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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/eslint.py | python | get_eslint_from_cache | (dest_file, platform, arch) | Get ESLint binary from mongodb's cache. | Get ESLint binary from mongodb's cache. | [
"Get",
"ESLint",
"binary",
"from",
"mongodb",
"s",
"cache",
"."
] | def get_eslint_from_cache(dest_file, platform, arch):
"""Get ESLint binary from mongodb's cache."""
# Get URL
if platform == "Linux":
url = ESLINT_HTTP_LINUX_CACHE
elif platform == "Darwin":
url = ESLINT_HTTP_DARWIN_CACHE
else:
raise ValueError('ESLint is not available as a binary for ' + platform)
dest_dir = tempfile.gettempdir()
temp_tar_file = os.path.join(dest_dir, "temp.tar.gz")
# Download the file
print("Downloading ESLint %s from %s, saving to %s" % (ESLINT_VERSION, url, temp_tar_file))
urllib.request.urlretrieve(url, temp_tar_file)
# pylint: disable=too-many-function-args
print("Extracting ESLint %s to %s" % (ESLINT_VERSION, dest_file))
eslint_distfile = ESLINT_SOURCE_TAR_BASE.substitute(platform=platform, arch=arch)
extract_eslint(temp_tar_file, eslint_distfile)
shutil.move(eslint_distfile, dest_file) | [
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vtraag/leidenalg | b53366829360e10922a2dbf57eb405a516c23bc9 | setup.py | python | BuildConfiguration.print_build_info | (self) | Prints the include and library path being used for debugging purposes. | Prints the include and library path being used for debugging purposes. | [
"Prints",
"the",
"include",
"and",
"library",
"path",
"being",
"used",
"for",
"debugging",
"purposes",
"."
] | def print_build_info(self):
"""Prints the include and library path being used for debugging purposes."""
if self.static_extension == "only_igraph":
build_type = "dynamic extension with vendored igraph source"
elif self.static_extension:
build_type = "static extension"
else:
build_type = "dynamic extension"
print("Build type: %s" % build_type)
print("Include path: %s" % " ".join(self.include_dirs))
if self.excluded_include_dirs:
print(" - excluding: %s" % " ".join(self.excluded_include_dirs))
print("Library path: %s" % " ".join(self.library_dirs))
if self.excluded_library_dirs:
print(" - excluding: %s" % " ".join(self.excluded_library_dirs))
print("Runtime library path: %s" % " ".join(self.runtime_library_dirs))
print("Linked dynamic libraries: %s" % " ".join(self.libraries))
print("Linked static libraries: %s" % " ".join(self.extra_objects))
print("Extra compiler options: %s" % " ".join(self.extra_compile_args))
print("Extra linker options: %s" % " ".join(self.extra_link_args)) | [
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] | https://github.com/vtraag/leidenalg/blob/b53366829360e10922a2dbf57eb405a516c23bc9/setup.py#L618-L637 | ||
fasiondog/hikyuu | 842751aa25283f9fdafc6f560ea262f79e67a307 | hikyuu/fetcher/proxy/proxy.py | python | request_with_local | (url) | return requests.get(url).text | 通过本机ip直接获取请求，访问失败将抛出异常 | 通过本机ip直接获取请求，访问失败将抛出异常 | [
"通过本机ip直接获取请求，访问失败将抛出异常"
] | def request_with_local(url):
"""通过本机ip直接获取请求，访问失败将抛出异常"""
return requests.get(url).text | [
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] | https://github.com/fasiondog/hikyuu/blob/842751aa25283f9fdafc6f560ea262f79e67a307/hikyuu/fetcher/proxy/proxy.py#L34-L36 | |
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/examples/image_retraining/retrain.py | python | get_random_cached_bottlenecks | (sess, image_lists, how_many, category,
bottleneck_dir, image_dir, jpeg_data_tensor,
bottleneck_tensor) | return bottlenecks, ground_truths | Retrieves bottleneck values for cached images.
If no distortions are being applied, this function can retrieve the cached
bottleneck values directly from disk for images. It picks a random set of
images from the specified category.
Args:
sess: Current TensorFlow Session.
image_lists: Dictionary of training images for each label.
how_many: The number of bottleneck values to return.
category: Name string of which set to pull from - training, testing, or
validation.
bottleneck_dir: Folder string holding cached files of bottleneck values.
image_dir: Root folder string of the subfolders containing the training
images.
jpeg_data_tensor: The layer to feed jpeg image data into.
bottleneck_tensor: The bottleneck output layer of the CNN graph.
Returns:
List of bottleneck arrays and their corresponding ground truths. | Retrieves bottleneck values for cached images. | [
"Retrieves",
"bottleneck",
"values",
"for",
"cached",
"images",
"."
] | def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
bottleneck_dir, image_dir, jpeg_data_tensor,
bottleneck_tensor):
"""Retrieves bottleneck values for cached images.
If no distortions are being applied, this function can retrieve the cached
bottleneck values directly from disk for images. It picks a random set of
images from the specified category.
Args:
sess: Current TensorFlow Session.
image_lists: Dictionary of training images for each label.
how_many: The number of bottleneck values to return.
category: Name string of which set to pull from - training, testing, or
validation.
bottleneck_dir: Folder string holding cached files of bottleneck values.
image_dir: Root folder string of the subfolders containing the training
images.
jpeg_data_tensor: The layer to feed jpeg image data into.
bottleneck_tensor: The bottleneck output layer of the CNN graph.
Returns:
List of bottleneck arrays and their corresponding ground truths.
"""
class_count = len(image_lists.keys())
bottlenecks = []
ground_truths = []
for unused_i in range(how_many):
label_index = random.randrange(class_count)
label_name = list(image_lists.keys())[label_index]
image_index = random.randrange(65536)
bottleneck = get_or_create_bottleneck(sess, image_lists, label_name,
image_index, image_dir, category,
bottleneck_dir, jpeg_data_tensor,
bottleneck_tensor)
ground_truth = np.zeros(class_count, dtype=np.float32)
ground_truth[label_index] = 1.0
bottlenecks.append(bottleneck)
ground_truths.append(ground_truth)
return bottlenecks, ground_truths | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/keras/python/keras/engine/training.py | python | Model.compile | (self,
optimizer,
loss,
metrics=None,
loss_weights=None,
sample_weight_mode=None,
**kwargs) | Configures the model for training.
Arguments:
optimizer: str (name of optimizer) or optimizer object.
See [optimizers](/optimizers).
loss: str (name of objective function) or objective function.
See [losses](/losses).
If the model has multiple outputs, you can use a different loss
on each output by passing a dictionary or a list of losses.
The loss value that will be minimized by the model
will then be the sum of all individual losses.
metrics: list of metrics to be evaluated by the model
during training and testing.
Typically you will use `metrics=['accuracy']`.
To specify different metrics for different outputs of a
multi-output model, you could also pass a dictionary,
such as `metrics={'output_a': 'accuracy'}`.
loss_weights: Optional list or dictionary specifying scalar
coefficients (Python floats) to weight the loss contributions
of different model outputs.
The loss value that will be minimized by the model
will then be the *weighted sum* of all individual losses,
weighted by the `loss_weights` coefficients.
If a list, it is expected to have a 1:1 mapping
to the model's outputs. If a tensor, it is expected to map
output names (strings) to scalar coefficients.
sample_weight_mode: if you need to do timestep-wise
sample weighting (2D weights), set this to `"temporal"`.
`None` defaults to sample-wise weights (1D).
If the model has multiple outputs, you can use a different
`sample_weight_mode` on each output by passing a
dictionary or a list of modes.
**kwargs: Additional arguments passed to `tf.Session.run`.
Raises:
ValueError: In case of invalid arguments for
`optimizer`, `loss`, `metrics` or `sample_weight_mode`.
RuntimeError: In case of ill-formulated optimization problem. | Configures the model for training. | [
"Configures",
"the",
"model",
"for",
"training",
"."
] | def compile(self,
optimizer,
loss,
metrics=None,
loss_weights=None,
sample_weight_mode=None,
**kwargs):
"""Configures the model for training.
Arguments:
optimizer: str (name of optimizer) or optimizer object.
See [optimizers](/optimizers).
loss: str (name of objective function) or objective function.
See [losses](/losses).
If the model has multiple outputs, you can use a different loss
on each output by passing a dictionary or a list of losses.
The loss value that will be minimized by the model
will then be the sum of all individual losses.
metrics: list of metrics to be evaluated by the model
during training and testing.
Typically you will use `metrics=['accuracy']`.
To specify different metrics for different outputs of a
multi-output model, you could also pass a dictionary,
such as `metrics={'output_a': 'accuracy'}`.
loss_weights: Optional list or dictionary specifying scalar
coefficients (Python floats) to weight the loss contributions
of different model outputs.
The loss value that will be minimized by the model
will then be the *weighted sum* of all individual losses,
weighted by the `loss_weights` coefficients.
If a list, it is expected to have a 1:1 mapping
to the model's outputs. If a tensor, it is expected to map
output names (strings) to scalar coefficients.
sample_weight_mode: if you need to do timestep-wise
sample weighting (2D weights), set this to `"temporal"`.
`None` defaults to sample-wise weights (1D).
If the model has multiple outputs, you can use a different
`sample_weight_mode` on each output by passing a
dictionary or a list of modes.
**kwargs: Additional arguments passed to `tf.Session.run`.
Raises:
ValueError: In case of invalid arguments for
`optimizer`, `loss`, `metrics` or `sample_weight_mode`.
RuntimeError: In case of ill-formulated optimization problem.
