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242,700 | 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_w=True, with_zero_w=False, sign_w=True, n_w=8, m_w=2, ste_fine_grained_w=True,
quantize_b=True, with_zero_b=False, sign_b=True, n_b=8, m_b=2, ste_fine_grained_b=True,):
"""Pow2 Quantized Convolution.
Pow2 Quantized Convolution is the convolution function,
except the definition of the inner product is modified.
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},
where :math:`Q(w_{n, m, i, j})` is the power-of-2 quantization function.
.. note::
1) if you would like to share weights between some layers, please
make sure to share the standard, floating value weights (`weight`)
and not the quantized weights (`quantized weight`)
2) The weights and the quantized weights become synced only after :func:`~nnabla._variable.Variable.forward` is called,
and not after a call to :func:`~nnabla._variable.Variable.backward`.
To access the parameters of the network, remember to call :func:`~nnabla._variable.Variable.forward` once before doing so, otherwise the
float weights and the quantized weights will not be in sync.
3) Quantized values are stored as floating point number for `quantized weight`,
since this function is only for simulation purposes.
Args:
inp (~nnabla.Variable): N-D array.
outmaps (int): Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (:obj:`tuple` of :obj:`int`): Padding sizes for dimensions.
stride (:obj:`tuple` of :obj:`int`): Stride sizes for dimensions.
dilation (:obj:`tuple` of :obj:`int`): Dilation sizes for dimensions.
group (int): Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`.
base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions.
fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
rng (numpy.random.RandomState): Random generator for Initializer.
with_bias (bool): Specify whether to include the bias term.
quantize_w (bool): Quantize weights if `True`.
sign_w (bool): Use signed quantization if `True`.
n_w (int): Bit width used for weight.
m_w (int): :math:`2^m` is upper bound and :math:`-2^m` is lower bound for weights. Default is 2.
ste_fine_grained_w (bool): STE is fine-grained if `True`.
quantize_b (bool): Quantize bias if `True`.
sign_b (bool): Use signed quantization if `True`.
n_b (int): Bit width used for bias.
m_b (int): :math:`2^m` is upper bound and :math:`-2^m` is lower bound for bias. Default is 2.
ste_fine_grained_b (bool): STE is fine-grained if `True`.
Returns:
:class:`~nnabla.Variable`: N-D array.
"""
if w_init is None:
w_init = UniformInitializer(
calc_uniform_lim_glorot(inp.shape[base_axis], outmaps, tuple(kernel)), rng=rng)
if with_bias and b_init is None:
b_init = ConstantInitializer()
# Floating Weight
w = get_parameter_or_create(
"W", (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
w_init, True, not fix_parameters)
# Quantized Weight
if quantize_w:
w_q = get_parameter_or_create(
"W_q", (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
w_init, False)
# Link computation graph
real_w_q = F.pow2_quantize(w, quantize=quantize_w,
sign=sign_w, with_zero=with_zero_w,
n=n_w, m=m_w, ste_fine_grained=ste_fine_grained_w,
outputs=[w_q.data])
real_w_q.persistent = True
else:
real_w_q = w
# Bias
# Floating
b = None
b_q = None
real_b_q = None
if with_bias:
b = get_parameter_or_create(
"b", (outmaps,), b_init, True, not fix_parameters)
if quantize_b:
b_q = get_parameter_or_create(
"b_q", (outmaps,), b_init, False)
# Link computation graph
real_b_q = F.pow2_quantize(b, quantize=quantize_b,
sign=sign_b, with_zero=with_zero_b,
n=n_b, m=m_b, ste_fine_grained=ste_fine_grained_b,
outputs=[b_q.data])
real_b_q.persistent = True
else:
real_b_q = b
return F.convolution(inp, real_w_q, real_b_q, base_axis, pad, stride, dilation, group) | 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,
quantize_w=True, with_zero_w=False, sign_w=True, n_w=8, m_w=2, ste_fine_grained_w=True,
quantize_b=True, with_zero_b=False, sign_b=True, n_b=8, m_b=2, ste_fine_grained_b=True,):
if w_init is None:
w_init = UniformInitializer(
calc_uniform_lim_glorot(inp.shape[base_axis], outmaps, tuple(kernel)), rng=rng)
if with_bias and b_init is None:
b_init = ConstantInitializer()
# Floating Weight
w = get_parameter_or_create(
"W", (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
w_init, True, not fix_parameters)
# Quantized Weight
if quantize_w:
w_q = get_parameter_or_create(
"W_q", (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
w_init, False)
# Link computation graph
real_w_q = F.pow2_quantize(w, quantize=quantize_w,
sign=sign_w, with_zero=with_zero_w,
n=n_w, m=m_w, ste_fine_grained=ste_fine_grained_w,
outputs=[w_q.data])
real_w_q.persistent = True
else:
real_w_q = w
# Bias
# Floating
b = None
b_q = None
real_b_q = None
if with_bias:
b = get_parameter_or_create(
"b", (outmaps,), b_init, True, not fix_parameters)
if quantize_b:
b_q = get_parameter_or_create(
"b_q", (outmaps,), b_init, False)
# Link computation graph
real_b_q = F.pow2_quantize(b, quantize=quantize_b,
sign=sign_b, with_zero=with_zero_b,
n=n_b, m=m_b, ste_fine_grained=ste_fine_grained_b,
outputs=[b_q.data])
real_b_q.persistent = True
else:
real_b_q = b
return F.convolution(inp, real_w_q, real_b_q, base_axis, pad, stride, dilation, group) | [
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"b... | Pow2 Quantized Convolution.
Pow2 Quantized Convolution is the convolution function,
except the definition of the inner product is modified.
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},
where :math:`Q(w_{n, m, i, j})` is the power-of-2 quantization function.
.. note::
1) if you would like to share weights between some layers, please
make sure to share the standard, floating value weights (`weight`)
and not the quantized weights (`quantized weight`)
2) The weights and the quantized weights become synced only after :func:`~nnabla._variable.Variable.forward` is called,
and not after a call to :func:`~nnabla._variable.Variable.backward`.
To access the parameters of the network, remember to call :func:`~nnabla._variable.Variable.forward` once before doing so, otherwise the
float weights and the quantized weights will not be in sync.
3) Quantized values are stored as floating point number for `quantized weight`,
since this function is only for simulation purposes.
Args:
inp (~nnabla.Variable): N-D array.
outmaps (int): Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (:obj:`tuple` of :obj:`int`): Padding sizes for dimensions.
stride (:obj:`tuple` of :obj:`int`): Stride sizes for dimensions.
dilation (:obj:`tuple` of :obj:`int`): Dilation sizes for dimensions.
group (int): Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`.
base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions.
fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
rng (numpy.random.RandomState): Random generator for Initializer.
with_bias (bool): Specify whether to include the bias term.
quantize_w (bool): Quantize weights if `True`.
sign_w (bool): Use signed quantization if `True`.
n_w (int): Bit width used for weight.
m_w (int): :math:`2^m` is upper bound and :math:`-2^m` is lower bound for weights. Default is 2.
ste_fine_grained_w (bool): STE is fine-grained if `True`.
quantize_b (bool): Quantize bias if `True`.
sign_b (bool): Use signed quantization if `True`.
n_b (int): Bit width used for bias.
m_b (int): :math:`2^m` is upper bound and :math:`-2^m` is lower bound for bias. Default is 2.
ste_fine_grained_b (bool): STE is fine-grained if `True`.
Returns:
:class:`~nnabla.Variable`: N-D array. | [
"Pow2",
"Quantized",
"Convolution",
"."
] | aaf3d33b7cbb38f2a03aa754178ba8f7c8481320 | https://github.com/sony/nnabla/blob/aaf3d33b7cbb38f2a03aa754178ba8f7c8481320/python/src/nnabla/parametric_functions.py#L2141-L2249 |
242,701 | 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,
except the definition of the inner product is modified.
The input-output relation of this function is as follows:
.. math::
y_j = \sum_{i} Q(w_{ji}) x_i,
where :math:`Q(w_{ji})` is the pruning function, i.e., `F.prune`.
.. note::
1) if you would like to share weights between some layers, please
make sure to share the standard, floating value weights (`weight`)
and not the quantized weights (`quantized weight`)
2) The weights and the quantized weights become synced only after :func:`~nnabla._variable.Variable.forward` is called,
and not after a call to :func:`~nnabla._variable.Variable.backward`.
To access the parameters of the network, remember to call :func:`~nnabla._variable.Variable.forward` once before doing so, otherwise the
float weights and the quantized weights will not be in sync.
3) CPU and GPU implementations now use float value for `quantized weight`,
since this function is only for simulation purposes.
Args:
inp (~nnabla.Variable): Input N-D array with shape (:math:`M_0 \\times \ldots \\times M_{B-1} \\times D_B \\times \ldots \\times D_N`). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (:obj:`int` or :obj:`tuple` of :obj:`int`): Number of output neurons per data.
base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias.
fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
rng (numpy.random.RandomState): Random generator for Initializer.
with_bias (bool): Specify whether to include the bias term.
prune_w (bool): Quantize weights if `True`.
rate_w (float): Pruning rate for weights.
prune_b (bool): Quantize bias if `True`.
rate_b (float): Pruning rate for bias.
