.py | NeuralNetwork.save_params | save_params | Save parameters to file. | [
"Save",
"parameters",
"to",
"file."
] | def save_params(self, path, new_thread=False):
save_logger.info(path)
param_variables = self.all_parameters
params = [p.get_value().copy() for p in param_variables]
if new_thread:
thread = Thread(target=save_network_params, args=(params, path))
thread.start()
else:
save_netwo... | ['def', 'save_params(self,', 'path,', 'new_thread=False):', 'save_logger.info(path)', 'param_variables', '=', 'self.all_parameters', 'params', '=', '[p.get_value().copy()', 'for', 'p', 'in', 'param_variables]', 'if', 'new_thread:', 'thread', '=', 'Thread(target=save_network_params,', 'args=(params,', 'path))', 'thread.... | 180,979 |
sek788432/Waymo-2D-Object-Detection | ddpg_agent.py | TD3Agent.critic_net | critic_net | Returns the output of the critic network. | [
"Returns",
"the",
"output",
"of",
"the",
"critic",
"network."
] | def critic_net(self, states, actions, for_critic_loss=False):
values1 = self._critic_net(states, actions, for_critic_loss=for_critic_loss)
values2 = self._critic_net2(states, actions, for_critic_loss=for_critic_loss)
if for_critic_loss:
return (values1, values2)
return values1 | ['def', 'critic_net(self,', 'states,', 'actions,', 'for_critic_loss=False):', 'values1', '=', 'self._critic_net(states,', 'actions,', 'for_critic_loss=for_critic_loss)', 'values2', '=', 'self._critic_net2(states,', 'actions,', 'for_critic_loss=for_critic_loss)', 'if', 'for_critic_loss:', 'return', '(values1,', 'values2... | 974,365 |
google-research/scenic | ops.py | tf_apply_to_image_mask_box | tf_apply_to_image_mask_box | Applies a function to a single element or each element in a batch. | [
"Applies",
"a",
"function",
"to",
"a",
"single",
"element",
"or",
"each",
"element",
"in",
"a",
"batch."
] | def tf_apply_to_image_mask_box(fn, image_or_images, mask_or_masks, box_or_boxes):
static_rank = len(image_or_images.get_shape().as_list())
if static_rank == 3:
return fn(image_or_images, mask_or_masks, box_or_boxes)
elif static_rank == 4:
aux = [fn(x, y, z) for (x, y, z) in zip(tf.unstack(im... | ['def', 'tf_apply_to_image_mask_box(fn,', 'image_or_images,', 'mask_or_masks,', 'box_or_boxes):', 'static_rank', '=', 'len(image_or_images.get_shape().as_list())', 'if', 'static_rank', '==', '3:', 'return', 'fn(image_or_images,', 'mask_or_masks,', 'box_or_boxes)', 'elif', 'static_rank', '==', '4:', 'aux', '=', '[fn(x,'... | 846,998 |
lanej5/bm | RBM.py | RBM.from_Values | from_Values | Initialize with trained weights. | [
"Initialize",
"with",
"trained",
"weights."
] | def from_Values(cls, weights):
(W, a, b) = (weights['W'], weights['a'], weights['b'])
assert W.shape[0] == a.shape[0] and W.shape[1] == b.shape[0]
rbm = cls(W.shape[0], W.shape[1])
rbm.W = W
rbm.a = a
rbm.b = b
return rbm | ['def', 'from_Values(cls,', 'weights):', '(W,', 'a,', 'b)', '=', "(weights['W'],", "weights['a'],", "weights['b'])", 'assert', 'W.shape[0]', '==', 'a.shape[0]', 'and', 'W.shape[1]', '==', 'b.shape[0]', 'rbm', '=', 'cls(W.shape[0],', 'W.shape[1])', 'rbm.W', '=', 'W', 'rbm.a', '=', 'a', 'rbm.b', '=', 'b', 'return', 'rbm'... | 461,833 |
tensorflow/data-validation | natural_language_stats_generator_test.py | NaturalLanguageStatsGeneratorTest.test_nl_generator_invalidation_check_empty_nld | test_nl_generator_invalidation_check_empty_nld | Tests generator invalidation whith empty natural language domain. | [
"Tests",
"generator",
"invalidation",
"whith",
"empty",
"natural",
"language",
"domain."
] | def test_nl_generator_invalidation_check_empty_nld(self):
generator = nlsg.NLStatsGenerator(self._schema, None, 0, 0, 0)
generator.setup()
accumulator = generator.create_accumulator()
self.assertFalse(accumulator.invalidate)
valid_input = pa.array([[0], [1]])
accumulator = generator.add_input(ac... | ['def', 'test_nl_generator_invalidation_check_empty_nld(self):', 'generator', '=', 'nlsg.NLStatsGenerator(self._schema,', 'None,', '0,', '0,', '0)', 'generator.setup()', 'accumulator', '=', 'generator.create_accumulator()', 'self.assertFalse(accumulator.invalidate)', 'valid_input', '=', 'pa.array([[0],', '[1]])', 'accu... | 497,508 |
zhoroh/ObjectDetection | functionalCV.py | resize | resize | Resize the input CV2 Image to the given size. | [
"Resize",
"the",
"input",
"CV2",
"Image",
"to",
"the",
"given",
"size."
] | def resize(img, size, interpolation=cv2.INTER_LINEAR):
if not _is_numpy_image(img):
raise TypeError('img should be nparrary Image. Got {}'.format(type(img)))
if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):
raise TypeError('Got inappropriate size arg... | ['def', 'resize(img,', 'size,', 'interpolation=cv2.INTER_LINEAR):', 'if', 'not', '_is_numpy_image(img):', 'raise', "TypeError('img", 'should', 'be', 'nparrary', 'Image.', 'Got', "{}'.format(type(img)))", 'if', 'not', '(isinstance(size,', 'int)', 'or', '(isinstance(size,', 'collections.Iterable)', 'and', 'len(size)', '=... | 743,332 |
tobegit3hub/deep_image_model | topn_ops.py | Load | Load | Load the TopN ops library and return the loaded module. | [
"Load",
"the",
"TopN",
"ops",
"library",
"and",
"return",
"the",
"loaded",
"module."