"""
loss = loss or {}
self.optimizer = optimizers.get(optimizer)
self.sample_weight_mode = sample_weight_mode
self.loss = loss
self.loss_weights = loss_weights
# Prepare loss functions.
if isinstance(loss, dict):
for name in loss:
if name not in self.output_names:
raise ValueError('Unknown entry in loss '
'dictionary: "' + name + '". '
'Only expected the following keys: ' +
str(self.output_names))
loss_functions = []
for name in self.output_names:
if name not in loss:
logging.warning(
'Output "' + name + '" missing from loss dictionary. '
'We assume this was done on purpose, '
'and we will not be expecting '
'any data to be passed to "' + name + '" during training.',
stacklevel=2)
loss_functions.append(losses.get(loss.get(name)))
elif isinstance(loss, list):
if len(loss) != len(self.outputs):
raise ValueError('When passing a list as loss, '
'it should have one entry per model outputs. '
'The model has ' + str(len(self.outputs)) +
' outputs, but you passed loss=' + str(loss))
loss_functions = [losses.get(l) for l in loss]
else:
loss_function = losses.get(loss)
loss_functions = [loss_function for _ in range(len(self.outputs))]
self.loss_functions = loss_functions
weighted_losses = [_weighted_masked_objective(fn) for fn in loss_functions]
skip_indices = []
self._feed_outputs = []
self._feed_output_names = []
self._feed_output_shapes = []
self._feed_loss_fns = []
for i in range(len(weighted_losses)):
if weighted_losses[i] is None:
skip_indices.append(i)
else:
self._feed_outputs.append(self.outputs[i])
self._feed_output_names.append(self.output_names[i])
self._feed_output_shapes.append(self.internal_output_shapes[i])
self._feed_loss_fns.append(self.loss_functions[i])
# Prepare output masks.
masks = self.compute_mask(self.inputs, mask=None)
if masks is None:
masks = [None for _ in self.outputs]
if not isinstance(masks, list):
masks = [masks]
# Prepare loss weights.
if loss_weights is None:
loss_weights_list = [1. for _ in range(len(self.outputs))]
elif isinstance(loss_weights, dict):
for name in loss_weights:
if name not in self.output_names:
raise ValueError('Unknown entry in loss_weights '
'dictionary: "' + name + '". '
'Only expected the following keys: ' +
str(self.output_names))
loss_weights_list = []
for name in self.output_names:
loss_weights_list.append(loss_weights.get(name, 1.))
elif isinstance(loss_weights, list):
if len(loss_weights) != len(self.outputs):
raise ValueError('When passing a list as loss_weights, '
'it should have one entry per model outputs. '
'The model has ' + str(len(self.outputs)) +
' outputs, but you passed loss_weights=' +
str(loss_weights))
loss_weights_list = loss_weights
else:
raise TypeError('Could not interpret loss_weights argument: ' +
str(loss_weights) + ' - expected a list of dicts.')
# Prepare sample weights.
sample_weights = []
sample_weight_modes = []
if isinstance(sample_weight_mode, dict):
for name in sample_weight_mode:
if name not in self.output_names:
raise ValueError('Unknown entry in '
'sample_weight_mode dictionary: "' + name + '". '
'Only expected the following keys: ' +
str(self.output_names))
for i, name in enumerate(self.output_names):
if i in skip_indices:
weight = None
sample_weight_modes.append(None)
else:
if name not in sample_weight_mode:
raise ValueError('Output "' + name +
'" missing from sample_weight_modes '
'dictionary')
if sample_weight_mode.get(name) == 'temporal':
weight = K.placeholder(ndim=2, name=name + '_sample_weights')
sample_weight_modes.append('temporal')
else:
weight = K.placeholder(ndim=1, name=name + '_sample_weights')
sample_weight_modes.append(None)
sample_weights.append(weight)
elif isinstance(sample_weight_mode, list):
if len(sample_weight_mode) != len(self.outputs):
raise ValueError('When passing a list as sample_weight_mode, '
'it should have one entry per model outputs. '
'The model has ' + str(len(self.outputs)) +
' outputs, but you passed '
'sample_weight_mode=' + str(sample_weight_mode))
for i in range(len(self.output_names)):
if i in skip_indices:
weight = None
sample_weight_modes.append(None)
else:
mode = sample_weight_mode[i]
name = self.output_names[i]
if mode == 'temporal':
weight = K.placeholder(ndim=2, name=name + '_sample_weights')
sample_weight_modes.append('temporal')
else:
weight = K.placeholder(ndim=1, name=name + '_sample_weights')
sample_weight_modes.append(None)
sample_weights.append(weight)
else:
for i, name in enumerate(self.output_names):
if i in skip_indices:
sample_weight_modes.append(None)
sample_weights.append(None)
else:
if sample_weight_mode == 'temporal':
sample_weights.append(
K.placeholder(ndim=2, name=name + '_sample_weights'))
sample_weight_modes.append('temporal')
else:
sample_weights.append(
K.placeholder(ndim=1, name=name + '_sample_weights'))
sample_weight_modes.append(None)
self.sample_weight_modes = sample_weight_modes
self._feed_sample_weight_modes = []
for i in range(len(self.outputs)):
if i not in skip_indices:
self._feed_sample_weight_modes.append(self.sample_weight_modes[i])
# Prepare targets of model.
self.targets = []
self._feed_targets = []
for i in range(len(self.outputs)):
if i in skip_indices:
self.targets.append(None)
else:
shape = self.internal_output_shapes[i]
name = self.output_names[i]
target = K.placeholder(
ndim=len(shape),
name=name + '_target',
sparse=K.is_sparse(self.outputs[i]),
dtype=K.dtype(self.outputs[i]))
self.targets.append(target)
self._feed_targets.append(target)
# Prepare metrics.
self.metrics = metrics
self.metrics_names = ['loss']
self.metrics_tensors = []
# Compute total loss.
total_loss = None
for i in range(len(self.outputs)):
if i in skip_indices:
continue
y_true = self.targets[i]
y_pred = self.outputs[i]
weighted_loss = weighted_losses[i]
sample_weight = sample_weights[i]
mask = masks[i]
loss_weight = loss_weights_list[i]
output_loss = weighted_loss(y_true, y_pred, sample_weight, mask)
if len(self.outputs) > 1:
self.metrics_tensors.append(output_loss)
self.metrics_names.append(self.output_names[i] + '_loss')
if total_loss is None:
total_loss = loss_weight * output_loss
else:
total_loss += loss_weight * output_loss
if total_loss is None:
if not self.losses:
raise RuntimeError('The model cannot be compiled '
'because it has no loss to optimize.')
else:
total_loss = 0.
# Add regularization penalties
# and other layer-specific losses.
for loss_tensor in self.losses:
total_loss += loss_tensor
# List of same size as output_names.
# contains tuples (metrics for output, names of metrics).
nested_metrics = _collect_metrics(metrics, self.output_names)
def append_metric(layer_num, metric_name, metric_tensor):
"""Helper function used in loop below."""
if len(self.output_names) > 1:
metric_name = self.output_layers[layer_num].name + '_' + metric_name
self.metrics_names.append(metric_name)
self.metrics_tensors.append(metric_tensor)
for i in range(len(self.outputs)):
if i in skip_indices:
continue
y_true = self.targets[i]
y_pred = self.outputs[i]
output_metrics = nested_metrics[i]
for metric in output_metrics:
if metric == 'accuracy' or metric == 'acc':
# custom handling of accuracy
# (because of class mode duality)
output_shape = self.internal_output_shapes[i]
acc_fn = None
if (output_shape[-1] == 1 or
self.loss_functions[i] == losses.binary_crossentropy):
# case: binary accuracy
acc_fn = metrics_module.binary_accuracy
elif self.loss_functions[i] == losses.sparse_categorical_crossentropy:
# case: categorical accuracy with sparse targets
acc_fn = metrics_module.sparse_categorical_accuracy
else:
acc_fn = metrics_module.categorical_accuracy
masked_fn = _masked_objective(acc_fn)
append_metric(i, 'acc', masked_fn(y_true, y_pred, mask=masks[i]))
else:
metric_fn = metrics_module.get(metric)
masked_metric_fn = _masked_objective(metric_fn)
metric_result = masked_metric_fn(y_true, y_pred, mask=masks[i])
metric_result = {metric_fn.__name__: metric_result}
for name, tensor in six.iteritems(metric_result):
append_metric(i, name, tensor)
# Prepare gradient updates and state updates.
self.total_loss = total_loss
self.sample_weights = sample_weights
self._feed_sample_weights = []
for i in range(len(self.sample_weights)):
if i not in skip_indices:
self._feed_sample_weights.append(sample_weights[i])
# Functions for train, test and predict will
# be compiled lazily when required.
# This saves time when the user is not using all functions.
self._function_kwargs = kwargs
self.train_function = None
self.test_function = None
self.predict_function = None
# Collected trainable weights, sorted in topological order.
trainable_weights = self.trainable_weights
self._collected_trainable_weights = trainable_weights | [
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Manu343726/siplasplas | 9fae7559f87087cf8ef34f04bd1e774b84b2ea9c | reference/cindex.py | python | Type.translation_unit | (self) | return self._tu | The TranslationUnit to which this Type is associated. | The TranslationUnit to which this Type is associated. | [
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"""The TranslationUnit to which this Type is associated."""
# If this triggers an AttributeError, the instance was not properly
# instantiated.
return self._tu | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/devil/devil/android/device_utils.py | python | _ParseModeString | (mode_str) | return mode | Parse a mode string, e.g. 'drwxrwxrwx', into a st_mode value.
Effectively the reverse of |mode_to_string| in, e.g.:
https://github.com/landley/toybox/blob/master/lib/lib.c#L896 | Parse a mode string, e.g. 'drwxrwxrwx', into a st_mode value. | [
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"""Parse a mode string, e.g. 'drwxrwxrwx', into a st_mode value.