Returns:
:class:`~nnabla.Variable`: :math:`(B + 1)`-D array. (:math:`M_0 \\times \ldots \\times M_{B-1} \\times L`)
"""
if not hasattr(n_outmaps, '__iter__'):
n_outmaps = [n_outmaps]
n_outmaps = list(n_outmaps)
n_outmap = int(np.prod(n_outmaps))
if w_init is None:
inmaps = np.prod(inp.shape[base_axis:])
w_init = UniformInitializer(
calc_uniform_lim_glorot(inmaps, n_outmap), rng=rng)
if with_bias and b_init is None:
b_init = ConstantInitializer()
# Floating Weight
w = get_parameter_or_create(
"W", [int(np.prod(inp.shape[base_axis:]))] + n_outmaps,
w_init, True, not fix_parameters)
# sparsed Weight
if prune_w:
w_q = get_parameter_or_create(
"W_q", [int(np.prod(inp.shape[base_axis:]))] + n_outmaps,
w_init, False)
# Link computation graph
real_w_q = F.prune(w, rate=rate_w, outputs=[w_q.data])
real_w_q.persistent = True
else:
real_w_q = w
# Bias
# Floating
real_b_q = None
if with_bias:
b = get_parameter_or_create(
"b", n_outmaps, b_init, True, not fix_parameters)
if prune_b:
b_q = get_parameter_or_create(
"b_q", n_outmaps, b_init, False)
# Link computation graph
real_b_q = F.prune(b, rate=rate_b, outputs=[b_q.data])
real_b_q.persistent = True
else:
real_b_q = b
return F.affine(inp, real_w_q, real_b_q, base_axis) | 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):
if not hasattr(n_outmaps, '__iter__'):
n_outmaps = [n_outmaps]
n_outmaps = list(n_outmaps)
n_outmap = int(np.prod(n_outmaps))
if w_init is None:
inmaps = np.prod(inp.shape[base_axis:])
w_init = UniformInitializer(
calc_uniform_lim_glorot(inmaps, n_outmap), rng=rng)
if with_bias and b_init is None:
b_init = ConstantInitializer()
# Floating Weight
w = get_parameter_or_create(
"W", [int(np.prod(inp.shape[base_axis:]))] + n_outmaps,
w_init, True, not fix_parameters)
# sparsed Weight
if prune_w:
w_q = get_parameter_or_create(
"W_q", [int(np.prod(inp.shape[base_axis:]))] + n_outmaps,
w_init, False)
# Link computation graph
real_w_q = F.prune(w, rate=rate_w, outputs=[w_q.data])
real_w_q.persistent = True
else:
real_w_q = w
# Bias
# Floating
real_b_q = None
if with_bias:
b = get_parameter_or_create(
"b", n_outmaps, b_init, True, not fix_parameters)
if prune_b:
b_q = get_parameter_or_create(
"b_q", n_outmaps, b_init, False)
# Link computation graph
real_b_q = F.prune(b, rate=rate_b, outputs=[b_q.data])
real_b_q.persistent = True
else:
real_b_q = b
return F.affine(inp, real_w_q, real_b_q, base_axis) | [
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... | Pruned Affine.
Pruned Affine is the affine function,
except the definition of the inner product is modified.
The input-output relation of this function is as follows:
.. math::
y_j = \sum_{i} Q(w_{ji}) x_i,
where :math:`Q(w_{ji})` is the pruning function, i.e., `F.prune`.
.. note::
1) if you would like to share weights between some layers, please
make sure to share the standard, floating value weights (`weight`)
and not the quantized weights (`quantized weight`)
2) The weights and the quantized weights become synced only after :func:`~nnabla._variable.Variable.forward` is called,
and not after a call to :func:`~nnabla._variable.Variable.backward`.
To access the parameters of the network, remember to call :func:`~nnabla._variable.Variable.forward` once before doing so, otherwise the
float weights and the quantized weights will not be in sync.
3) CPU and GPU implementations now use float value for `quantized weight`,
since this function is only for simulation purposes.
Args:
inp (~nnabla.Variable): Input N-D array with shape (:math:`M_0 \\times \ldots \\times M_{B-1} \\times D_B \\times \ldots \\times D_N`). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (:obj:`int` or :obj:`tuple` of :obj:`int`): Number of output neurons per data.
base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias.
fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
rng (numpy.random.RandomState): Random generator for Initializer.
with_bias (bool): Specify whether to include the bias term.
prune_w (bool): Quantize weights if `True`.
rate_w (float): Pruning rate for weights.
prune_b (bool): Quantize bias if `True`.
rate_b (float): Pruning rate for bias.
Returns:
:class:`~nnabla.Variable`: :math:`(B + 1)`-D array. (:math:`M_0 \\times \ldots \\times M_{B-1} \\times L`) | [
"Pruned",
"Affine",
"."
] | aaf3d33b7cbb38f2a03aa754178ba8f7c8481320 | https://github.com/sony/nnabla/blob/aaf3d33b7cbb38f2a03aa754178ba8f7c8481320/python/src/nnabla/parametric_functions.py#L2258-L2351 |
242,702 | 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=0.9):
"""Pruned Convolution.
Pruned Convolution is the convolution function,
except the definition of the inner product is modified.
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},
where :math:`Q(w_{ji})` is the pruning function, i.e., `F.prune`.
.. note::
1) if you would like to share weights between some layers, please
make sure to share the standard, floating value weights (`weight`)
and not the quantized weights (`quantized weight`)
2) The weights and the quantized weights become synced only after :func:`~nnabla._variable.Variable.forward` is called,
and not after a call to :func:`~nnabla._variable.Variable.backward`.
To access the parameters of the network, remember to call :func:`~nnabla._variable.Variable.forward` once before doing so, otherwise the
float weights and the quantized weights will not be in sync.
3) CPU and GPU implementations now use float value for `quantized weight`,
since this function is only for simulation purposes.
Args:
inp (~nnabla.Variable): N-D array.
outmaps (int): Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (:obj:`tuple` of :obj:`int`): Padding sizes for dimensions.
stride (:obj:`tuple` of :obj:`int`): Stride sizes for dimensions.
dilation (:obj:`tuple` of :obj:`int`): Dilation sizes for dimensions.
group (int): Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias.
base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions.
fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
rng (numpy.random.RandomState): Random generator for Initializer.
with_bias (bool): Specify whether to include the bias term.
prune_w (bool): Quantize weights if `True`.
rate_w (float): Pruning rate for weights.
prune_b (bool): Quantize bias if `True`.
rate_b (float): Pruning rate for bias.
Returns:
:class:`~nnabla.Variable`: N-D array.
"""
if w_init is None:
w_init = UniformInitializer(
calc_uniform_lim_glorot(inp.shape[base_axis], outmaps, tuple(kernel)), rng=rng)
if with_bias and b_init is None:
b_init = ConstantInitializer()
# Floating Weight
w = get_parameter_or_create(
"W", (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
w_init, True, not fix_parameters)
# Quantized Weight
if prune_w:
w_q = get_parameter_or_create(
"W_q", (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
w_init, False)
# Link computation graph
real_w_q = F.prune(w, rate=rate_w, outputs=[w_q.data])
real_w_q.persistent = True
else:
real_w_q = w
# Bias
# Floating
real_b_q = None
if with_bias:
b = get_parameter_or_create(
"b", (outmaps,), b_init, True, not fix_parameters)
if prune_b:
b_q = get_parameter_or_create(
"b_q", (outmaps,), b_init, False)
# Link computation graph
real_b_q = F.prune(b, rate=rate_b, outputs=[b_q.data])
real_b_q.persistent = True
else:
real_b_q = b
return F.convolution(inp, real_w_q, real_b_q, base_axis, pad, stride, dilation, group) | 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=0.9):
if w_init is None:
w_init = UniformInitializer(
calc_uniform_lim_glorot(inp.shape[base_axis], outmaps, tuple(kernel)), rng=rng)
if with_bias and b_init is None:
b_init = ConstantInitializer()
# Floating Weight
w = get_parameter_or_create(
"W", (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
w_init, True, not fix_parameters)
# Quantized Weight
if prune_w:
w_q = get_parameter_or_create(
"W_q", (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
w_init, False)
# Link computation graph
real_w_q = F.prune(w, rate=rate_w, outputs=[w_q.data])
real_w_q.persistent = True
else:
real_w_q = w
# Bias
# Floating
real_b_q = None
if with_bias:
b = get_parameter_or_create(
"b", (outmaps,), b_init, True, not fix_parameters)
if prune_b:
b_q = get_parameter_or_create(
"b_q", (outmaps,), b_init, False)
# Link computation graph
real_b_q = F.prune(b, rate=rate_b, outputs=[b_q.data])
real_b_q.persistent = True
else:
real_b_q = b
return F.convolution(inp, real_w_q, real_b_q, base_axis, pad, stride, dilation, group) | [
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Pruned Convolution is the convolution function,
except the definition of the inner product is modified.
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},
where :math:`Q(w_{ji})` is the pruning function, i.e., `F.prune`.
.. note::
1) if you would like to share weights between some layers, please
make sure to share the standard, floating value weights (`weight`)
and not the quantized weights (`quantized weight`)
2) The weights and the quantized weights become synced only after :func:`~nnabla._variable.Variable.forward` is called,
and not after a call to :func:`~nnabla._variable.Variable.backward`.
To access the parameters of the network, remember to call :func:`~nnabla._variable.Variable.forward` once before doing so, otherwise the
float weights and the quantized weights will not be in sync.
3) CPU and GPU implementations now use float value for `quantized weight`,
since this function is only for simulation purposes.
Args:
inp (~nnabla.Variable): N-D array.
outmaps (int): Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (:obj:`tuple` of :obj:`int`): Padding sizes for dimensions.
stride (:obj:`tuple` of :obj:`int`): Stride sizes for dimensions.
dilation (:obj:`tuple` of :obj:`int`): Dilation sizes for dimensions.
group (int): Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias.
base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions.
fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
rng (numpy.random.RandomState): Random generator for Initializer.
with_bias (bool): Specify whether to include the bias term.
prune_w (bool): Quantize weights if `True`.
rate_w (float): Pruning rate for weights.
prune_b (bool): Quantize bias if `True`.
rate_b (float): Pruning rate for bias.
