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 |
|---|---|---|---|---|---|---|---|---|
huawei-noah/xingtian | share_memory.py | ClusterShareMemory.delete | delete | Delete data according to name. | [
"Delete",
"data",
"according",
"to",
"name."
] | def delete(self):
self.var.delete() | ['def', 'delete(self):', 'self.var.delete()'] | 968,366 |
napratin/lumos | rpc.py | start_server_thread | start_server_thread | Start RPC server thread (asynchronous), return Thread object immediately. | [
"Start",
"RPC",
"server",
"thread",
"(asynchronous),",
"return",
"Thread",
"object",
"immediately."
] | def start_server_thread(daemon=True, *args, **kwargs):
rpcServerThread = Thread(target=start_server, name='RPC-Server', args=args, kwargs=kwargs)
rpcServerThread.daemon = daemon
rpcServerThread.start()
time.sleep(0.01)
return rpcServerThread | ['def', 'start_server_thread(daemon=True,', '*args,', '**kwargs):', 'rpcServerThread', '=', 'Thread(target=start_server,', "name='RPC-Server',", 'args=args,', 'kwargs=kwargs)', 'rpcServerThread.daemon', '=', 'daemon', 'rpcServerThread.start()', 'time.sleep(0.01)', 'return', 'rpcServerThread'] | 617,603 |
thaines/helit | viewer.py | Viewer.del_layer | del_layer | Terminates a layer given an ident. | [
"Terminates",
"a",
"layer",
"given",
"an",
"ident."
] | def del_layer(self, ident):
self.layers[ident] = None | ['def', 'del_layer(self,', 'ident):', 'self.layers[ident]', '=', 'None'] | 592,740 |
carlos-ferras/Sequence-ToolKit | NodeLibrary.py | NodeLibrary.reload | reload | Reload Node classes in this library. | [
"Reload",
"Node",
"classes",
"in",
"this",
"library."
] | def reload(self):
raise NotImplementedError() | ['def', 'reload(self):', 'raise', 'NotImplementedError()'] | 876,784 |
instadeepai/jumanji | utils.py | init_graph_merge | init_graph_merge | Merge two graphs and initialize the setting to add new edges. | [
"Merge",
"two",
"graphs",
"and",
"initialize",
"the",
"setting",
"to",
"add",
"new",
"edges."
] | def init_graph_merge(graph_a: Graph, graph_b: Graph, num_edges: int, max_degree: int) -> Graph:
edges = jnp.ones((num_edges, 2), dtype=jnp.int32) * EMPTY_EDGE
num_edges_a = graph_a.edges.shape[0]
num_edges_b = graph_b.edges.shape[0]
num_edges_ab = num_edges_a + num_edges_b
edges = edges.at[0:num_edg... | ['def', 'init_graph_merge(graph_a:', 'Graph,', 'graph_b:', 'Graph,', 'num_edges:', 'int,', 'max_degree:', 'int)', '->', 'Graph:', 'edges', '=', 'jnp.ones((num_edges,', '2),', 'dtype=jnp.int32)', '*', 'EMPTY_EDGE', 'num_edges_a', '=', 'graph_a.edges.shape[0]', 'num_edges_b', '=', 'graph_b.edges.shape[0]', 'num_edges_ab'... | 594,411 |
YuriyGuts/snake-ai-reinforcement | environment.py | Environment.new_episode | new_episode | Reset the environment and begin a new episode. | [
"Reset",
"the",
"environment",
"and",
"begin",
"a",
"new",
"episode."
] | def new_episode(self):
self.field.create_level()
self.stats.reset()
self.timestep_index = 0
self.snake = Snake(self.field.find_snake_head(), length=self.initial_snake_length)
self.field.place_snake(self.snake)
self.generate_fruit()
self.current_action = None
self.is_game_over = False
... | ['def', 'new_episode(self):', 'self.field.create_level()', 'self.stats.reset()', 'self.timestep_index', '=', '0', 'self.snake', '=', 'Snake(self.field.find_snake_head(),', 'length=self.initial_snake_length)', 'self.field.place_snake(self.snake)', 'self.generate_fruit()', 'self.current_action', '=', 'None', 'self.is_gam... | 352,170 |
google/balloon-learning-environment | vae.py | FieldShape.num_flow_fields | num_flow_fields | Returns the number of flow fields generated by the decoder. | [
"Returns",
"the",
"number",
"of",
"flow",
"fields",
"generated",
"by",
"the",
"decoder."
] | def num_flow_fields(self) -> int:
return self.pressure_slices * self.time_slices | ['def', 'num_flow_fields(self)', '->', 'int:', 'return', 'self.pressure_slices', '*', 'self.time_slices'] | 422,442 |
tensorflow/hub | tf_utils.py | get_composite_tensor_type_spec | get_composite_tensor_type_spec | Returns the TypeSpec for `x`, or `None` if it's not a composite tensor. | [
"Returns",
"the",
"TypeSpec",
"for",
"`x`,",
"or",
"`None`",
"if",
"it's",
"not",
"a",
"composite",
"tensor."
] | def get_composite_tensor_type_spec(x):
type_spec = getattr(x, '__tf_type_spec__', None)
if type_spec is None:
return getattr(x, '_type_spec', None)
else:
return type_spec() | ['def', 'get_composite_tensor_type_spec(x):', 'type_spec', '=', 'getattr(x,', "'__tf_type_spec__',", 'None)', 'if', 'type_spec', 'is', 'None:', 'return', 'getattr(x,', "'_type_spec',", 'None)', 'else:', 'return', 'type_spec()'] | 571,052 |
RonMcKay/OODRetrieval | discover.py | Discovery.get_nearest_neighbors | get_nearest_neighbors | Computes nearest neighbors to the specified index in the collection of segment crops. | [
"Computes",
"nearest",
"neighbors",
"to",
"the",
"specified",
"index",
"in",
"the",
"collection",
"of",
"segment",
"crops."
] | def get_nearest_neighbors(self, ind, metric='cos'):
if metric == 'euclid':
dists = self.lp_dist(self.embeddings[ind], self.embeddings, d=2)
else:
dists = self.cos_dist(self.embeddings[ind], self.embeddings)
return np.argsort(dists)[1:self.n_neighbors + 1] | ['def', 'get_nearest_neighbors(self,', 'ind,', "metric='cos'):", 'if', 'metric', '==', "'euclid':", 'dists', '=', 'self.lp_dist(self.embeddings[ind],', 'self.embeddings,', 'd=2)', 'else:', 'dists', '=', 'self.cos_dist(self.embeddings[ind],', 'self.embeddings)', 'return', 'np.argsort(dists)[1:self.n_neighbors', '+', '1]... | 756,660 |
dstallmann/transfer_learning_twinvae | DeepView.py | DeepView.get_artist_sample | get_artist_sample | Maps the location of an embedded point to it's image. | [
"Maps",
"the",
"location",
"of",
"an",
"embedded",
"point",
"to",
"it's",
"image."
] | def get_artist_sample(self, point):
sample_id = np.argmin(np.linalg.norm(self.embedded - point, axis=1))
sample = self.samples[sample_id]
sample = sample + np.abs(sample.min())
sample = sample / sample.max()
(yp, yt) = (int(self.y_pred[sample_id]), int(self.y_true[sample_id]))
return (sample, yp... | ['def', 'get_artist_sample(self,', 'point):', 'sample_id', '=', 'np.argmin(np.linalg.norm(self.embedded', '-', 'point,', 'axis=1))', 'sample', '=', 'self.samples[sample_id]', 'sample', '=', 'sample', '+', 'np.abs(sample.min())', 'sample', '=', 'sample', '/', 'sample.max()', '(yp,', 'yt)', '=', '(int(self.y_pred[sample_... | 964,689 |
flavioschneider/rl-transfer- | test_conjugate_gradient_optimizer.py | TestFiniteDifferenceHVP.test_finite_difference_hvp_2x2_non_diagonal | test_finite_difference_hvp_2x2_non_diagonal | Test Hessian-vector product for a function with two variables whose Hessian is non-diagonal. | [
"Test",
"Hessian-vector",
"product",
"for",
"a",
"function",
"with",
"two",
"variables",
"whose",
"Hessian",
"is",
"non-diagonal."
] | def test_finite_difference_hvp_2x2_non_diagonal(self, a_val, b_val, x_val, y_val, vector):
a_val = [a_val]
b_val = [b_val]
vector = np.array([vector], dtype=np.float32)
policy = HelperPolicy(n_vars=2)
params = policy.get_params()
(x, y) = (params[0], params[1])
a = tf.constant(a_val)
b =... | ['def', 'test_finite_difference_hvp_2x2_non_diagonal(self,', 'a_val,', 'b_val,', 'x_val,', 'y_val,', 'vector):', 'a_val', '=', '[a_val]', 'b_val', '=', '[b_val]', 'vector', '=', 'np.array([vector],', 'dtype=np.float32)', 'policy', '=', 'HelperPolicy(n_vars=2)', 'params', '=', 'policy.get_params()', '(x,', 'y)', '=', '(... | 861,773 |
scikit-learn/scikit-learn | test_set_output.py | test__wrap_in_pandas_container_column_errors | test__wrap_in_pandas_container_column_errors | If a callable `columns` errors, it has the same semantics as columns=None. | [
"If",
"a",
"callable",
"`columns`",
"errors,",
"it",
"has",
"the",
"same",
"semantics",
"as",
"columns=None."
] | def test__wrap_in_pandas_container_column_errors():
pd = pytest.importorskip('pandas')
def get_columns():
raise ValueError('No feature names defined')
X_df = pd.DataFrame({'feat1': [1, 2, 3], 'feat2': [3, 4, 5]})
X_wrapped = _wrap_in_pandas_container(X_df, columns=get_columns)
assert_array_... | ['def', 'test__wrap_in_pandas_container_column_errors():', 'pd', '=', "pytest.importorskip('pandas')", 'def', 'get_columns():', 'raise', "ValueError('No", 'feature', 'names', "defined')", 'X_df', '=', "pd.DataFrame({'feat1':", '[1,', '2,', '3],', "'feat2':", '[3,', '4,', '5]})', 'X_wrapped', '=', '_wrap_in_pandas_conta... | 854,375 |
aisingapore/PeekingDuck | utils.py | letterbox | letterbox | Resizes a rectangular image to a padded rectangular image. | [
"Resizes",
"a",
"rectangular",
"image",
"to",
"a",
"padded",
"rectangular",
"image."
