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sony/nnabla | python/src/nnabla/utils/image_utils/pypng_utils.py | imsave | def imsave(path, img, channel_first=False, as_uint16=False, auto_scale=True):
"""
Save image by pypng module.
Args:
path (str): output filename
img (numpy.ndarray): Image array to save. Image shape is considered as (height, width, channel) by default.
channel_first:
This... | python | def imsave(path, img, channel_first=False, as_uint16=False, auto_scale=True):
"""
Save image by pypng module.
Args:
path (str): output filename
img (numpy.ndarray): Image array to save. Image shape is considered as (height, width, channel) by default.
channel_first:
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sony/nnabla | python/src/nnabla/context.py | context_scope | def context_scope(ctx):
"""
Context as Python context.
.. code-block:: python
import nnabla as nn
import nnabla.functions as F
x = nn.Variable([2, 3 ,4])
ctx = nnabla_ext.cuda.context('0')
with context_scope(ctx):
# Inside with scope, the specified conte... | python | def context_scope(ctx):
"""
Context as Python context.
.. code-block:: python
import nnabla as nn
import nnabla.functions as F
x = nn.Variable([2, 3 ,4])
ctx = nnabla_ext.cuda.context('0')
with context_scope(ctx):
# Inside with scope, the specified conte... | [
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import nnabla as nn
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sony/nnabla | python/src/nnabla/utils/converter/onnx/exporter.py | generate_scalar_constant | def generate_scalar_constant(output_name, tensor_name, scalar):
"""Convert a scalar value to a Constant buffer.
This is mainly used for xxScalar operators."""
t = onnx.helper.make_tensor(tensor_name,
data_type=TensorProto.FLOAT,
dims=[1], vals=... | python | def generate_scalar_constant(output_name, tensor_name, scalar):
"""Convert a scalar value to a Constant buffer.
This is mainly used for xxScalar operators."""
t = onnx.helper.make_tensor(tensor_name,
data_type=TensorProto.FLOAT,
dims=[1], vals=... | [
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sony/nnabla | python/src/nnabla/utils/converter/onnx/exporter.py | replace_negative_size_with_batch_size | def replace_negative_size_with_batch_size(shape, batch_size):
"""Replace all dimensions with negative values to batch size"""
sl = []
for d in shape.dim:
if d < 0:
# Negative size means batch size
sl.append(batch_size)
else:
sl.append(d)
out_shape = nn... | python | def replace_negative_size_with_batch_size(shape, batch_size):
"""Replace all dimensions with negative values to batch size"""
sl = []
for d in shape.dim:
if d < 0:
# Negative size means batch size
sl.append(batch_size)
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sl.append(d)
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sony/nnabla | python/src/nnabla/utils/converter/onnx/exporter.py | OnnxExporter.BinarySigmoid | def BinarySigmoid(self, func):
'''
Currently, caffe2 does not support this function.
'''
n = onnx.helper.make_node(
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return [n] | python | def BinarySigmoid(self, func):
'''
Currently, caffe2 does not support this function.
'''
n = onnx.helper.make_node(
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func.input,
func.output,
alpha=1.0,
beta=0.0
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return [n] | [
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sony/nnabla | python/src/nnabla/experimental/graph_converters/sequential.py | SequentialConverter.convert | def convert(self, vroot, entry_variables):
"""Convert a given graph.
Convert a given graph using the `converters` in the order of the registeration, i.e., sequentially.
Args:
vroot (:obj:`Variable`): NNabla Variable
entry_variables (:obj:`Variable`): Entry variable from... | python | def convert(self, vroot, entry_variables):
"""Convert a given graph.
Convert a given graph using the `converters` in the order of the registeration, i.e., sequentially.
Args:
vroot (:obj:`Variable`): NNabla Variable
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sony/nnabla | python/src/nnabla/initializer.py | calc_normal_std_he_forward | def calc_normal_std_he_forward(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the standard deviation proposed by He et al.
.. math::
\sigma = \sqrt{\frac{2}{NK}}
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
outmaps (int): Map size of an output Variable, :math:`M`.... | python | def calc_normal_std_he_forward(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the standard deviation proposed by He et al.
.. math::
\sigma = \sqrt{\frac{2}{NK}}
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inmaps (int): Map size of an input Variable, :math:`N`.
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sony/nnabla | python/src/nnabla/initializer.py | calc_normal_std_glorot | def calc_normal_std_glorot(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the standard deviation proposed by Glorot et al.
.. math::
\sigma = \sqrt{\frac{2}{NK + M}}
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
outmaps (int): Map size of an output Variable, :math:... | python | def calc_normal_std_glorot(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the standard deviation proposed by Glorot et al.
.. math::
\sigma = \sqrt{\frac{2}{NK + M}}
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inmaps (int): Map size of an input Variable, :math:`N`.
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sony/nnabla | python/src/nnabla/initializer.py | calc_uniform_lim_glorot | def calc_uniform_lim_glorot(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the lower bound and the upper bound of the uniform distribution proposed by Glorot et al.
.. math::
b &= \sqrt{\frac{6}{NK + M}}\\
a &= -b
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
... | python | def calc_uniform_lim_glorot(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the lower bound and the upper bound of the uniform distribution proposed by Glorot et al.
.. math::
b &= \sqrt{\frac{6}{NK + M}}\\
a &= -b
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
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sony/nnabla | python/src/nnabla/utils/save.py | _get_unique_function_name | def _get_unique_function_name(function_type, functions):
'''Get a unique function name.
Args:
function_type(str): Name of Function. Ex) Convolution, Affine
functions(OrderedDict of (str, Function)
Returns: str
A unique function name
'''
function_name = function_name_base = ... | python | def _get_unique_function_name(function_type, functions):
'''Get a unique function name.
Args:
function_type(str): Name of Function. Ex) Convolution, Affine
functions(OrderedDict of (str, Function)
Returns: str
A unique function name
'''
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sony/nnabla | python/src/nnabla/utils/save.py | _get_unique_variable_name | def _get_unique_variable_name(vname, variables):
'''Get a unique variable name.
Args:
vname(str): A candidate name.
variable(OrderedDict of str and Variable)
Returns: str
A unique variable name
'''
count = 2
vname_base = vname
while vname in variables:
vname... | python | def _get_unique_variable_name(vname, variables):
'''Get a unique variable name.