Returns:
:class:`~nnabla.Variable`: N-D array. | [
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] | aaf3d33b7cbb38f2a03aa754178ba8f7c8481320 | https://github.com/sony/nnabla/blob/aaf3d33b7cbb38f2a03aa754178ba8f7c8481320/python/src/nnabla/parametric_functions.py#L2360-L2451 |
242,703 | 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 following:
.. math::
f_t&&=\\sigma(W_fx_t+U_fh_{t-1}+b_f) \\\\
i_t&&=\\sigma(W_ix_t+U_ih_{t-1}+b_i) \\\\
o_t&&=\\sigma(W_ox_t+U_oh_{t-1}+b_o) \\\\
c_t&&=f_t\\odot c_{t-1}+i_t\\odot\\tanh(W_cx_t+U_ch_{t-1}+b_c) \\\\
h_t&&=o_t\\odot\\tanh(c_t).
References:
S. Hochreiter, and J. Schmidhuber. "Long Short-Term Memory."
Neural Computation. 1997.
Args:
x (~nnabla.Variable): Input N-D array with shape (batch_size, input_size).
h (~nnabla.Variable): Input N-D array with shape (batch_size, state_size).
c (~nnabla.Variable): Input N-D array with shape (batch_size, state_size).
state_size (int): Internal state size is set to `state_size`.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`.
fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
Returns:
:class:`~nnabla.Variable`
"""
xh = F.concatenate(*(x, h), axis=1)
iofc = affine(xh, (4, state_size), w_init=w_init,
b_init=b_init, fix_parameters=fix_parameters)
i_t, o_t, f_t, gate = F.split(iofc, axis=1)
c_t = F.sigmoid(f_t) * c + F.sigmoid(i_t) * F.tanh(gate)
h_t = F.sigmoid(o_t) * F.tanh(c_t)
return h_t, c_t | python | def lstm_cell(x, h, c, state_size, w_init=None, b_init=None, fix_parameters=False):
xh = F.concatenate(*(x, h), axis=1)
iofc = affine(xh, (4, state_size), w_init=w_init,
b_init=b_init, fix_parameters=fix_parameters)
i_t, o_t, f_t, gate = F.split(iofc, axis=1)
c_t = F.sigmoid(f_t) * c + F.sigmoid(i_t) * F.tanh(gate)
h_t = F.sigmoid(o_t) * F.tanh(c_t)
return h_t, c_t | [
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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 following:
.. math::
f_t&&=\\sigma(W_fx_t+U_fh_{t-1}+b_f) \\\\
i_t&&=\\sigma(W_ix_t+U_ih_{t-1}+b_i) \\\\
o_t&&=\\sigma(W_ox_t+U_oh_{t-1}+b_o) \\\\
c_t&&=f_t\\odot c_{t-1}+i_t\\odot\\tanh(W_cx_t+U_ch_{t-1}+b_c) \\\\
h_t&&=o_t\\odot\\tanh(c_t).
References:
S. Hochreiter, and J. Schmidhuber. "Long Short-Term Memory."
Neural Computation. 1997.
Args:
x (~nnabla.Variable): Input N-D array with shape (batch_size, input_size).
h (~nnabla.Variable): Input N-D array with shape (batch_size, state_size).
c (~nnabla.Variable): Input N-D array with shape (batch_size, state_size).
state_size (int): Internal state size is set to `state_size`.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`.
fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
Returns:
:class:`~nnabla.Variable` | [
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] | aaf3d33b7cbb38f2a03aa754178ba8f7c8481320 | https://github.com/sony/nnabla/blob/aaf3d33b7cbb38f2a03aa754178ba8f7c8481320/python/src/nnabla/parametric_functions.py#L2459-L2497 |
242,704 | 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 computed by the power iteration.
References:
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida,
"Spectral Normalization for Generative Adversarial Networks",
International Conference on Learning Representations. 2018.
Args:
W (~nnabla.Variable): Input N-D array with shape. This is normally network parameter.
dim (`int`): Output dimension. Default is 0. If the dimension is not 0, then the specified dimension becomes the most-left dimension by transposing.
itr (`int`): Number of iterations. Default is 1.
eps (`float`): Epsilon for the normalization. Default is 1e-12.
test (`bool`): Use test mode. Default is False.
Returns:
~nnabla.Variable: Spectrally normalized :math:`W_{sn}` with the same shape as :math:`W`.
Example:
.. code-block:: python
import nnabla as nn
import nnabla.parametric_functions as PF
b, c, h, w = 4, 64, 32, 32
# Spectrally normalized convolution
apply_w = lambda w: PF.spectral_norm(w, dim=0)
h = nn.Variable.from_numpy_array(np.random.randn(b, c, h, w))
h = PF.convolution(h, with_bias=False, apply_w=apply_w)
# Spectrally normalized affine
apply_w = lambda w: PF.spectral_norm(w, dim=1)
h = nn.Variable.from_numpy_array(np.random.randn(b, c))
h = PF.affine(h, with_bias=False, apply_w=apply_w)
# Spectrally normalized embed
apply_w = lambda w: PF.spectral_norm(w, dim=1)
h = nn.Variable.from_numpy_array(np.random.randn(b, c))
h = PF.embed(h, c, apply_w=apply_w)
"""
assert (0 <= dim and dim < len(w.shape)
), "`dim` must be `0 <= dim and dim < len(w.shape)`."
assert 0 < itr, "`itr` must be greater than 0."
assert 0 < eps, "`eps` must be greater than 0."
if dim == len(w.shape) - 1:
w_sn = _spectral_norm_outer_most_dim(w, dim=dim, itr=itr, eps=eps, test=test,
u_init=u_init, fix_parameters=fix_parameters)
else:
w_sn = _spectral_norm(w, dim=dim, itr=itr, eps=eps, test=test,
u_init=u_init, fix_parameters=fix_parameters)
return w_sn | python | def spectral_norm(w, dim=0, itr=1, eps=1e-12, test=False, u_init=None, fix_parameters=True):
assert (0 <= dim and dim < len(w.shape)
), "`dim` must be `0 <= dim and dim < len(w.shape)`."
assert 0 < itr, "`itr` must be greater than 0."
assert 0 < eps, "`eps` must be greater than 0."
if dim == len(w.shape) - 1:
w_sn = _spectral_norm_outer_most_dim(w, dim=dim, itr=itr, eps=eps, test=test,
u_init=u_init, fix_parameters=fix_parameters)
else:
w_sn = _spectral_norm(w, dim=dim, itr=itr, eps=eps, test=test,
u_init=u_init, fix_parameters=fix_parameters)
return w_sn | [
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... | 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 computed by the power iteration.
References:
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida,
"Spectral Normalization for Generative Adversarial Networks",
International Conference on Learning Representations. 2018.
Args:
W (~nnabla.Variable): Input N-D array with shape. This is normally network parameter.
dim (`int`): Output dimension. Default is 0. If the dimension is not 0, then the specified dimension becomes the most-left dimension by transposing.
itr (`int`): Number of iterations. Default is 1.
eps (`float`): Epsilon for the normalization. Default is 1e-12.
test (`bool`): Use test mode. Default is False.
Returns:
~nnabla.Variable: Spectrally normalized :math:`W_{sn}` with the same shape as :math:`W`.
Example:
.. code-block:: python
import nnabla as nn
import nnabla.parametric_functions as PF
b, c, h, w = 4, 64, 32, 32
# Spectrally normalized convolution
apply_w = lambda w: PF.spectral_norm(w, dim=0)
h = nn.Variable.from_numpy_array(np.random.randn(b, c, h, w))
h = PF.convolution(h, with_bias=False, apply_w=apply_w)
# Spectrally normalized affine
apply_w = lambda w: PF.spectral_norm(w, dim=1)
h = nn.Variable.from_numpy_array(np.random.randn(b, c))
h = PF.affine(h, with_bias=False, apply_w=apply_w)
# Spectrally normalized embed
apply_w = lambda w: PF.spectral_norm(w, dim=1)
h = nn.Variable.from_numpy_array(np.random.randn(b, c))
h = PF.embed(h, c, apply_w=apply_w) | [
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"."
] | aaf3d33b7cbb38f2a03aa754178ba8f7c8481320 | https://github.com/sony/nnabla/blob/aaf3d33b7cbb38f2a03aa754178ba8f7c8481320/python/src/nnabla/parametric_functions.py#L2556-L2618 |
242,705 | 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):
self.h.data.zero()
self.c.data.zero() | [
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242,706 | 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 - self.lap_time
total_time = now - self.start
self.lap_time = now
return lap_time, total_time | python | def lap(self):
now = time.time()
lap_time = now - self.lap_time
total_time = now - self.start
self.lap_time = now
return lap_time, total_time | [
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Returns:
float: Lap time. The duration from the previous call of ``lap()``
or initialization at first call.
float: Total time. The duration from initialization. | [
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242,707 | 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)
print('class = {}'.format(fb.func_ins.name), file=self.file)
print('inspecs = {}'.format(repr(fb.inspecs)), file=self.file)
print('func_args = {}'.format(repr(fb.func_args)), file=self.file)
print('func_kwargs = {}'.format(repr(fb.func_kwargs)), file=self.file)
print('ext = ({}, {})'.format(
repr(fb.ext), repr(fb.ext_kwargs)), file=self.file)
if self.setup_stat is not None:
self._write_a_stat('setup', self.setup_stat)
if self.foward_stat is not None:
self._write_a_stat('forward', self.forward_stat)
if self.backward_stat is not None:
self._write_a_stat('backward', self.backward_stat) | python | def write(self, fb):
print('[{}.{}]'.format(fb.module, fb.func.__name__), file=self.file)
print('class = {}'.format(fb.func_ins.name), file=self.file)
print('inspecs = {}'.format(repr(fb.inspecs)), file=self.file)
print('func_args = {}'.format(repr(fb.func_args)), file=self.file)
print('func_kwargs = {}'.format(repr(fb.func_kwargs)), file=self.file)
print('ext = ({}, {})'.format(
repr(fb.ext), repr(fb.ext_kwargs)), file=self.file)
if self.setup_stat is not None:
self._write_a_stat('setup', self.setup_stat)
if self.foward_stat is not None:
self._write_a_stat('forward', self.forward_stat)
if self.backward_stat is not None:
self._write_a_stat('backward', self.backward_stat) | [
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242,708 | 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 + self.func_args), **self.func_kwargs)
if not hasattr(outputs, '__iter__'):
self.outputs = [outputs]
else:
self.outputs = outputs
self.func_ins = self.outputs[0].parent
self.inputs = self.func_ins.inputs | python | def _setup(self, delete=True):
if delete:
self.clear()
with nn.context_scope(self.ctx):
outputs = self.func(
*(self.inputs_f + self.func_args), **self.func_kwargs)
if not hasattr(outputs, '__iter__'):
self.outputs = [outputs]
else:
self.outputs = outputs
self.func_ins = self.outputs[0].parent
self.inputs = self.func_ins.inputs | [
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242,709 | sony/nnabla | python/benchmark/function/function_benchmark.py | FunctionBenchmark.benchmark_setup | def benchmark_setup(self):
"""Benchmark setup execution.