] | def letterbox(image: np.ndarray, height: int, width: int, color: Tuple[float, float, float]=(127.5, 127.5, 127.5)) -> np.ndarray:
shape = image.shape[:2]
ratio = min(float(height) / shape[0], float(width) / shape[1])
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio))
width_padding = (width -... | ['def', 'letterbox(image:', 'np.ndarray,', 'height:', 'int,', 'width:', 'int,', 'color:', 'Tuple[float,', 'float,', 'float]=(127.5,', '127.5,', '127.5))', '->', 'np.ndarray:', 'shape', '=', 'image.shape[:2]', 'ratio', '=', 'min(float(height)', '/', 'shape[0],', 'float(width)', '/', 'shape[1])', 'new_shape', '=', '(roun... | 766,984 |
Speedwagon13/CS-3600-Introduction-to-- | _ast_gen.py | ASTCodeGenerator.generate | generate | Generates the code into file, an open file buffer. | [
"Generates",
"the",
"code",
"into",
"file,",
"an",
"open",
"file",
"buffer."
] | def generate(self, file=None):
src = Template(_PROLOGUE_COMMENT).substitute(cfg_filename=self.cfg_filename)
src += _PROLOGUE_CODE
for node_cfg in self.node_cfg:
src += node_cfg.generate_source() + '\n\n'
file.write(src) | ['def', 'generate(self,', 'file=None):', 'src', '=', 'Template(_PROLOGUE_COMMENT).substitute(cfg_filename=self.cfg_filename)', 'src', '+=', '_PROLOGUE_CODE', 'for', 'node_cfg', 'in', 'self.node_cfg:', 'src', '+=', 'node_cfg.generate_source()', '+', "'\\n\\n'", 'file.write(src)'] | 219,786 |
devashish-patel/webcam-motion-detector | parse.py | splitport | splitport | splitport('host:port') --> 'host', 'port'. | [
"splitport('host:port')",
"-->",
"'host',",
"'port'."
] | def splitport(host):
global _portprog
if _portprog is None:
import re
_portprog = re.compile('^(.*):([0-9]+)$')
match = _portprog.match(host)
if match:
return match.group(1, 2)
return (host, None) | ['def', 'splitport(host):', 'global', '_portprog', 'if', '_portprog', 'is', 'None:', 'import', 're', '_portprog', '=', "re.compile('^(.*):([0-9]+)$')", 'match', '=', '_portprog.match(host)', 'if', 'match:', 'return', 'match.group(1,', '2)', 'return', '(host,', 'None)'] | 978,118 |
openvinotoolkit/training_extensions | random_augment.py | contrast | contrast | Applies contrast adjustment to an image. | [
"Applies",
"contrast",
"adjustment",
"to",
"an",
"image."
] | def contrast(img, value, max_value, bias=0):
value = _float_parameter(value, max_value) + bias
return (PIL.ImageEnhance.Contrast(img).enhance(value), value) | ['def', 'contrast(img,', 'value,', 'max_value,', 'bias=0):', 'value', '=', '_float_parameter(value,', 'max_value)', '+', 'bias', 'return', '(PIL.ImageEnhance.Contrast(img).enhance(value),', 'value)'] | 903,989 |
GeekLiB/keras | generic_utils.py | func_dump | func_dump | Serialize user defined function. | [
"Serialize",
"user",
"defined",
"function."
] | def func_dump(func):
code = marshal.dumps(func.__code__).decode('raw_unicode_escape')
defaults = func.__defaults__
if func.__closure__:
closure = tuple((c.cell_contents for c in func.__closure__))
else:
closure = None
return (code, defaults, closure) | ['def', 'func_dump(func):', 'code', '=', "marshal.dumps(func.__code__).decode('raw_unicode_escape')", 'defaults', '=', 'func.__defaults__', 'if', 'func.__closure__:', 'closure', '=', 'tuple((c.cell_contents', 'for', 'c', 'in', 'func.__closure__))', 'else:', 'closure', '=', 'None', 'return', '(code,', 'defaults,', 'clos... | 247,888 |
43Carrig/recurrent_neural_networks_practice | event_file_writer_v2.py | EventFileWriterV2.add_event | add_event | Adds an event to the event file. | [
"Adds",
"an",
"event",
"to",
"the",
"event",
"file."
] | def add_event(self, event):
if not self._closed:
event_pb = event.SerializeToString()
self._session.run(self._add_event_op, feed_dict={self._event_placeholder: event_pb}) | ['def', 'add_event(self,', 'event):', 'if', 'not', 'self._closed:', 'event_pb', '=', 'event.SerializeToString()', 'self._session.run(self._add_event_op,', 'feed_dict={self._event_placeholder:', 'event_pb})'] | 339,455 |
aws/sagemaker-python-sdk | cache.py | LRUCache.clear | clear | Deletes all elements from the cache. | [
"Deletes",
"all",
"elements",
"from",
"the",
"cache."
] | def clear(self) -> None:
self._lru_cache.clear() | ['def', 'clear(self)', '->', 'None:', 'self._lru_cache.clear()'] | 830,562 |
rudranil723/mini-main | base.py | GeoIP2.info | info | Return information about the GeoIP library and databases in use. | [
"Return",
"information",
"about",
"the",
"GeoIP",
"library",
"and",
"databases",
"in",
"use."
] | def info(self):
meta = self._reader.metadata()
return 'GeoIP Library:\n\t%s.%s\n' % (meta.binary_format_major_version, meta.binary_format_minor_version) | ['def', 'info(self):', 'meta', '=', 'self._reader.metadata()', 'return', "'GeoIP", "Library:\\n\\t%s.%s\\n'", '%', '(meta.binary_format_major_version,', 'meta.binary_format_minor_version)'] | 315,257 |
sek788432/Waymo-2D-Object-Detection | maskrcnn.py | MaskRCNNTask.build_losses | build_losses | Build Mask R-CNN losses. | [
"Build",
"Mask",
"R-CNN",
"losses."
] | def build_losses(self, outputs: Mapping[str, Any], labels: Mapping[str, Any], aux_losses: Optional[Any]=None):
params = self.task_config
cascade_ious = params.model.roi_sampler.cascade_iou_thresholds
rpn_score_loss_fn = maskrcnn_losses.RpnScoreLoss(tf.shape(outputs['box_outputs'])[1])
rpn_box_loss_fn = ... | ['def', 'build_losses(self,', 'outputs:', 'Mapping[str,', 'Any],', 'labels:', 'Mapping[str,', 'Any],', 'aux_losses:', 'Optional[Any]=None):', 'params', '=', 'self.task_config', 'cascade_ious', '=', 'params.model.roi_sampler.cascade_iou_thresholds', 'rpn_score_loss_fn', '=', "maskrcnn_losses.RpnScoreLoss(tf.shape(output... | 973,459 |
openvinotoolkit/training_extensions | cross_focal_loss.py | OrdinaryFocalLoss.forward | forward | Forward function for focal loss. | [
"Forward",
"function",
"for",
"focal",
"loss."
] | def forward(self, input, target, label_weights=None, avg_factor=None, reduction='mean', **kwars):
if target.numel() == 0:
return 0.0 * input.sum()
CE = F.cross_entropy(input, target, reduction='none')
p = torch.exp(-CE)
loss = (1 - p) ** self.gamma * CE
if label_weights is not None:
... | ['def', 'forward(self,', 'input,', 'target,', 'label_weights=None,', 'avg_factor=None,', "reduction='mean',", '**kwars):', 'if', 'target.numel()', '==', '0:', 'return', '0.0', '*', 'input.sum()', 'CE', '=', 'F.cross_entropy(input,', 'target,', "reduction='none')", 'p', '=', 'torch.exp(-CE)', 'loss', '=', '(1', '-', 'p)... | 918,193 |
QData/deepWordBug | __init__.py | percentage | percentage | Check for an integer percentage value with optional percent sign. | [
"Check",
"for",
"an",
"integer",
"percentage",
"value",
"with",
"optional",
"percent",
"sign."
] | def percentage(argument):
try:
argument = argument.rstrip(' %')
except AttributeError:
pass
return nonnegative_int(argument) | ['def', 'percentage(argument):', 'try:', 'argument', '=', "argument.rstrip('", "%')", 'except', 'AttributeError:', 'pass', 'return', 'nonnegative_int(argument)'] | 542,227 |
YiSyuanChen/MTL-ABS | loss.py | LossComputeBase.monolithic_compute_loss | monolithic_compute_loss | Compute the forward loss for the batch. | [
"Compute",
"the",
"forward",
"loss",
"for",
"the",
"batch."
] | def monolithic_compute_loss(self, batch, output):
shard_state = self._make_shard_state(batch, output)
(_, batch_stats) = self._compute_loss(batch, **shard_state)
return batch_stats | ['def', 'monolithic_compute_loss(self,', 'batch,', 'output):', 'shard_state', '=', 'self._make_shard_state(batch,', 'output)', '(_,', 'batch_stats)', '=', 'self._compute_loss(batch,', '**shard_state)', 'return', 'batch_stats'] | 642,801 |
chainer/chainer | evaluator.py | Evaluator.get_iterator | get_iterator | Returns the iterator of the given name. | [
"Returns",
"the",
"iterator",
"of",
"the",
"given",
"name."
] | def get_iterator(self, name):
return self._iterators[name] | ['def', 'get_iterator(self,', 'name):', 'return', 'self._iterators[name]'] | 477,535 |
Eric3911/OpenAGI | msdd_diarizer.py | MSDD_module.conv_scale_weights | conv_scale_weights | Use multiple Convnet layers to estimate the scale weights based on the cluster-average embedding and input embedding sequence. | [
"Use",
"multiple",
"Convnet",
"layers",
"to",
"estimate",
"the",
"scale",
"weights",
"based",
"on",
"the",
"cluster-average",
"embedding",
"and",
"input",
"embedding",
"sequence."