Effectively the reverse of |mode_to_string| in, e.g.:
https://github.com/landley/toybox/blob/master/lib/lib.c#L896
"""
if not _FILE_MODE_RE.match(mode_str):
raise ValueError('Unexpected file mode %r', mode_str)
mode = _FILE_MODE_KIND[mode_str[0]]
for c, flag in zip(mode_str[1:], _FILE_MODE_PERMS):
if c != '-' and c.islower():
mode |= flag
for c, (t, flag) in zip(mode_str[3::3], _FILE_MODE_SPECIAL):
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mode |= flag
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/converters/onnx/_operators_nd.py | python | _convert_gru | (
builder, node, graph, err
) | convert to CoreML GRU Layer:
https://github.com/apple/coremltools/blob/655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492/mlmodel/format/NeuralNetwork.proto#L3104 | convert to CoreML GRU Layer:
https://github.com/apple/coremltools/blob/655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492/mlmodel/format/NeuralNetwork.proto#L3104 | [
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builder, node, graph, err
): # type: (NeuralNetworkBuilder, Node, Graph, ErrorHandling) -> None
"""
convert to CoreML GRU Layer:
https://github.com/apple/coremltools/blob/655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492/mlmodel/format/NeuralNetwork.proto#L3104
"""
def get_weights(W, W_name, R, R_name, B):
"""
Helper routine to return weights in CoreML LSTM required format
"""
W = np.expand_dims(np.expand_dims(W, 3), 3)
R = np.expand_dims(np.expand_dims(R, 3), 3)
if W is None:
err.missing_initializer(
node,
"Weight tensor: {} not found in the graph initializer".format(W_name),
)
if R is None:
err.missing_initializer(
node,
"Weight tensor: {} not found in the graph initializer".format(R_name),
)
W_z, W_r, W_h = np.split(np.squeeze(W), 3) # type: ignore
R_z, R_r, R_h = np.split(np.squeeze(R), 3) # type: ignore
W_x = [W_z, W_r, W_h]
W_h = [R_z, R_r, R_h]
b = None
if B is not None:
b_Wz, b_Wr, b_Wh, b_Rz, b_Rr, b_Rh = np.split(np.squeeze(B), 6) # type: ignore
b = [b_Wz + b_Rz, b_Wr + b_Rr, b_Wh + b_Rh]
return W_x, W_h, b
def expand_dim(node_name, input_name, output_name, axes):
builder.add_expand_dims(
name=node_name, input_name=input_name, output_name=output_name, axes=axes
)
# Read attributes
# activation alpha and beta
if "activation_alpha" in node.attrs or "activation_beta" in node.attrs:
err.unsupported_feature_warning(
node, "Activation parameter alpha and beta are currently not used"
)
inner_activation = "SIGMOID"
output_activation = "TANH"
if "activations" in node.attrs:
activations_list = node.attrs["activations"]
if len(activations_list) < 2:
err.unsupported_op_configuration(
builder,
node,
graph,
"Error in ONNX model: Less number of activations provided",
)
inner_activation = activations_list[0].upper()
output_activation = activations_list[1].upper()
# Extract direction from ONNX attribute
direction = node.attrs.get("direction", "forward")
if direction == "bidirectional":
return err.unsupported_op_configuration(
builder,
node,
graph,
"Bidirectional GRU not supported!! Please consider adding custom conversion function/layer",
)
hidden_size = node.attrs.get("hidden_size")
# Read inputs
W_name = node.inputs[1]
R_name = node.inputs[2]
B = None
if len(node.inputs) > 3:
B_name = node.inputs[3]
B = node.input_tensors.get(B_name, None)
if W_name not in node.input_tensors or R_name not in node.input_tensors:
return err.unsupported_op_configuration(
builder,
node,
graph,
"Input and Recursion weights must be known!! Please consider adding custom conversion function/layer",
)
W = node.input_tensors.get(W_name, None)
R = node.input_tensors.get(R_name, None)
# Get weights for forward direction
W_x, W_h, b = get_weights(W, W_name, R, R_name, B)
# shape of input
input_size = W_x[0].shape[1]
# Get input and output for hidden and cell
input_h = node.inputs[5] if len(node.inputs) > 5 else node.inputs[0] + "_h_input"
output_h = (
node.outputs[1] if len(node.outputs) > 1 else node.outputs[0] + "_h_output"
)
output_h_5d = output_h + "_5d"
if len(node.inputs) < 6:
# if input is not present in the network, load they as constant
if node.inputs[0] not in graph.shape_dict:
err.unsupported_op_configuration(
builder, node, graph, "Input shape not represented within Graph"
)
# Input is represented as [Seq Len, Batch Size, Input Size]
batch_size = graph.shape_dict[node.inputs[0]][1]
builder.add_load_constant_nd(
name=node.name + "_load_initial_h",
output_name=input_h,
constant_value=0.0,
shape=[1, batch_size, hidden_size],
)
# CoreML GRU expects 5-d tensor
# Expand dimensions of input to 5-d for compatibility
input_rank = builder._get_rank(node.inputs[0])
if input_rank == -1:
return err.unsupported_op_configuration(
builder, node, graph, "Rank unknown for input"
)
if input_rank < 5:
add_nodes = 5 - input_rank
# TODO: Add one expand instead of adding one after another for input, h
expand_dim(
node.name + "_expand_in_0",
node.inputs[0],
node.inputs[0] + "_expand_out_0",
[input_rank],
)
expand_dim(
node.name + "_expand_in_h_0",
input_h,
input_h + "_expand_out_h_0",
[input_rank],
)
for i in range(1, add_nodes):
i_str = str(i)
i_p_str = str(i - 1)
expand_dim(
node.name + "_expand_in_" + i_str,
node.inputs[0] + "_expand_out_" + i_p_str,
node.inputs[0] + "_expand_out_" + i_str,
[input_rank + i],
)
expand_dim(
node.name + "_expand_in_h_" + i_str,
input_h + "_expand_out_h_" + i_p_str,
input_h + "_expand_out_h_" + i_str,
[input_rank + i],
)
builder.add_gru(
name=node.name,
W_h=W_h,
W_x=W_x,
b=b,
hidden_size=hidden_size,
input_size=input_size,
input_names=[
node.inputs[0] + "_expand_out_" + str(add_nodes - 1),
input_h + "_expand_out_h_" + str(add_nodes - 1),
],
output_names=[node.outputs[0] + "_5d_out", output_h_5d],
inner_activation=inner_activation,
activation=output_activation,
output_all=True,
reverse_input=(direction == "reverse"),
)
# CoreML output is [Seq Len, Batch Size, Num Dir * Hidden Size, 1, 1]
# Return output as [Seq Len, Num Dir, Batch Size, Hidden Size]
# Following steps:
# a. Reshape and split hidden size for direction [Seq Len, Batch Size, Num Dir, Hidden Size, 1]
# b. Squeeze last dimension [Seq Len, Batch Size, Num Dir, Hidden Size]
# c. Permute to fix the order [Seq Len, Num Dir, Batch Size, Hidden Size, 1]
builder.add_rank_preserving_reshape(
name=node.name + "_reshape_",
input_name=node.outputs[0] + "_5d_out",
output_name=node.outputs[0] + "_5d_reshaped",
output_shape=[0, 0, 1, -1, 0],
)
builder.add_squeeze(
name=node.name + "_squeeze_out",
input_name=node.outputs[0] + "_5d_reshaped",
output_name=node.outputs[0] + "_4d",
axes=[-1],
)
builder.add_transpose(
name=node.name + "_transpose",
axes=[0, 2, 1, 3],
input_name=node.outputs[0] + "_4d",
output_name=node.outputs[0],
)
# Squeeze dimensions of output_h
builder.add_squeeze(
name=node.name + "_squeeze_out_h",
input_name=output_h_5d,
output_name=output_h,
axes=[-1, -2],
) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib-tk/Tix.py | python | tixCommand.tix_resetoptions | (self, newScheme, newFontSet, newScmPrio=None) | Resets the scheme and fontset of the Tix application to
newScheme and newFontSet, respectively. This affects only those
widgets created after this call. Therefore, it is best to call the
resetoptions command before the creation of any widgets in a Tix
application.
The optional parameter newScmPrio can be given to reset the
priority level of the Tk options set by the Tix schemes.
Because of the way Tk handles the X option database, after Tix has
been has imported and inited, it is not possible to reset the color
schemes and font sets using the tix config command. Instead, the
tix_resetoptions command must be used. | Resets the scheme and fontset of the Tix application to
newScheme and newFontSet, respectively. This affects only those
widgets created after this call. Therefore, it is best to call the
resetoptions command before the creation of any widgets in a Tix
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Jinja2/py2/jinja2/meta.py | python | find_undeclared_variables | (ast) | return codegen.undeclared_identifiers | Returns a set of all variables in the AST that will be looked up from
the context at runtime. Because at compile time it's not known which
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runtime, all variables are returned.
>>> from jinja2 import Environment, meta
>>> env = Environment()
>>> ast = env.parse('{% set foo = 42 %}{{ bar + foo }}')
>>> meta.find_undeclared_variables(ast) == set(['bar'])
True
.. admonition:: Implementation
Internally the code generator is used for finding undeclared variables.
This is good to know because the code generator might raise a
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>>> from jinja2 import Environment, meta
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>>> ast = env.parse('{% set foo = 42 %}{{ bar + foo }}')
>>> meta.find_undeclared_variables(ast) == set(['bar'])
True
.. admonition:: Implementation
Internally the code generator is used for finding undeclared variables.
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codegen = TrackingCodeGenerator(ast.environment)
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/resource_variable_ops.py | python | BaseResourceVariable._assign_dependencies | (self) | Makes assignments depend on the cached value, if any.
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if self._cached_value is not None:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/pydoc.py | python | Helper.getline | (self, prompt) | Read one line, using input() when appropriate. | Read one line, using input() when appropriate. | [
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self.output.flush()
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/combo.py | python | BitmapComboBox.Insert | (*args, **kwargs) | return _combo.BitmapComboBox_Insert(*args, **kwargs) | Insert(self, String item, Bitmap bitmap, int pos, PyObject clientData=None) -> int
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PropertyGrid.GetImageSize | (*args, **kwargs) | return _propgrid.PropertyGrid_GetImageSize(*args, **kwargs) | GetImageSize(self, PGProperty p=None, int item=-1) -> Size | GetImageSize(self, PGProperty p=None, int item=-1) -> Size | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/locations.py | python | write_delete_marker_file | (directory) | Write the pip delete marker file into this directory. | Write the pip delete marker file into this directory. | [
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facebookincubator/BOLT | 88c70afe9d388ad430cc150cc158641701397f70 | llvm/utils/sort_includes.py | python | sort_includes | (f) | Sort the #include lines of a specific file. | Sort the #include lines of a specific file. | [
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"""Sort the #include lines of a specific file."""