Args:
vname(str): A candidate name.
variable(OrderedDict of str and Variable)
Returns: str
A unique variable name
'''
count = 2
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sony/nnabla | python/src/nnabla/functions.py | sum | def sum(x, axis=None, keepdims=False):
"""Reduction along axes with sum operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which the sum is
calculated. Passing the default value `None` will reduce all dimensions.
keepdims ... | python | def sum(x, axis=None, keepdims=False):
"""Reduction along axes with sum operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which the sum is
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sony/nnabla | python/src/nnabla/functions.py | mean | def mean(x, axis=None, keepdims=False):
"""Reduction along axes with mean operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which mean is
calculated. Passing the default value `None` will reduce all dimensions.
keepdims (... | python | def mean(x, axis=None, keepdims=False):
"""Reduction along axes with mean operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which mean is
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sony/nnabla | python/src/nnabla/functions.py | prod | def prod(x, axis=None, keepdims=False):
"""Reduction along axes with product operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which product is
calculated. Passing the default value `None` will reduce all dimensions.
keep... | python | def prod(x, axis=None, keepdims=False):
"""Reduction along axes with product operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which product is
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sony/nnabla | python/src/nnabla/functions.py | reduce | def reduce(x, op='sum'):
"""Reduction function with given operation.
Args:
x (Variable): An input.
op (str): 'sum' or 'mean'.
Note:
This is deprecated. Use ``mean`` or ``sum`` instead.
"""
import warnings
warnings.warn(
"Deprecated API. Use ``sum`` or ``mean`` ... | python | def reduce(x, op='sum'):
"""Reduction function with given operation.
Args:
x (Variable): An input.
op (str): 'sum' or 'mean'.
Note:
This is deprecated. Use ``mean`` or ``sum`` instead.
"""
import warnings
warnings.warn(
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sony/nnabla | python/src/nnabla/functions.py | split | def split(x, axis=0):
"""
Split arrays at the specified axis.
It returns a number corresponding the size of the given
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Args:
x(~nnabla.Variable): N-D array
axis(int): Axis
Returns: A :obj:`tuple` of :obj:`~nnabla.Variab... | python | def split(x, axis=0):
"""
Split arrays at the specified axis.
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sony/nnabla | python/src/nnabla/functions.py | batch_normalization | def batch_normalization(x, beta, gamma, mean, variance, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, n_outputs=None):
r"""
Batch normalization.
.. math::
\begin{eqnarray}
\mu &=& \frac{1}{M} \sum x_i \\
\sigma^2 &=& \frac{1}{M} \sum \left(x_i - \mu\ri... | python | def batch_normalization(x, beta, gamma, mean, variance, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, n_outputs=None):
r"""
Batch normalization.
.. math::
\begin{eqnarray}
\mu &=& \frac{1}{M} \sum x_i \\
\sigma^2 &=& \frac{1}{M} \sum \left(x_i - \mu\ri... | [
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sony/nnabla | python/src/nnabla/functions.py | fixed_point_quantize | def fixed_point_quantize(x, sign=True, n=8, delta=2**-4, quantize=True, ste_fine_grained=True, outputs=None):
r"""Fixed Point Quantize
Args:
x (Variable): An input variable.
sign (bool): Indicate the signed number or the unsigned number. Default is true.
n (int): Bit width used. Note th... | python | def fixed_point_quantize(x, sign=True, n=8, delta=2**-4, quantize=True, ste_fine_grained=True, outputs=None):
r"""Fixed Point Quantize
Args:
x (Variable): An input variable.
sign (bool): Indicate the signed number or the unsigned number. Default is true.
n (int): Bit width used. Note th... | [
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sony/nnabla | python/src/nnabla/functions.py | pow2_quantize | def pow2_quantize(x, sign=True, with_zero=True, n=8, m=1, quantize=True, ste_fine_grained=True, outputs=None):
r"""Pow2 Quantize
Args:
x (Variable): An input variable.
sign (bool): Indicate the signed number or the unsigned number. Default is true.
with_zero (bool): Indicate using zero ... | python | def pow2_quantize(x, sign=True, with_zero=True, n=8, m=1, quantize=True, ste_fine_grained=True, outputs=None):
r"""Pow2 Quantize
Args:
x (Variable): An input variable.
sign (bool): Indicate the signed number or the unsigned number. Default is true.
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sony/nnabla | python/src/nnabla/functions.py | clip_by_value | def clip_by_value(x, min, max):
r"""Clip inputs by values.
.. math::
y = \begin{cases}
max & (x > max) \\
x & (otherwise) \\
min & (x < min)
\end{cases}.
Args:
x (Variable): An input variable.
min (Variable): A min variab... | python | def clip_by_value(x, min, max):
r"""Clip inputs by values.
.. math::
y = \begin{cases}
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x & (otherwise) \\
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x (Variable): An input variable.
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sony/nnabla | python/src/nnabla/functions.py | interpolate | def interpolate(x, scale=None, output_size=None, mode='linear', align_corners=None):
'''
Resize an ND array with interpolation.
Scaling factors for spatial dimensions are determined by either
``scale`` or ``output_size``.
``nd = len(scale)`` or ``nd = len(output_size)`` determines the number of
... | python | def interpolate(x, scale=None, output_size=None, mode='linear', align_corners=None):
'''
Resize an ND array with interpolation.
Scaling factors for spatial dimensions are determined by either
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sony/nnabla | python/src/nnabla/functions.py | sort | def sort(x, axis=-1, reverse=False, with_index=False, only_index=False):
"""Sorts the elements of `x` along a given `axis` in ascending order
by value. A negative `axis` counts from the last dimension of `x`,
so the default of -1 sorts along the last dimension. If `reverse`
is True, then the elements ar... | python | def sort(x, axis=-1, reverse=False, with_index=False, only_index=False):
"""Sorts the elements of `x` along a given `axis` in ascending order
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sony/nnabla | python/src/nnabla/utils/download.py | download | def download(url, output_file=None, open_file=True, allow_overwrite=False):
'''Download a file from URL.
Args:
url (str): URL.
output_file (str, optional): If given, the downloaded file is written to the given path.
open_file (bool): If True, it returns an opened file stream of the down... | python | def download(url, output_file=None, open_file=True, allow_overwrite=False):
'''Download a file from URL.
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url (str): URL.
output_file (str, optional): If given, the downloaded file is written to the given path.