] | def conv_scale_weights(self, ms_avg_embs_perm, ms_emb_seq_single):
ms_cnn_input_seq = torch.cat([ms_avg_embs_perm, ms_emb_seq_single], dim=2)
ms_cnn_input_seq = ms_cnn_input_seq.unsqueeze(2).flatten(0, 1)
conv_out = self.conv_forward(ms_cnn_input_seq, conv_module=self.conv[0], bn_module=self.conv_bn[0], fir... | ['def', 'conv_scale_weights(self,', 'ms_avg_embs_perm,', 'ms_emb_seq_single):', 'ms_cnn_input_seq', '=', 'torch.cat([ms_avg_embs_perm,', 'ms_emb_seq_single],', 'dim=2)', 'ms_cnn_input_seq', '=', 'ms_cnn_input_seq.unsqueeze(2).flatten(0,', '1)', 'conv_out', '=', 'self.conv_forward(ms_cnn_input_seq,', 'conv_module=self.c... | 272,589 |
atulkum/object_detection | FPN.py | add_topdown_lateral_module | add_topdown_lateral_module | Add a top-down lateral module. | [
"Add",
"a",
"top-down",
"lateral",
"module."
] | def add_topdown_lateral_module(model, fpn_top, fpn_lateral, fpn_bottom, dim_top, dim_lateral):
lat = model.Conv(fpn_lateral, fpn_bottom + '_lateral', dim_in=dim_lateral, dim_out=dim_top, kernel=1, pad=0, stride=1, weight_init=const_fill(0.0) if cfg.FPN.ZERO_INIT_LATERAL else ('XavierFill', {}), bias_init=const_fill... | ['def', 'add_topdown_lateral_module(model,', 'fpn_top,', 'fpn_lateral,', 'fpn_bottom,', 'dim_top,', 'dim_lateral):', 'lat', '=', 'model.Conv(fpn_lateral,', 'fpn_bottom', '+', "'_lateral',", 'dim_in=dim_lateral,', 'dim_out=dim_top,', 'kernel=1,', 'pad=0,', 'stride=1,', 'weight_init=const_fill(0.0)', 'if', 'cfg.FPN.ZERO_... | 772,667 |
jogisuda/QuantumSentenceTransformer | QuantumSentenceTransformer.py | QuantumSentenceTransformer.forward | forward | Defining how tensors are supposed to move through the *dressed* quantum net. | [
"Defining",
"how",
"tensors",
"are",
"supposed",
"to",
"move",
"through",
"the",
"*dressed*",
"quantum",
"net."
] | def forward(self, input_text):
input_features = self.sentence_transformer.encode(input_text, convert_to_tensor=True)
pre_out = self.pre_net(input_features)
q_in = torch.tanh(pre_out) * np.pi / 2.0
q_out = torch.Tensor(0, n_qubits)
q_out = q_out.to(self.device)
for elem in q_in:
q_out_ele... | ['def', 'forward(self,', 'input_text):', 'input_features', '=', 'self.sentence_transformer.encode(input_text,', 'convert_to_tensor=True)', 'pre_out', '=', 'self.pre_net(input_features)', 'q_in', '=', 'torch.tanh(pre_out)', '*', 'np.pi', '/', '2.0', 'q_out', '=', 'torch.Tensor(0,', 'n_qubits)', 'q_out', '=', 'q_out.to(s... | 835,530 |
ldkong1205/LaserMix | base_points.py | BasePoints.to | to | Convert current points to a specific device. | [
"Convert",
"current",
"points",
"to",
"a",
"specific",
"device."
] | def to(self, device: Union[str, torch.device], *args, **kwargs) -> 'BasePoints':
original_type = type(self)
return original_type(self.tensor.to(device, *args, **kwargs), points_dim=self.points_dim, attribute_dims=self.attribute_dims) | ['def', 'to(self,', 'device:', 'Union[str,', 'torch.device],', '*args,', '**kwargs)', '->', "'BasePoints':", 'original_type', '=', 'type(self)', 'return', 'original_type(self.tensor.to(device,', '*args,', '**kwargs),', 'points_dim=self.points_dim,', 'attribute_dims=self.attribute_dims)'] | 624,442 |
ChenhongyiYang/PGD | gaussian_target.py | get_topk_from_heatmap | get_topk_from_heatmap | Get top k positions from heatmap. | [
"Get",
"top",
"k",
"positions",
"from",
"heatmap."
] | def get_topk_from_heatmap(scores, k=20):
(batch, _, height, width) = scores.size()
(topk_scores, topk_inds) = torch.topk(scores.view(batch, -1), k)
topk_clses = topk_inds // (height * width)
topk_inds = topk_inds % (height * width)
topk_ys = topk_inds // width
topk_xs = (topk_inds % width).int()... | ['def', 'get_topk_from_heatmap(scores,', 'k=20):', '(batch,', '_,', 'height,', 'width)', '=', 'scores.size()', '(topk_scores,', 'topk_inds)', '=', 'torch.topk(scores.view(batch,', '-1),', 'k)', 'topk_clses', '=', 'topk_inds', '//', '(height', '*', 'width)', 'topk_inds', '=', 'topk_inds', '%', '(height', '*', 'width)', ... | 768,266 |
Kvatsx/Artificial-Intelligence-Assignments | zmqstream.py | ZMQStream.set_close_callback | set_close_callback | Call the given callback when the stream is closed. | [
"Call",
"the",
"given",
"callback",
"when",
"the",
"stream",
"is",
"closed."
] | def set_close_callback(self, callback):
self._close_callback = stack_context.wrap(callback) | ['def', 'set_close_callback(self,', 'callback):', 'self._close_callback', '=', 'stack_context.wrap(callback)'] | 79,204 |
megvii-research/MSCL | bsn.py | PEM.forward_train | forward_train | Define the computation performed at every call when training. | [
"Define",
"the",
"computation",
"performed",
"at",
"every",
"call",
"when",
"training."
] | def forward_train(self, bsp_feature, reference_temporal_iou):
pem_output = self._forward(bsp_feature)
reference_temporal_iou = torch.cat(list(reference_temporal_iou))
device = pem_output.device
reference_temporal_iou = reference_temporal_iou.to(device)
anchors_temporal_iou = pem_output.view(-1)
... | ['def', 'forward_train(self,', 'bsp_feature,', 'reference_temporal_iou):', 'pem_output', '=', 'self._forward(bsp_feature)', 'reference_temporal_iou', '=', 'torch.cat(list(reference_temporal_iou))', 'device', '=', 'pem_output.device', 'reference_temporal_iou', '=', 'reference_temporal_iou.to(device)', 'anchors_temporal_... | 264,934 |
devashish-patel/webcam-motion-detector | document.py | Document.template | template | A Jinja2 template to use for rendering this document. | [
"A",
"Jinja2",
"template",
"to",
"use",
"for",
"rendering",
"this",
"document."
] | def template(self):
return self._template | ['def', 'template(self):', 'return', 'self._template'] | 977,286 |
ivanmontero/autobot | test_utils_summarization.py | SummarizationDataProcessingTest.test_fit_to_block_sequence_fit_exactly | test_fit_to_block_sequence_fit_exactly | Do nothing if the sequence is the right size. | [
"Do",
"nothing",
"if",
"the",
"sequence",
"is",
"the",
"right",
"size."
] | def test_fit_to_block_sequence_fit_exactly(self):
sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output) | ['def', 'test_fit_to_block_sequence_fit_exactly(self):', 'sequence', '=', '[1,', '2,', '3,', '4,', '5,', '6,', '7,', '8,', '9,', '10]', 'expected_output', '=', '[1,', '2,', '3,', '4,', '5,', '6,', '7,', '8,', '9,', '10]', 'self.assertEqual(truncate_or_pad(sequence,', 'self.block_size,', '0),', 'expected_output)'] | 417,767 |
aws/sagemaker-python-sdk | image_uris.py | ImageURIRetrieveImportFromRenamer.node_should_be_modified | node_should_be_modified | Checks if the import statement imports ``get_image_uri`` from the correct module. | [
"Checks",
"if",
"the",
"import",
"statement",
"imports",
"``get_image_uri``",
"from",
"the",
"correct",
"module."
] | def node_should_be_modified(self, node):
return node is not None and node.module in GET_IMAGE_URI_NAMESPACES and any((name.name == GET_IMAGE_URI_NAME for name in node.names)) | ['def', 'node_should_be_modified(self,', 'node):', 'return', 'node', 'is', 'not', 'None', 'and', 'node.module', 'in', 'GET_IMAGE_URI_NAMESPACES', 'and', 'any((name.name', '==', 'GET_IMAGE_URI_NAME', 'for', 'name', 'in', 'node.names))'] | 829,836 |
jbwang1997/CrossKD | augment_wrappers.py | level_to_mag | level_to_mag | Map from level to magnitude. | [
"Map",
"from",
"level",
"to",
"magnitude."
] | def level_to_mag(level: Optional[int], min_mag: float, max_mag: float) -> float:
if level is None:
return round(np.random.rand() * (max_mag - min_mag) + min_mag, 1)
else:
return round(level / _MAX_LEVEL * (max_mag - min_mag) + min_mag, 1) | ['def', 'level_to_mag(level:', 'Optional[int],', 'min_mag:', 'float,', 'max_mag:', 'float)', '->', 'float:', 'if', 'level', 'is', 'None:', 'return', 'round(np.random.rand()', '*', '(max_mag', '-', 'min_mag)', '+', 'min_mag,', '1)', 'else:', 'return', 'round(level', '/', '_MAX_LEVEL', '*', '(max_mag', '-', 'min_mag)', '... | 490,762 |
klickmal/ContextNet | layer.py | SELayer.forward | forward | Forward propagate a `inputs` for SE Layer. | [
"Forward",
"propagate",
"a",
"`inputs`",
"for",
"SE",
"Layer."