] | def Load():
with _ops_lock:
global _topn_ops
if not _topn_ops:
ops_path = tf.resource_loader.get_path_to_datafile(TOPN_OPS_FILE)
tf.logging.info('data path: %s', ops_path)
_topn_ops = tf.load_op_library(ops_path)
assert _topn_ops, 'Could not load topn_... | ['def', 'Load():', 'with', '_ops_lock:', 'global', '_topn_ops', 'if', 'not', '_topn_ops:', 'ops_path', '=', 'tf.resource_loader.get_path_to_datafile(TOPN_OPS_FILE)', "tf.logging.info('data", 'path:', "%s',", 'ops_path)', '_topn_ops', '=', 'tf.load_op_library(ops_path)', 'assert', '_topn_ops,', "'Could", 'not', 'load', ... | 182,122 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | generator_utils.py | tfrecord_iterator | tfrecord_iterator | Yields records from TFRecord files. | [
"Yields",
"records",
"from",
"TFRecord",
"files."
] | def tfrecord_iterator(filenames, gzipped=False, example_spec=None):
with tf.Graph().as_default():
dataset = tf.data.Dataset.from_tensor_slices(filenames)
def _load_records(filename):
return tf.data.TFRecordDataset(filename, compression_type=tf.constant('GZIP') if gzipped else None, buff... | ['def', 'tfrecord_iterator(filenames,', 'gzipped=False,', 'example_spec=None):', 'with', 'tf.Graph().as_default():', 'dataset', '=', 'tf.data.Dataset.from_tensor_slices(filenames)', 'def', '_load_records(filename):', 'return', 'tf.data.TFRecordDataset(filename,', "compression_type=tf.constant('GZIP')", 'if', 'gzipped',... | 964,870 |
joaquimcampos/DeepSplines | ds_utils.py | json_load | json_load | Load a json file. | [
"Load",
"a",
"json",
"file."
] | def json_load(json_filename):
try:
with open(json_filename) as jsonfile:
results_dict = json.load(jsonfile)
except FileNotFoundError:
print(f'File {json_filename} not found...')
raise
return results_dict | ['def', 'json_load(json_filename):', 'try:', 'with', 'open(json_filename)', 'as', 'jsonfile:', 'results_dict', '=', 'json.load(jsonfile)', 'except', 'FileNotFoundError:', "print(f'File", '{json_filename}', 'not', "found...')", 'raise', 'return', 'results_dict'] | 540,049 |
myothida/Supervised-Machine-Learning | test_kernel_pca.py | test_kernel_pca_invalid_parameters | test_kernel_pca_invalid_parameters | Check that kPCA raises an error if the parameters are invalid Tests fitting inverse transform with a precomputed kernel raises a ValueError. | [
"Check",
"that",
"kPCA",
"raises",
"an",
"error",
"if",
"the",
"parameters",
"are",
"invalid",
"Tests",
"fitting",
"inverse",
"transform",
"with",
"a",
"precomputed",
"kernel",
"raises",
"a",
"ValueError."
] | def test_kernel_pca_invalid_parameters():
estimator = KernelPCA(n_components=10, fit_inverse_transform=True, kernel='precomputed')
err_ms = 'Cannot fit_inverse_transform with a precomputed kernel'
with pytest.raises(ValueError, match=err_ms):
estimator.fit(np.random.randn(10, 10)) | ['def', 'test_kernel_pca_invalid_parameters():', 'estimator', '=', 'KernelPCA(n_components=10,', 'fit_inverse_transform=True,', "kernel='precomputed')", 'err_ms', '=', "'Cannot", 'fit_inverse_transform', 'with', 'a', 'precomputed', "kernel'", 'with', 'pytest.raises(ValueError,', 'match=err_ms):', 'estimator.fit(np.rand... | 363,668 |
arshpreetsingh/quantopian-machinelearning | magic_arguments.py | construct_parser | construct_parser | Construct an argument parser using the function decorations. | [
"Construct",
"an",
"argument",
"parser",
"using",
"the",
"function",
"decorations."
] | def construct_parser(magic_func):
kwds = getattr(magic_func, 'argcmd_kwds', {})
if 'description' not in kwds:
kwds['description'] = getattr(magic_func, '__doc__', None)
arg_name = real_name(magic_func)
parser = MagicArgumentParser(arg_name, **kwds)
group = None
for deco in magic_func.dec... | ['def', 'construct_parser(magic_func):', 'kwds', '=', 'getattr(magic_func,', "'argcmd_kwds',", '{})', 'if', "'description'", 'not', 'in', 'kwds:', "kwds['description']", '=', 'getattr(magic_func,', "'__doc__',", 'None)', 'arg_name', '=', 'real_name(magic_func)', 'parser', '=', 'MagicArgumentParser(arg_name,', '**kwds)'... | 886,380 |
david8862/tf-keras-deeplabv3p-model-set | layers.py | DeeplabDepthwiseConv2D | DeeplabDepthwiseConv2D | Wrapper to set Deeplab parameters for DepthwiseConv2D. | [
"Wrapper",
"to",
"set",
"Deeplab",
"parameters",
"for",
"DepthwiseConv2D."
] | def DeeplabDepthwiseConv2D(*args, **kwargs):
deeplab_conv_kwargs = {'kernel_regularizer': l2(L2_FACTOR)}
deeplab_conv_kwargs['bias_regularizer'] = l2(L2_FACTOR)
deeplab_conv_kwargs.update(kwargs)
return DepthwiseConv2D(*args, **deeplab_conv_kwargs) | ['def', 'DeeplabDepthwiseConv2D(*args,', '**kwargs):', 'deeplab_conv_kwargs', '=', "{'kernel_regularizer':", 'l2(L2_FACTOR)}', "deeplab_conv_kwargs['bias_regularizer']", '=', 'l2(L2_FACTOR)', 'deeplab_conv_kwargs.update(kwargs)', 'return', 'DepthwiseConv2D(*args,', '**deeplab_conv_kwargs)'] | 914,165 |
Speedwagon13/CS-3600-Introduction-to-- | tclobj.py | FromObj | FromObj | Convert a TclObj pointer into a Python object. | [
"Convert",
"a",
"TclObj",
"pointer",
"into",
"a",
"Python",
"object."