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sony/nnabla | python/src/nnabla/utils/image_utils/cv2_utils.py | imread | def imread(path, grayscale=False, size=None, interpolate="bilinear",
channel_first=False, as_uint16=False, num_channels=-1):
"""
Read image by cv2 module.
Args:
path (str or 'file object'): File path or object to read.
grayscale (bool):
size (tupple of int):
(... | python | def imread(path, grayscale=False, size=None, interpolate="bilinear",
channel_first=False, as_uint16=False, num_channels=-1):
"""
Read image by cv2 module.
Args:
path (str or 'file object'): File path or object to read.
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size (tupple of int):
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sony/nnabla | python/src/nnabla/utils/learning_rate_scheduler.py | PolynomialScheduler.get_learning_rate | def get_learning_rate(self, iter):
'''
Get learning rate with polymomial decay based on current iteration.
Args:
iter (int): current iteration (starting with 0).
Returns:
float: Learning rate
'''
return self.init_lr * ((1.0 - iter * 1.0 / self.ma... | python | def get_learning_rate(self, iter):
'''
Get learning rate with polymomial decay based on current iteration.
Args:
iter (int): current iteration (starting with 0).
Returns:
float: Learning rate
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sony/nnabla | python/src/nnabla/utils/learning_rate_scheduler.py | CosineScheduler.get_learning_rate | def get_learning_rate(self, iter):
'''
Get learning rate with cosine decay based on current iteration.
Args:
iter (int): Current iteration (starting with 0).
Returns:
float: Learning rate
'''
return self.init_lr * ((math.cos(iter * 1.0 / (self.ma... | python | def get_learning_rate(self, iter):
'''
Get learning rate with cosine decay based on current iteration.
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iter (int): Current iteration (starting with 0).
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float: Learning rate
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sony/nnabla | python/src/nnabla/parametric_functions.py | affine | def affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
apply_w=None, apply_b=None):
"""
The affine layer, also known as the fully connected layer. Computes
.. math::
{\\mathbf y} = {\\mathbf A} {\... | python | def affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
apply_w=None, apply_b=None):
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{\\mathbf y} = {\\mathbf A} {\... | [
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sony/nnabla | python/src/nnabla/parametric_functions.py | binary_weight_affine | def binary_weight_affine(inp, n_outmaps,
base_axis=1, quantize_zero_to=1.0,
w_init=None, wb_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True):
"""Binary Weight Affine, multiplier-less inner-product with a scale factor.
... | python | def binary_weight_affine(inp, n_outmaps,
base_axis=1, quantize_zero_to=1.0,
w_init=None, wb_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True):
"""Binary Weight Affine, multiplier-less inner-product with a scale factor.
... | [
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sony/nnabla | python/src/nnabla/parametric_functions.py | inq_affine | def inq_affine(inp, n_outmaps, base_axis=1, num_bits=4,
inq_iterations=(), selection_algorithm='random',
seed=-1, w_init=None, i_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True):
"""Incremental Network Quantization Affine Layer
During training... | python | def inq_affine(inp, n_outmaps, base_axis=1, num_bits=4,
inq_iterations=(), selection_algorithm='random',
seed=-1, w_init=None, i_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True):
"""Incremental Network Quantization Affine Layer
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sony/nnabla | python/src/nnabla/parametric_functions.py | binary_connect_convolution | def binary_connect_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
quantize_zero_to=1.0,
w_init=None, wb_init=None, b_init=None,
base_axis=1, fix_parameters=False,... | python | def binary_connect_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
quantize_zero_to=1.0,
w_init=None, wb_init=None, b_init=None,
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.. math::
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sony/nnabla | python/src/nnabla/parametric_functions.py | inq_convolution | def inq_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
num_bits=4, inq_iterations=(), selection_algorithm='random',
seed=-1, w_init=None, i_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=Non... | python | def inq_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
num_bits=4, inq_iterations=(), selection_algorithm='random',
seed=-1, w_init=None, i_init=None, b_init=None,
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sony/nnabla | python/src/nnabla/parametric_functions.py | depthwise_convolution | def depthwise_convolution(inp, kernel, pad=None, stride=None, dilation=None,
multiplier=1, w_init=None, b_init=None, base_axis=1,
fix_parameters=False, rng=None, with_bias=True):
"""
N-D Depthwise Convolution with a bias term.
Reference:
- F. Chollet... | python | def depthwise_convolution(inp, kernel, pad=None, stride=None, dilation=None,
multiplier=1, w_init=None, b_init=None, base_axis=1,
fix_parameters=False, rng=None, with_bias=True):
"""
N-D Depthwise Convolution with a bias term.
Reference:
- F. Chollet... | [
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Reference:
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sony/nnabla | python/src/nnabla/parametric_functions.py | batch_normalization | def batch_normalization(inp, axes=[1], decay_rate=0.9, eps=1e-5,
batch_stat=True, output_stat=False, fix_parameters=False,
param_init=None):
"""
Batch normalization layer.
.. math::
\\begin{array}{lcl}
\\mu &=& \\frac{1}{M} \\sum x_i\\\\
... | python | def batch_normalization(inp, axes=[1], decay_rate=0.9, eps=1e-5,
batch_stat=True, output_stat=False, fix_parameters=False,
param_init=None):
"""
Batch normalization layer.
.. math::
\\begin{array}{lcl}
\\mu &=& \\frac{1}{M} \\sum x_i\\\\
... | [
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\\hat{x}_i &=& \\frac{x_i - \\mu}{\\sqrt{\\sigma^2 + \\epsilon }}\\\\
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sony/nnabla | python/src/nnabla/parametric_functions.py | mean_subtraction | def mean_subtraction(inp, base_axis=1, update_running_mean=True, fix_parameters=False):
"""
Mean subtraction layer.
It subtracts the mean of the elements of the input array,
and normalizes it to :math:`0`. Preprocessing arrays with this function has the effect of improving accuracy
in various tasks... | python | def mean_subtraction(inp, base_axis=1, update_running_mean=True, fix_parameters=False):
"""
Mean subtraction layer.