] | def forward(self, inputs: Tensor, input_lengths: Tensor) -> Tuple[Tensor, Tensor]:
residual = inputs
seq_lengths = inputs.size(2)
inputs = inputs.sum(dim=2) / input_lengths.unsqueeze(1)
output = self.sequential(inputs)
output = output.sigmoid().unsqueeze(2)
output = output.repeat(1, 1, seq_lengt... | ['def', 'forward(self,', 'inputs:', 'Tensor,', 'input_lengths:', 'Tensor)', '->', 'Tuple[Tensor,', 'Tensor]:', 'residual', '=', 'inputs', 'seq_lengths', '=', 'inputs.size(2)', 'inputs', '=', 'inputs.sum(dim=2)', '/', 'input_lengths.unsqueeze(1)', 'output', '=', 'self.sequential(inputs)', 'output', '=', 'output.sigmoid(... | 136,368 |
rlgraph/rlgraph | sac_networks.py | SACValueNetwork.build_value_function | build_value_function | Builds a dense stack and optionally an image stack. | [
"Builds",
"a",
"dense",
"stack",
"and",
"optionally",
"an",
"image",
"stack."
] | def build_value_function(self):
if self.use_image_stack:
image_components = []
dense_components = []
for layer_spec in self.network_spec:
if layer_spec['type'] in ['conv2d', 'reshape']:
image_components.append(Layer.from_spec(layer_spec))
self.image_stack ... | ['def', 'build_value_function(self):', 'if', 'self.use_image_stack:', 'image_components', '=', '[]', 'dense_components', '=', '[]', 'for', 'layer_spec', 'in', 'self.network_spec:', 'if', "layer_spec['type']", 'in', "['conv2d',", "'reshape']:", 'image_components.append(Layer.from_spec(layer_spec))', 'self.image_stack', ... | 862,514 |
google-research/rigl | shuffled_mask_test.py | ShuffledMaskTest.test_run_conv | test_run_conv | Tests if the driver for shuffled training runs correctly with CNN. | [
"Tests",
"if",
"the",
"driver",
"for",
"shuffled",
"training",
"runs",
"correctly",
"with",
"CNN."
] | def test_run_conv(self):
experiment_dir = tempfile.mkdtemp()
eval_flags = dict(epochs=1, experiment_dir=experiment_dir, model='MNIST_CNN')
with flagsaver.flagsaver(**eval_flags):
shuffled_mask.main([])
outfile = path.join(experiment_dir, '*', 'events.out.tfevents.*')
files = glob.glob(outfil... | ['def', 'test_run_conv(self):', 'experiment_dir', '=', 'tempfile.mkdtemp()', 'eval_flags', '=', 'dict(epochs=1,', 'experiment_dir=experiment_dir,', "model='MNIST_CNN')", 'with', 'flagsaver.flagsaver(**eval_flags):', 'shuffled_mask.main([])', 'outfile', '=', 'path.join(experiment_dir,', "'*',", "'events.out.tfevents.*')... | 841,400 |
devashish-patel/webcam-motion-detector | bccache.py | Bucket.bytecode_to_string | bytecode_to_string | Return the bytecode as string. | [
"Return",
"the",
"bytecode",
"as",
"string."
] | def bytecode_to_string(self):
out = BytesIO()
self.write_bytecode(out)
return out.getvalue() | ['def', 'bytecode_to_string(self):', 'out', '=', 'BytesIO()', 'self.write_bytecode(out)', 'return', 'out.getvalue()'] | 979,630 |
Djaizz/Djaizz | token_classification.py | PreTrainedHuggingFaceTokenClassifier.predict | predict | Classify Tokens in Text(s). | [
"Classify",
"Tokens",
"in",
"Text(s)."
] | def predict(self, text_or_texts: Union[TokenClassificationInputType, Sequence[TokenClassificationInputType]]) -> Union[TokenClassificationOutputType, Sequence[TokenClassificationOutputType]]:
single_text: bool = isinstance(text_or_texts, str)
if not (single_text or isinstance(text_or_texts, list)):
text... | ['def', 'predict(self,', 'text_or_texts:', 'Union[TokenClassificationInputType,', 'Sequence[TokenClassificationInputType]])', '->', 'Union[TokenClassificationOutputType,', 'Sequence[TokenClassificationOutputType]]:', 'single_text:', 'bool', '=', 'isinstance(text_or_texts,', 'str)', 'if', 'not', '(single_text', 'or', 'i... | 189,457 |
voxel51/fiftyone | utils.py | extract_kwargs_for_function | extract_kwargs_for_function | Extracts keyword arguments for the given function from the given kwargs. | [
"Extracts",
"keyword",
"arguments",
"for",
"the",
"given",
"function",
"from",
"the",
"given",
"kwargs."
] | def extract_kwargs_for_function(fcn, kwargs):
return _extract_kwargs(fcn, kwargs) | ['def', 'extract_kwargs_for_function(fcn,', 'kwargs):', 'return', '_extract_kwargs(fcn,', 'kwargs)'] | 583,425 |
omarmhaimdat/twitter_nlp_native_swift | api.py | Api.GetListTimeline | GetListTimeline | Fetch the sequence of Status messages for a given List ID. | [
"Fetch",
"the",
"sequence",
"of",
"Status",
"messages",
"for",
"a",
"given",
"List",
"ID."
] | def GetListTimeline(self, list_id=None, slug=None, owner_id=None, owner_screen_name=None, since_id=None, max_id=None, count=None, include_rts=True, include_entities=True, return_json=False):
url = '%s/lists/statuses.json' % self.base_url
parameters = {}
parameters.update(self._IDList(list_id=list_id, slug=s... | ['def', 'GetListTimeline(self,', 'list_id=None,', 'slug=None,', 'owner_id=None,', 'owner_screen_name=None,', 'since_id=None,', 'max_id=None,', 'count=None,', 'include_rts=True,', 'include_entities=True,', 'return_json=False):', 'url', '=', "'%s/lists/statuses.json'", '%', 'self.base_url', 'parameters', '=', '{}', 'para... | 955,159 |
zihuitang/medical_AI_platform | mailbox.py | Mailbox.update | update | Change the messages that correspond to certain keys. | [
"Change",
"the",
"messages",
"that",
"correspond",
"to",
"certain",
"keys."
] | def update(self, arg=None):
if hasattr(arg, 'iteritems'):
source = arg.iteritems()
elif hasattr(arg, 'items'):
source = arg.items()
else:
source = arg
bad_key = False
for (key, message) in source:
try:
self[key] = message
except KeyError:
... | ['def', 'update(self,', 'arg=None):', 'if', 'hasattr(arg,', "'iteritems'):", 'source', '=', 'arg.iteritems()', 'elif', 'hasattr(arg,', "'items'):", 'source', '=', 'arg.items()', 'else:', 'source', '=', 'arg', 'bad_key', '=', 'False', 'for', '(key,', 'message)', 'in', 'source:', 'try:', 'self[key]', '=', 'message', 'exc... | 280,719 |
flavioschneider/rl-transfer- | td3_pendulum.py | td3_pendulum | td3_pendulum | Train TD3 with InvertedDoublePendulum-v2 environment. | [
"Train",
"TD3",
"with",
"InvertedDoublePendulum-v2",
"environment."
] | def td3_pendulum(ctxt=None, seed=1):
set_seed(seed)
n_epochs = 750
steps_per_epoch = 40
sampler_batch_size = 100
num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
trainer = Trainer(ctxt)
env = normalize(GymEnv('InvertedDoublePendulum-v2'))
policy = DeterministicMLPPolicy(en... | ['def', 'td3_pendulum(ctxt=None,', 'seed=1):', 'set_seed(seed)', 'n_epochs', '=', '750', 'steps_per_epoch', '=', '40', 'sampler_batch_size', '=', '100', 'num_timesteps', '=', 'n_epochs', '*', 'steps_per_epoch', '*', 'sampler_batch_size', 'trainer', '=', 'Trainer(ctxt)', 'env', '=', "normalize(GymEnv('InvertedDoublePend... | 861,146 |
Ruturaj123/Flowchart-Detection | stats_accumulator_ops.py | StatsAccumulator.add | add | Updates the stats accumulator. | [
"Updates",
"the",
"stats",
"accumulator."
] | def add(self, stamp_token, partition_ids, feature_ids, gradients, hessians):
(partition_ids, feature_ids, gradients, hessians) = self._make_summary(partition_ids, feature_ids, gradients, hessians)
if self._is_scalar:
return gen_stats_accumulator_ops.stats_accumulator_scalar_add([self._resource_handle], ... | ['def', 'add(self,', 'stamp_token,', 'partition_ids,', 'feature_ids,', 'gradients,', 'hessians):', '(partition_ids,', 'feature_ids,', 'gradients,', 'hessians)', '=', 'self._make_summary(partition_ids,', 'feature_ids,', 'gradients,', 'hessians)', 'if', 'self._is_scalar:', 'return', 'gen_stats_accumulator_ops.stats_accum... | 586,889 |
liaorongfan/DeepPersonality | draw.py | pil_to_tensor | pil_to_tensor | Convert a PIL image to a tensor. | [
"Convert",
"a",
"PIL",
"image",
"to",
"a",
"tensor."
] | def pil_to_tensor(pil_image):
pil_image = np.array(pil_image)
if len(pil_image.shape) == 2:
pil_image = pil_image[:, :, None]
return torch.tensor(pil_image, dtype=torch.float32).permute(2, 0, 1) / 255 | ['def', 'pil_to_tensor(pil_image):', 'pil_image', '=', 'np.array(pil_image)', 'if', 'len(pil_image.shape)', '==', '2:', 'pil_image', '=', 'pil_image[:,', ':,', 'None]', 'return', 'torch.tensor(pil_image,', 'dtype=torch.float32).permute(2,', '0,', '1)', '/', '255'] | 539,242 |
ludwig-ai/ludwig | metrics_printed_table.py | get_metric_value_or_empty | get_metric_value_or_empty | Returns the metric value if it exists or empty. | [
"Returns",
"the",
"metric",
"value",
"if",
"it",
"exists",
"or",
"empty."
] | def get_metric_value_or_empty(metrics_log: Dict[str, List[TrainerMetric]], metric_name: str):
if metric_name not in metrics_log:
return ''
return metrics_log[metric_name][-1][-1] | ['def', 'get_metric_value_or_empty(metrics_log:', 'Dict[str,', 'List[TrainerMetric]],', 'metric_name:', 'str):', 'if', 'metric_name', 'not', 'in', 'metrics_log:', 'return', "''", 'return', 'metrics_log[metric_name][-1][-1]'] | 617,121 |
gunthercox/ChatterBot | reading.py | IndexReader.doc_count_all | doc_count_all | Returns the total number of documents, DELETED OR UNDELETED, in this reader. | [
"Returns",
"the",
"total",
"number",
"of",
"documents,",
"DELETED",
"OR",
"UNDELETED,",
"in",
"this",
"reader."