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return
ext = os.path.splitext(f.name)[1]
if ext not in ['.cpp', '.c', '.h', '.inc', '.def']:
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lines = f.readlines()
look_for_api_header = ext in ['.cpp', '.c']
found_headers = False
headers_begin = 0
headers_end = 0
api_headers = []
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llvm_headers = []
system_headers = []
for (i, l) in enumerate(lines):
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if l.startswith('#include'):
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found_headers = True
headers_end = i
header = l[len('#include'):].lstrip()
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look_for_api_header = False
continue
if (header.startswith('<') or header.startswith('"gtest/') or
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continue
if (header.startswith('"clang/') or header.startswith('"clang-c/') or
header.startswith('"polly/')):
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continue
if (header.startswith('"llvm/') or header.startswith('"llvm-c/')):
llvm_headers.append(header)
continue
local_headers.append(header)
continue
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if found_headers:
break
if l.startswith('//') or l.startswith('#define') or l.startswith('#ifndef'):
continue
break
if not found_headers:
return
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subproject_headers = sorted(set(subproject_headers))
llvm_headers = sorted(set(llvm_headers))
system_headers = sorted(set(system_headers))
headers = api_headers + local_headers + subproject_headers + llvm_headers + system_headers
header_lines = ['#include ' + h for h in headers]
lines = lines[:headers_begin] + header_lines + lines[headers_end + 1:]
f.seek(0)
f.truncate()
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Tools/bgen/bgen/bgenOutput.py | python | OutHeader2 | (text) | Output a level 2 header comment (uses '-' dashes). | Output a level 2 header comment (uses '-' dashes). | [
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OpenArkStudio/ARK | a7f8413dd416cd1ac5b12adbdd84f010f59f11e2 | build/tools/config_tool/config_xml.py | python | get_node_by_keyvalue | (nodelist, kv_map) | return result_nodes | find all nodes that match the key-value
nodelist: node list
kv_map: property map
return: all matched nodes | find all nodes that match the key-value
nodelist: node list
kv_map: property map
return: all matched nodes | [
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kv_map: property map
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_windows.py | python | PageSetupDialogData.EnablePrinter | (*args, **kwargs) | return _windows_.PageSetupDialogData_EnablePrinter(*args, **kwargs) | EnablePrinter(self, bool flag) | EnablePrinter(self, bool flag) | [
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kismetwireless/kismet | a7c0dc270c960fb1f58bd9cec4601c201885fd4e | capture_bt_geiger/KismetCaptureBtGeiger/kismetexternal/__init__.py | python | ExternalInterface.publish_event | (self, event, content_json) | Publish an event on the eventbus; see the docs for additional info and
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | clang/tools/scan-build-py/libscanbuild/analyze.py | python | filter_debug_flags | (opts, continuation=dispatch_ctu) | return continuation(opts) | Filter out nondebug macros when requested. | Filter out nondebug macros when requested. | [
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if opts.pop('force_debug'):
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apiaryio/snowcrash | b5b39faa85f88ee17459edf39fdc6fe4fc70d2e3 | tools/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings.GetCompilerPdbName | (self, config, expand_special) | return pdbname | Get the pdb file name that should be used for compiler invocations, or
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qboticslabs/mastering_ros | d83e78f30acc45b0f18522c1d5fae3a7f52974b9 | chapter_10_codes/seven_dof_arm_gazebo/scripts/pick_and_place_working_1.py | python | CokeCanPickAndPlace._create_pickup_goal | (self, group, target, grasps) | return goal | Create a MoveIt! PickupGoal | Create a MoveIt! PickupGoal | [
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"""
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# Create goal:
goal = PickupGoal()
goal.group_name = group
goal.target_name = target
goal.possible_grasps.extend(grasps)
goal.allowed_touch_objects.append(target)
goal.support_surface_name = self._table_object_name
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goal.allowed_planning_time = 7.0
goal.planning_options.planning_scene_diff.is_diff = True
goal.planning_options.planning_scene_diff.robot_state.is_diff = True
goal.planning_options.plan_only = False
goal.planning_options.replan = True
goal.planning_options.replan_attempts = 20
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/virtualenv/virtualenv.py | python | fixup_pth_and_egg_link | (home_dir, sys_path=None) | Makes .pth and .egg-link files use relative paths | Makes .pth and .egg-link files use relative paths | [
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home_dir = os.path.normcase(os.path.abspath(home_dir))
if sys_path is None:
sys_path = sys.path
for path in sys_path:
if not path:
path = '.'
if not os.path.isdir(path):
continue
path = os.path.normcase(os.path.abspath(path))
if not path.startswith(home_dir):
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continue
for filename in os.listdir(path):
filename = os.path.join(path, filename)
if filename.endswith('.pth'):
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fixup_pth_file(filename)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PropertyGridInterface.GetPropertyHelpString | (*args, **kwargs) | return _propgrid.PropertyGridInterface_GetPropertyHelpString(*args, **kwargs) | GetPropertyHelpString(self, PGPropArg id) -> String | GetPropertyHelpString(self, PGPropArg id) -> String | [
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pymcuprog/configgenerator.py | python | ConfigGenerator._add_register | (self, name, value) | return reg | Shortcut to add a register into the XML
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danxuhk/ContinuousCRF-CNN | 2b6dcaf179620f118b225ed12c890414ca828e21 | scripts/cpp_lint.py | python | _CppLintState.SetCountingStyle | (self, counting_style) | Sets the module's counting options. | Sets the module's counting options. | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/training/saver.py | python | _add_collection_def | (meta_graph_def, key) | Adds a collection to MetaGraphDef protocol buffer.
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ucb-bar/esp-llvm | 8aec2ae754fd66d4e73b9b777a9f20c4583a0f03 | examples/Kaleidoscope/MCJIT/lazy/genk-timing.py | python | KScriptGenerator.setCallWeighting | (self, weight) | Sets the probably of generating a function call | Sets the probably of generating a function call | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/optimize/_trustregion_constr/qp_subproblem.py | python | reinforce_box_boundaries | (x, lb, ub) | return np.minimum(np.maximum(x, lb), ub) | Return clipped value of x | Return clipped value of x | [
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eric612/MobileNet-YOLO | 69b4441cb3ec8d553fbdef788ad033e246f901bd | scripts/cpp_lint.py | python | CheckAltTokens | (filename, clean_lines, linenum, error) | Check alternative keywords being used in boolean expressions.
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filename: The name of the current file.
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linenum: The number of the line to check.
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/osr.py | python | SpatialReference.ExportToUSGS | (self, *args) | return _osr.SpatialReference_ExportToUSGS(self, *args) | r"""ExportToUSGS(SpatialReference self) -> OGRErr | r"""ExportToUSGS(SpatialReference self) -> OGRErr | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcodeproj_file.py | python | PBXGroup.TakeOverOnlyChild | (self, recurse=False) | If this PBXGroup has only one child and it's also a PBXGroup, take
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google/mysql-protobuf | 467cda676afaa49e762c5c9164a43f6ad31a1fbf | protobuf/python/google/protobuf/internal/decoder.py | python | _RaiseInvalidWireType | (buffer, pos, end) | Skip function for unknown wire types. Raises an exception. | Skip function for unknown wire types. Raises an exception. | [
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] | def _RaiseInvalidWireType(buffer, pos, end):
"""Skip function for unknown wire types. Raises an exception."""
raise _DecodeError('Tag had invalid wire type.') | [
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digibyte/digibyte | 0b8a04fb06d5470a15168e2f675aec57bcc24dac | contrib/devtools/security-check.py | python | get_ELF_program_headers | (executable) | return headers | Return type and flags for ELF program headers | Return type and flags for ELF program headers | [
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'''Return type and flags for ELF program headers'''
p = subprocess.Popen([READELF_CMD, '-l', '-W', executable], stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE, universal_newlines=True)
(stdout, stderr) = p.communicate()
if p.returncode:
raise IOError('Error opening file')
in_headers = False
count = 0
headers = []
for line in stdout.splitlines():
if line.startswith('Program Headers:'):
in_headers = True
if line == '':
in_headers = False
if in_headers:
if count == 1: # header line
ofs_typ = line.find('Type')
ofs_offset = line.find('Offset')
ofs_flags = line.find('Flg')
ofs_align = line.find('Align')
if ofs_typ == -1 or ofs_offset == -1 or ofs_flags == -1 or ofs_align == -1:
raise ValueError('Cannot parse elfread -lW output')
elif count > 1:
typ = line[ofs_typ:ofs_offset].rstrip()
flags = line[ofs_flags:ofs_align].rstrip()
headers.append((typ, flags))
count += 1
return headers | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/textwrap.py | python | dedent | (text) | return text | Remove any common leading whitespace from every line in `text`.
This can be used to make triple-quoted strings line up with the left
edge of the display, while still presenting them in the source code
in indented form.
Note that tabs and spaces are both treated as whitespace, but they
are not equal: the lines " hello" and "\thello" are
considered to have no common leading whitespace. (This behaviour is
new in Python 2.5; older versions of this module incorrectly
expanded tabs before searching for common leading whitespace.) | Remove any common leading whitespace from every line in `text`. | [
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"any",
"common",
"leading",
"whitespace",
"from",
"every",
"line",
"in",
"text",
"."
] | def dedent(text):
"""Remove any common leading whitespace from every line in `text`.
This can be used to make triple-quoted strings line up with the left
edge of the display, while still presenting them in the source code
in indented form.
Note that tabs and spaces are both treated as whitespace, but they
are not equal: the lines " hello" and "\thello" are
considered to have no common leading whitespace. (This behaviour is
new in Python 2.5; older versions of this module incorrectly
expanded tabs before searching for common leading whitespace.)
"""
# Look for the longest leading string of spaces and tabs common to
# all lines.
margin = None
text = _whitespace_only_re.sub('', text)
indents = _leading_whitespace_re.findall(text)
for indent in indents:
if margin is None:
margin = indent
# Current line more deeply indented than previous winner:
# no change (previous winner is still on top).
elif indent.startswith(margin):
pass
# Current line consistent with and no deeper than previous winner:
# it's the new winner.
elif margin.startswith(indent):
margin = indent
# Current line and previous winner have no common whitespace:
# there is no margin.
else:
margin = ""
break
# sanity check (testing/debugging only)
if 0 and margin:
for line in text.split("\n"):
assert not line or line.startswith(margin), \
"line = %r, margin = %r" % (line, margin)
if margin:
text = re.sub(r'(?m)^' + margin, '', text)
return text | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.DocumentStartExtend | (*args, **kwargs) | return _stc.StyledTextCtrl_DocumentStartExtend(*args, **kwargs) | DocumentStartExtend(self)
Move caret to first position in document extending selection to new caret position. | DocumentStartExtend(self) | [
"DocumentStartExtend",
"(",
"self",
")"
] | def DocumentStartExtend(*args, **kwargs):
"""
DocumentStartExtend(self)
Move caret to first position in document extending selection to new caret position.