It subtracts the mean of the elements of the input array,
and normalizes it to :math:`0`. Preprocessing arrays with this function has the effect of improving accuracy
in various tasks... | [
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At training time, this function is defined as
.. math::
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sony/nnabla | python/src/nnabla/parametric_functions.py | prelu | def prelu(inp, base_axis=1, shared=True, fix_parameters=False):
"""
Parametrized Rectified Linear Unit function defined as
.. math::
y_i = \max(0, x_i) + w_i \min(0, -x_i)
where negative slope :math:`w` is learned and can vary across channels (an
axis specified with base_axis). Weights are... | python | def prelu(inp, base_axis=1, shared=True, fix_parameters=False):
"""
Parametrized Rectified Linear Unit function defined as
.. math::
y_i = \max(0, x_i) + w_i \min(0, -x_i)
where negative slope :math:`w` is learned and can vary across channels (an
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sony/nnabla | python/src/nnabla/parametric_functions.py | fixed_point_quantized_affine | def fixed_point_quantized_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
quantize_w=True, sign_w=True, n_w=8, delta_w=2**-4, ... | python | def fixed_point_quantized_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
quantize_w=True, sign_w=True, n_w=8, delta_w=2**-4, ... | [
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.. math::
y_j = \sum_{i} Q(w_{ji}) x_i,
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sony/nnabla | python/src/nnabla/parametric_functions.py | fixed_point_quantized_convolution | def fixed_point_quantized_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None, with_bias=True,
... | python | def fixed_point_quantized_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None, with_bias=True,
... | [
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Fixed-Point Quantized Convolution is the convolution function,
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The input-output relation of this function is as follows:
.. math::
y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} Q(w_{n, m, i, j}) x_{m, a + i,... | [
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sony/nnabla | python/src/nnabla/parametric_functions.py | pow2_quantized_affine | def pow2_quantized_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
quantize_w=True, sign_w=True, with_zero_w=False, n_w=8, m_w=2, ste_fine_grained_w=True,... | python | def pow2_quantized_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
quantize_w=True, sign_w=True, with_zero_w=False, n_w=8, m_w=2, ste_fine_grained_w=True,... | [
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Pow2 Quantized Affine is the affine function,
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The input-output relation of this function is as follows:
.. math::
y_j = \sum_{i} Q(w_{ji}) x_i,
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sony/nnabla | python/src/nnabla/parametric_functions.py | pow2_quantized_convolution | def pow2_quantized_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None, with_bias=True,
quantize_... | python | def pow2_quantized_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None, with_bias=True,
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Pow2 Quantized Convolution is the convolution function,
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The input-output relation of this function is as follows:
.. math::
y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} Q(w_{n, m, i, j}) x_{m, a + i, b + j},
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sony/nnabla | python/src/nnabla/parametric_functions.py | pruned_affine | def pruned_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
prune_w=True, rate_w=0.9, prune_b=True, rate_b=0.9):
"""Pruned Affine.
Pruned Affine is the affine function,
exce... | python | def pruned_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
prune_w=True, rate_w=0.9, prune_b=True, rate_b=0.9):
"""Pruned Affine.
Pruned Affine is the affine function,
exce... | [
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Pruned Affine is the affine function,
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The input-output relation of this function is as follows:
.. math::
y_j = \sum_{i} Q(w_{ji}) x_i,
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.. note:... | [
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sony/nnabla | python/src/nnabla/parametric_functions.py | pruned_convolution | def pruned_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None, with_bias=True,
prune_w=True, rate_w=0.9, prune_b=True, rate_b=0.... | python | def pruned_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None, with_bias=True,
prune_w=True, rate_w=0.9, prune_b=True, rate_b=0.... | [
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sony/nnabla | python/src/nnabla/parametric_functions.py | lstm_cell | def lstm_cell(x, h, c, state_size, w_init=None, b_init=None, fix_parameters=False):
"""Long Short-Term Memory.
Long Short-Term Memory, or LSTM, is a building block for recurrent neural networks (RNN) layers.
LSTM unit consists of a cell and input, output, forget gates whose functions are defined as followi... | python | def lstm_cell(x, h, c, state_size, w_init=None, b_init=None, fix_parameters=False):
"""Long Short-Term Memory.
Long Short-Term Memory, or LSTM, is a building block for recurrent neural networks (RNN) layers.
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sony/nnabla | python/src/nnabla/parametric_functions.py | spectral_norm | def spectral_norm(w, dim=0, itr=1, eps=1e-12, test=False, u_init=None, fix_parameters=True):
"""Spectral Normalization.
.. math::
W_{sn} = \\frac{W}{\\sigma(W)}.
where :math:`W` is the input matrix, and the :math:`\\sigma(W)` is the spectral norm of :math:`W`. The spectral norm is approximately c... | python | def spectral_norm(w, dim=0, itr=1, eps=1e-12, test=False, u_init=None, fix_parameters=True):
"""Spectral Normalization.
.. math::
W_{sn} = \\frac{W}{\\sigma(W)}.
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sony/nnabla | python/src/nnabla/parametric_functions.py | LSTMCell.reset_state | def reset_state(self):
"""
Resets states h and c to zero.
"""
self.h.data.zero()
self.c.data.zero() | python | def reset_state(self):
"""
Resets states h and c to zero.
"""
self.h.data.zero()
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sony/nnabla | python/benchmark/function/function_benchmark.py | Timer.lap | def lap(self):
"""Calculate lap time.
Returns:
float: Lap time. The duration from the previous call of ``lap()``
or initialization at first call.
float: Total time. The duration from initialization.
"""
now = time.time()
lap_time = now -... | python | def lap(self):
"""Calculate lap time.
Returns:
float: Lap time. The duration from the previous call of ``lap()``
or initialization at first call.
float: Total time. The duration from initialization.
"""
now = time.time()
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sony/nnabla | python/benchmark/function/function_benchmark.py | FunctionBenchmarkWriter.write | def write(self, fb):
"""Write a single function benchmark.
Args:
fb (FunctionBenchmark): FunctionBenchmark class instance.
Before passing to this, you should call ``fb.benchmark()``.
"""
print('[{}.{}]'.format(fb.module, fb.func.__name__), file=self.file)
... | python | def write(self, fb):
"""Write a single function benchmark.
Args:
fb (FunctionBenchmark): FunctionBenchmark class instance.
Before passing to this, you should call ``fb.benchmark()``.
"""
print('[{}.{}]'.format(fb.module, fb.func.__name__), file=self.file)
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sony/nnabla | python/benchmark/function/function_benchmark.py | FunctionBenchmark._setup | def _setup(self, delete=True):
"""Create a function instance and execute setup.