] | def FromObj(app, value):
typeCache = app._typeCache
if not value.typePtr:
buf = tkffi.buffer(value.bytes, value.length)
return FromTclString(buf[:])
if value.typePtr in (typeCache.BooleanType, typeCache.OldBooleanType):
value_ptr = tkffi.new('int*')
if tklib.Tcl_GetBooleanFro... | ['def', 'FromObj(app,', 'value):', 'typeCache', '=', 'app._typeCache', 'if', 'not', 'value.typePtr:', 'buf', '=', 'tkffi.buffer(value.bytes,', 'value.length)', 'return', 'FromTclString(buf[:])', 'if', 'value.typePtr', 'in', '(typeCache.BooleanType,', 'typeCache.OldBooleanType):', 'value_ptr', '=', "tkffi.new('int*')", ... | 219,801 |
cvhciKIT/sloth | container.py | JsonContainer.serializeToFile | serializeToFile | Overwritten to write JSON files. | [
"Overwritten",
"to",
"write",
"JSON",
"files."
] | def serializeToFile(self, fname, annotations):
f = open(fname, 'w')
json.dump(annotations, f, indent=4, separators=(',', ': '), sort_keys=True)
f.write('\n') | ['def', 'serializeToFile(self,', 'fname,', 'annotations):', 'f', '=', 'open(fname,', "'w')", 'json.dump(annotations,', 'f,', 'indent=4,', "separators=(',',", "':", "'),", 'sort_keys=True)', "f.write('\\n')"] | 878,361 |
billstark/receipt-scanner | image_to_records.py | TFRecordsConverter.write_tfrecords_file | write_tfrecords_file | Writes out TFRecords file. | [
"Writes",
"out",
"TFRecords",
"file."
] | def write_tfrecords_file(self, output_path, indices):
writer = tf.python_io.TFRecordWriter(output_path)
for i in indices:
filename = self.filenames[i]
label = self.labels[i]
with tf.gfile.FastGFile(filename, 'rb') as f:
im_data = f.read()
example = tf.train.Example(fe... | ['def', 'write_tfrecords_file(self,', 'output_path,', 'indices):', 'writer', '=', 'tf.python_io.TFRecordWriter(output_path)', 'for', 'i', 'in', 'indices:', 'filename', '=', 'self.filenames[i]', 'label', '=', 'self.labels[i]', 'with', 'tf.gfile.FastGFile(filename,', "'rb')", 'as', 'f:', 'im_data', '=', 'f.read()', 'exam... | 832,060 |
surafelml/adapt-mnmt | reducer.py | pad_n_with_identity | pad_n_with_identity | Pads each input tensors with identity values up to ``max(sequence_lengths)`` for each batch. | [
"Pads",
"each",
"input",
"tensors",
"with",
"identity",
"values",
"up",
"to",
"``max(sequence_lengths)``",
"for",
"each",
"batch."
] | def pad_n_with_identity(inputs, sequence_lengths, identity_values=0):
max_sequence_length = tf.reduce_max(sequence_lengths, axis=0)
maxlen = tf.reduce_max([tf.shape(x)[1] for x in inputs])
padded = [pad_with_identity(x, length, max_sequence_length, identity_values=identity_values, maxlen=maxlen) for (x, len... | ['def', 'pad_n_with_identity(inputs,', 'sequence_lengths,', 'identity_values=0):', 'max_sequence_length', '=', 'tf.reduce_max(sequence_lengths,', 'axis=0)', 'maxlen', '=', 'tf.reduce_max([tf.shape(x)[1]', 'for', 'x', 'in', 'inputs])', 'padded', '=', '[pad_with_identity(x,', 'length,', 'max_sequence_length,', 'identity_... | 407,960 |
triaquae/triaquae | cookie.py | CookieTest.test_max_cookie_length | test_max_cookie_length | Tests that, if the data exceeds what is allowed in a cookie, older messages are removed before saving (and returned by the ``update`` method). | [
"Tests",
"that,",
"if",
"the",
"data",
"exceeds",
"what",
"is",
"allowed",
"in",
"a",
"cookie,",
"older",
"messages",
"are",
"removed",
"before",
"saving",
"(and",
"returned",
"by",
"the",
"``update``",
"method)."
] | def test_max_cookie_length(self):
storage = self.get_storage()
response = self.get_response()
msg_size = int((CookieStorage.max_cookie_size - 54) / 4.5 - 37)
for i in range(5):
storage.add(constants.INFO, str(i) * msg_size)
unstored_messages = storage.update(response)
cookie_storing = se... | ['def', 'test_max_cookie_length(self):', 'storage', '=', 'self.get_storage()', 'response', '=', 'self.get_response()', 'msg_size', '=', 'int((CookieStorage.max_cookie_size', '-', '54)', '/', '4.5', '-', '37)', 'for', 'i', 'in', 'range(5):', 'storage.add(constants.INFO,', 'str(i)', '*', 'msg_size)', 'unstored_messages',... | 358,134 |
xiaoxiong74/Object-Detection-and-Tracking | sort.py | KalmanBoxTracker.predict | predict | Advances the state vector and returns the predicted bounding box estimate. | [
"Advances",
"the",
"state",
"vector",
"and",
"returns",
"the",
"predicted",
"bounding",
"box",
"estimate."
] | def predict(self):
if self.kf.x[6] + self.kf.x[2] <= 0:
self.kf.x[6] *= 0.0
self.kf.predict()
self.age += 1
if self.time_since_update > 0:
self.hit_streak = 0
self.time_since_update += 1
self.history.append(convert_x_to_bbox(self.kf.x))
return self.history[-1] | ['def', 'predict(self):', 'if', 'self.kf.x[6]', '+', 'self.kf.x[2]', '<=', '0:', 'self.kf.x[6]', '*=', '0.0', 'self.kf.predict()', 'self.age', '+=', '1', 'if', 'self.time_since_update', '>', '0:', 'self.hit_streak', '=', '0', 'self.time_since_update', '+=', '1', 'self.history.append(convert_x_to_bbox(self.kf.x))', 'ret... | 726,080 |
huawei-noah/xingtian | learner.py | TrainWorker.record_reward | record_reward | Record reward in train. | [
"Record",
"reward",
"in",
"train."