"""
return _stc.StyledTextCtrl_DocumentStartExtend(*args, **kwargs) | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/front/tf/ObjectDetectionAPI.py | python | ObjectDetectionAPIProposalReplacement.insert_detection_output_instead_of_proposal | (graph: Graph, match: SubgraphMatch,
pipeline_config: PipelineConfig) | return {'proposal_node': ObjectDetectionAPIProposalReplacement.ie_to_tf_proposals(graph, proposal_node, match,
pipeline_config,
max_proposals)} | The function inserts DetectionOutput operation instead of Proposal operation which may result in an increase of
the accuracy for some models. The function is enabled with the custom attribute "operation_to_insert" with
value "DetectionOutput" in the transformation configuration file section for the
"ObjectDetectionAPIProposalReplacement" transformation.
:param graph: the graph to operate on
:param match: the object containing information about the matched sub-graph
:param pipeline_config: object containing information from the pipeline.config file of the model
:return: the dictionary with mapping information needed for other transformations | The function inserts DetectionOutput operation instead of Proposal operation which may result in an increase of
the accuracy for some models. The function is enabled with the custom attribute "operation_to_insert" with
value "DetectionOutput" in the transformation configuration file section for the
"ObjectDetectionAPIProposalReplacement" transformation. | [
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] | def insert_detection_output_instead_of_proposal(graph: Graph, match: SubgraphMatch,
pipeline_config: PipelineConfig):
"""
The function inserts DetectionOutput operation instead of Proposal operation which may result in an increase of
the accuracy for some models. The function is enabled with the custom attribute "operation_to_insert" with
value "DetectionOutput" in the transformation configuration file section for the
"ObjectDetectionAPIProposalReplacement" transformation.
:param graph: the graph to operate on
:param match: the object containing information about the matched sub-graph
:param pipeline_config: object containing information from the pipeline.config file of the model
:return: the dictionary with mapping information needed for other transformations
"""
max_proposals = _value_or_raise(match, pipeline_config, 'first_stage_max_proposals')
# Convolution/matmul node that produces classes confidence
# Transpose result of the tensor with classes confidences so it will be in a correct layout for Softmax
class_conf_nodes = backward_bfs_for_operation(match.single_input_node(1)[0], ['Add'])
assert len(class_conf_nodes) >= 1, 'Expected to find nodes of type "Add" starting from the node "{}" in ' \
'backward direction'.format(match.single_input_node(1)[0].id)
class_conf = class_conf_nodes[0]
# prepare input with class confidences. The DetectionOutput operation which will consume this tensor as a
# second input expects probabilities to be normalized with SoftMax operation per each bounding box class. In
# order to do this we first reshape the tensor so the last dimension contains probability for 2 classes
# (background and foreground) for each bounding box. Before feeding this tensor to the DO operation the tensor
# is flattened to the shape [num_batches, num_classes * num_bounding_boxes]
reshape_conf = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1, 2]),
dict(name='predictions/Reshape'))
# transpose from NCHW to NHWC will be inserted as input to the Reshape automatically. This is expected
class_conf.out_port(0).disconnect()
class_conf.out_port(0).connect(reshape_conf.in_port(0))
softmax_conf = Softmax(graph, dict(axis=2, name=reshape_conf.id + '/Softmax')).create_node([reshape_conf])
flattened_conf = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1]),
dict(name=softmax_conf.name + '/Flatten'), softmax_conf)
# prepare input with bounding boxes shape offsets
offsets = backward_bfs_for_operation(match.single_input_node(0)[0], ['Add'])[0]
flatten_offsets = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1]),
dict(name=offsets.soft_get('name', offsets.id) + '/Flatten'),
offsets)
# TensorFlow produces anchor boxes in absolute coordinates in YXYX order. Need to normalize them to [0, 1]
# interval and append a tensor with variances. Refer to the ObjectDetectionAPISSDPostprocessorReplacement
# transformation comments about variances. The YXYX->XYXY order change will be performed with the output of the
# inserted DetectionOutput operation
yxyx_anchors = match.single_input_node(2)[0]
# get the input image height and width to divide the anchors values by it
initial_input_node_name = 'input_tensor' if 'input_tensor' in graph.nodes else 'image_tensor'
if initial_input_node_name not in graph.nodes():
raise Error('Input node "{}" of the graph is not found. Do not run the Model Optimizer with '
'"--input" command line parameter.'.format(initial_input_node_name))
parameter_node = Node(graph, initial_input_node_name)
input_shape = Shape(graph, {'name': parameter_node.name}).create_node([parameter_node])
input_image_hw = node_to_get_shape_value_of_indices(input_shape, [1, 2]) # NHWC layout
hwhw = create_op_with_const_inputs(graph, Tile, {1: int64_array([2])}, {'name': 'image_hwhw'}, input_image_hw)
hwhw_float = Cast(graph, {'dst_type': np.float32}).create_node([hwhw])
scaled_anchors = Div(graph, {'name': 'scaled_anchors'}).create_node([yxyx_anchors, hwhw_float])
flattened_anchors = create_op_with_const_inputs(graph, Reshape, {1: int64_array([1, 1, -1])},
{'name': 'flattened_anchors'}, scaled_anchors)
cropped_anchors = AttributedClamp(graph, {'min': 0.0, 'max': 1.0, 'name': 'clamped_yxyx',
'nchw_layout': True}).create_node([flattened_anchors])
# the input tensor "scaled_anchors" for the "flattened_anchors" may be 4D. In order to avoid inserting Transpose
# operation mark the "flattened_anchors" with the correct data layout
mark_as_correct_data_layout(flattened_anchors)
# create tensor of shape [4] with variance values which then are tiled by the number of boxes which is obtained
# from the 'yxyx_anchors' node
variances = Const(graph, {'value': _variance_from_pipeline_config(pipeline_config)}).create_node()
anchors_shape = Shape(graph, {'name': 'anchors_shape'}).create_node([yxyx_anchors])
anchors_count = node_to_get_shape_value_of_indices(anchors_shape, [0])
tiled_variances = Tile(graph, {'name': 'tiled_variances'}).create_node([variances, anchors_count])
reshaped_tiled_variances = create_op_with_const_inputs(graph, Reshape, {1: int64_array([1, 1, -1])},
{'name': 'flattened_variances'}, tiled_variances)
# now we can merge actual anchors coordinates with a tensor with variances as it is expected by the
# DetectionOutput operation
duplicate_anchors = Concat(graph, {'axis': 1, 'name': 'anchors_with_variances'}).create_node(
[cropped_anchors, reshaped_tiled_variances])
do = DetectionOutput(graph,
{'background_label_id': 0,
'clip_after_nms': True,
'clip_before_nms': False,
'code_type': 'caffe.PriorBoxParameter.CENTER_SIZE',
'confidence_threshold': 0.0,
'decrease_label_id': False,
'input_height': 1,
'input_width': 1,
'keep_top_k': max_proposals,
'normalized': True,
'objectness_score': 0,
'share_location': True,
'top_k': 6000,
'variance_encoded_in_target': False,
'nms_threshold': _value_or_raise(match, pipeline_config, 'first_stage_nms_iou_threshold'),
'name': 'first_do',
}).create_node([flatten_offsets, flattened_conf, duplicate_anchors])
# DetectionOutput output tensor has YXYX box coordinates order
# switch to 3D to avoid issues that part of the model with 4D shapes should be inferred in NCHW layout
do_3d = create_op_with_const_inputs(graph, Squeeze, {1: int64_array(0)}, {'name': do.name + '/SqueezeDO'}, do)
mark_as_correct_data_layout(do_3d)
# DetectionOutput output tensor produces a tensor of tuples with the following 7 elements:
# [batch_id, class_id, confidence, x1, y1, x2, y2]. Here we split the DetectionOutput result into the 7
# tensors with each of these elements for predictions. Then we crop predicted box coordinates (scaled) to be
# within [0, 1] range (as it is predicted in the TF model) and then combine tensors back to the Proposal
# operation output format: [batch_id, x1, y1, x2, y2].
do_split = create_op_node_with_second_input(graph, Split, int64_array(2), {'num_splits': 7,
'name': do.name + '/Split'}, do_3d)
coords = Concat(graph, {'axis': -1, 'in_ports_count': 4, 'name': do_split.name + '/coords'}).create_node()
# concat bounding boxes with the same order (XYXY) as Proposal produces
for port_idx in range(4):
do_split.out_port(3 + port_idx).connect(coords.in_port(port_idx))
clamped_coords = AttributedClamp(graph, {'min': 0.0, 'max': 1.0, 'name': 'clamped_xyxy'}).create_node([coords])
# prepare final proposal boxes [batch_id, x1, y1, x2, y2]
proposal_node = Concat(graph, {'axis': -1, 'in_ports_count': 2, 'name': 'proposals'}).create_node()
do_split.out_port(0).connect(proposal_node.in_port(0))
clamped_coords.out_port(0).connect(proposal_node.in_port(1))
return {'proposal_node': ObjectDetectionAPIProposalReplacement.ie_to_tf_proposals(graph, proposal_node, match,
pipeline_config,
max_proposals)} | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/lib2to3/pgen2/parse.py | python | Parser.push | (self, type, newdfa, newstate, context) | Push a nonterminal. (Internal) | Push a nonterminal. (Internal) | [
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/jinja2/compiler.py | python | CodeGenerator.pull_locals | (self, frame) | Pull all the references identifiers into the local scope. | Pull all the references identifiers into the local scope. | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/gdal.py | python | Dataset.AddFieldDomain | (self, *args) | return _gdal.Dataset_AddFieldDomain(self, *args) | r"""AddFieldDomain(Dataset self, FieldDomain fieldDomain) -> bool | r"""AddFieldDomain(Dataset self, FieldDomain fieldDomain) -> bool | [
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nnrg/opennero | 43e12a1bcba6e228639db3886fec1dc47ddc24cb | mods/Maze/client.py | python | pauseAgent | (ui) | return closure | return a function that pauses and continues the agent | return a function that pauses and continues the agent | [
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nileshkulkarni/csm | 0e6e0e7d4f725fd36f2414c0be4b9d83197aa1fc | csm/utils/transformations.py | python | vector_product | (v0, v1, axis=0) | return numpy.cross(v0, v1, axis=axis) | Return vector perpendicular to vectors.