"""
def f():
self._setup()
self.mod_ext.synchronize(**self.ext_kwargs)
f() # Ignore first
self.setup_stat = self._calc_benchmark_stat(f) | python | def benchmark_setup(self):
def f():
self._setup()
self.mod_ext.synchronize(**self.ext_kwargs)
f() # Ignore first
self.setup_stat = self._calc_benchmark_stat(f) | [
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242,710 | sony/nnabla | python/benchmark/function/function_benchmark.py | FunctionBenchmark.benchmark_forward | def benchmark_forward(self):
"""Benchmark forward execution.
"""
self._setup()
def f():
self._forward()
self.mod_ext.synchronize(**self.ext_kwargs)
f() # Ignore first
self.forward_stat = self._calc_benchmark_stat(f) | python | def benchmark_forward(self):
self._setup()
def f():
self._forward()
self.mod_ext.synchronize(**self.ext_kwargs)
f() # Ignore first
self.forward_stat = self._calc_benchmark_stat(f) | [
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242,711 | 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,
this benchmark system considers the error is because the function
doesn't support backward operation, then set the benchmark
``None``.
"""
try:
self._benchmark_backward()
except RuntimeError as e:
# Seems like not implemented.
print(e)
self.mod_ext.synchronize(**self.ext_kwargs)
self.backward_stat = None | python | def benchmark_backward(self):
try:
self._benchmark_backward()
except RuntimeError as e:
# Seems like not implemented.
print(e)
self.mod_ext.synchronize(**self.ext_kwargs)
self.backward_stat = None | [
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242,712 | 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':
pass
else:
raise ValueError("Unknown data type config is given %s" % type_config)
return nn.Context(backends, array_classes()[0], '') | python | def context(type_config='float', **kw):
backends = ['cpu:float']
if type_config == 'half':
backends = ['cpu:half', 'cpu:float']
elif type_config == 'float':
pass
else:
raise ValueError("Unknown data type config is given %s" % type_config)
return nn.Context(backends, array_classes()[0], '') | [
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242,713 | 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
FLOAT32, it affects the memory consumption.
'''
size_mapping = {
'FLOAT32': 4,
'FIXED16': 2,
'FIXED8': 1
}
var_dict = settings['variables']
buffer_index = 0
info._variable_sizes = []
info._variable_buffer_index = collections.OrderedDict()
info._variable_buffer_size = collections.OrderedDict()
info._buffer_ids = {}
for n, v in enumerate(info._network.variable):
byte_per_item = size_mapping.get(var_dict.get(
v.name, 'FLOAT32').split('_')[0], 4)
size = nnabla.utils.converter.calc_shape_size(
v.shape, info._batch_size) * byte_per_item
info._variable_sizes.append(size)
if v.type == 'Buffer':
info._variable_buffer_index[buffer_index] = [n]
for vid in info._variable_buffer_index[buffer_index]:
info._buffer_ids[vid] = buffer_index
info._variable_buffer_size[buffer_index] = size
buffer_index += 1 | python | 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
FLOAT32, it affects the memory consumption.
'''
size_mapping = {
'FLOAT32': 4,
'FIXED16': 2,
'FIXED8': 1
}
var_dict = settings['variables']
buffer_index = 0
info._variable_sizes = []
info._variable_buffer_index = collections.OrderedDict()
info._variable_buffer_size = collections.OrderedDict()
info._buffer_ids = {}
for n, v in enumerate(info._network.variable):
byte_per_item = size_mapping.get(var_dict.get(
v.name, 'FLOAT32').split('_')[0], 4)
size = nnabla.utils.converter.calc_shape_size(
v.shape, info._batch_size) * byte_per_item
info._variable_sizes.append(size)
if v.type == 'Buffer':
info._variable_buffer_index[buffer_index] = [n]
for vid in info._variable_buffer_index[buffer_index]:
info._buffer_ids[vid] = buffer_index
info._variable_buffer_size[buffer_index] = size
buffer_index += 1 | [
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242,714 | 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 = fd.read().splitlines()
return self._category_names | python | 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 = fd.read().splitlines()
return self._category_names | [
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242,715 | 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 self.check_same(f, b):
raise AssertionError()
args_info = ", ".join(["{}: {}".format(k, v)
for k, v in f["args_info"]])
out = [f["parameter_scope"], f["function_name"], f["inputs_shape"], args_info,
f["mean_time"], b["mean_time"], f["n_run"], b["n_run"]]
writer.writerow(out)
writer.writerow([])
writer.writerow(["forward all", self.gb.result["forward_all"]])
writer.writerow(
["forward_all_n_run", self.gb.result["n_run_forward_all"]])
writer.writerow([])
writer.writerow(["backward all", self.gb.result["backward_all"]])
writer.writerow(
["backward_all_n_run", self.gb.result["n_run_backward_all"]])
if set(self.gb.result.keys()) >= {"training", "n_run_training"}:
writer.writerow([])
writer.writerow(
["training(forward + backward + update)", self.gb.result["training"]])
writer.writerow(
["training_n_run", self.gb.result["n_run_training"]]) | python | def write(self):
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 self.check_same(f, b):
raise AssertionError()
args_info = ", ".join(["{}: {}".format(k, v)
for k, v in f["args_info"]])
out = [f["parameter_scope"], f["function_name"], f["inputs_shape"], args_info,
f["mean_time"], b["mean_time"], f["n_run"], b["n_run"]]
writer.writerow(out)
writer.writerow([])
writer.writerow(["forward all", self.gb.result["forward_all"]])
writer.writerow(
["forward_all_n_run", self.gb.result["n_run_forward_all"]])
writer.writerow([])
writer.writerow(["backward all", self.gb.result["backward_all"]])
writer.writerow(
["backward_all_n_run", self.gb.result["n_run_backward_all"]])
if set(self.gb.result.keys()) >= {"training", "n_run_training"}:
writer.writerow([])
writer.writerow(
["training(forward + backward + update)", self.gb.result["training"]])
writer.writerow(
["training_n_run", self.gb.result["n_run_training"]]) | [
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242,716 | 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.plot`.
Note:
matplotlib package is required.
'''
import matplotlib.pyplot as plt
if plot_kwargs is None:
plot_kwargs = {}
data = np.genfromtxt(filename, dtype='i8,f4', names=['k', 'v'])
index = data['k']
values = data['v']
plt.plot(index, values, **plot_kwargs) | 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.plot`.
Note:
matplotlib package is required.
'''
import matplotlib.pyplot as plt
if plot_kwargs is None:
plot_kwargs = {}
data = np.genfromtxt(filename, dtype='i8,f4', names=['k', 'v'])
index = data['k']
values = data['v']
plt.plot(index, values, **plot_kwargs) | [
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242,717 | 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.
unit (str):
Time unit chosen from ``'s'``, ``'m'``, ``'h'``, or ``'d'``.
plot_kwags (dict, optional):
Keyward arguments passed to :function:`matplotlib.pyplot.plot`.
Note:
matplotlib package is required.
'''
import matplotlib.pyplot as plt
if plot_kwargs is None:
plot_kwargs = {}
data_column = 3 if elapsed else 1
data = np.genfromtxt(filename, dtype='i8,f4',
usecols=(0, data_column), names=['k', 'v'])
index = data['k']
values = data['v']
if unit == 's':
pass
elif unit == 'm':
values /= 60
elif unit == 'h':
values /= 3600
elif unit == 'd':
values /= 3600 * 24
else:
raise ValueError('The argument `unit` must be chosen from {s|m|h|d}.')
plt.plot(index, values, **plot_kwargs) | 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.
elapsed (bool): If ``True``, it plots the total elapsed time.
unit (str):
Time unit chosen from ``'s'``, ``'m'``, ``'h'``, or ``'d'``.
plot_kwags (dict, optional):
Keyward arguments passed to :function:`matplotlib.pyplot.plot`.
Note:
matplotlib package is required.
'''
import matplotlib.pyplot as plt
if plot_kwargs is None:
plot_kwargs = {}
data_column = 3 if elapsed else 1
data = np.genfromtxt(filename, dtype='i8,f4',
usecols=(0, data_column), names=['k', 'v'])
index = data['k']
values = data['v']
if unit == 's':
pass
elif unit == 'm':
values /= 60
elif unit == 'h':
values /= 3600
elif unit == 'd':
values /= 3600 * 24
else:
raise ValueError('The argument `unit` must be chosen from {s|m|h|d}.')
plt.plot(index, values, **plot_kwargs) | [
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Time unit chosen from ``'s'``, ``'m'``, ``'h'``, or ``'d'``.
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242,718 | 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.