] | def record_reward(self, train_data):
broker_id = get_msg_info(train_data, 'broker_id')
explorer_id = get_msg_info(train_data, 'explorer_id')
agent_id = get_msg_info(train_data, 'agent_id')
key = (broker_id, explorer_id, agent_id)
self._train_data_counter[key] += 1
self.alg.dist_model_policy.add_... | ['def', 'record_reward(self,', 'train_data):', 'broker_id', '=', 'get_msg_info(train_data,', "'broker_id')", 'explorer_id', '=', 'get_msg_info(train_data,', "'explorer_id')", 'agent_id', '=', 'get_msg_info(train_data,', "'agent_id')", 'key', '=', '(broker_id,', 'explorer_id,', 'agent_id)', 'self._train_data_counter[key... | 962,214 |
sktime/sktime | test_all_estimators.py | TestAllEstimators.test_save_estimators_to_file | test_save_estimators_to_file | Check if saved estimators onto disk can be loaded correctly. | [
"Check",
"if",
"saved",
"estimators",
"onto",
"disk",
"can",
"be",
"loaded",
"correctly."
] | def test_save_estimators_to_file(self, estimator_instance, scenario, method_nsc_arraylike):
method_nsc = method_nsc_arraylike
if isinstance(estimator_instance, BaseForecaster) and method_nsc == 'predict_proba':
return None
estimator = estimator_instance
set_random_state(estimator)
scenario.r... | ['def', 'test_save_estimators_to_file(self,', 'estimator_instance,', 'scenario,', 'method_nsc_arraylike):', 'method_nsc', '=', 'method_nsc_arraylike', 'if', 'isinstance(estimator_instance,', 'BaseForecaster)', 'and', 'method_nsc', '==', "'predict_proba':", 'return', 'None', 'estimator', '=', 'estimator_instance', 'set_... | 877,624 |
tensorflow/privacy | common_test_utils.py | get_computed_and_true_norms_from_model | get_computed_and_true_norms_from_model | Generates relevant norms from an input model and other specs. | [
"Generates",
"relevant",
"norms",
"from",
"an",
"input",
"model",
"and",
"other",
"specs."
] | def get_computed_and_true_norms_from_model(model: tf.keras.Model, per_example_loss_fn: Optional[Callable[[tf.Tensor, tf.Tensor], tf.Tensor]], num_microbatches: Optional[int], x_batch: tf.Tensor, weight_batch: Optional[tf.Tensor]=None, rng_seed: int=777, registry: layer_registry.LayerRegistry=None, partial: bool=False):... | ['def', 'get_computed_and_true_norms_from_model(model:', 'tf.keras.Model,', 'per_example_loss_fn:', 'Optional[Callable[[tf.Tensor,', 'tf.Tensor],', 'tf.Tensor]],', 'num_microbatches:', 'Optional[int],', 'x_batch:', 'tf.Tensor,', 'weight_batch:', 'Optional[tf.Tensor]=None,', 'rng_seed:', 'int=777,', 'registry:', 'layer_... | 824,771 |
43Carrig/recurrent_neural_networks_practice | control_flow_ops.py | CondContext.AddValue | AddValue | Add `val` to the current context and its outer context recursively. | [
"Add",
"`val`",
"to",
"the",
"current",
"context",
"and",
"its",
"outer",
"context",
"recursively."
] | def AddValue(self, val):
if val.name in self._values:
result = self._external_values.get(val.name)
result = val if result is None else result
else:
result = val
self._values.add(val.name)
if self._outer_context:
result = self._outer_context.AddValue(val)
... | ['def', 'AddValue(self,', 'val):', 'if', 'val.name', 'in', 'self._values:', 'result', '=', 'self._external_values.get(val.name)', 'result', '=', 'val', 'if', 'result', 'is', 'None', 'else', 'result', 'else:', 'result', '=', 'val', 'self._values.add(val.name)', 'if', 'self._outer_context:', 'result', '=', 'self._outer_c... | 337,170 |
LPRowe/artificial-intelligence-snake | visualize.py | plot_stats | plot_stats | Plots the population's average and best fitness. | [
"Plots",
"the",
"population's",
"average",
"and",
"best",
"fitness."
] | def plot_stats(statistics, ylog=False, view=False, filename='avg_fitness.svg'):
if plt is None:
warnings.warn('This display is not available due to a missing optional dependency (matplotlib)')
return
generation = range(len(statistics.most_fit_genomes))
best_fitness = [c.fitness for c in stat... | ['def', 'plot_stats(statistics,', 'ylog=False,', 'view=False,', "filename='avg_fitness.svg'):", 'if', 'plt', 'is', 'None:', "warnings.warn('This", 'display', 'is', 'not', 'available', 'due', 'to', 'a', 'missing', 'optional', 'dependency', "(matplotlib)')", 'return', 'generation', '=', 'range(len(statistics.most_fit_gen... | 91,512 |
caiiiac/Machine-Learning-with-Python | artist.py | Artist.get_snap | get_snap | Returns the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center Only supported by the Agg and MacOSX backends. | [
"Returns",
"the",
"snap",
"setting",
"which",
"may",
"be:",
"*",
"True:",
"snap",
"vertices",
"to",
"the",
"nearest",
"pixel",
"center",
"*",
"False:",
"leave",
"vertices",
"as-is",
"*",
"None:",
"(auto)",
"If",
"the",
"path",
"contains",
"only",
"rectilinea... | def get_snap(self):
if rcParams['path.snap']:
return self._snap
else:
return False | ['def', 'get_snap(self):', 'if', "rcParams['path.snap']:", 'return', 'self._snap', 'else:', 'return', 'False'] | 714,902 |
RasaHQ/rasa | trackers.py | DialogueStateTracker.is_paused | is_paused | State whether the tracker is currently paused. | [
"State",
"whether",
"the",
"tracker",
"is",
"currently",
"paused."
] | def is_paused(self) -> bool:
return self._paused | ['def', 'is_paused(self)', '->', 'bool:', 'return', 'self._paused'] | 837,537 |
THUNLP-MT/THUCC | bottle.py | BaseResponse.charset | charset | Return the charset specified in the content-type header (default: utf8). | [
"Return",
"the",
"charset",
"specified",
"in",
"the",
"content-type",
"header",
"(default:",
"utf8)."
] | def charset(self, default='UTF-8'):
if 'charset=' in self.content_type:
return self.content_type.split('charset=')[-1].split(';')[0].strip()
return default | ['def', 'charset(self,', "default='UTF-8'):", 'if', "'charset='", 'in', 'self.content_type:', 'return', "self.content_type.split('charset=')[-1].split(';')[0].strip()", 'return', 'default'] | 916,436 |
fanoping/Computer-Vision | thread_demo.py | noThreading | noThreading | Grab and show video frames without multithreading. | [
"Grab",
"and",
"show",
"video",
"frames",
"without",
"multithreading."