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>>> v1 = [[0, 3, 0], [0, 0, 3], [0, 0, 3], [3, 3, 3]]
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>>> numpy.allclose(v, [[0, 0, 6], [0, -6, 0], [6, 0, 0], [0, -6, 6]])
True
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Vipermdl/OCR_detection_IC15 | 8eebd353d6fac97f5832a138d7af3bd3071670db | base/base_data_loader.py | python | BaseDataLoader.__len__ | (self) | return self._n_samples() // self.batch_size | :return: Total number of batches | :return: Total number of batches | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/html2.py | python | WebView.SetEditable | (*args, **kwargs) | return _html2.WebView_SetEditable(*args, **kwargs) | SetEditable(self, bool enable=True) | SetEditable(self, bool enable=True) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/context.py | python | BaseContext.get_logger | (self) | return get_logger() | Return package logger -- if it does not already exist then
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cinder/Cinder | e83f5bb9c01a63eec20168d02953a0879e5100f7 | docs/libs/pystache/context.py | python | ContextStack._get_simple | (self, name) | Query the stack for a non-dotted name. | Query the stack for a non-dotted name. | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/multi_threads_helpers.py | python | IntegratePeaksThread.set_integrated_peak_info | (self, scan_number, peak_integration_dict) | return | set the integrated peak information including
* calculate Lorentz correction
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* add motor step information
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# get peak information
peak_info_obj = self._mainWindow.controller.get_peak_info(self._expNumber, scan_number)
# get Q-vector of the peak center and calculate |Q| from it
peak_center_q = peak_info_obj.get_peak_centre_v3d().norm()
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wavelength = self._mainWindow.controller.get_wave_length(self._expNumber, [scan_number])
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except AssertionError as ass_err:
return str(ass_err)
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# TODO/FIXME/NOW2 : peak center Q shall be from calculation!
lorentz_factor = peak_integration_utility.calculate_lorentz_correction_factor(peak_center_q, wavelength,
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peak_info_obj.lorentz_correction_factor = lorentz_factor
# set motor
peak_info_obj.set_motor(motor_name, motor_step, motor_std_dev)
# set peak integration dictionary
peak_info_obj.set_integration(peak_integration_dict)
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | llvm/utils/lit/lit/llvm/subst.py | python | ToolSubst.__init__ | (self, key, command=None, pre=r'.-^/\<', post='-.', verbatim=False,
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livecode/livecode | 4606a10ea10b16d5071d0f9f263ccdd7ede8b31d | gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings.GetCflagsCC | (self, config) | return ['/TP'] + self._GetPchFlags(config, '.cc') | Returns the flags that need to be added to .cc compilations. | Returns the flags that need to be added to .cc compilations. | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/debug/wrappers/framework.py | python | NonInteractiveDebugWrapperSession.prepare_run_debug_urls | (self, fetches, feed_dict) | Abstract method to be implemented by concrete subclasses.
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fetches: Same as the `fetches` argument to `Session.run()`
feed_dict: Same as the `feed_dict` argument to `Session.run()`
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feed_dict: Same as the `feed_dict` argument to `Session.run()`
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/gdal-utils/osgeo_utils/auxiliary/base.py | python | get_byte | (number: int, i: int) | return (number & (0xff << (i * 8))) >> (i * 8) | returns the i-th byte from an integer | returns the i-th byte from an integer | [
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zeroc-ice/ice | 6df7df6039674d58fb5ab9a08e46f28591a210f7 | python/python/Ice/__init__.py | python | Value.ice_staticId | () | return '::Ice::Object' | Obtains the type id of this Slice class or interface.
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/resmokelib/hang_analyzer/dumper.py | python | Dumper.__init__ | (self, root_logger: logging.Logger, dbg_output: str) | Initialize dumper. | Initialize dumper. | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/skia/tools/copyright/fileparser.py | python | CParser.FindAllCommentBlocks | (self, file_contents) | return self._comment_pattern.findall(file_contents) | Returns a list of all comment blocks within these file contents. | Returns a list of all comment blocks within these file contents. | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/guess-the-majority-in-a-hidden-array.py | python | Solution.guessMajority | (self, reader) | return 3 if count_a > count_b else idx_b | :type reader: ArrayReader
:rtype: integer | :type reader: ArrayReader
:rtype: integer | [
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"""
:type reader: ArrayReader
:rtype: integer
"""
count_a, count_b, idx_b = 1, 0, None
value_0_1_2_3 = reader.query(0, 1, 2, 3)
for i in reversed(xrange(4, reader.length())):
value_0_1_2_i = reader.query(0, 1, 2, i)
if value_0_1_2_i == value_0_1_2_3: # nums[i] == nums[3]
count_a = count_a+1
else:
count_b, idx_b = count_b+1, i
value_0_1_2_4 = value_0_1_2_i
for i in xrange(3):
value_a_b_3_4 = reader.query(*[v for v in [0, 1, 2, 3, 4] if v != i])
if value_a_b_3_4 == value_0_1_2_4: # nums[i] == nums[3]
count_a = count_a+1
else:
count_b, idx_b = count_b+1, i
if count_a == count_b:
return -1
return 3 if count_a > count_b else idx_b | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/msw/gizmos.py | python | TreeListColumnInfo.SetImage | (*args, **kwargs) | return _gizmos.TreeListColumnInfo_SetImage(*args, **kwargs) | SetImage(self, int image) | SetImage(self, int image) | [
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"""SetImage(self, int image)"""
return _gizmos.TreeListColumnInfo_SetImage(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/buttonpanel.py | python | ButtonInfo.Draw | (self, dc, rect) | Draws the button on :class:`ButtonPanel`. Actually the drawing is done in :class:`BPArt`.
:param `dc`: an instance of :class:`DC`;
:param Rect `rect`: the main caption text client rectangle. | Draws the button on :class:`ButtonPanel`. Actually the drawing is done in :class:`BPArt`. | [
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Draws the button on :class:`ButtonPanel`. Actually the drawing is done in :class:`BPArt`.
:param `dc`: an instance of :class:`DC`;
:param Rect `rect`: the main caption text client rectangle.
"""
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isVertical = self._parent.IsVertical()
text = self.GetText()
buttonStatus = self.GetStatus()
isToggled = self.GetToggled()
textAlignment = self.GetTextAlignment()
self._parent._art.DrawButton(dc, rect, buttonBitmap, isVertical,
buttonStatus, isToggled, textAlignment, text)
self.SetRect(rect) | [
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pymcuprog/nvmpic.py | python | NvmAccessProviderCmsisDapPic.read_device_id | (self) | return id_array | Get the device info from the device
:returns: Device ID raw bytes (Little endian) | Get the device info from the device | [
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"""
Get the device info from the device
:returns: Device ID raw bytes (Little endian)
"""
pic_id = self.pic.read_id()
id_array = binary.pack_le16(pic_id)
self.logger.info("Device ID read out: '%04X'", pic_id)
return id_array | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/distutils/cmd.py | python | Command.move_file | (self, src, dst, level=1) | return file_util.move_file(src, dst, dry_run=self.dry_run) | Move a file respecting dry-run flag. | Move a file respecting dry-run flag. | [
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return file_util.move_file(src, dst, dry_run=self.dry_run) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/decimal.py | python | Decimal.logical_or | (self, other, context=None) | return _dec_from_triple(0, result.lstrip('0') or '0', 0) | Applies an 'or' operation between self and other's digits. | Applies an 'or' operation between self and other's digits. | [
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"""Applies an 'or' operation between self and other's digits."""
if context is None:
context = getcontext()
other = _convert_other(other, raiseit=True)
if not self._islogical() or not other._islogical():
return context._raise_error(InvalidOperation)
# fill to context.prec
(opa, opb) = self._fill_logical(context, self._int, other._int)
# make the operation, and clean starting zeroes
result = "".join([str(int(a)|int(b)) for a,b in zip(opa,opb)])
return _dec_from_triple(0, result.lstrip('0') or '0', 0) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/compiler/pyassem.py | python | FlowGraph.getBlocksInOrder | (self) | return order | Return the blocks in reverse postorder
i.e. each node appears before all of its successors | Return the blocks in reverse postorder | [
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"""Return the blocks in reverse postorder
i.e. each node appears before all of its successors
"""
order = order_blocks(self.entry, self.exit)
return order | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/logging/__init__.py | python | Logger.isEnabledFor | (self, level) | Is this logger enabled for level 'level'? | Is this logger enabled for level 'level'? | [
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"""
Is this logger enabled for level 'level'?
"""
try:
return self._cache[level]
except KeyError:
_acquireLock()
try:
if self.manager.disable >= level:
is_enabled = self._cache[level] = False
else:
is_enabled = self._cache[level] = (
level >= self.getEffectiveLevel()
)
finally:
_releaseLock()
return is_enabled | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathUtils.py | python | getOffsetArea | (
fcShape,
offset,
removeHoles=False,
# Default: XY plane
plane=Part.makeCircle(10),
tolerance=1e-4,
) | return offsetShape | Make an offset area of a shape, projected onto a plane.
Positive offsets expand the area, negative offsets shrink it.
Inspired by _buildPathArea() from PathAreaOp.py module. Adjustments made
based on notes by @sliptonic at this webpage:
https://github.com/sliptonic/FreeCAD/wiki/PathArea-notes. | Make an offset area of a shape, projected onto a plane.
Positive offsets expand the area, negative offsets shrink it.
Inspired by _buildPathArea() from PathAreaOp.py module. Adjustments made
based on notes by | [
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plane=Part.makeCircle(10),
tolerance=1e-4,
):
"""Make an offset area of a shape, projected onto a plane.
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Inspired by _buildPathArea() from PathAreaOp.py module. Adjustments made
based on notes by @sliptonic at this webpage:
https://github.com/sliptonic/FreeCAD/wiki/PathArea-notes."""