"""
self.buf.append(value)
if (index - self.flush_at) < self.interval:
return
value = np.mean(self.buf)
if self.verbose:
logger.info("iter={} {{{}}}={}".format(index, self.name, value))
if self.fd is not None:
print("{} {:g}".format(index, value), file=self.fd)
self.flush_at = index
self.buf = [] | python | def add(self, index, value):
self.buf.append(value)
if (index - self.flush_at) < self.interval:
return
value = np.mean(self.buf)
if self.verbose:
logger.info("iter={} {{{}}}={}".format(index, self.name, value))
if self.fd is not None:
print("{} {:g}".format(index, value), file=self.fd)
self.flush_at = index
self.buf = [] | [
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242,719 | sony/nnabla | python/src/nnabla/monitor.py | MonitorTimeElapsed.add | def add(self, index):
"""Calculate time elapsed from the point previously called
this method or this object is created to this is called.
Args:
index (int): Index to be displayed, and be used to take intervals.
"""
if (index - self.flush_at) < self.interval:
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it = index - self.flush_at
self.lap = now
if self.verbose:
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if self.fd is not None:
print("{} {} {} {}".format(index, elapsed,
it, elapsed_total), file=self.fd)
self.flush_at = index | python | def add(self, index):
if (index - self.flush_at) < self.interval:
return
now = time.time()
elapsed = now - self.lap
elapsed_total = now - self.start
it = index - self.flush_at
self.lap = now
if self.verbose:
logger.info("iter={} {{{}}}={}[sec/{}iter] {}[sec]".format(
index, self.name, elapsed, it, elapsed_total))
if self.fd is not None:
print("{} {} {} {}".format(index, elapsed,
it, elapsed_total), file=self.fd)
self.flush_at = index | [
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242,720 | 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`):
A minibatch of images with ``(N, ..., C, H, W)`` format.
If C == 2, blue channel is appended with ones. If C > 3,
the array will be sliced to remove C > 3 sub-array.
"""
import nnabla as nn
from nnabla.utils.image_utils import imsave
if index != 0 and (index + 1) % self.interval != 0:
return
if isinstance(var, nn.Variable):
data = var.d.copy()
elif isinstance(var, nn.NdArray):
data = var.data.copy()
else:
assert isinstance(var, np.ndarray)
data = var.copy()
assert data.ndim > 2
channels = data.shape[-3]
data = data.reshape(-1, *data.shape[-3:])
data = data[:min(data.shape[0], self.num_images)]
data = self.normalize_method(data)
if channels > 3:
data = data[:, :3]
elif channels == 2:
data = np.concatenate(
[data, np.ones((data.shape[0], 1) + data.shape[-2:])], axis=1)
path_tmpl = os.path.join(self.save_dir, '{:06d}-{}.png')
for j in range(min(self.num_images, data.shape[0])):
img = data[j].transpose(1, 2, 0)
if img.shape[-1] == 1:
img = img[..., 0]
path = path_tmpl.format(index, '{:03d}'.format(j))
imsave(path, img)
if self.verbose:
logger.info("iter={} {{{}}} are written to {}.".format(
index, self.name, path_tmpl.format(index, '*'))) | python | def add(self, index, var):
import nnabla as nn
from nnabla.utils.image_utils import imsave
if index != 0 and (index + 1) % self.interval != 0:
return
if isinstance(var, nn.Variable):
data = var.d.copy()
elif isinstance(var, nn.NdArray):
data = var.data.copy()
else:
assert isinstance(var, np.ndarray)
data = var.copy()
assert data.ndim > 2
channels = data.shape[-3]
data = data.reshape(-1, *data.shape[-3:])
data = data[:min(data.shape[0], self.num_images)]
data = self.normalize_method(data)
if channels > 3:
data = data[:, :3]
elif channels == 2:
data = np.concatenate(
[data, np.ones((data.shape[0], 1) + data.shape[-2:])], axis=1)
path_tmpl = os.path.join(self.save_dir, '{:06d}-{}.png')
for j in range(min(self.num_images, data.shape[0])):
img = data[j].transpose(1, 2, 0)
if img.shape[-1] == 1:
img = img[..., 0]
path = path_tmpl.format(index, '{:03d}'.format(j))
imsave(path, img)
if self.verbose:
logger.info("iter={} {{{}}} are written to {}.".format(
index, self.name, path_tmpl.format(index, '*'))) | [
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A minibatch of images with ``(N, ..., C, H, W)`` format.
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242,721 | 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=None,
epoch_begin_callbacks=[],
epoch_end_callbacks=[]):
"""A generator that ``yield`` s minibatch data as a tuple, as defined in ``load_func`` .
It can unlimitedly yield minibatches at your request, queried from the provided data.
Args:
load_func (function): Takes a single argument `i`, an index of an
example in your dataset to be loaded, and returns a tuple of data.
Every call by any index `i` must return a tuple of arrays with
the same shape.
num_examples (int): Number of examples in your dataset. Random sequence
of indexes is generated according to this number.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator.
Here is an example of `load_func` which returns an image and a label of a
classification dataset.
.. code-block:: python
import numpy as np
from nnabla.utils.image_utils import imread
image_paths = load_image_paths()
labels = load_labels()
def my_load_func(i):
'''
Returns:
image: c x h x w array
label: 0-shape array
'''
img = imread(image_paths[i]).astype('float32')
return np.rollaxis(img, 2), np.array(labels[i])
"""
return data_iterator(SimpleDataSource(load_func,
num_examples,
shuffle=shuffle,
rng=rng),
batch_size=batch_size,
with_memory_cache=with_memory_cache,
with_file_cache=with_file_cache,
cache_dir=cache_dir,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks) | 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=None,
epoch_begin_callbacks=[],
epoch_end_callbacks=[]):
return data_iterator(SimpleDataSource(load_func,
num_examples,
shuffle=shuffle,
rng=rng),
batch_size=batch_size,
with_memory_cache=with_memory_cache,
with_file_cache=with_file_cache,
cache_dir=cache_dir,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks) | [
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It can unlimitedly yield minibatches at your request, queried from the provided data.
Args:
load_func (function): Takes a single argument `i`, an index of an
example in your dataset to be loaded, and returns a tuple of data.
Every call by any index `i` must return a tuple of arrays with
the same shape.
num_examples (int): Number of examples in your dataset. Random sequence
of indexes is generated according to this number.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator.
Here is an example of `load_func` which returns an image and a label of a
classification dataset.
.. code-block:: python
import numpy as np
from nnabla.utils.image_utils import imread
image_paths = load_image_paths()
labels = load_labels()
def my_load_func(i):
'''
Returns:
image: c x h x w array
label: 0-shape array
'''
img = imread(image_paths[i]).astype('float32')
return np.rollaxis(img, 2), np.array(labels[i]) | [
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242,722 | 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,
cache_dir=None,
epoch_begin_callbacks=[],
epoch_end_callbacks=[]):
'''data_iterator_csv_dataset
Get data directly from a dataset provided as a CSV file.
You can read files located on the local file system, http(s) servers or Amazon AWS S3 storage.
For example,
.. code-block:: python
batch = data_iterator_csv_dataset('CSV_FILE.csv', batch_size, shuffle=True)
Args:
uri (str): Location of dataset CSV file.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
normalize (bool): If True, each sample in the data gets normalized by a factor of 255.
Default is True.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator
'''
ds = CsvDataSource(uri,
shuffle=shuffle,
rng=rng,
normalize=normalize)
return data_iterator(ds,
batch_size=batch_size,
with_memory_cache=with_memory_cache,
with_file_cache=with_file_cache,
cache_dir=cache_dir,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks) | python | def data_iterator_csv_dataset(uri,
batch_size,
shuffle=False,
rng=None,
normalize=True,
with_memory_cache=True,
with_file_cache=True,
cache_dir=None,
epoch_begin_callbacks=[],
epoch_end_callbacks=[]):
'''data_iterator_csv_dataset
Get data directly from a dataset provided as a CSV file.
You can read files located on the local file system, http(s) servers or Amazon AWS S3 storage.
For example,
.. code-block:: python
batch = data_iterator_csv_dataset('CSV_FILE.csv', batch_size, shuffle=True)
Args:
uri (str): Location of dataset CSV file.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
normalize (bool): If True, each sample in the data gets normalized by a factor of 255.
Default is True.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator
'''
ds = CsvDataSource(uri,
shuffle=shuffle,
rng=rng,
normalize=normalize)
return data_iterator(ds,
batch_size=batch_size,
with_memory_cache=with_memory_cache,
with_file_cache=with_file_cache,
cache_dir=cache_dir,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks) | [
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Get data directly from a dataset provided as a CSV file.
You can read files located on the local file system, http(s) servers or Amazon AWS S3 storage.
For example,
.. code-block:: python
batch = data_iterator_csv_dataset('CSV_FILE.csv', batch_size, shuffle=True)
Args:
uri (str): Location of dataset CSV file.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
normalize (bool): If True, each sample in the data gets normalized by a factor of 255.
Default is True.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
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242,723 | 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=[]):
'''data_iterator_cache
Get data from the cache directory.
Cache files are read from the local file system.
For example,
.. code-block:: python
batch = data_iterator_cache('CACHE_DIR', batch_size, shuffle=True)
Args:
uri (str): Location of directory with cache files.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
normalize (bool): If True, each sample in the data gets normalized by a factor of 255.
Default is True.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator
'''
ds = CacheDataSource(uri,
shuffle=shuffle,
rng=rng,
normalize=normalize)
return data_iterator(ds,
batch_size=batch_size,
with_memory_cache=with_memory_cache,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks) | python | def data_iterator_cache(uri,
batch_size,
shuffle=False,
rng=None,
normalize=True,
with_memory_cache=True,
epoch_begin_callbacks=[],
epoch_end_callbacks=[]):
'''data_iterator_cache
Get data from the cache directory.
Cache files are read from the local file system.
For example,
.. code-block:: python
batch = data_iterator_cache('CACHE_DIR', batch_size, shuffle=True)
Args:
uri (str): Location of directory with cache files.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
normalize (bool): If True, each sample in the data gets normalized by a factor of 255.