] | def doc_count_all(self):
raise NotImplementedError | ['def', 'doc_count_all(self):', 'raise', 'NotImplementedError'] | 526,356 |
ludwig-ai/ludwig | utils.py | register_parameter_config | register_parameter_config | Register a parameter config class by name. | [
"Register",
"a",
"parameter",
"config",
"class",
"by",
"name."
] | def register_parameter_config(name: str) -> Callable:
def wrap(cls: Type['BaseParameterConfig']) -> Type['BaseParameterConfig']:
parameter_config_registry[name] = cls
return cls
return wrap | ['def', 'register_parameter_config(name:', 'str)', '->', 'Callable:', 'def', 'wrap(cls:', "Type['BaseParameterConfig'])", '->', "Type['BaseParameterConfig']:", 'parameter_config_registry[name]', '=', 'cls', 'return', 'cls', 'return', 'wrap'] | 616,991 |
dustin/twitty-twister | test_twitter.py | TwitterMonitorTest.test_connectConnecting | test_connectConnecting | Don't connect while connecting. | [
"Don't",
"connect",
"while",
"connecting."
] | def test_connectConnecting(self):
self.setUpState('connecting')
self.assertRaises(twitter.Error, self.monitor.connect)
self.clock.advance(0)
self.assertEqual(1, len(self.api.filterCalls), 'Extra connect') | ['def', 'test_connectConnecting(self):', "self.setUpState('connecting')", 'self.assertRaises(twitter.Error,', 'self.monitor.connect)', 'self.clock.advance(0)', 'self.assertEqual(1,', 'len(self.api.filterCalls),', "'Extra", "connect')"] | 426,523 |
wonheeML/mtl-ssl | box_list.py | BoxList.get_lefttop_coordinates_and_sizes | get_lefttop_coordinates_and_sizes | Computes the left-top coordinates, height and width of the boxes. | [
"Computes",
"the",
"left-top",
"coordinates,",
"height",
"and",
"width",
"of",
"the",
"boxes."
] | def get_lefttop_coordinates_and_sizes(self, scope=None):
with tf.name_scope(scope, 'get_lefttop_coordinates_and_sizes'):
box_corners = self.get()
(ymin, xmin, ymax, xmax) = tf.unstack(tf.transpose(box_corners))
width = xmax - xmin
height = ymax - ymin
return [ymin, xmin, heig... | ['def', 'get_lefttop_coordinates_and_sizes(self,', 'scope=None):', 'with', 'tf.name_scope(scope,', "'get_lefttop_coordinates_and_sizes'):", 'box_corners', '=', 'self.get()', '(ymin,', 'xmin,', 'ymax,', 'xmax)', '=', 'tf.unstack(tf.transpose(box_corners))', 'width', '=', 'xmax', '-', 'xmin', 'height', '=', 'ymax', '-', ... | 642,965 |
Farama-Foundation/Gymnasium | test_shared_memory.py | test_non_space | test_non_space | Test the use of non-space types on the shared memory functions. | [
"Test",
"the",
"use",
"of",
"non-space",
"types",
"on",
"the",
"shared",
"memory",
"functions."
] | def test_non_space():
with pytest.raises(TypeError, match=re.escape("The space provided to `create_shared_memory` is not a gymnasium Space instance, type: <class 'str'>, space")):
create_shared_memory('space')
with pytest.raises(TypeError, match=re.escape("The space provided to `read_from_shared_memory`... | ['def', 'test_non_space():', 'with', 'pytest.raises(TypeError,', 'match=re.escape("The', 'space', 'provided', 'to', '`create_shared_memory`', 'is', 'not', 'a', 'gymnasium', 'Space', 'instance,', 'type:', '<class', "'str'>,", 'space")):', "create_shared_memory('space')", 'with', 'pytest.raises(TypeError,', 'match=re.esc... | 573,553 |
Atharv24/DanceGeneration | animate_view.py | generate | generate | Generates Z data for the points in the X, Y meshgrid and parameter phi. | [
"Generates",
"Z",
"data",
"for",
"the",
"points",
"in",
"the",
"X,",
"Y",
"meshgrid",
"and",
"parameter",
"phi."
] | def generate(X, Y, phi):
R = 1 - np.sqrt(X ** 2 + Y ** 2)
return np.cos(2 * np.pi * X + phi) * R | ['def', 'generate(X,', 'Y,', 'phi):', 'R', '=', '1', '-', 'np.sqrt(X', '**', '2', '+', 'Y', '**', '2)', 'return', 'np.cos(2', '*', 'np.pi', '*', 'X', '+', 'phi)', '*', 'R'] | 497,021 |
Eric3911/OpenAGI | numba_utils.py | numba_cuda_is_supported | numba_cuda_is_supported | Tests if an appropriate version of numba is installed, and if it is, if cuda is supported properly within it. | [
"Tests",
"if",
"an",
"appropriate",
"version",
"of",
"numba",
"is",
"installed,",
"and",
"if",
"it",
"is,",
"if",
"cuda",
"is",
"supported",
"properly",
"within",
"it."
] | def numba_cuda_is_supported(min_version: str) -> bool:
module_available = numba_cpu_is_supported(min_version)
if module_available is None:
return False
if module_available is True:
from numba import cuda
if hasattr(cuda, 'is_supported_version'):
try:
cuda_... | ['def', 'numba_cuda_is_supported(min_version:', 'str)', '->', 'bool:', 'module_available', '=', 'numba_cpu_is_supported(min_version)', 'if', 'module_available', 'is', 'None:', 'return', 'False', 'if', 'module_available', 'is', 'True:', 'from', 'numba', 'import', 'cuda', 'if', 'hasattr(cuda,', "'is_supported_version'):"... | 274,098 |
robustness-gym/robustness-gym | testbench.py | TestBench.load | load | Load a testbench from disk. | [
"Load",
"a",
"testbench",
"from",
"disk."
] | def load(cls, path: str) -> TestBench:
savedir = pathlib.Path(path)
slices = []
for sl_path in tqdm(list((savedir / 'slices').glob('*'))):
try:
slices.append(DataPanel.load_from_disk(str(sl_path)))
except FileNotFoundError:
continue
metrics = dill.load(open(str(sa... | ['def', 'load(cls,', 'path:', 'str)', '->', 'TestBench:', 'savedir', '=', 'pathlib.Path(path)', 'slices', '=', '[]', 'for', 'sl_path', 'in', 'tqdm(list((savedir', '/', "'slices').glob('*'))):", 'try:', 'slices.append(DataPanel.load_from_disk(str(sl_path)))', 'except', 'FileNotFoundError:', 'continue', 'metrics', '=', '... | 826,297 |
sktime/sktime | test_window_summarizer.py | count_gt100 | count_gt100 | Count how many observations lie above threshold 100. | [
"Count",
"how",
"many",
"observations",
"lie",
"above",
"threshold",
"100."
] | def count_gt100(x):
return np.sum((x > 100)[::-1]) | ['def', 'count_gt100(x):', 'return', 'np.sum((x', '>', '100)[::-1])'] | 877,926 |
jimtin/Stock_Comparison | compiler.py | Identifiers.add_special | add_special | Register a special name like `loop`. | [
"Register",
"a",
"special",
"name",
"like",
"`loop`."
] | def add_special(self, name):
self.undeclared.discard(name)
self.declared.add(name) | ['def', 'add_special(self,', 'name):', 'self.undeclared.discard(name)', 'self.declared.add(name)'] | 385,705 |
dustin/twitty-twister | test_twitter.py | TwitterMonitorTest.test_stopServiceAfterReconnect | test_stopServiceAfterReconnect | Stopping the service after waiting is fine. | [
"Stopping",
"the",
"service",
"after",
"waiting",
"is",
"fine."
] | def test_stopServiceAfterReconnect(self):
self.setUpState('waiting')
self.clock.advance(DELAY_INITIAL)
self.assertEqual(2, len(self.api.filterCalls))
self.monitor.stopService()
self.clock.advance(0) | ['def', 'test_stopServiceAfterReconnect(self):', "self.setUpState('waiting')", 'self.clock.advance(DELAY_INITIAL)', 'self.assertEqual(2,', 'len(self.api.filterCalls))', 'self.monitor.stopService()', 'self.clock.advance(0)'] | 426,519 |
mnielsen/neural-networks-and-deep-learning | mnist.py | plot_mnist_digit | plot_mnist_digit | Plot a single MNIST image. | [
"Plot",
"a",
"single",
"MNIST",
"image."
] | def plot_mnist_digit(image):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image, cmap=matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show() | ['def', 'plot_mnist_digit(image):', 'fig', '=', 'plt.figure()', 'ax', '=', 'fig.add_subplot(1,', '1,', '1)', 'ax.matshow(image,', 'cmap=matplotlib.cm.binary)', 'plt.xticks(np.array([]))', 'plt.yticks(np.array([]))', 'plt.show()'] | 722,057 |
siddhanthaldar/PyTorch_Object_Detection | encoder.py | DataEncoder.iou | iou | Compute the intersection over union of two set of boxes, each box is [x1,y1,x2,y2]. | [
"Compute",
"the",
"intersection",
"over",
"union",
"of",
"two",
"set",
"of",
"boxes,",
"each",
"box",
"is",
"[x1,y1,x2,y2]."