Args:
delete (bool): Delete buffered variables.
"""
if delete:
self.clear()
with nn.context_scope(self.ctx):
outputs = self.func(
*(self.inputs_f ... | python | def _setup(self, delete=True):
"""Create a function instance and execute setup.
Args:
delete (bool): Delete buffered variables.
"""
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self.clear()
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sony/nnabla | python/benchmark/function/function_benchmark.py | FunctionBenchmark.benchmark_setup | def benchmark_setup(self):
"""Benchmark setup execution.
"""
def f():
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"""Benchmark setup execution.
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sony/nnabla | python/benchmark/function/function_benchmark.py | FunctionBenchmark.benchmark_forward | def benchmark_forward(self):
"""Benchmark forward execution.
"""
self._setup()
def f():
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"""Benchmark forward execution.
"""
self._setup()
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sony/nnabla | python/benchmark/function/function_benchmark.py | FunctionBenchmark.benchmark_backward | def benchmark_backward(self):
"""Benchmark backward execution.
Note:
If backward execution throws any exception,
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``None``.
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Note:
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sony/nnabla | python/src/nnabla_ext/cpu/__init__.py | context | def context(type_config='float', **kw):
"""CPU Context."""
backends = ['cpu:float']
if type_config == 'half':
backends = ['cpu:half', 'cpu:float']
elif type_config == 'float':
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raise ValueError("Unknown data type config is given %s" % type_config)
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"""CPU Context."""
backends = ['cpu:float']
if type_config == 'half':
backends = ['cpu:half', 'cpu:float']
elif type_config == 'float':
pass
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raise ValueError("Unknown data type config is given %s" % type_config)
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sony/nnabla | python/src/nnabla/utils/converter/nnablart/utils.py | revise_buffer_size | def revise_buffer_size(info, settings):
'''
This function is used to revise buffer size, use byte
as its unit, instead of data item.
This is only used for nnb, not for csrc.
When settings contains user customized data type, not pure
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'''
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'''
This function is used to revise buffer size, use byte
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This is only used for nnb, not for csrc.
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sony/nnabla | python/src/nnabla/models/imagenet/base.py | ImageNetBase.category_names | def category_names(self):
'''
Returns category names of 1000 ImageNet classes.
'''
if hasattr(self, '_category_names'):
return self._category_names
with open(os.path.join(os.path.dirname(__file__), 'category_names.txt'), 'r') as fd:
self._category_names = ... | python | def category_names(self):
'''
Returns category names of 1000 ImageNet classes.
'''
if hasattr(self, '_category_names'):
return self._category_names
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sony/nnabla | python/src/nnabla/utils/profiler.py | GraphProfilerCsvWriter.write | def write(self):
"""
Write result to the file.
The output file is specified by ``file``.
"""
writer = csv.writer(self.file)
for f, b in zip(self.gb.result["forward"], self.gb.result["backward"]):
f = f._asdict()
b = b._asdict()
if not ... | python | def write(self):
"""
Write result to the file.
The output file is specified by ``file``.
"""
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sony/nnabla | python/src/nnabla/monitor.py | plot_series | def plot_series(filename, plot_kwargs=None):
'''Plot series data from MonitorSeries output text file.
Args:
filename (str): Path to *.series.txt file produced by :obj:`~nnabla.MonitorSeries` class.
plot_kwags (dict, optional):
Keyward arguments passed to :function:`matplotlib.pyplot... | python | def plot_series(filename, plot_kwargs=None):
'''Plot series data from MonitorSeries output text file.
Args:
filename (str): Path to *.series.txt file produced by :obj:`~nnabla.MonitorSeries` class.
plot_kwags (dict, optional):
Keyward arguments passed to :function:`matplotlib.pyplot... | [
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sony/nnabla | python/src/nnabla/monitor.py | plot_time_elapsed | def plot_time_elapsed(filename, elapsed=False, unit='s', plot_kwargs=None):
'''Plot series data from MonitorTimeElapsed output text file.
Args:
filename (str): Path to *.series.txt file produced by :obj:`~nnabla.MonitorSeries` class.
elapsed (bool): If ``True``, it plots the total elapsed time.... | python | def plot_time_elapsed(filename, elapsed=False, unit='s', plot_kwargs=None):
'''Plot series data from MonitorTimeElapsed output text file.
Args:
filename (str): Path to *.series.txt file produced by :obj:`~nnabla.MonitorSeries` class.
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sony/nnabla | python/src/nnabla/monitor.py | MonitorSeries.add | def add(self, index, value):
"""Add a value to the series.
Args:
index (int): Index.
value (float): Value.
"""
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"""Add a value to the series.
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index (int): Index.
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sony/nnabla | python/src/nnabla/monitor.py | MonitorTimeElapsed.add | def add(self, index):
"""Calculate time elapsed from the point previously called
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Args:
index (int): Index to be displayed, and be used to take intervals.
"""
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sony/nnabla | python/src/nnabla/monitor.py | MonitorImage.add | def add(self, index, var):
"""Add a minibatch of images to the monitor.
Args:
index (int): Index.
var (:obj:`~nnabla.Variable`, :obj:`~nnabla.NdArray`, or :obj:`~numpy.ndarray`):
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If C == 2, ... | python | def add(self, index, var):
"""Add a minibatch of images to the monitor.
Args:
index (int): Index.
var (:obj:`~nnabla.Variable`, :obj:`~nnabla.NdArray`, or :obj:`~numpy.ndarray`):
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sony/nnabla | python/src/nnabla/utils/data_iterator.py | data_iterator_simple | def data_iterator_simple(load_func,
num_examples,
batch_size,
shuffle=False,
rng=None,
with_memory_cache=True,
with_file_cache=True,
cache_dir=No... | python | def data_iterator_simple(load_func,
num_examples,
batch_size,
shuffle=False,
rng=None,
with_memory_cache=True,
with_file_cache=True,
cache_dir=No... | [
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sony/nnabla | python/src/nnabla/utils/data_iterator.py | data_iterator_csv_dataset | def data_iterator_csv_dataset(uri,
batch_size,
shuffle=False,
rng=None,
normalize=True,
with_memory_cache=True,
with_file_cache=True,
... | python | def data_iterator_csv_dataset(uri,
batch_size,
shuffle=False,
rng=None,
normalize=True,
with_memory_cache=True,
with_file_cache=True,
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batch = data_iterator_csv_dataset('CSV_FILE.csv', batch_size, shuffle=True)
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sony/nnabla | python/src/nnabla/utils/data_iterator.py | data_iterator_cache | def data_iterator_cache(uri,
batch_size,
shuffle=False,
rng=None,
normalize=True,
with_memory_cache=True,
epoch_begin_callbacks=[],
epoch_end_callbacks=... | python | def data_iterator_cache(uri,
batch_size,
shuffle=False,
rng=None,
normalize=True,
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epoch_begin_callbacks=[],
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Get data from the cache directory.