PathLog.debug("getOffsetArea()")
areaParams = {}
areaParams["Offset"] = offset
areaParams["Fill"] = 1 # 1
areaParams["Outline"] = removeHoles
areaParams["Coplanar"] = 0
areaParams["SectionCount"] = 1 # -1 = full(all per depthparams??) sections
areaParams["Reorient"] = True
areaParams["OpenMode"] = 0
areaParams["MaxArcPoints"] = 400 # 400
areaParams["Project"] = True
areaParams["FitArcs"] = False # Can be buggy & expensive
areaParams["Deflection"] = tolerance
areaParams["Accuracy"] = tolerance
areaParams["Tolerance"] = 1e-5 # Equal point tolerance
areaParams["Simplify"] = True
areaParams["CleanDistance"] = tolerance / 5
area = Path.Area() # Create instance of Area() class object
# Set working plane normal to Z=1
area.setPlane(makeWorkplane(plane))
area.add(fcShape)
area.setParams(**areaParams) # set parameters
offsetShape = area.getShape()
if not offsetShape.Faces:
return False
return offsetShape | [
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/Chem/MolStandardize/charge.py | python | Uncharger.uncharge | (self, mol) | return mol | Neutralize molecule by adding/removing hydrogens. Attempts to preserve zwitterions.
:param mol: The molecule to uncharge.
:type mol: :rdkit:`Mol <Chem.rdchem.Mol-class.html>`
:return: The uncharged molecule.
:rtype: :rdkit:`Mol <Chem.rdchem.Mol-class.html>` | Neutralize molecule by adding/removing hydrogens. Attempts to preserve zwitterions. | [
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"""Neutralize molecule by adding/removing hydrogens. Attempts to preserve zwitterions.
:param mol: The molecule to uncharge.
:type mol: :rdkit:`Mol <Chem.rdchem.Mol-class.html>`
:return: The uncharged molecule.
:rtype: :rdkit:`Mol <Chem.rdchem.Mol-class.html>`
"""
log.debug('Running Uncharger')
mol = copy.deepcopy(mol)
# Get atom ids for matches
p = [x[0] for x in mol.GetSubstructMatches(self._pos_h)]
q = [x[0] for x in mol.GetSubstructMatches(self._pos_quat)]
n = [x[0] for x in mol.GetSubstructMatches(self._neg)]
a = [x[0] for x in mol.GetSubstructMatches(self._neg_acid)]
# Neutralize negative charges
if q:
# Surplus negative charges more than non-neutralizable positive charges
neg_surplus = len(n) - len(q)
if a and neg_surplus > 0:
# zwitterion with more negative charges than quaternary positive centres
while neg_surplus > 0 and a:
# Add hydrogen to first negative acid atom, increase formal charge
# Until quaternary positive == negative total or no more negative acid
atom = mol.GetAtomWithIdx(a.pop(0))
atom.SetNumExplicitHs(atom.GetNumExplicitHs() + 1)
atom.SetFormalCharge(atom.GetFormalCharge() + 1)
neg_surplus -= 1
log.info('Removed negative charge')
else:
for atom in [mol.GetAtomWithIdx(x) for x in n]:
while atom.GetFormalCharge() < 0:
atom.SetNumExplicitHs(atom.GetNumExplicitHs() + 1)
atom.SetFormalCharge(atom.GetFormalCharge() + 1)
log.info('Removed negative charge')
# Neutralize positive charges
for atom in [mol.GetAtomWithIdx(x) for x in p]:
# Remove hydrogen and reduce formal change until neutral or no more hydrogens
while atom.GetFormalCharge() > 0 and atom.GetNumExplicitHs() > 0:
atom.SetNumExplicitHs(atom.GetNumExplicitHs() - 1)
atom.SetFormalCharge(atom.GetFormalCharge() - 1)
log.info('Removed positive charge')
return mol | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/framework/tensor_util.py | python | _GetDenseDimensions | (list_of_lists) | Returns the inferred dense dimensions of a list of lists. | Returns the inferred dense dimensions of a list of lists. | [
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"""Returns the inferred dense dimensions of a list of lists."""
if not isinstance(list_of_lists, (list, tuple)):
return []
elif not list_of_lists:
return [0]
else:
return [len(list_of_lists)] + _GetDenseDimensions(list_of_lists[0]) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/ApplyPaalmanPingsCorrection.py | python | ApplyPaalmanPingsCorrection._get_factor_workspaces | (self) | return {factor: self._get_correction_factor_workspace(factor) for factor in self._factors} | :return: A dictionary of the factors to the factor workspaces. | :return: A dictionary of the factors to the factor workspaces. | [
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"""
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return {factor: self._get_correction_factor_workspace(factor) for factor in self._factors} | [
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yrnkrn/zapcc | c6a8aa30006d997eff0d60fd37b0e62b8aa0ea50 | tools/clang/bindings/python/clang/cindex.py | python | Cursor.is_pure_virtual_method | (self) | return conf.lib.clang_CXXMethod_isPureVirtual(self) | Returns True if the cursor refers to a C++ member function or member
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/v8/third_party/jinja2/filters.py | python | evalcontextfilter | (f) | return f | Decorator for marking eval-context dependent filters. An eval
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potassco/clingo | e0c91d8f95cc28de1c480a871f9c97c30de83d40 | libpyclingo/clingo/backend.py | python | Observer.theory_term_string | (self, term_id : int, name : str) | Observe string theory terms.
Parameters
----------
term_id
The id of the term.
name
The string value of the term. | Observe string theory terms. | [
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Observe string theory terms.
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term_id
The id of the term.
name
The string value of the term.
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cmu-db/noisepage | 79276e68fe83322f1249e8a8be96bd63c583ae56 | build-support/cpplint.py | python | IsInitializerList | (clean_lines, linenum) | return False | Check if current line is inside constructor initializer list.
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clean_lines: A CleansedLines instance containing the file.
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Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
Returns:
True if current line appears to be inside constructor initializer
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"""
for i in xrange(linenum, 1, -1):
line = clean_lines.elided[i]
if i == linenum:
remove_function_body = Match(r'^(.*)\{\s*$', line)
if remove_function_body:
line = remove_function_body.group(1)
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jeog/TDAmeritradeAPI | 91c738afd7d57b54f6231170bd64c2550fafd34d | python/tdma_api/execute.py | python | OrderTicket.get_duration | (self) | return clib.get_val('OrderTicket_GetDuration_ABI', c_int, self._obj) | Returns duration type as ORDER_DURATION_[] constant. | Returns duration type as ORDER_DURATION_[] constant. | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/slim/quantization/imperative/fuse_utils.py | python | fuse_layers | (model, layers_to_fuse, inplace=False) | return model | fuse layers in layers_to_fuse
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'''
fuse layers in layers_to_fuse
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layers_to_fuse(list): The layers' names to be fused. For
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | bindings/python/htcondor/dags/dag.py | python | DAG.edges | (
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | InputStream.readline | (*args, **kwargs) | return _core_.InputStream_readline(*args, **kwargs) | readline(self, int size=-1) -> PyObject | readline(self, int size=-1) -> PyObject | [
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telefonicaid/fiware-orion | 27c3202b9ddcfb9e3635a0af8d373f76e89b1d24 | scripts/cpplint.py | python | CleansedLines._CollapseStrings | (elided) | return elided | Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
Args:
elided: The line being processed.
Returns:
The line with collapsed strings. | Collapses strings and chars on a line to simple "" or '' blocks. | [
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"""Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
Args:
elided: The line being processed.
Returns:
The line with collapsed strings.
"""
if not _RE_PATTERN_INCLUDE.match(elided):
# Remove escaped characters first to make quote/single quote collapsing
# basic. Things that look like escaped characters shouldn't occur
# outside of strings and chars.
elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided)
elided = _RE_PATTERN_CLEANSE_LINE_SINGLE_QUOTES.sub("''", elided)
elided = _RE_PATTERN_CLEANSE_LINE_DOUBLE_QUOTES.sub('""', elided)
return elided | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/ndarray/ndarray.py | python | NDArray.one_hot | (self, *args, **kwargs) | return op.one_hot(self, *args, **kwargs) | Convenience fluent method for :py:func:`one_hot`.
The arguments are the same as for :py:func:`one_hot`, with
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The arguments are the same as for :py:func:`one_hot`, with
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextBuffer.EndParagraphSpacing | (*args, **kwargs) | return _richtext.RichTextBuffer_EndParagraphSpacing(*args, **kwargs) | EndParagraphSpacing(self) -> bool | EndParagraphSpacing(self) -> bool | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | TextAttr.SetBulletFont | (*args, **kwargs) | return _controls_.TextAttr_SetBulletFont(*args, **kwargs) | SetBulletFont(self, String bulletFont) | SetBulletFont(self, String bulletFont) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/variables.py | python | PartitionedVariable._distribute_strategy | (self) | return None | The `tf.distribute.Strategy` that this variable was created under. | The `tf.distribute.Strategy` that this variable was created under. | [
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# NOTE(yuefengz): Today, no partitioned variables in a distribute strategy.
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taichi-dev/taichi | 973c04d6ba40f34e9e3bd5a28ae0ee0802f136a6 | python/taichi/lang/impl.py | python | _Root.shape | (self) | return _root_fb.root.shape | Same as :func:`taichi.SNode.shape` | Same as :func:`taichi.SNode.shape` | [
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funnyzhou/Adaptive_Feeding | 9c78182331d8c0ea28de47226e805776c638d46f | lib/datasets/pascal_voc.py | python | pascal_voc._load_pascal_annotation | (self, index) | return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_overlaps' : overlaps,
'flipped' : False,
'seg_areas' : seg_areas} | Load image and bounding boxes info from XML file in the PASCAL VOC
format. | Load image and bounding boxes info from XML file in the PASCAL VOC
format. | [
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"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_overlaps' : overlaps,
'flipped' : False,
'seg_areas' : seg_areas} | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/dateutil/parser.py | python | _parsems | (value) | Parse a I[.F] seconds value into (seconds, microseconds). | Parse a I[.F] seconds value into (seconds, microseconds). | [
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if "." not in value:
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i, f = value.split(".")
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/os.py | python | execlp | (file, *args) | execlp(file, *args)
Execute the executable file (which is searched for along $PATH)
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NVIDIA/TensorRT | 42805f078052daad1a98bc5965974fcffaad0960 | tools/Polygraphy/polygraphy/tools/args/base.py | python | BaseArgs.register | (self, maker) | Registers another argument group with this one.