] | def iou(self, box1, box2):
N = box1.size(0)
M = box2.size(0)
lt = torch.max(box1[:, :2].unsqueeze(1).expand(N, M, 2), box2[:, :2].unsqueeze(0).expand(N, M, 2))
rb = torch.min(box1[:, 2:].unsqueeze(1).expand(N, M, 2), box2[:, 2:].unsqueeze(0).expand(N, M, 2))
wh = rb - lt
wh[wh < 0] = 0
inter... | ['def', 'iou(self,', 'box1,', 'box2):', 'N', '=', 'box1.size(0)', 'M', '=', 'box2.size(0)', 'lt', '=', 'torch.max(box1[:,', ':2].unsqueeze(1).expand(N,', 'M,', '2),', 'box2[:,', ':2].unsqueeze(0).expand(N,', 'M,', '2))', 'rb', '=', 'torch.min(box1[:,', '2:].unsqueeze(1).expand(N,', 'M,', '2),', 'box2[:,', '2:].unsqueez... | 815,552 |
dojoteef/dvae | rbm.py | MarginalRBMType1Generic.cross_entropy_from_hierarchical | cross_entropy_from_hierarchical | Computes a sampling-based estimate of the cross-entropy from a hierarchical posterior to marginal. | [
"Computes",
"a",
"sampling-based",
"estimate",
"of",
"the",
"cross-entropy",
"from",
"a",
"hierarchical",
"posterior",
"to",
"marginal."
] | def cross_entropy_from_hierarchical(self, post_samples, is_training=False):
neg_log_prob = -self.log_prob(post_samples, is_training)
return neg_log_prob | ['def', 'cross_entropy_from_hierarchical(self,', 'post_samples,', 'is_training=False):', 'neg_log_prob', '=', '-self.log_prob(post_samples,', 'is_training)', 'return', 'neg_log_prob'] | 554,863 |
TengXiaoDai/DistributedCrawling | punycode.py | selective_len | selective_len | Return the length of str, considering only characters below max. | [
"Return",
"the",
"length",
"of",
"str,",
"considering",
"only",
"characters",
"below",
"max."
] | def selective_len(str, max):
res = 0
for c in str:
if ord(c) < max:
res += 1
return res | ['def', 'selective_len(str,', 'max):', 'res', '=', '0', 'for', 'c', 'in', 'str:', 'if', 'ord(c)', '<', 'max:', 'res', '+=', '1', 'return', 'res'] | 188,184 |
greydanus/mr_london | OleFileIO.py | OleMetadata.dump | dump | Dump all metadata, for debugging purposes. | [
"Dump",
"all",
"metadata,",
"for",
"debugging",
"purposes."
] | def dump(self):
print('Properties from SummaryInformation stream:')
for prop in self.SUMMARY_ATTRIBS:
value = getattr(self, prop)
print('- %s: %s' % (prop, repr(value)))
print('Properties from DocumentSummaryInformation stream:')
for prop in self.DOCSUM_ATTRIBS:
value = getattr(s... | ['def', 'dump(self):', "print('Properties", 'from', 'SummaryInformation', "stream:')", 'for', 'prop', 'in', 'self.SUMMARY_ATTRIBS:', 'value', '=', 'getattr(self,', 'prop)', "print('-", '%s:', "%s'", '%', '(prop,', 'repr(value)))', "print('Properties", 'from', 'DocumentSummaryInformation', "stream:')", 'for', 'prop', 'i... | 263,258 |
instadeepai/jumanji | env.py | RubiksCube.animate | animate | Creates an animated gif of the cube based on the sequence of states. | [
"Creates",
"an",
"animated",
"gif",
"of",
"the",
"cube",
"based",
"on",
"the",
"sequence",
"of",
"states."
] | def animate(self, states: Sequence[State], interval: int=200, save_path: Optional[str]=None) -> matplotlib.animation.FuncAnimation:
return self._viewer.animate(states=states, interval=interval, save_path=save_path) | ['def', 'animate(self,', 'states:', 'Sequence[State],', 'interval:', 'int=200,', 'save_path:', 'Optional[str]=None)', '->', 'matplotlib.animation.FuncAnimation:', 'return', 'self._viewer.animate(states=states,', 'interval=interval,', 'save_path=save_path)'] | 594,097 |
microsoft/maro | grass_executor.py | GrassExecutor.push_data | push_data | Push data from local to remote MARO Cluster. | [
"Push",
"data",
"from",
"local",
"to",
"remote",
"MARO",
"Cluster."
] | def push_data(self, local_path: str, remote_path: str) -> None:
if not remote_path.startswith('/'):
raise FileOperationError(f"Invalid remote path: {remote_path}\nShould be started with '/'")
FileSynchronizer.copy_files_to_node(local_path=local_path, remote_dir=f'{GlobalPaths.MARO_SHARED}/clusters/{self... | ['def', 'push_data(self,', 'local_path:', 'str,', 'remote_path:', 'str)', '->', 'None:', 'if', 'not', "remote_path.startswith('/'):", 'raise', 'FileOperationError(f"Invalid', 'remote', 'path:', '{remote_path}\\nShould', 'be', 'started', 'with', '\'/\'")', 'FileSynchronizer.copy_files_to_node(local_path=local_path,', "r... | 628,161 |
microsoft/logrl | train_atari.py | create_agent | create_agent | Creates a DQN agent. | [
"Creates",
"a",
"DQN",
"agent."
] | def create_agent(sess, environment, summary_writer=None):
if not FLAGS.debug_mode:
summary_writer = None
if FLAGS.agent_name == 'dqn':
return dqn_agent.DQNAgent(sess, num_actions=environment.action_space.n, summary_writer=summary_writer)
elif FLAGS.agent_name == 'log_dqn':
return log... | ['def', 'create_agent(sess,', 'environment,', 'summary_writer=None):', 'if', 'not', 'FLAGS.debug_mode:', 'summary_writer', '=', 'None', 'if', 'FLAGS.agent_name', '==', "'dqn':", 'return', 'dqn_agent.DQNAgent(sess,', 'num_actions=environment.action_space.n,', 'summary_writer=summary_writer)', 'elif', 'FLAGS.agent_name',... | 615,712 |
santhoshkolloju/Abstractive-Summarization-With-Transfer- | data_decoders.py | TextDataDecoder.decode | decode | Decodes the data to return the tensors specified by the list of items. | [
"Decodes",
"the",
"data",
"to",
"return",
"the",
"tensors",
"specified",
"by",
"the",
"list",
"of",
"items."
] | def decode(self, data, items):
if self._split_level == 'word':
tokens = tf.string_split([data], delimiter=self._delimiter).values
elif self._split_level == 'char':
raise NotImplementedError
else:
raise ValueError('Unknown split level: %s' % self._split_level)
if self._max_seq_len... | ['def', 'decode(self,', 'data,', 'items):', 'if', 'self._split_level', '==', "'word':", 'tokens', '=', 'tf.string_split([data],', 'delimiter=self._delimiter).values', 'elif', 'self._split_level', '==', "'char':", 'raise', 'NotImplementedError', 'else:', 'raise', "ValueError('Unknown", 'split', 'level:', "%s'", '%', 'se... | 406,003 |
aeon-toolkit/aeon | test_benchmarks.py | test_add_task_string_entrypoint | test_add_task_string_entrypoint | Test adding task using string of entrypoint. | [
"Test",
"adding",
"task",
"using",
"string",
"of",
"entrypoint."
] | def test_add_task_string_entrypoint(tmp_path):
benchmark = benchmarks.BaseBenchmark()
benchmark.add_estimator(NaiveForecaster(strategy='drift'))
benchmark._add_task('aeon.benchmarking.tests.test_benchmarks:factory_estimator_class_task')
results_file = tmp_path / 'results.csv'
results_df = benchmark.... | ['def', 'test_add_task_string_entrypoint(tmp_path):', 'benchmark', '=', 'benchmarks.BaseBenchmark()', "benchmark.add_estimator(NaiveForecaster(strategy='drift'))", "benchmark._add_task('aeon.benchmarking.tests.test_benchmarks:factory_estimator_class_task')", 'results_file', '=', 'tmp_path', '/', "'results.csv'", 'resul... | 399,188 |
Megvii-BaseDetection/cvpods | mobilenet.py | make_stage | make_stage | Create a mobilenetv2 stage by creating many blocks. | [
"Create",
"a",
"mobilenetv2",
"stage",
"by",
"creating",
"many",
"blocks."
] | def make_stage(num_blocks, input_channels, output_channels, stride, expand_ratio, norm, activation):
blocks = []
blocks.append(InvertedResBlock(input_channels, output_channels, stride=stride, expand_ratio=expand_ratio, norm=norm, activation=activation, use_shortcut=False))
for i in range(num_blocks - 1):
... | ['def', 'make_stage(num_blocks,', 'input_channels,', 'output_channels,', 'stride,', 'expand_ratio,', 'norm,', 'activation):', 'blocks', '=', '[]', 'blocks.append(InvertedResBlock(input_channels,', 'output_channels,', 'stride=stride,', 'expand_ratio=expand_ratio,', 'norm=norm,', 'activation=activation,', 'use_shortcut=F... | 522,945 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | mtf_image_transformer.py | MtfImageTransformer.create_positional_emb_2d | create_positional_emb_2d | Learned 2d positional embedding for images. | [
"Learned",
"2d",
"positional",
"embedding",
"for",
"images."
] | def create_positional_emb_2d(self, targets, max_length_dim, model_dim):
mesh = targets.mesh
hparams = self._hparams
activation_dtype = self.set_activation_type()
rows_dim = mtf.Dimension('rows', hparams.img_len)
cols_dim = mtf.Dimension('cols', hparams.img_len * hparams.num_channels)
positional_... | ['def', 'create_positional_emb_2d(self,', 'targets,', 'max_length_dim,', 'model_dim):', 'mesh', '=', 'targets.mesh', 'hparams', '=', 'self._hparams', 'activation_dtype', '=', 'self.set_activation_type()', 'rows_dim', '=', "mtf.Dimension('rows',", 'hparams.img_len)', 'cols_dim', '=', "mtf.Dimension('cols',", 'hparams.im... | 965,532 |
sktime/sktime | test_fh.py | test_empty_range_in_fh | test_empty_range_in_fh | Test when ``range`` has zero length. | [
"Test",
"when",
"``range``",
"has",
"zero",
"length."
] | def test_empty_range_in_fh():
empty_range = ForecastingHorizon(values=range(-5))
assert (empty_range == ForecastingHorizon(values=[])).all() | ['def', 'test_empty_range_in_fh():', 'empty_range', '=', 'ForecastingHorizon(values=range(-5))', 'assert', '(empty_range', '==', 'ForecastingHorizon(values=[])).all()'] | 877,180 |
arshpreetsingh/quantopian-machinelearning | frontend_widget.py | FrontendHighlighter.rehighlightBlock | rehighlightBlock | Reimplemented to temporarily enable highlighting if disabled. | [
"Reimplemented",
"to",
"temporarily",
"enable",
"highlighting",
"if",
"disabled."