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.. code-block:: python
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sony/nnabla | python/src/nnabla/utils/data_iterator.py | data_iterator_concat_datasets | def data_iterator_concat_datasets(data_source_list,
batch_size,
shuffle=False,
rng=None,
with_memory_cache=True,
with_file_cache=False,
... | python | def data_iterator_concat_datasets(data_source_list,
batch_size,
shuffle=False,
rng=None,
with_memory_cache=True,
with_file_cache=False,
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sony/nnabla | python/src/nnabla/utils/data_iterator.py | DataIterator.slice | def slice(self, rng, num_of_slices=None, slice_pos=None,
slice_start=None, slice_end=None,
cache_dir=None):
'''
Slices the data iterator so that newly generated data iterator has access to limited portion of the original data.
Args:
rng (numpy.random.Rand... | python | def slice(self, rng, num_of_slices=None, slice_pos=None,
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sony/nnabla | python/src/nnabla/auto_forward.py | auto_forward | def auto_forward(auto=True):
"""
Context for dynamic graph execution mode.
Args:
auto (bool): Whether forward computation is executed during a
computation graph construction.
Returns: bool
"""
global __auto_forward_state
prev = __auto_forward_state
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"""
Context for dynamic graph execution mode.
Args:
auto (bool): Whether forward computation is executed during a
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Returns: bool
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sony/nnabla | python/src/nnabla/utils/function_profile.py | FunctionProfile.print_stats | def print_stats(self, reset=True):
'''Manually print profiling result.
Args:
reset (bool): If False is specified, the profiling statistics so
far is maintained. If ``True`` (default),
:obj:`~reset_stats`
is called to reset the profiling statis... | python | def print_stats(self, reset=True):
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sony/nnabla | python/src/nnabla/models/utils.py | get_model_home | def get_model_home():
'''
Returns a root folder path for downloading models.
'''
d = os.path.join(get_data_home(), 'nnp_models')
if not os.path.isdir(d):
os.makedirs(d)
return d | python | def get_model_home():
'''
Returns a root folder path for downloading models.
'''
d = os.path.join(get_data_home(), 'nnp_models')
if not os.path.isdir(d):
os.makedirs(d)
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sony/nnabla | python/src/nnabla/models/utils.py | get_model_url_base | def get_model_url_base():
'''
Returns a root folder for models.
'''
url_base = get_model_url_base_from_env()
if url_base is not None:
logger.info('NNBLA_MODELS_URL_BASE is set as {}.'.format(url_base))
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url_base = 'https://nnabla.org/pretrained-models/nnp_models/'
return... | python | def get_model_url_base():
'''
Returns a root folder for models.
'''
url_base = get_model_url_base_from_env()
if url_base is not None:
logger.info('NNBLA_MODELS_URL_BASE is set as {}.'.format(url_base))
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sony/nnabla | python/src/nnabla/utils/data_source_loader.py | load_image_imread | def load_image_imread(file, shape=None, max_range=1.0):
'''
Load image from file like object.
:param file: Image contents
:type file: file like object.
:param shape: shape of output array
e.g. (3, 128, 192) : n_color, height, width.
:type shape: tuple of int
:param float max_range: ... | python | def load_image_imread(file, shape=None, max_range=1.0):
'''
Load image from file like object.
:param file: Image contents
:type file: file like object.
:param shape: shape of output array
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sony/nnabla | python/src/nnabla/utils/data_source_loader.py | load_csv | def load_csv(file, shape=None, normalize=False):
"""
Load CSV file.
:param file: CSV file.
:type file: file like object
:param shape : data array is reshape to this shape.
:type shape: tuple of int
:return: numpy array
"""
value_list = []
if six.PY2:
for row in csv.read... | python | def load_csv(file, shape=None, normalize=False):
"""
Load CSV file.
:param file: CSV file.
:type file: file like object
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:type shape: tuple of int
:return: numpy array
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vleaf (`nnabla.Variable`): End variable. All variables and functions which can be traversed from this variable are shown in the reuslt.
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sony/nnabla | python/src/nnabla/experimental/parametric_function_class/module.py | Module.get_modules | def get_modules(self, memo=None, prefix=""):
"""Get modules.
This function is internally used as the helper method for other methods.
Args:
memo (set, optional): Module set in order to memorize to visit.
prefix (str, optional): Prefix to a specific parameter name.
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"""Get modules.
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memo (set, optional): Module set in order to memorize to visit.
prefix (str, optional): Prefix to a specific parameter name.
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jazzband/django-push-notifications | push_notifications/fields.py | HexIntegerField.get_prep_value | def get_prep_value(self, value):
""" Return the integer value to be stored from the hex string """
if value is None or value == "":
return None
if isinstance(value, six.string_types):
value = _hex_string_to_unsigned_integer(value)
if _using_signed_storage():
value = _unsigned_to_signed_integer(value)
... | python | def get_prep_value(self, value):
""" Return the integer value to be stored from the hex string """
if value is None or value == "":
return None
if isinstance(value, six.string_types):
value = _hex_string_to_unsigned_integer(value)
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jazzband/django-push-notifications | push_notifications/fields.py | HexIntegerField.from_db_value | def from_db_value(self, value, expression, connection, context):
""" Return an unsigned int representation from all db backends """
if value is None:
return value
if _using_signed_storage():
value = _signed_to_unsigned_integer(value)
return value | python | def from_db_value(self, value, expression, connection, context):
""" Return an unsigned int representation from all db backends """
if value is None:
return value
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value = _signed_to_unsigned_integer(value)
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jazzband/django-push-notifications | push_notifications/fields.py | HexIntegerField.to_python | def to_python(self, value):
""" Return a str representation of the hexadecimal """
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jazzband/django-push-notifications | push_notifications/apns.py | apns_send_bulk_message | def apns_send_bulk_message(
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Sends an APNS notification to one or more registration_ids.