Default is True.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator
'''
ds = CacheDataSource(uri,
shuffle=shuffle,
rng=rng,
normalize=normalize)
return data_iterator(ds,
batch_size=batch_size,
with_memory_cache=with_memory_cache,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks) | [
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Get data from the cache directory.
Cache files are read from the local file system.
For example,
.. code-block:: python
batch = data_iterator_cache('CACHE_DIR', batch_size, shuffle=True)
Args:
uri (str): Location of directory with cache files.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
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If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
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:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
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242,724 | 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,
cache_dir=None,
epoch_begin_callbacks=[],
epoch_end_callbacks=[]):
'''data_iterator_concat_datasets
Get data from multiple datasets.
For example,
.. code-block:: python
batch = data_iterator_concat_datasets([DataSource0, DataSource1, ...], batch_size)
Args:
data_source_list (list of DataSource): list of datasets.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator
'''
ds = ConcatDataSource(data_source_list,
shuffle=shuffle,
rng=rng)
return data_iterator(ds,
batch_size=batch_size,
with_memory_cache=with_memory_cache,
with_file_cache=with_file_cache,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks) | python | def data_iterator_concat_datasets(data_source_list,
batch_size,
shuffle=False,
rng=None,
with_memory_cache=True,
with_file_cache=False,
cache_dir=None,
epoch_begin_callbacks=[],
epoch_end_callbacks=[]):
'''data_iterator_concat_datasets
Get data from multiple datasets.
For example,
.. code-block:: python
batch = data_iterator_concat_datasets([DataSource0, DataSource1, ...], batch_size)
Args:
data_source_list (list of DataSource): list of datasets.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator
'''
ds = ConcatDataSource(data_source_list,
shuffle=shuffle,
rng=rng)
return data_iterator(ds,
batch_size=batch_size,
with_memory_cache=with_memory_cache,
with_file_cache=with_file_cache,
epoch_begin_callbacks=epoch_begin_callbacks,
epoch_end_callbacks=epoch_end_callbacks) | [
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Get data from multiple datasets.
For example,
.. code-block:: python
batch = data_iterator_concat_datasets([DataSource0, DataSource1, ...], batch_size)
Args:
data_source_list (list of DataSource): list of datasets.
batch_size (int): Size of data unit.
shuffle (bool):
Indicates whether the dataset is shuffled or not.
Default value is False.
rng (None or :obj:`numpy.random.RandomState`): Numpy random number
generator.
with_memory_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithMemoryCache`
to wrap ``data_source``. It is a good idea to set this as true unless
data_source provides on-memory data.
Default value is True.
with_file_cache (bool):
If ``True``, use :py:class:`.data_source.DataSourceWithFileCache`
to wrap ``data_source``.
If ``data_source`` is slow, enabling this option a is good idea.
Default value is False.
cache_dir (str):
Location of file_cache.
If this value is None, :py:class:`.data_source.DataSourceWithFileCache`
creates file caches implicitly on temporary directory and erases them all
when data_iterator is finished.
Otherwise, :py:class:`.data_source.DataSourceWithFileCache` keeps created cache.
Default is None.
epoch_begin_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the beginning of an epoch.
epoch_end_callbacks (list of functions): An item is a function
which takes an epoch index as an argument. These are called
at the end of an epoch.
Returns:
:py:class:`DataIterator <nnabla.utils.data_iterator.DataIterator>`:
Instance of DataIterator | [
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242,725 | 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.RandomState): Random generator for Initializer.
num_of_slices(int): Total number of slices to be made. Muts be used together with `slice_pos`.
slice_pos(int): Position of the slice to be assigned to the new data iterator. Must be used together with `num_of_slices`.
slice_start(int): Starting position of the range to be sliced into new data iterator. Must be used together with `slice_end`.
slice_end(int) : End position of the range to be sliced into new data iterator. Must be used together with `slice_start`.
cache_dir(str) : Directory to save cache files
Example:
.. code-block:: python
from nnabla.utils.data_iterator import data_iterator_simple
import numpy as np
def load_func1(index):
d = np.ones((2, 2)) * index
return d
di = data_iterator_simple(load_func1, 1000, batch_size=3)
di_s1 = di.slice(None, num_of_slices=10, slice_pos=0)
di_s2 = di.slice(None, num_of_slices=10, slice_pos=1)
di_s3 = di.slice(None, slice_start=100, slice_end=200)
di_s4 = di.slice(None, slice_start=300, slice_end=400)
'''
if num_of_slices is not None and slice_pos is not None and slice_start is None and slice_end is None:
size = self._size // num_of_slices
amount = self._size % num_of_slices
slice_start = slice_pos * size
if slice_pos < amount:
slice_start += slice_pos
else:
slice_start += amount
slice_end = slice_start + size
if slice_end > self._size:
slice_start -= (slice_end - self._size)
slice_end = self._size
elif num_of_slices is None and slice_pos is None and slice_start is not None and slice_end is not None:
pass
else:
logger.critical(
'You must specify position(num_of_slice and slice_pos) or range(slice_start and slice_end).')
return None
if cache_dir is None:
ds = self._data_source
while '_data_source' in dir(ds):
if '_cache_dir' in dir(ds):
cache_dir = ds._cache_dir
ds = ds._data_source
if cache_dir is None:
return DataIterator(
DataSourceWithMemoryCache(
SlicedDataSource(
self._data_source,
self._data_source.shuffle,
slice_start=slice_start,
slice_end=slice_end),
shuffle=self._shuffle,
rng=rng),
self._batch_size)
else:
return DataIterator(
DataSourceWithMemoryCache(
DataSourceWithFileCache(
SlicedDataSource(
self._data_source,
self._data_source.shuffle,
slice_start=slice_start,
slice_end=slice_end),
cache_dir=cache_dir,
cache_file_name_prefix='cache_sliced_{:08d}_{:08d}'.format(
slice_start,
slice_end),
shuffle=self._shuffle,
rng=rng),
shuffle=self._shuffle,
rng=rng),
self._batch_size) | python | 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.RandomState): Random generator for Initializer.
num_of_slices(int): Total number of slices to be made. Muts be used together with `slice_pos`.
slice_pos(int): Position of the slice to be assigned to the new data iterator. Must be used together with `num_of_slices`.
slice_start(int): Starting position of the range to be sliced into new data iterator. Must be used together with `slice_end`.
slice_end(int) : End position of the range to be sliced into new data iterator. Must be used together with `slice_start`.
cache_dir(str) : Directory to save cache files
Example:
.. code-block:: python
from nnabla.utils.data_iterator import data_iterator_simple
import numpy as np
def load_func1(index):
d = np.ones((2, 2)) * index
return d
di = data_iterator_simple(load_func1, 1000, batch_size=3)
di_s1 = di.slice(None, num_of_slices=10, slice_pos=0)
di_s2 = di.slice(None, num_of_slices=10, slice_pos=1)
di_s3 = di.slice(None, slice_start=100, slice_end=200)
di_s4 = di.slice(None, slice_start=300, slice_end=400)
'''
if num_of_slices is not None and slice_pos is not None and slice_start is None and slice_end is None:
size = self._size // num_of_slices
amount = self._size % num_of_slices
slice_start = slice_pos * size
if slice_pos < amount:
slice_start += slice_pos
else:
slice_start += amount
slice_end = slice_start + size
if slice_end > self._size:
slice_start -= (slice_end - self._size)
slice_end = self._size
elif num_of_slices is None and slice_pos is None and slice_start is not None and slice_end is not None:
pass
else:
logger.critical(
'You must specify position(num_of_slice and slice_pos) or range(slice_start and slice_end).')
return None
if cache_dir is None:
ds = self._data_source
while '_data_source' in dir(ds):
if '_cache_dir' in dir(ds):
cache_dir = ds._cache_dir
ds = ds._data_source
if cache_dir is None:
return DataIterator(
DataSourceWithMemoryCache(
SlicedDataSource(
self._data_source,
self._data_source.shuffle,
slice_start=slice_start,
slice_end=slice_end),
shuffle=self._shuffle,
rng=rng),
self._batch_size)
else:
return DataIterator(
DataSourceWithMemoryCache(
DataSourceWithFileCache(
SlicedDataSource(
self._data_source,
self._data_source.shuffle,
slice_start=slice_start,
slice_end=slice_end),
cache_dir=cache_dir,
cache_file_name_prefix='cache_sliced_{:08d}_{:08d}'.format(
slice_start,
slice_end),
shuffle=self._shuffle,
rng=rng),
shuffle=self._shuffle,
rng=rng),
self._batch_size) | [
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Args:
rng (numpy.random.RandomState): Random generator for Initializer.
num_of_slices(int): Total number of slices to be made. Muts be used together with `slice_pos`.
slice_pos(int): Position of the slice to be assigned to the new data iterator. Must be used together with `num_of_slices`.
slice_start(int): Starting position of the range to be sliced into new data iterator. Must be used together with `slice_end`.
slice_end(int) : End position of the range to be sliced into new data iterator. Must be used together with `slice_start`.
cache_dir(str) : Directory to save cache files
Example:
.. code-block:: python
from nnabla.utils.data_iterator import data_iterator_simple
import numpy as np
def load_func1(index):
d = np.ones((2, 2)) * index
return d
di = data_iterator_simple(load_func1, 1000, batch_size=3)
di_s1 = di.slice(None, num_of_slices=10, slice_pos=0)
di_s2 = di.slice(None, num_of_slices=10, slice_pos=1)
di_s3 = di.slice(None, slice_start=100, slice_end=200)
di_s4 = di.slice(None, slice_start=300, slice_end=400) | [
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242,726 | 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
__auto_forward_state = auto
yield
__auto_forward_state = prev | python | def auto_forward(auto=True):
global __auto_forward_state
prev = __auto_forward_state
__auto_forward_state = auto
yield
__auto_forward_state = prev | [
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Args:
auto (bool): Whether forward computation is executed during a
computation graph construction.