] | def rehighlightBlock(self, block):
old = self.highlighting_on
self.highlighting_on = True
super(FrontendHighlighter, self).rehighlightBlock(block)
self.highlighting_on = old | ['def', 'rehighlightBlock(self,', 'block):', 'old', '=', 'self.highlighting_on', 'self.highlighting_on', '=', 'True', 'super(FrontendHighlighter,', 'self).rehighlightBlock(block)', 'self.highlighting_on', '=', 'old'] | 892,873 |
lhotse-speech/lhotse | stcmds.py | stcmds | stcmds | Stcmds ASR data preparation. | [
"Stcmds",
"ASR",
"data",
"preparation."
] | def stcmds(corpus_dir: Pathlike, output_dir: Pathlike):
prepare_stcmds(corpus_dir, output_dir=output_dir) | ['def', 'stcmds(corpus_dir:', 'Pathlike,', 'output_dir:', 'Pathlike):', 'prepare_stcmds(corpus_dir,', 'output_dir=output_dir)'] | 600,628 |
deon-gracias/artificial-intelligence-practicals | node.py | Node.child_node | child_node | Get the child node from applying the given action. | [
"Get",
"the",
"child",
"node",
"from",
"applying",
"the",
"given",
"action."
] | def child_node(self, problem, action):
next_node = problem.result(self.state, action)
return Node(next_node, self, action) | ['def', 'child_node(self,', 'problem,', 'action):', 'next_node', '=', 'problem.result(self.state,', 'action)', 'return', 'Node(next_node,', 'self,', 'action)'] | 91,349 |
Ikomia-dev/IkomiaApi | datadictIO.py | DataDictIO.save | save | Save data dict as JSON. | [
"Save",
"data",
"dict",
"as",
"JSON."
] | def save(self, path):
with open(path, 'w') as outfile:
json.dump(self.data, outfile) | ['def', 'save(self,', 'path):', 'with', 'open(path,', "'w')", 'as', 'outfile:', 'json.dump(self.data,', 'outfile)'] | 598,668 |
43Carrig/recurrent_neural_networks_practice | conversion.py | node_to_graph | node_to_graph | Convert Python code to equivalent TF graph mode code. | [
"Convert",
"Python",
"code",
"to",
"equivalent",
"TF",
"graph",
"mode",
"code."
] | def node_to_graph(node, context, rewrite_errors=True):
node = converter.standard_analysis(node, context, is_initial=True)
context.info.source_code = None
node = converter.apply_(node, context, decorators)
node = converter.apply_(node, context, directives)
node = converter.apply_(node, context, break... | ['def', 'node_to_graph(node,', 'context,', 'rewrite_errors=True):', 'node', '=', 'converter.standard_analysis(node,', 'context,', 'is_initial=True)', 'context.info.source_code', '=', 'None', 'node', '=', 'converter.apply_(node,', 'context,', 'decorators)', 'node', '=', 'converter.apply_(node,', 'context,', 'directives)... | 312,359 |
microsoft/nni | data.py | get_buckets | get_buckets | Get bucket by length. | [
"Get",
"bucket",
"by",
"length."
] | def get_buckets(min_length, max_length, bucket_count):
if bucket_count <= 0:
return [max_length]
unit_length = int((max_length - min_length) // bucket_count)
buckets = [min_length + unit_length * (i + 1) for i in range(0, bucket_count)]
buckets[-1] = max_length
return buckets | ['def', 'get_buckets(min_length,', 'max_length,', 'bucket_count):', 'if', 'bucket_count', '<=', '0:', 'return', '[max_length]', 'unit_length', '=', 'int((max_length', '-', 'min_length)', '//', 'bucket_count)', 'buckets', '=', '[min_length', '+', 'unit_length', '*', '(i', '+', '1)', 'for', 'i', 'in', 'range(0,', 'bucket... | 728,047 |
thaines/helit | model.py | Model.getSample | getSample | Returns the sample associated with the given index. | [
"Returns",
"the",
"sample",
"associated",
"with",
"the",
"given",
"index."
] | def getSample(self, s):
return self.sample[s] | ['def', 'getSample(self,', 's):', 'return', 'self.sample[s]'] | 591,460 |
hamza-murad/AALU | compare_comply_v1.py | Contexts.from_dict | from_dict | Initialize a Contexts object from a json dictionary. | [
"Initialize",
"a",
"Contexts",
"object",
"from",
"a",
"json",
"dictionary."
] | def from_dict(cls, _dict: Dict) -> 'Contexts':
args = {}
valid_keys = ['text', 'location']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError('Unrecognized keys detected in dictionary for class Contexts: ' + ', '.join(bad_keys))
if 'text' in _dict:
args['tex... | ['def', 'from_dict(cls,', '_dict:', 'Dict)', '->', "'Contexts':", 'args', '=', '{}', 'valid_keys', '=', "['text',", "'location']", 'bad_keys', '=', 'set(_dict.keys())', '-', 'set(valid_keys)', 'if', 'bad_keys:', 'raise', "ValueError('Unrecognized", 'keys', 'detected', 'in', 'dictionary', 'for', 'class', 'Contexts:', "'... | 5,370 |
open-mmlab/mmsegmentation | amg.py | rle_to_mask | rle_to_mask | Compute a binary mask from an uncompressed RLE. | [
"Compute",
"a",
"binary",
"mask",
"from",
"an",
"uncompressed",
"RLE."
] | def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
(h, w) = rle['size']
mask = np.empty(h * w, dtype=bool)
idx = 0
parity = False
for count in rle['counts']:
mask[idx:idx + count] = parity
idx += count
parity ^= True
mask = mask.reshape(w, h)
return mask.transpose() | ['def', 'rle_to_mask(rle:', 'Dict[str,', 'Any])', '->', 'np.ndarray:', '(h,', 'w)', '=', "rle['size']", 'mask', '=', 'np.empty(h', '*', 'w,', 'dtype=bool)', 'idx', '=', '0', 'parity', '=', 'False', 'for', 'count', 'in', "rle['counts']:", 'mask[idx:idx', '+', 'count]', '=', 'parity', 'idx', '+=', 'count', 'parity', '^='... | 625,576 |
sunishsheth2009/ChatterBot | debug.py | ProcessedTraceback.render_as_text | render_as_text | Return a string with the traceback. | [
"Return",
"a",
"string",
"with",
"the",
"traceback."
] | def render_as_text(self, limit=None):
lines = traceback.format_exception(self.exc_type, self.exc_value, self.frames[0], limit=limit)
return ''.join(lines).rstrip() | ['def', 'render_as_text(self,', 'limit=None):', 'lines', '=', 'traceback.format_exception(self.exc_type,', 'self.exc_value,', 'self.frames[0],', 'limit=limit)', 'return', "''.join(lines).rstrip()"] | 478,988 |
MegEngine/Transfer-Learning-Library | dst.py | shift_log | shift_log | First shift, then calculate log for numerical stability. | [
"First",
"shift,",
"then",
"calculate",
"log",
"for",
"numerical",
"stability."
] | def shift_log(x, offset=1e-06):
return torch.log(torch.clamp(x + offset, max=1.0)) | ['def', 'shift_log(x,', 'offset=1e-06):', 'return', 'torch.log(torch.clamp(x', '+', 'offset,', 'max=1.0))'] | 921,223 |
jason718/game-feature-learning | tools.py | SimpleTransformer.set_mean | set_mean | Set the mean to subtract for centering the data. | [
"Set",
"the",
"mean",
"to",
"subtract",
"for",
"centering",
"the",
"data."
] | def set_mean(self, mean):
self.mean = mean | ['def', 'set_mean(self,', 'mean):', 'self.mean', '=', 'mean'] | 199,445 |
gunthercox/ChatterBot | interfaces.py | MapperProperty.create_row_processor | create_row_processor | Return a 3-tuple consisting of three row processing functions. | [
"Return",
"a",
"3-tuple",
"consisting",
"of",
"three",
"row",
"processing",
"functions."
] | def create_row_processor(self, context, path, reduced_path, mapper, row, adapter):
return (None, None, None) | ['def', 'create_row_processor(self,', 'context,', 'path,', 'reduced_path,', 'mapper,', 'row,', 'adapter):', 'return', '(None,', 'None,', 'None)'] | 481,366 |
cslu-nlp/nlup | perceptron.py | Perceptron.predict | predict | Predicts most likely class for a feature vector. | [
"Predicts",
"most",
"likely",
"class",
"for",
"a",
"feature",
"vector."
] | def predict(self, phi):
scores = self.scores(phi)
(argmax_score, _) = max(scores.items(), key=itemgetter(1))
return argmax_score | ['def', 'predict(self,', 'phi):', 'scores', '=', 'self.scores(phi)', '(argmax_score,', '_)', '=', 'max(scores.items(),', 'key=itemgetter(1))', 'return', 'argmax_score'] | 731,728 |
kubeflow/pipelines | pipeline_with_nested_conditions_yaml.py | random_num_op | random_num_op | Generate a random number between low and high. | [
"Generate",
"a",
"random",
"number",
"between",
"low",
"and",
"high."
] | def random_num_op(low, high):
return components.load_component_from_text('\n name: Generate random number\n outputs:\n - {name: output, type: Integer}\n implementation:\n container:\n image: python:alpine3.6\n command:\n - sh\n - -c\n args:\n ... | ['def', 'random_num_op(low,', 'high):', 'return', "components.load_component_from_text('\\n", 'name:', 'Generate', 'random', 'number\\n', 'outputs:\\n', '-', '{name:', 'output,', 'type:', 'Integer}\\n', 'implementation:\\n', 'container:\\n', 'image:', 'python:alpine3.6\\n', 'command:\\n', '-', 'sh\\n', '-', '-c\\n', 'a... | 780,336 |
43Carrig/recurrent_neural_networks_practice | stats_ops.py | FertileStatsVariableSavable.restore | restore | Restores the associated tree from 'restored_tensors'. | [
"Restores",
"the",
"associated",
"tree",
"from",
"'restored_tensors'."