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jazzband/django-push-notifications | push_notifications/gcm.py | _cm_send_request | def _cm_send_request(
registration_ids, data, cloud_type="GCM", application_id=None,
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):
"""
Sends a FCM or GCM notification to one or more registration_ids as json data.
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"""
payload = {"registration_ids": registration_ids} if registra... | python | def _cm_send_request(
registration_ids, data, cloud_type="GCM", application_id=None,
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Sends a FCM or GCM notification to one or more registration_ids as json data.
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jazzband/django-push-notifications | push_notifications/gcm.py | _cm_handle_canonical_id | def _cm_handle_canonical_id(canonical_id, current_id, cloud_type):
"""
Handle situation when FCM server response contains canonical ID
"""
devices = GCMDevice.objects.filter(cloud_message_type=cloud_type)
if devices.filter(registration_id=canonical_id, active=True).exists():
devices.filter(registration_id=curren... | python | def _cm_handle_canonical_id(canonical_id, current_id, cloud_type):
"""
Handle situation when FCM server response contains canonical ID
"""
devices = GCMDevice.objects.filter(cloud_message_type=cloud_type)
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jazzband/django-push-notifications | push_notifications/conf/app.py | AppConfig._validate_applications | def _validate_applications(self, apps):
"""Validate the application collection"""
for application_id, application_config in apps.items():
self._validate_config(application_id, application_config)
application_config["APPLICATION_ID"] = application_id | python | def _validate_applications(self, apps):
"""Validate the application collection"""
for application_id, application_config in apps.items():
self._validate_config(application_id, application_config)
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jazzband/django-push-notifications | push_notifications/conf/app.py | AppConfig._validate_apns_certificate | def _validate_apns_certificate(self, certfile):
"""Validate the APNS certificate at startup."""
try:
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content = f.read()
check_apns_certificate(content)
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"""Validate the APNS certificate at startup."""
try:
with open(certfile, "r") as f:
content = f.read()
check_apns_certificate(content)
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jazzband/django-push-notifications | push_notifications/conf/app.py | AppConfig._validate_allowed_settings | def _validate_allowed_settings(self, application_id, application_config, allowed_settings):
"""Confirm only allowed settings are present."""
for setting_key in application_config.keys():
if setting_key not in allowed_settings:
raise ImproperlyConfigured(
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"""Confirm only allowed settings are present."""
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jazzband/django-push-notifications | push_notifications/conf/app.py | AppConfig._validate_required_settings | def _validate_required_settings(
self, application_id, application_config, required_settings
):
"""All required keys must be present"""
for setting_key in required_settings:
if setting_key not in application_config.keys():
raise ImproperlyConfigured(
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"""All required keys must be present"""
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raise ImproperlyConfigured(
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jazzband/django-push-notifications | push_notifications/conf/app.py | AppConfig._get_application_settings | def _get_application_settings(self, application_id, platform, settings_key):
"""
Walks through PUSH_NOTIFICATIONS_SETTINGS to find the correct setting value
or raises ImproperlyConfigured.
"""
if not application_id:
conf_cls = "push_notifications.conf.AppConfig"
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"""
Walks through PUSH_NOTIFICATIONS_SETTINGS to find the correct setting value
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jazzband/django-push-notifications | push_notifications/wns.py | _wns_authenticate | def _wns_authenticate(scope="notify.windows.com", application_id=None):
"""
Requests an Access token for WNS communication.
:return: dict: {'access_token': <str>, 'expires_in': <int>, 'token_type': 'bearer'}
"""
client_id = get_manager().get_wns_package_security_id(application_id)
client_secret = get_manager().g... | python | def _wns_authenticate(scope="notify.windows.com", application_id=None):
"""
Requests an Access token for WNS communication.
:return: dict: {'access_token': <str>, 'expires_in': <int>, 'token_type': 'bearer'}
"""
client_id = get_manager().get_wns_package_security_id(application_id)
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jazzband/django-push-notifications | push_notifications/wns.py | _wns_send | def _wns_send(uri, data, wns_type="wns/toast", application_id=None):
"""
Sends a notification data and authentication to WNS.
:param uri: str: The device's unique notification URI
:param data: dict: The notification data to be sent.
:return:
"""
access_token = _wns_authenticate(application_id=application_id)
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"""
Sends a notification data and authentication to WNS.
:param uri: str: The device's unique notification URI
:param data: dict: The notification data to be sent.
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jazzband/django-push-notifications | push_notifications/wns.py | _wns_prepare_toast | def _wns_prepare_toast(data, **kwargs):
"""
Creates the xml tree for a `toast` notification
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{
"text": ["Title text", "Message Text", "Another message!"],
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}
:return: str
"""
root = ET.Element("toast")
visu... | python | def _wns_prepare_toast(data, **kwargs):
"""
Creates the xml tree for a `toast` notification
:param data: dict: The notification data to be converted to an xml tree.
{
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jazzband/django-push-notifications | push_notifications/wns.py | wns_send_bulk_message | def wns_send_bulk_message(
uri_list, message=None, xml_data=None, raw_data=None, application_id=None, **kwargs
):
"""
WNS doesn't support bulk notification, so we loop through each uri.
:param uri_list: list: A list of uris the notification will be sent to.
:param message: str: The notification data to be sent.
... | python | def wns_send_bulk_message(
uri_list, message=None, xml_data=None, raw_data=None, application_id=None, **kwargs
):
"""
WNS doesn't support bulk notification, so we loop through each uri.
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jazzband/django-push-notifications | push_notifications/wns.py | _add_sub_elements_from_dict | def _add_sub_elements_from_dict(parent, sub_dict):
"""
Add SubElements to the parent element.
:param parent: ElementTree.Element: The parent element for the newly created SubElement.
:param sub_dict: dict: Used to create a new SubElement. See `dict_to_xml_schema`
method docstring for more information. e.g.:
{"e... | python | def _add_sub_elements_from_dict(parent, sub_dict):
"""
Add SubElements to the parent element.
:param parent: ElementTree.Element: The parent element for the newly created SubElement.