Returns: bool | [
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] | aaf3d33b7cbb38f2a03aa754178ba8f7c8481320 | https://github.com/sony/nnabla/blob/aaf3d33b7cbb38f2a03aa754178ba8f7c8481320/python/src/nnabla/auto_forward.py#L23-L38 |
242,727 | 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 statistics.
'''
if not self.ncalls:
return
stats = self.stats
code = self.fn.__code__
print('--- Function Profiling ---')
print('File "{}", line {}, function {}'.format(
code.co_filename,
code.co_firstlineno,
self.fn.__name__))
stats.sort_stats(*self.sort_keys)
stats.print_stats(*self.print_restrictions)
print('--------------------------')
if reset:
self.reset_stats() | python | 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 statistics.
'''
if not self.ncalls:
return
stats = self.stats
code = self.fn.__code__
print('--- Function Profiling ---')
print('File "{}", line {}, function {}'.format(
code.co_filename,
code.co_firstlineno,
self.fn.__name__))
stats.sort_stats(*self.sort_keys)
stats.print_stats(*self.print_restrictions)
print('--------------------------')
if reset:
self.reset_stats() | [
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242,728 | 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)
return d | [
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242,729 | 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))
else:
url_base = 'https://nnabla.org/pretrained-models/nnp_models/'
return url_base | 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))
else:
url_base = 'https://nnabla.org/pretrained-models/nnp_models/'
return url_base | [
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242,730 | 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: the value of return array ranges from 0 to `max_range`.
:return: numpy array
'''
img255 = imread(
file) # return value is from zero to 255 (even if the image has 16-bitdepth.)
if len(img255.shape) == 2: # gray image
height, width = img255.shape
if shape is None:
out_height, out_width, out_n_color = height, width, 1
else:
out_n_color, out_height, out_width = shape
assert(out_n_color == 1)
if out_height != height or out_width != width:
# imresize returns 0 to 255 image.
img255 = imresize(img255, (out_height, out_width))
img255 = img255.reshape((out_n_color, out_height, out_width))
elif len(img255.shape) == 3: # RGB image
height, width, n_color = img255.shape
if shape is None:
out_height, out_width, out_n_color = height, width, n_color
else:
out_n_color, out_height, out_width = shape
assert(out_n_color == n_color)
if out_height != height or out_width != width or out_n_color != n_color:
# imresize returns 0 to 255 image.
img255 = imresize(img255, (out_height, out_width, out_n_color))
img255 = img255.transpose(2, 0, 1)
if max_range < 0 or max_range == 255.0:
return img255
else:
return img255 * (max_range / 255.0) | 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
e.g. (3, 128, 192) : n_color, height, width.
:type shape: tuple of int
:param float max_range: the value of return array ranges from 0 to `max_range`.
:return: numpy array
'''
img255 = imread(
file) # return value is from zero to 255 (even if the image has 16-bitdepth.)
if len(img255.shape) == 2: # gray image
height, width = img255.shape
if shape is None:
out_height, out_width, out_n_color = height, width, 1
else:
out_n_color, out_height, out_width = shape
assert(out_n_color == 1)
if out_height != height or out_width != width:
# imresize returns 0 to 255 image.
img255 = imresize(img255, (out_height, out_width))
img255 = img255.reshape((out_n_color, out_height, out_width))
elif len(img255.shape) == 3: # RGB image
height, width, n_color = img255.shape
if shape is None:
out_height, out_width, out_n_color = height, width, n_color
else:
out_n_color, out_height, out_width = shape
assert(out_n_color == n_color)
if out_height != height or out_width != width or out_n_color != n_color:
# imresize returns 0 to 255 image.
img255 = imresize(img255, (out_height, out_width, out_n_color))
img255 = img255.transpose(2, 0, 1)
if max_range < 0 or max_range == 255.0:
return img255
else:
return img255 * (max_range / 255.0) | [
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242,731 | 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.reader(file):
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elif six.PY34:
for row in csv.reader([l.decode('utf-8') for l in file.readlines()]):
value_list.append(list(map(float, row)))
if shape is None:
return numpy.array(value_list)
else:
return numpy.array(value_list).reshape(shape) | python | def load_csv(file, shape=None, normalize=False):
value_list = []
if six.PY2:
for row in csv.reader(file):
value_list.append(list(map(float, row)))
elif six.PY34:
for row in csv.reader([l.decode('utf-8') for l in file.readlines()]):
value_list.append(list(map(float, row)))
if shape is None:
return numpy.array(value_list)
else:
return numpy.array(value_list).reshape(shape) | [
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242,732 | sony/nnabla | python/src/nnabla/experimental/viewers.py | SimpleGraph.save | def save(self, vleaf, fpath, cleanup=False, format=None):
"""Save the graph to a given file path.
Args:
vleaf (`nnabla.Variable`): End variable. All variables and functions which can be traversed from this variable are shown in the reuslt.
fpath (`str`): The file path used to save.
cleanup (`bool`): Clean up the source file after rendering. Default is False.
format (str):
Force overwrite ``format`` (``'pdf', 'png', ...)``) configuration.
"""
graph = self.create_graphviz_digraph(vleaf, format=format)
graph.render(fpath, cleanup=cleanup) | python | def save(self, vleaf, fpath, cleanup=False, format=None):
graph = self.create_graphviz_digraph(vleaf, format=format)
graph.render(fpath, cleanup=cleanup) | [
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242,733 | sony/nnabla | python/src/nnabla/experimental/viewers.py | SimpleGraph.view | def view(self, vleaf, fpath=None, cleanup=True, format=None):
"""View the graph.
Args:
vleaf (`nnabla.Variable`): End variable. All variables and functions which can be traversed from this variable are shown in the reuslt.
fpath (`str`): The file path used to save.
cleanup (`bool`): Clean up the source file after rendering. Default is True.
format (str):
Force overwrite ``format`` (``'pdf', 'png', ...)``) configuration.
"""
graph = self.create_graphviz_digraph(vleaf, format=format)
graph.view(fpath, cleanup=cleanup) | python | def view(self, vleaf, fpath=None, cleanup=True, format=None):
graph = self.create_graphviz_digraph(vleaf, format=format)
graph.view(fpath, cleanup=cleanup) | [
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242,734 | 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.
Yields:
`Module`: The module class.
"""
if memo is None:
memo = set()
if self not in memo:
memo.add(self)
yield prefix, self
for k, v in self.__dict__.items():
if not isinstance(v, Module):
continue
name, module = k, v
submodule_prefix = "{}/{}".format(prefix,
name) if prefix != "" else name
for m in module.get_modules(memo, submodule_prefix):
yield m | python | def get_modules(self, memo=None, prefix=""):
if memo is None:
memo = set()
if self not in memo:
memo.add(self)
yield prefix, self
for k, v in self.__dict__.items():
if not isinstance(v, Module):
continue
name, module = k, v
submodule_prefix = "{}/{}".format(prefix,
name) if prefix != "" else name
for m in module.get_modules(memo, submodule_prefix):
yield m | [
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242,735 | 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)
return value | python | def get_prep_value(self, value):
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)
return value | [
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242,736 | 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):
if value is None:
return value
if _using_signed_storage():
value = _signed_to_unsigned_integer(value)
return value | [
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242,737 | jazzband/django-push-notifications | push_notifications/fields.py | HexIntegerField.to_python | def to_python(self, value):
""" Return a str representation of the hexadecimal """
if isinstance(value, six.string_types):
return value
if value is None:
return value
return _unsigned_integer_to_hex_string(value) | python | def to_python(self, value):
if isinstance(value, six.string_types):
return value
if value is None:
return value
return _unsigned_integer_to_hex_string(value) | [
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242,738 | jazzband/django-push-notifications | push_notifications/apns.py | apns_send_bulk_message | def apns_send_bulk_message(
registration_ids, alert, application_id=None, certfile=None, **kwargs
):
"""
Sends an APNS notification to one or more registration_ids.
The registration_ids argument needs to be a list.
Note that if set alert should always be a string. If it is not set,
it won"t be included in the notification. You will need to pass None
to this for silent notifications.
"""
results = _apns_send(
registration_ids, alert, batch=True, application_id=application_id,
certfile=certfile, **kwargs
)
inactive_tokens = [token for token, result in results.items() if result == "Unregistered"]
models.APNSDevice.objects.filter(registration_id__in=inactive_tokens).update(active=False)
return results | python | def apns_send_bulk_message(
registration_ids, alert, application_id=None, certfile=None, **kwargs
):
results = _apns_send(
registration_ids, alert, batch=True, application_id=application_id,
certfile=certfile, **kwargs
)
inactive_tokens = [token for token, result in results.items() if result == "Unregistered"]
models.APNSDevice.objects.filter(registration_id__in=inactive_tokens).update(active=False)
return results | [
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242,739 | jazzband/django-push-notifications | push_notifications/gcm.py | _cm_send_request | def _cm_send_request(
registration_ids, data, cloud_type="GCM", application_id=None,
use_fcm_notifications=True, **kwargs
):
"""
Sends a FCM or GCM notification to one or more registration_ids as json data.
The registration_ids needs to be a list.