] | def noThreading(source=0):
cap = cv2.VideoCapture(source)
cps = CountsPerSec().start()
while True:
(grabbed, frame) = cap.read()
if not grabbed or cv2.waitKey(1) == ord('q'):
break
frame = putIterationsPerSec(frame, cps.countsPerSec())
cv2.imshow('Video', frame)
... | ['def', 'noThreading(source=0):', 'cap', '=', 'cv2.VideoCapture(source)', 'cps', '=', 'CountsPerSec().start()', 'while', 'True:', '(grabbed,', 'frame)', '=', 'cap.read()', 'if', 'not', 'grabbed', 'or', 'cv2.waitKey(1)', '==', "ord('q'):", 'break', 'frame', '=', 'putIterationsPerSec(frame,', 'cps.countsPerSec())', "cv2.... | 459,834 |
xiaoaleiBLUE/computer_vision | eval.py | label2str | label2str | Predicted sequence to string. | [
"Predicted",
"sequence",
"to",
"string."
] | def label2str(preds, probs, label_dict, eos='EOS'):
results = []
for idx in preds:
if label_dict[idx] == eos:
break
results.append(label_dict[idx])
probabilities = probs[:min(len(results) + 1, cfg.seq_len + 1)]
return (''.join(results), probabilities) | ['def', 'label2str(preds,', 'probs,', 'label_dict,', "eos='EOS'):", 'results', '=', '[]', 'for', 'idx', 'in', 'preds:', 'if', 'label_dict[idx]', '==', 'eos:', 'break', 'results.append(label_dict[idx])', 'probabilities', '=', 'probs[:min(len(results)', '+', '1,', 'cfg.seq_len', '+', '1)]', 'return', "(''.join(results),"... | 501,415 |
yinyunie/ScenePriors | camera_visualization.py | plot_cameras | plot_cameras | Plots a set of `cameras` objects into the maplotlib axis `ax` with color `color`. | [
"Plots",
"a",
"set",
"of",
"`cameras`",
"objects",
"into",
"the",
"maplotlib",
"axis",
"`ax`",
"with",
"color",
"`color`."
] | def plot_cameras(ax, cameras, color: str='blue'):
cam_wires_canonical = get_camera_wireframe().cuda()[None]
cam_trans = cameras.get_world_to_view_transform().inverse()
cam_wires_trans = cam_trans.transform_points(cam_wires_canonical)
plot_handles = []
for wire in cam_wires_trans:
(x_, z_, y_... | ['def', 'plot_cameras(ax,', 'cameras,', 'color:', "str='blue'):", 'cam_wires_canonical', '=', 'get_camera_wireframe().cuda()[None]', 'cam_trans', '=', 'cameras.get_world_to_view_transform().inverse()', 'cam_wires_trans', '=', 'cam_trans.transform_points(cam_wires_canonical)', 'plot_handles', '=', '[]', 'for', 'wire', '... | 329,572 |
KalleHallden/InstaAutomator | test_half.py | TestHalf.test_half_correctness | test_half_correctness | Take every finite float16, and check the casting functions with a manual conversion. | [
"Take",
"every",
"finite",
"float16,",
"and",
"check",
"the",
"casting",
"functions",
"with",
"a",
"manual",
"conversion."
] | def test_half_correctness(self):
a_bits = self.finite_f16.view(dtype=uint16)
a_sgn = (-1.0) ** ((a_bits & 32768) >> 15)
a_exp = np.array((a_bits & 31744) >> 10, dtype=np.int32) - 15
a_man = (a_bits & 1023) * 2.0 ** (-10)
a_man[a_exp != -15] += 1
a_exp[a_exp == -15] = -14
a_manual = a_sgn * a... | ['def', 'test_half_correctness(self):', 'a_bits', '=', 'self.finite_f16.view(dtype=uint16)', 'a_sgn', '=', '(-1.0)', '**', '((a_bits', '&', '32768)', '>>', '15)', 'a_exp', '=', 'np.array((a_bits', '&', '31744)', '>>', '10,', 'dtype=np.int32)', '-', '15', 'a_man', '=', '(a_bits', '&', '1023)', '*', '2.0', '**', '(-10)',... | 243,124 |
erikdelange/Reinforcement-Learning-Maze | qtable.py | QTableModel.q | q | Get q values for all actions for a certain state. | [
"Get",
"q",
"values",
"for",
"all",
"actions",
"for",
"a",
"certain",
"state."
] | def q(self, state):
if type(state) == np.ndarray:
state = tuple(state.flatten())
return np.array([self.Q.get((state, action), 0.0) for action in self.environment.actions]) | ['def', 'q(self,', 'state):', 'if', 'type(state)', '==', 'np.ndarray:', 'state', '=', 'tuple(state.flatten())', 'return', 'np.array([self.Q.get((state,', 'action),', '0.0)', 'for', 'action', 'in', 'self.environment.actions])'] | 286,500 |
JonasLandman/QCNN | link.py | Link.netloc | netloc | This can contain auth information. | [
"This",
"can",
"contain",
"auth",
"information."
] | def netloc(self):
return self._parsed_url.netloc | ['def', 'netloc(self):', 'return', 'self._parsed_url.netloc'] | 302,833 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | thinkstats2.py | LeastSquares | LeastSquares | Computes a linear least squares fit for ys as a function of xs. | [
"Computes",
"a",
"linear",
"least",
"squares",
"fit",
"for",
"ys",
"as",
"a",
"function",
"of",
"xs."
] | def LeastSquares(xs, ys):
(meanx, varx) = MeanVar(xs)
meany = Mean(ys)
slope = Cov(xs, ys, meanx, meany) / varx
inter = meany - slope * meanx
return (inter, slope) | ['def', 'LeastSquares(xs,', 'ys):', '(meanx,', 'varx)', '=', 'MeanVar(xs)', 'meany', '=', 'Mean(ys)', 'slope', '=', 'Cov(xs,', 'ys,', 'meanx,', 'meany)', '/', 'varx', 'inter', '=', 'meany', '-', 'slope', '*', 'meanx', 'return', '(inter,', 'slope)'] | 19,719 |
tensorflow/agents | tf_driver.py | TFDriver.run | run | Run policy in environment given initial time_step and policy_state. | [
"Run",
"policy",
"in",
"environment",
"given",
"initial",
"time_step",
"and",
"policy_state."