:param sub_dict: dict: Used to create a new SubElement. See `dict_to_xml_schema`
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jazzband/django-push-notifications | push_notifications/wns.py | _add_element_attrs | def _add_element_attrs(elem, attrs):
"""
Add attributes to the given element.
:param elem: ElementTree.Element: The element the attributes are being added to.
:param attrs: dict: A dictionary of attributes. e.g.:
{"attribute1": "value", "attribute2": "another"}
:return: ElementTree.Element
"""
for attr, value... | python | def _add_element_attrs(elem, attrs):
"""
Add attributes to the given element.
:param elem: ElementTree.Element: The element the attributes are being added to.
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{"attribute1": "value", "attribute2": "another"}
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skydive-project/skydive | contrib/python/api/skydive/websocket/client.py | WSClient.login | def login(self, host_spec="", username="", password=""):
""" Authenticate with infrastructure via the Skydive analyzer
This method will also set the authentication cookie to be used in
the future requests
:param host_spec: Host IP and port (e.g. 192.168.10.1:8082)
:type host_spe... | python | def login(self, host_spec="", username="", password=""):
""" Authenticate with infrastructure via the Skydive analyzer
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:param host_spec: Host IP and port (e.g. 192.168.10.1:8082)
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kivy/buildozer | buildozer/targets/android.py | TargetAndroid._sdkmanager | def _sdkmanager(self, *args, **kwargs):
"""Call the sdkmanager in our Android SDK with the given arguments."""
# Use the android-sdk dir as cwd by default
kwargs['cwd'] = kwargs.get('cwd', self.android_sdk_dir)
command = self.sdkmanager_path + ' ' + ' '.join(args)
return_child = ... | python | def _sdkmanager(self, *args, **kwargs):
"""Call the sdkmanager in our Android SDK with the given arguments."""
# Use the android-sdk dir as cwd by default
kwargs['cwd'] = kwargs.get('cwd', self.android_sdk_dir)
command = self.sdkmanager_path + ' ' + ' '.join(args)
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kivy/buildozer | buildozer/targets/android.py | TargetAndroid._android_get_installed_platform_tools_version | def _android_get_installed_platform_tools_version(self):
"""
Crudely parse out the installed platform-tools version
"""
platform_tools_dir = os.path.join(
self.android_sdk_dir,
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if not os.path.exists(platform_tools_dir):
retu... | python | def _android_get_installed_platform_tools_version(self):
"""
Crudely parse out the installed platform-tools version
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platform_tools_dir = os.path.join(
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kivy/buildozer | buildozer/targets/android.py | TargetAndroid._android_update_sdk | def _android_update_sdk(self, *sdkmanager_commands):
"""Update the tools and package-tools if possible"""
auto_accept_license = self.buildozer.config.getbooldefault(
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if auto_accept_license:
# `SIGPIPE` is not being reported som... | python | def _android_update_sdk(self, *sdkmanager_commands):
"""Update the tools and package-tools if possible"""
auto_accept_license = self.buildozer.config.getbooldefault(
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kivy/buildozer | buildozer/targets/android.py | TargetAndroid.cmd_logcat | def cmd_logcat(self, *args):
'''Show the log from the device
'''
self.check_requirements()
serial = self.serials[0:]
if not serial:
return
filters = self.buildozer.config.getrawdefault(
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'''Show the log from the device
'''
self.check_requirements()
serial = self.serials[0:]
if not serial:
return
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kivy/buildozer | buildozer/target.py | Target.path_or_git_url | def path_or_git_url(self, repo, owner='kivy', branch='master',
url_format='https://github.com/{owner}/{repo}.git',
platform=None,
squash_hyphen=True):
"""Get source location for a git checkout
This method will check the `buildozer.... | python | def path_or_git_url(self, repo, owner='kivy', branch='master',
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platform=None,
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kivy/buildozer | buildozer/target.py | Target.install_or_update_repo | def install_or_update_repo(self, repo, **kwargs):
"""Install or update a git repository into the platform directory.
This will clone the contents of a git repository to
`buildozer.platform_dir`. The location of this repo can be
speficied via URL and branch name, or via a custom (local)
... | python | def install_or_update_repo(self, repo, **kwargs):
"""Install or update a git repository into the platform directory.
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kivy/buildozer | buildozer/__init__.py | set_config_token_from_env | def set_config_token_from_env(section, token, config):
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environment variable. If the variable exists, sets the config entry to
its value.
The environment variable checked is of the form SECTION_TOKEN, all
upper case, with any dots replac... | python | def set_config_token_from_env(section, token, config):
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] | 586152c6ce2b6cde4d5a081d9711f9cb037a901c | https://github.com/kivy/buildozer/blob/586152c6ce2b6cde4d5a081d9711f9cb037a901c/buildozer/__init__.py#L1252-L1270 | train |
kivy/buildozer | buildozer/__init__.py | Buildozer.prepare_for_build | def prepare_for_build(self):
'''Prepare the build.
'''
assert(self.target is not None)
if hasattr(self.target, '_build_prepared'):
return
self.info('Preparing build')
self.info('Check requirements for {0}'.format(self.targetname))
self.target.check_r... | python | def prepare_for_build(self):
'''Prepare the build.
'''
assert(self.target is not None)
if hasattr(self.target, '_build_prepared'):
return
self.info('Preparing build')
self.info('Check requirements for {0}'.format(self.targetname))
self.target.check_r... | [
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] | 586152c6ce2b6cde4d5a081d9711f9cb037a901c | https://github.com/kivy/buildozer/blob/586152c6ce2b6cde4d5a081d9711f9cb037a901c/buildozer/__init__.py#L173-L198 | train |
kivy/buildozer | buildozer/__init__.py | Buildozer.build | def build(self):
'''Do the build.
The target can set build_mode to 'release' or 'debug' before calling
this method.
(:meth:`prepare_for_build` must have been call before.)
'''
assert(self.target is not None)
assert(hasattr(self.target, '_build_prepared'))
... | python | def build(self):
'''Do the build.
The target can set build_mode to 'release' or 'debug' before calling
this method.
(:meth:`prepare_for_build` must have been call before.)
'''
assert(self.target is not None)
assert(hasattr(self.target, '_build_prepared'))
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] | 586152c6ce2b6cde4d5a081d9711f9cb037a901c | https://github.com/kivy/buildozer/blob/586152c6ce2b6cde4d5a081d9711f9cb037a901c/buildozer/__init__.py#L200-L225 | train |
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