] | def restore(self, restored_tensors, unused_restored_shapes):
with ops.control_dependencies([self._create_op]):
return gen_stats_ops.fertile_stats_deserialize(self._stats_handle, restored_tensors[0], params=self.params.serialized_params_proto) | ['def', 'restore(self,', 'restored_tensors,', 'unused_restored_shapes):', 'with', 'ops.control_dependencies([self._create_op]):', 'return', 'gen_stats_ops.fertile_stats_deserialize(self._stats_handle,', 'restored_tensors[0],', 'params=self.params.serialized_params_proto)'] | 335,375 |
aimclub/FEDOT | base_cache_db.py | BaseCacheDB.reset | reset | Drops all scores from working table and resets efficiency table values to zero. | [
"Drops",
"all",
"scores",
"from",
"working",
"table",
"and",
"resets",
"efficiency",
"table",
"values",
"to",
"zero."
] | def reset(self):
with closing(sqlite3.connect(self.db_path)) as conn:
with conn:
cur = conn.cursor()
if self.use_stats:
self._reset_eff(cur)
self._reset_main(cur) | ['def', 'reset(self):', 'with', 'closing(sqlite3.connect(self.db_path))', 'as', 'conn:', 'with', 'conn:', 'cur', '=', 'conn.cursor()', 'if', 'self.use_stats:', 'self._reset_eff(cur)', 'self._reset_main(cur)'] | 545,618 |
shankyb9/College-Information-Chatbot-System | Utils.py | sentences | sentences | Split the string s into a list of sentences. | [
"Split",
"the",
"string",
"s",
"into",
"a",
"list",
"of",
"sentences."
] | def sentences(s):
try:
s + ''
except:
raise TypeError('s must be a string')
pos = 0
sentenceList = []
l = len(s)
while pos < l:
try:
q = s.index('?', pos)
except:
q = l + 1
try:
e = s.index('!', pos)
except:
... | ['def', 'sentences(s):', 'try:', 's', '+', "''", 'except:', 'raise', "TypeError('s", 'must', 'be', 'a', "string')", 'pos', '=', '0', 'sentenceList', '=', '[]', 'l', '=', 'len(s)', 'while', 'pos', '<', 'l:', 'try:', 'q', '=', "s.index('?',", 'pos)', 'except:', 'q', '=', 'l', '+', '1', 'try:', 'e', '=', "s.index('!',", '... | 125,053 |
Ruturaj123/Flowchart-Detection | variable_scope.py | VariableScope.reuse_variables | reuse_variables | Reuse variables in this scope. | [
"Reuse",
"variables",
"in",
"this",
"scope."
] | def reuse_variables(self):
self._reuse = True | ['def', 'reuse_variables(self):', 'self._reuse', '=', 'True'] | 606,199 |
Jittor/JDet | representation.py | Representation.is_trivial | is_trivial | Whether this representation is trivial or not. | [
"Whether",
"this",
"representation",
"is",
"trivial",
"or",
"not."
] | def is_trivial(self) -> bool:
return self.irreducible and self.group.trivial_representation.name == self.irreps[0] | ['def', 'is_trivial(self)', '->', 'bool:', 'return', 'self.irreducible', 'and', 'self.group.trivial_representation.name', '==', 'self.irreps[0]'] | 577,850 |
google-research/scenic | dataset_utils.py | add_image_and_boxes | add_image_and_boxes | Same as add_image with additional support boxes. | [
"Same",
"as",
"add_image",
"with",
"additional",
"support",
"boxes."
] | def add_image_and_boxes(parser_builder: builders.BaseParserBuilder, sampler_builder: builders.SamplerBuilder, decoder_builder: builders.DecoderBuilder, preprocessor_builder: builders.PreprocessorBuilder, postprocessor_builder: builders.PostprocessorBuilder, input_feature_name: str='image/encoded', output_feature_name: ... | ['def', 'add_image_and_boxes(parser_builder:', 'builders.BaseParserBuilder,', 'sampler_builder:', 'builders.SamplerBuilder,', 'decoder_builder:', 'builders.DecoderBuilder,', 'preprocessor_builder:', 'builders.PreprocessorBuilder,', 'postprocessor_builder:', 'builders.PostprocessorBuilder,', 'input_feature_name:', "str=... | 847,107 |
rifqind/Agent-Programs-3KS1 | mask_test.py | MaskTypeTest.test_set_at__default_value | test_set_at__default_value | Ensure individual mask bits are set using the default value. | [
"Ensure",
"individual",
"mask",
"bits",
"are",
"set",
"using",
"the",
"default",
"value."
] | def test_set_at__default_value(self):
(width, height) = (3, 21)
mask0 = pygame.mask.Mask((width, height))
mask1 = pygame.mask.Mask((width, height), fill=True)
mask0_expected_count = 1
mask1_expected_count = mask1.count()
expected_bit = 1
pos = (width - 1, height - 1)
mask0.set_at(pos)
... | ['def', 'test_set_at__default_value(self):', '(width,', 'height)', '=', '(3,', '21)', 'mask0', '=', 'pygame.mask.Mask((width,', 'height))', 'mask1', '=', 'pygame.mask.Mask((width,', 'height),', 'fill=True)', 'mask0_expected_count', '=', '1', 'mask1_expected_count', '=', 'mask1.count()', 'expected_bit', '=', '1', 'pos',... | 45,812 |
microsoft/InnerEye-DeepLearning | run_ml.py | MLRunner.is_normal_run_or_crossval_child_0 | is_normal_run_or_crossval_child_0 | Returns True if the present run is a non-crossvalidation run, or child run 0 of a crossvalidation run. | [
"Returns",
"True",
"if",
"the",
"present",
"run",
"is",
"a",
"non-crossvalidation",
"run,",
"or",
"child",
"run",
"0",
"of",
"a",
"crossvalidation",
"run."
] | def is_normal_run_or_crossval_child_0(self) -> bool:
if self.container.perform_cross_validation:
return self.container.cross_validation_split_index == 0
return True | ['def', 'is_normal_run_or_crossval_child_0(self)', '->', 'bool:', 'if', 'self.container.perform_cross_validation:', 'return', 'self.container.cross_validation_split_index', '==', '0', 'return', 'True'] | 613,067 |
ratschlab/dpsom | somvae_model.py | SOMVAE.z_q | z_q | Aggregates the respective closest embedding for every encoding. | [
"Aggregates",
"the",
"respective",
"closest",
"embedding",
"for",
"every",
"encoding."
] | def z_q(self):
k_1 = self.k // self.som_dim[1]
k_2 = self.k % self.som_dim[1]
k_stacked = tf.stack([k_1, k_2], axis=1)
z_q = tf.gather_nd(self.embeddings, k_stacked)
return z_q | ['def', 'z_q(self):', 'k_1', '=', 'self.k', '//', 'self.som_dim[1]', 'k_2', '=', 'self.k', '%', 'self.som_dim[1]', 'k_stacked', '=', 'tf.stack([k_1,', 'k_2],', 'axis=1)', 'z_q', '=', 'tf.gather_nd(self.embeddings,', 'k_stacked)', 'return', 'z_q'] | 167,018 |
jimtin/Stock_Comparison | frontend_widget.py | FrontendWidget.copy_raw | copy_raw | Copy the currently selected text to the clipboard without attempting to remove prompts or otherwise alter the text. | [
"Copy",
"the",
"currently",
"selected",
"text",
"to",
"the",
"clipboard",
"without",
"attempting",
"to",
"remove",
"prompts",
"or",
"otherwise",
"alter",
"the",
"text."
] | def copy_raw(self):
self._control.copy() | ['def', 'copy_raw(self):', 'self._control.copy()'] | 358,561 |
thaines/helit | line_overlay_layer.py | LineOverlayLayer.get_line | get_line | Returns the LineGraph object being rendered, or None if there is none. | [
"Returns",
"the",
"LineGraph",
"object",
"being",
"rendered,",
"or",
"None",
"if",
"there",
"is",
"none."
] | def get_line(self):
return self.line | ['def', 'get_line(self):', 'return', 'self.line'] | 591,993 |
acutesoftware/AIKIF | cls_context.py | where_am_i | where_am_i | high level function that can estimate where user is based on predefined setups. | [
"high",
"level",
"function",
"that",
"can",
"estimate",
"where",
"user",
"is",
"based",
"on",
"predefined",
"setups."
] | def where_am_i():
locations = {'Work': 0, 'Home': 0}
for ssid in scan_for_ssids():
for l in logged_ssids:
if l['name'] == ssid:
locations[l['location']] += 1
print('Where Am I: SSIDS Matching Home = ', locations['Home'], ' SSIDs matching Work = ', locations['Work'])
r... | ['def', 'where_am_i():', 'locations', '=', "{'Work':", '0,', "'Home':", '0}', 'for', 'ssid', 'in', 'scan_for_ssids():', 'for', 'l', 'in', 'logged_ssids:', 'if', "l['name']", '==', 'ssid:', "locations[l['location']]", '+=', '1', "print('Where", 'Am', 'I:', 'SSIDS', 'Matching', 'Home', '=', "',", "locations['Home'],", "'... | 85,806 |
google/deepvariant | testdata.py | init | init | Initialize global variables from flag values. | [
"Initialize",
"global",
"variables",
"from",
"flag",
"values."
] | def init():
global CHR20_FASTA
global CHR20_BAM
global CHR20_BAM_FIRST_HALF
global CHR20_BAM_SECOND_HALF
global NOCHR20_BAM
global CHR20_CRAM
global GOLDEN_TRAINING_EXAMPLES
global GOLDEN_CALLING_CANDIDATES
global GOLDEN_CANDIDATE_POSITIONS
global GOLDEN_CALLING_EXAMPLES
glob... | ['def', 'init():', 'global', 'CHR20_FASTA', 'global', 'CHR20_BAM', 'global', 'CHR20_BAM_FIRST_HALF', 'global', 'CHR20_BAM_SECOND_HALF', 'global', 'NOCHR20_BAM', 'global', 'CHR20_CRAM', 'global', 'GOLDEN_TRAINING_EXAMPLES', 'global', 'GOLDEN_CALLING_CANDIDATES', 'global', 'GOLDEN_CANDIDATE_POSITIONS', 'global', 'GOLDEN_... | 540,441 |
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