] | def run(self, time_step: ts.TimeStep, policy_state: types.NestedTensor=()) -> Tuple[ts.TimeStep, types.NestedTensor]:
num_steps = tf.constant(0.0)
num_episodes = tf.constant(0.0)
while num_steps < self._max_steps and num_episodes < self._max_episodes:
action_step = self.policy.action(time_step, poli... | ['def', 'run(self,', 'time_step:', 'ts.TimeStep,', 'policy_state:', 'types.NestedTensor=())', '->', 'Tuple[ts.TimeStep,', 'types.NestedTensor]:', 'num_steps', '=', 'tf.constant(0.0)', 'num_episodes', '=', 'tf.constant(0.0)', 'while', 'num_steps', '<', 'self._max_steps', 'and', 'num_episodes', '<', 'self._max_episodes:'... | 23,404 |
AgnostiqHQ/covalent | runtime_sampler.py | QiskitRuntimeSampler.post_process | post_process | Post-process a single circuit result. | [
"Post-process",
"a",
"single",
"circuit",
"result."
] | def post_process(self, *args):
results = []
metadatas = []
for (i, circuit) in enumerate(self._active_circuits):
circuit = self._active_circuits[i]
job = self._active_jobs[i]
job_result = job.result()
self._num_executions += 1
assert len(job_result.quasi_dists) == 1
... | ['def', 'post_process(self,', '*args):', 'results', '=', '[]', 'metadatas', '=', '[]', 'for', '(i,', 'circuit)', 'in', 'enumerate(self._active_circuits):', 'circuit', '=', 'self._active_circuits[i]', 'job', '=', 'self._active_jobs[i]', 'job_result', '=', 'job.result()', 'self._num_executions', '+=', '1', 'assert', 'len... | 489,419 |
ZhAnGToNG1/transfer_learning_cspt | sabl_head.py | SABLHead.attention_pool | attention_pool | Extract direction-specific features fx and fy with attention methanism. | [
"Extract",
"direction-specific",
"features",
"fx",
"and",
"fy",
"with",
"attention",
"methanism."
] | def attention_pool(self, reg_x):
reg_fx = reg_x
reg_fy = reg_x
reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid()
reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid()
reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2)
reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3)
reg_f... | ['def', 'attention_pool(self,', 'reg_x):', 'reg_fx', '=', 'reg_x', 'reg_fy', '=', 'reg_x', 'reg_fx_att', '=', 'self.reg_conv_att_x(reg_fx).sigmoid()', 'reg_fy_att', '=', 'self.reg_conv_att_y(reg_fy).sigmoid()', 'reg_fx_att', '=', 'reg_fx_att', '/', 'reg_fx_att.sum(dim=2).unsqueeze(2)', 'reg_fy_att', '=', 'reg_fy_att', ... | 964,254 |
43Carrig/recurrent_neural_networks_practice | training.py | _TrainingExecutor.run_ps | run_ps | Runs task parameter server (in training cluster spec). | [
"Runs",
"task",
"parameter",
"server",
"(in",
"training",
"cluster",
"spec)."
] | def run_ps(self):
config = self._estimator.config
server = self._start_std_server(config)
server.join() | ['def', 'run_ps(self):', 'config', '=', 'self._estimator.config', 'server', '=', 'self._start_std_server(config)', 'server.join()'] | 336,198 |
muhanzhang/D-VAE | test_mlp.py | run_conv_nnet2_classif | run_conv_nnet2_classif | Run the train function returned by build_conv_nnet2_classif on one device. | [
"Run",
"the",
"train",
"function",
"returned",
"by",
"build_conv_nnet2_classif",
"on",
"one",
"device."
] | def run_conv_nnet2_classif(use_gpu, seed, isize, ksize, bsize, n_train=10, check_isfinite=True, pickle=False, verbose=0, version=-1):
utt.seed_rng(seed)
(train, params, x_shape, y_shape, mode) = build_conv_nnet2_classif(use_gpu=use_gpu, isize=isize, ksize=ksize, n_batch=bsize, verbose=verbose, version=version, ... | ['def', 'run_conv_nnet2_classif(use_gpu,', 'seed,', 'isize,', 'ksize,', 'bsize,', 'n_train=10,', 'check_isfinite=True,', 'pickle=False,', 'verbose=0,', 'version=-1):', 'utt.seed_rng(seed)', '(train,', 'params,', 'x_shape,', 'y_shape,', 'mode)', '=', 'build_conv_nnet2_classif(use_gpu=use_gpu,', 'isize=isize,', 'ksize=ks... | 525,177 |
microsoft/maro | client.py | StreamitClient.close | close | Close current client connection. | [
"Close",
"current",
"client",
"connection."
] | def close(self):
if self._is_started and self._sender is not None and self._sender.is_alive():
self._put(MessageType.Close, None)
self._sender.join()
self._is_started = False | ['def', 'close(self):', 'if', 'self._is_started', 'and', 'self._sender', 'is', 'not', 'None', 'and', 'self._sender.is_alive():', 'self._put(MessageType.Close,', 'None)', 'self._sender.join()', 'self._is_started', '=', 'False'] | 628,707 |
aisingapore/PeekingDuck | base.py | ThresholdCheckerMixin.check_bounds | check_bounds | Checks if the configuration value(s) specified by `key` satisfies the specified bounds. | [
"Checks",
"if",
"the",
"configuration",
"value(s)",
"specified",
"by",
"`key`",
"satisfies",
"the",
"specified",
"bounds."
] | def check_bounds(self, key: Union[str, List[str]], interval: str) -> None:
if self.interval_pattern.match(interval) is None:
raise ValueError('Badly formatted interval')
left_bracket = interval[0]
right_bracket = interval[-1]
(lower, upper) = [float(value.strip()) for value in interval[1:-1].spl... | ['def', 'check_bounds(self,', 'key:', 'Union[str,', 'List[str]],', 'interval:', 'str)', '->', 'None:', 'if', 'self.interval_pattern.match(interval)', 'is', 'None:', 'raise', "ValueError('Badly", 'formatted', "interval')", 'left_bracket', '=', 'interval[0]', 'right_bracket', '=', 'interval[-1]', '(lower,', 'upper)', '='... | 766,808 |
som-shahlab/femr | jax.py | embedding_dot_bwd | embedding_dot_bwd | The backward pass for embedding dot. | [
"The",
"backward",
"pass",
"for",
"embedding",
"dot."