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/gn/bin/gyp_flag_compare.py | python | Comparison.__init__ | (self, gyp_target, gn_target=None) | Creates a comparison of a GN and GYP target. If the target names differ
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if gn_target is None:
gn_target = gyp_target
self._gyp_target = gyp_target
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | Joystick.GetXMin | (*args, **kwargs) | return _misc_.Joystick_GetXMin(*args, **kwargs) | GetXMin(self) -> int | GetXMin(self) -> int | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/_abcoll.py | python | MutableSequence.append | (self, value) | S.append(object) -- append object to the end of the sequence | S.append(object) -- append object to the end of the sequence | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/dynamodb/item.py | python | Item.delete_attribute | (self, attr_name, attr_value=None) | Queue the deletion of an attribute from an item in DynamoDB.
This call will result in a UpdateItem request being issued
with update action of DELETE when the save method is called.
:type attr_name: str
:param attr_name: Name of the attribute you want to alter.
:type attr_value: set
:param attr_value: A set of values to be removed from the attribute.
This parameter is optional. If None, the whole attribute is
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Queue the deletion of an attribute from an item in DynamoDB.
This call will result in a UpdateItem request being issued
with update action of DELETE when the save method is called.
:type attr_name: str
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:type attr_value: set
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self._updates[attr_name] = ("DELETE", attr_value) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatnotebook.py | python | FNBDropSource.GiveFeedback | (self, effect) | return False | You may give some custom UI feedback during the drag and drop operation
in this function. It is called on each mouse move, so your implementation
must not be too slow.
:param `effect`: the effect to implement. One of ``wx.DragCopy``, ``wx.DragMove``,
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:return: Return ``False`` if you want default feedback, or ``True`` if you
implement your own feedback. The return values is ignored under GTK.
:note: To show your own custom drag and drop UI feedback, you must override
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"""
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in this function. It is called on each mouse move, so your implementation
must not be too slow.
:param `effect`: the effect to implement. One of ``wx.DragCopy``, ``wx.DragMove``,
``wx.DragLink`` and ``wx.DragNone``.
:return: Return ``False`` if you want default feedback, or ``True`` if you
implement your own feedback. The return values is ignored under GTK.
:note: To show your own custom drag and drop UI feedback, you must override
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"""
self._win.DrawDragHint()
return False | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/retdec-3.2/scripts/retdec-utils.py | python | CmdRunner._get_memory_from_measured_output | (self, output) | return memory | Get memory in MB from output string generated by `config.LOG_TIME`.
`/usr/bin/time` format is expected. | Get memory in MB from output string generated by `config.LOG_TIME`.
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`/usr/bin/time` format is expected."""
memory = 0
s = re.search(r'Maximum resident set size \(kbytes\): (\d+)', output)
if s:
g = s.group(1)
if g:
memory_kb = int(g)
memory = int(memory_kb / 1024) # to MB
if memory == 0:
memory = 1
return memory | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/server/wsgi/wms/ogc/common/configs.py | python | Configs.__init__ | (self, config_file, default_values=None) | Load config key/value pairs from the config file. | Load config key/value pairs from the config file. | [
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"""Load config key/value pairs from the config file."""
if default_values:
self._config_values = default_values
else:
self._config_values = {}
try:
fp = open(config_file)
for line in fp:
line = line.strip()
if line and line[0] != "#":
(key, value) = line.split(" ")
self._config_values[key] = value
fp.close()
except IOError:
pass
except:
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hszhao/PSPNet | cf7e5a99ba37e46118026e96be5821a9bc63bde0 | python/caffe/io.py | python | Transformer.set_raw_scale | (self, in_, scale) | Set the scale of raw features s.t. the input blob = input * scale.
While Python represents images in [0, 1], certain Caffe models
like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
of these models must be 255.
Parameters
----------
in_ : which input to assign this scale factor
scale : scale coefficient | Set the scale of raw features s.t. the input blob = input * scale.
While Python represents images in [0, 1], certain Caffe models
like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
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like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
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Parameters
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scale : scale coefficient
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self.__check_input(in_)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Image.RGBtoHSV | (*args, **kwargs) | return _core_.Image_RGBtoHSV(*args, **kwargs) | RGBtoHSV(Image_RGBValue rgb) -> Image_HSVValue
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return _core_.Image_RGBtoHSV(*args, **kwargs) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/math_ops.py | python | _maybe_get_dtype | (x) | return x | Returns a numpy type if available from x. Skips if x is numpy.ndarray. | Returns a numpy type if available from x. Skips if x is numpy.ndarray. | [
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# Don't put np.ndarray in this list, because np.result_type looks at the
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if isinstance(x, numbers.Real):
return x
if isinstance(x, ops.Tensor):
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/difflib.py | python | SequenceMatcher.get_grouped_opcodes | (self, n=3) | Isolate change clusters by eliminating ranges with no changes.
Return a generator of groups with upto n lines of context.
Each group is in the same format as returned by get_opcodes().
>>> from pprint import pprint
>>> a = map(str, range(1,40))
>>> b = a[:]
>>> b[8:8] = ['i'] # Make an insertion
>>> b[20] += 'x' # Make a replacement
>>> b[23:28] = [] # Make a deletion
>>> b[30] += 'y' # Make another replacement
>>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))
[[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],
[('equal', 16, 19, 17, 20),
('replace', 19, 20, 20, 21),
('equal', 20, 22, 21, 23),
('delete', 22, 27, 23, 23),
('equal', 27, 30, 23, 26)],
[('equal', 31, 34, 27, 30),
('replace', 34, 35, 30, 31),
('equal', 35, 38, 31, 34)]] | Isolate change clusters by eliminating ranges with no changes. | [
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""" Isolate change clusters by eliminating ranges with no changes.
Return a generator of groups with upto n lines of context.
Each group is in the same format as returned by get_opcodes().
>>> from pprint import pprint
>>> a = map(str, range(1,40))
>>> b = a[:]
>>> b[8:8] = ['i'] # Make an insertion
>>> b[20] += 'x' # Make a replacement
>>> b[23:28] = [] # Make a deletion
>>> b[30] += 'y' # Make another replacement
>>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))
[[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],
[('equal', 16, 19, 17, 20),
('replace', 19, 20, 20, 21),
('equal', 20, 22, 21, 23),
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('equal', 27, 30, 23, 26)],
[('equal', 31, 34, 27, 30),
('replace', 34, 35, 30, 31),
('equal', 35, 38, 31, 34)]]
"""
codes = self.get_opcodes()
if not codes:
codes = [("equal", 0, 1, 0, 1)]
# Fixup leading and trailing groups if they show no changes.
if codes[0][0] == 'equal':
tag, i1, i2, j1, j2 = codes[0]
codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2
if codes[-1][0] == 'equal':
tag, i1, i2, j1, j2 = codes[-1]
codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n)
nn = n + n
group = []
for tag, i1, i2, j1, j2 in codes:
# End the current group and start a new one whenever
# there is a large range with no changes.
if tag == 'equal' and i2-i1 > nn:
group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n)))
yield group
group = []
i1, j1 = max(i1, i2-n), max(j1, j2-n)
group.append((tag, i1, i2, j1 ,j2))
if group and not (len(group)==1 and group[0][0] == 'equal'):
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/protorpc/protorpc/definition.py | python | define_service | (service_descriptor, module) | return service_class | Define a new service proxy.
Args:
service_descriptor: ServiceDescriptor class that describes the service.
module: Module to add service to. Request and response types are found
relative to this module.
Returns:
Service class proxy capable of communicating with a remote server. | Define a new service proxy. | [
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"""Define a new service proxy.
Args:
service_descriptor: ServiceDescriptor class that describes the service.
module: Module to add service to. Request and response types are found
relative to this module.
Returns:
Service class proxy capable of communicating with a remote server.
"""
class_dict = {'__module__': module.__name__}
class_name = service_descriptor.name.encode('utf-8')
for method_descriptor in service_descriptor.methods or []:
request_definition = messages.find_definition(
method_descriptor.request_type, module)
response_definition = messages.find_definition(
method_descriptor.response_type, module)
method_name = method_descriptor.name.encode('utf-8')
def remote_method(self, request):
"""Actual service method."""
raise NotImplementedError('Method is not implemented')
remote_method.__name__ = method_name
remote_method_decorator = remote.method(request_definition,
response_definition)
class_dict[method_name] = remote_method_decorator(remote_method)
service_class = type(class_name, (remote.Service,), class_dict)
return service_class | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_differentialevolution.py | python | DifferentialEvolutionSolver._randtobest1 | (self, samples) | return bprime | randtobest1bin, randtobest1exp | randtobest1bin, randtobest1exp | [
"randtobest1bin",
"randtobest1exp"
] | def _randtobest1(self, samples):
"""randtobest1bin, randtobest1exp"""
r0, r1, r2 = samples[:3]
bprime = np.copy(self.population[r0])
bprime += self.scale * (self.population[0] - bprime)
bprime += self.scale * (self.population[r1] -
self.population[r2])
return bprime | [
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tangzhenyu/Scene-Text-Understanding | 0f7ffc7aea5971a50cdc03d33d0a41075285948b | SynthText_Chinese/text_utils.py | python | TextSource.is_good | (self, txt, f=0.35) | return [ (len(l)> self.min_nchar
and self.check_symb_frac(l,f)
and is_txt(l)) for l in txt ] | T/F return : T iff the lines in txt (a list of txt lines)
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3. Has at-least self.min_nchar characters
4. Not all characters are i,x,0,O,- | T/F return : T iff the lines in txt (a list of txt lines)
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A given line l is valid iff:
1. It is not empty.
2. symbol_fraction > f
3. Has at-least self.min_nchar characters
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/linalg_grad.py | python | _BatchMatrixDeterminantGrad | (op, grad) | return multipliers * a_adj_inv | Gradient for BatchMatrixDeterminant. | Gradient for BatchMatrixDeterminant. | [
"Gradient",
"for",
"BatchMatrixDeterminant",
"."
] | def _BatchMatrixDeterminantGrad(op, grad):
"""Gradient for BatchMatrixDeterminant."""
a = op.inputs[0]
c = op.outputs[0]
a_adj_inv = linalg_ops.batch_matrix_inverse(a, adjoint=True)
multipliers = array_ops.reshape(
grad * c, array_ops.concat(0, [array_ops.shape(c), [1, 1]]))
return multipliers * a_adj_inv | [
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