] | def embedding_dot_bwd(res: Tuple[Array, Array, Array], g: Array) -> Tuple[Array, Array, None]:
(a, b, indices) = res
(da, db) = embedding_dot_backward_p.bind(a, b, indices, g)
return (da, db, None) | ['def', 'embedding_dot_bwd(res:', 'Tuple[Array,', 'Array,', 'Array],', 'g:', 'Array)', '->', 'Tuple[Array,', 'Array,', 'None]:', '(a,', 'b,', 'indices)', '=', 'res', '(da,', 'db)', '=', 'embedding_dot_backward_p.bind(a,', 'b,', 'indices,', 'g)', 'return', '(da,', 'db,', 'None)'] | 179,738 |
sktime/sktime | t.py | TDistribution.ppf | ppf | Quantile function = percent point function = inverse cdf. | [
"Quantile",
"function",
"=",
"percent",
"point",
"function",
"=",
"inverse",
"cdf."
] | def ppf(self, p):
d = self.loc[p.index, p.columns]
ppf_arr = p.to_numpy(copy=True)
ppf_arr[p.values == 0.5] = 0.0
ppf_arr[p.values <= 0] = -np.inf
ppf_arr[p.values >= 1] = np.inf
mask1 = (p.values < 0.5) & (p.values > 0)
mask2 = (p.values < 1) & (p.values > 0.5)
ppf_arr[mask1] = 1 / beta... | ['def', 'ppf(self,', 'p):', 'd', '=', 'self.loc[p.index,', 'p.columns]', 'ppf_arr', '=', 'p.to_numpy(copy=True)', 'ppf_arr[p.values', '==', '0.5]', '=', '0.0', 'ppf_arr[p.values', '<=', '0]', '=', '-np.inf', 'ppf_arr[p.values', '>=', '1]', '=', 'np.inf', 'mask1', '=', '(p.values', '<', '0.5)', '&', '(p.values', '>', '0... | 877,487 |
Eric3911/OpenAGI | pann_model.py | init_layer | init_layer | Initialize a Linear or Convolutional layer. | [
"Initialize",
"a",
"Linear",
"or",
"Convolutional",
"layer."
] | def init_layer(layer):
nn.init.xavier_uniform_(layer.weight)
if hasattr(layer, 'bias'):
if layer.bias is not None:
layer.bias.data.fill_(0.0) | ['def', 'init_layer(layer):', 'nn.init.xavier_uniform_(layer.weight)', 'if', 'hasattr(layer,', "'bias'):", 'if', 'layer.bias', 'is', 'not', 'None:', 'layer.bias.data.fill_(0.0)'] | 250,748 |
rudranil723/mini-main | _regex_core.py | is_hexadecimal | is_hexadecimal | Checks whether a string is hexadecimal. | [
"Checks",
"whether",
"a",
"string",
"is",
"hexadecimal."
] | def is_hexadecimal(string):
return all((ch in HEX_DIGITS for ch in string)) | ['def', 'is_hexadecimal(string):', 'return', 'all((ch', 'in', 'HEX_DIGITS', 'for', 'ch', 'in', 'string))'] | 269,807 |
yahoo/Prototrain | stanford_online_products.py | parse | parse | Replace paths to images with the images themselves, cropped to bounding boxes if available and distorted if augmentation (otherwise only resized). | [
"Replace",
"paths",
"to",
"images",
"with",
"the",
"images",
"themselves,",
"cropped",
"to",
"bounding",
"boxes",
"if",
"available",
"and",
"distorted",
"if",
"augmentation",
"(otherwise",
"only",
"resized)."
] | def parse(path_dict, augmentation=True):
result = {}
for (key, val) in path_dict.items():
if key + '_bbox' in path_dict:
result[key] = get_image(val, augmentation=augmentation, bbox=path_dict[key + '_bbox'])
elif key == 'id' or key == 'url' or key == 'labels' or (key == 'category'):
... | ['def', 'parse(path_dict,', 'augmentation=True):', 'result', '=', '{}', 'for', '(key,', 'val)', 'in', 'path_dict.items():', 'if', 'key', '+', "'_bbox'", 'in', 'path_dict:', 'result[key]', '=', 'get_image(val,', 'augmentation=augmentation,', 'bbox=path_dict[key', '+', "'_bbox'])", 'elif', 'key', '==', "'id'", 'or', 'key... | 818,092 |
georghess/voxel-mae | encoder_decoder.py | EncoderDecoder3D.extract_feat | extract_feat | Extract features from points. | [
"Extract",
"features",
"from",
"points."
] | def extract_feat(self, points):
x = self.backbone(points)
if self.with_neck:
x = self.neck(x)
return x | ['def', 'extract_feat(self,', 'points):', 'x', '=', 'self.backbone(points)', 'if', 'self.with_neck:', 'x', '=', 'self.neck(x)', 'return', 'x'] | 380,756 |
salesforce/CodeRL | check_repo.py | check_models_are_in_init | check_models_are_in_init | Checks all models defined in the library are in the main init. | [
"Checks",
"all",
"models",
"defined",
"in",
"the",
"library",
"are",
"in",
"the",
"main",
"init."
] | def check_models_are_in_init():
models_not_in_init = []
dir_transformers = dir(transformers)
for module in get_model_modules():
models_not_in_init += [model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers]
models_not_in_init = [model for model in m... | ['def', 'check_models_are_in_init():', 'models_not_in_init', '=', '[]', 'dir_transformers', '=', 'dir(transformers)', 'for', 'module', 'in', 'get_model_modules():', 'models_not_in_init', '+=', '[model[0]', 'for', 'model', 'in', 'get_models(module,', 'include_pretrained=True)', 'if', 'model[0]', 'not', 'in', 'dir_transf... | 495,741 |
intel/neural-compressor | gptq.py | trace_gptq_target_blocks | trace_gptq_target_blocks | Search transformer stacked structures, which is critical in LLMs and GPTQ execution. | [
"Search",
"transformer",
"stacked",
"structures,",
"which",
"is",
"critical",
"in",
"LLMs",
"and",
"GPTQ",
"execution."
] | def trace_gptq_target_blocks(module, module_types=[torch.nn.ModuleList]):
gptq_related_blocks = {'embeddings': {}, 'transformers_pre': {}, 'transformers_name': '', 'transformers': [], 'transformers_post': {}}
for (n, m) in module.named_modules():
if type(m) in module_types:
gptq_related_bloc... | ['def', 'trace_gptq_target_blocks(module,', 'module_types=[torch.nn.ModuleList]):', 'gptq_related_blocks', '=', "{'embeddings':", '{},', "'transformers_pre':", '{},', "'transformers_name':", "'',", "'transformers':", '[],', "'transformers_post':", '{}}', 'for', '(n,', 'm)', 'in', 'module.named_modules():', 'if', 'type(... | 737,863 |
tonybeltramelli/Graphics-And-Vision | Image.py | Image.Right | Right | Set the image provided by the right camera. | [
"Set",
"the",
"image",
"provided",
"by",
"the",
"right",
"camera."
] | def Right(self, value):
self.__right = value | ['def', 'Right(self,', 'value):', 'self.__right', '=', 'value'] | 580,632 |
NREL/sup3r | surface.py | SurfaceSpatialMetModel.feature_inds_rh | feature_inds_rh | Get the feature index values for the relative humidity features. | [
"Get",
"the",
"feature",
"index",
"values",
"for",
"the",
"relative",
"humidity",
"features."
] | def feature_inds_rh(self):
inds = [i for (i, name) in enumerate(self._features) if fnmatch(name, 'relativehumidity_*')]
return inds | ['def', 'feature_inds_rh(self):', 'inds', '=', '[i', 'for', '(i,', 'name)', 'in', 'enumerate(self._features)', 'if', 'fnmatch(name,', "'relativehumidity_*')]", 'return', 'inds'] | 912,062 |
shivendrapratap2/Computer-Vision | data_load.py | resize | resize | Resize the input PIL image to the given size. | [
"Resize",
"the",
"input",
"PIL",
"image",
"to",
"the",
"given",
"size."
] | def resize(img, boxes, size, max_size=1000):
(w, h) = img.size
if isinstance(size, int):
size_min = min(w, h)
size_max = max(w, h)
sw = sh = float(size) / size_min
if sw * size_max > max_size:
sw = sh = float(max_size) / size_max
ow = int(w * sw + 0.5)
... | ['def', 'resize(img,', 'boxes,', 'size,', 'max_size=1000):', '(w,', 'h)', '=', 'img.size', 'if', 'isinstance(size,', 'int):', 'size_min', '=', 'min(w,', 'h)', 'size_max', '=', 'max(w,', 'h)', 'sw', '=', 'sh', '=', 'float(size)', '/', 'size_min', 'if', 'sw', '*', 'size_max', '>', 'max_size:', 'sw', '=', 'sh', '=', 'floa... | 458,155 |
asyml/texar | xlnet_classifier_test.py | XLNetClassifierTest.test_model_loading | test_model_loading | Tests model loading functionality. | [
"Tests",
"model",
"loading",
"functionality."
] | def test_model_loading(self):
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])
for pretrained_model_name in XLNetClassifier.available_checkpoints():
classifier = XLNetClassifier(pretrained_model_name=pretrained_model_name)
(_, _) = classifier(inputs) | ['def', 'test_model_loading(self):', 'inputs', '=', 'tf.placeholder(dtype=tf.int32,', 'shape=[None,', 'None])', 'for', 'pretrained_model_name', 'in', 'XLNetClassifier.available_checkpoints():', 'classifier', '=', 'XLNetClassifier(pretrained_model_name=pretrained_model_name)', '(_,', '_)', '=', 'classifier(inputs)'] | 924,350 |
victordibia/data2vis | beam_search.py | hyp_score | hyp_score | Calculates scores for beam search hypotheses. | [
"Calculates",
"scores",
"for",
"beam",
"search",
"hypotheses."
] | def hyp_score(log_probs, sequence_lengths, config):
length_penality_ = length_penalty(sequence_lengths=sequence_lengths, penalty_factor=config.length_penalty_weight)
score = log_probs / length_penality_
return score | ['def', 'hyp_score(log_probs,', 'sequence_lengths,', 'config):', 'length_penality_', '=', 'length_penalty(sequence_lengths=sequence_lengths,', 'penalty_factor=config.length_penalty_weight)', 'score', '=', 'log_probs', '/', 'length_penality_', 'return', 'score'] | 126,851 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | aggregate_experiment_results.py | print_results_table | print_results_table | Print human readable results table to stdout. | [
"Print",
"human",
"readable",
"results",
"table",
"to",
"stdout."
] | def print_results_table(results_table):
print('')
print('=== Results Table ===')
print('Format: # reps [success rate, avg total NPE]')
def info_str(info_row):
if not info_row[0]:
return '0'
return '%s [%s, %s]' % (str(info_row[0]).ljust(2), info_row[1], info_row[2])
nc =... | ['def', 'print_results_table(results_table):', "print('')", "print('===", 'Results', 'Table', "===')", "print('Format:", '#', 'reps', '[success', 'rate,', 'avg', 'total', "NPE]')", 'def', 'info_str(info_row):', 'if', 'not', 'info_row[0]:', 'return', "'0'", 'return', "'%s", '[%s,', "%s]'", '%', '(str(info_row[0]).ljust(... | 52,666 |
xuannianz/SAPD | pascal.py | PascalVocGenerator.load_annotations | load_annotations | Load annotations for an image_index. | [
"Load",
"annotations",
"for",
"an",
"image_index."
] | def load_annotations(self, image_index):
filename = self.image_names[image_index] + '.xml'
try:
tree = ET.parse(os.path.join(self.data_dir, 'Annotations', filename))
return self.__parse_annotations(tree.getroot())
except ET.ParseError as e:
raise_from(ValueError('invalid annotations ... | ['def', 'load_annotations(self,', 'image_index):', 'filename', '=', 'self.image_names[image_index]', '+', "'.xml'", 'try:', 'tree', '=', 'ET.parse(os.path.join(self.data_dir,', "'Annotations',", 'filename))', 'return', 'self.__parse_annotations(tree.getroot())', 'except', 'ET.ParseError', 'as', 'e:', "raise_from(ValueE... | 845,466 |
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