"""
payload = {"registration_ids": registration_ids} if registration_ids else {}
data = data.copy()
# If using FCM, optionnally autodiscovers notification related keys
# https://firebase.google.com/docs/cloud-messaging/concept-options#notifications_and_data_messages
if cloud_type == "FCM" and use_fcm_notifications:
notification_payload = {}
if "message" in data:
notification_payload["body"] = data.pop("message", None)
for key in FCM_NOTIFICATIONS_PAYLOAD_KEYS:
value_from_extra = data.pop(key, None)
if value_from_extra:
notification_payload[key] = value_from_extra
value_from_kwargs = kwargs.pop(key, None)
if value_from_kwargs:
notification_payload[key] = value_from_kwargs
if notification_payload:
payload["notification"] = notification_payload
if data:
payload["data"] = data
# Attach any additional non falsy keyword args (targets, options)
# See ref : https://firebase.google.com/docs/cloud-messaging/http-server-ref#table1
payload.update({
k: v for k, v in kwargs.items() if v and (k in FCM_TARGETS_KEYS or k in FCM_OPTIONS_KEYS)
})
# Sort the keys for deterministic output (useful for tests)
json_payload = json.dumps(payload, separators=(",", ":"), sort_keys=True).encode("utf-8")
# Sends requests and handles the response
if cloud_type == "GCM":
response = json.loads(_gcm_send(
json_payload, "application/json", application_id=application_id
))
elif cloud_type == "FCM":
response = json.loads(_fcm_send(
json_payload, "application/json", application_id=application_id
))
else:
raise ImproperlyConfigured("cloud_type must be FCM or GCM not %s" % str(cloud_type))
return _cm_handle_response(registration_ids, response, cloud_type, application_id) | python | def _cm_send_request(
registration_ids, data, cloud_type="GCM", application_id=None,
use_fcm_notifications=True, **kwargs
):
payload = {"registration_ids": registration_ids} if registration_ids else {}
data = data.copy()
# If using FCM, optionnally autodiscovers notification related keys
# https://firebase.google.com/docs/cloud-messaging/concept-options#notifications_and_data_messages
if cloud_type == "FCM" and use_fcm_notifications:
notification_payload = {}
if "message" in data:
notification_payload["body"] = data.pop("message", None)
for key in FCM_NOTIFICATIONS_PAYLOAD_KEYS:
value_from_extra = data.pop(key, None)
if value_from_extra:
notification_payload[key] = value_from_extra
value_from_kwargs = kwargs.pop(key, None)
if value_from_kwargs:
notification_payload[key] = value_from_kwargs
if notification_payload:
payload["notification"] = notification_payload
if data:
payload["data"] = data
# Attach any additional non falsy keyword args (targets, options)
# See ref : https://firebase.google.com/docs/cloud-messaging/http-server-ref#table1
payload.update({
k: v for k, v in kwargs.items() if v and (k in FCM_TARGETS_KEYS or k in FCM_OPTIONS_KEYS)
})
# Sort the keys for deterministic output (useful for tests)
json_payload = json.dumps(payload, separators=(",", ":"), sort_keys=True).encode("utf-8")
# Sends requests and handles the response
if cloud_type == "GCM":
response = json.loads(_gcm_send(
json_payload, "application/json", application_id=application_id
))
elif cloud_type == "FCM":
response = json.loads(_fcm_send(
json_payload, "application/json", application_id=application_id
))
else:
raise ImproperlyConfigured("cloud_type must be FCM or GCM not %s" % str(cloud_type))
return _cm_handle_response(registration_ids, response, cloud_type, application_id) | [
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242,740 | 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=current_id).update(active=False)
else:
devices.filter(registration_id=current_id).update(registration_id=canonical_id) | python | def _cm_handle_canonical_id(canonical_id, current_id, cloud_type):
devices = GCMDevice.objects.filter(cloud_message_type=cloud_type)
if devices.filter(registration_id=canonical_id, active=True).exists():
devices.filter(registration_id=current_id).update(active=False)
else:
devices.filter(registration_id=current_id).update(registration_id=canonical_id) | [
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242,741 | 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):
for application_id, application_config in apps.items():
self._validate_config(application_id, application_config)
application_config["APPLICATION_ID"] = application_id | [
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242,742 | 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:
with open(certfile, "r") as f:
content = f.read()
check_apns_certificate(content)
except Exception as e:
raise ImproperlyConfigured(
"The APNS certificate file at %r is not readable: %s" % (certfile, e)
) | python | def _validate_apns_certificate(self, certfile):
try:
with open(certfile, "r") as f:
content = f.read()
check_apns_certificate(content)
except Exception as e:
raise ImproperlyConfigured(
"The APNS certificate file at %r is not readable: %s" % (certfile, e)
) | [
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242,743 | 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(
"Platform {}, app {} does not support the setting: {}.".format(
application_config["PLATFORM"], application_id, setting_key
)
) | python | def _validate_allowed_settings(self, application_id, application_config, allowed_settings):
for setting_key in application_config.keys():
if setting_key not in allowed_settings:
raise ImproperlyConfigured(
"Platform {}, app {} does not support the setting: {}.".format(
application_config["PLATFORM"], application_id, setting_key
)
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242,744 | 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(
MISSING_SETTING.format(
application_id=application_id, setting=setting_key
)
) | python | def _validate_required_settings(
self, application_id, application_config, required_settings
):
for setting_key in required_settings:
if setting_key not in application_config.keys():
raise ImproperlyConfigured(
MISSING_SETTING.format(
application_id=application_id, setting=setting_key
)
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242,745 | 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"
raise ImproperlyConfigured(
"{} requires the application_id be specified at all times.".format(conf_cls)
)
# verify that the application config exists
app_config = self._settings.get("APPLICATIONS").get(application_id, None)
if app_config is None:
raise ImproperlyConfigured(
"No application configured with application_id: {}.".format(application_id)
)
# fetch a setting for the incorrect type of platform
if app_config.get("PLATFORM") != platform:
raise ImproperlyConfigured(
SETTING_MISMATCH.format(
application_id=application_id,
platform=app_config.get("PLATFORM"),
setting=settings_key
)
)
# finally, try to fetch the setting
if settings_key not in app_config:
raise ImproperlyConfigured(
MISSING_SETTING.format(
application_id=application_id, setting=settings_key
)
)
return app_config.get(settings_key) | python | def _get_application_settings(self, application_id, platform, settings_key):
if not application_id:
conf_cls = "push_notifications.conf.AppConfig"
raise ImproperlyConfigured(
"{} requires the application_id be specified at all times.".format(conf_cls)
)
# verify that the application config exists
app_config = self._settings.get("APPLICATIONS").get(application_id, None)
if app_config is None:
raise ImproperlyConfigured(
"No application configured with application_id: {}.".format(application_id)
)
# fetch a setting for the incorrect type of platform
if app_config.get("PLATFORM") != platform:
raise ImproperlyConfigured(
SETTING_MISMATCH.format(
application_id=application_id,
platform=app_config.get("PLATFORM"),
setting=settings_key
)
)
# finally, try to fetch the setting
if settings_key not in app_config:
raise ImproperlyConfigured(
MISSING_SETTING.format(
application_id=application_id, setting=settings_key
)
)
return app_config.get(settings_key) | [
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242,746 | 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().get_wns_secret_key(application_id)
if not client_id:
raise ImproperlyConfigured(
'You need to set PUSH_NOTIFICATIONS_SETTINGS["WNS_PACKAGE_SECURITY_ID"] to use WNS.'
)
if not client_secret:
raise ImproperlyConfigured(
'You need to set PUSH_NOTIFICATIONS_SETTINGS["WNS_SECRET_KEY"] to use WNS.'
)
headers = {
"Content-Type": "application/x-www-form-urlencoded",
}
params = {
"grant_type": "client_credentials",
"client_id": client_id,
"client_secret": client_secret,
"scope": scope,
}
data = urlencode(params).encode("utf-8")
request = Request(SETTINGS["WNS_ACCESS_URL"], data=data, headers=headers)
try:
response = urlopen(request)
except HTTPError as err:
if err.code == 400:
# One of your settings is probably jacked up.
# https://msdn.microsoft.com/en-us/library/windows/apps/xaml/hh868245
raise WNSAuthenticationError("Authentication failed, check your WNS settings.")
raise err
oauth_data = response.read().decode("utf-8")
try:
oauth_data = json.loads(oauth_data)
except Exception:
# Upstream WNS issue
raise WNSAuthenticationError("Received invalid JSON data from WNS.")
access_token = oauth_data.get("access_token")
if not access_token:
# Upstream WNS issue
raise WNSAuthenticationError("Access token missing from WNS response.")
return access_token | python | def _wns_authenticate(scope="notify.windows.com", application_id=None):
client_id = get_manager().get_wns_package_security_id(application_id)
client_secret = get_manager().get_wns_secret_key(application_id)
if not client_id:
raise ImproperlyConfigured(
'You need to set PUSH_NOTIFICATIONS_SETTINGS["WNS_PACKAGE_SECURITY_ID"] to use WNS.'
)
if not client_secret:
raise ImproperlyConfigured(
'You need to set PUSH_NOTIFICATIONS_SETTINGS["WNS_SECRET_KEY"] to use WNS.'
)
headers = {
"Content-Type": "application/x-www-form-urlencoded",
}
params = {
"grant_type": "client_credentials",
"client_id": client_id,
"client_secret": client_secret,
"scope": scope,
}
data = urlencode(params).encode("utf-8")
request = Request(SETTINGS["WNS_ACCESS_URL"], data=data, headers=headers)
try:
response = urlopen(request)
except HTTPError as err:
if err.code == 400:
# One of your settings is probably jacked up.
# https://msdn.microsoft.com/en-us/library/windows/apps/xaml/hh868245
raise WNSAuthenticationError("Authentication failed, check your WNS settings.")
raise err
oauth_data = response.read().decode("utf-8")
try:
oauth_data = json.loads(oauth_data)
except Exception:
# Upstream WNS issue
raise WNSAuthenticationError("Received invalid JSON data from WNS.")
access_token = oauth_data.get("access_token")
if not access_token:
# Upstream WNS issue
raise WNSAuthenticationError("Access token missing from WNS response.")
return access_token | [
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242,747 | 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)
content_type = "text/xml"
if wns_type == "wns/raw":
content_type = "application/octet-stream"
headers = {
# content_type is "text/xml" (toast/badge/tile) | "application/octet-stream" (raw)
"Content-Type": content_type,
"Authorization": "Bearer %s" % (access_token),
"X-WNS-Type": wns_type, # wns/toast | wns/badge | wns/tile | wns/raw
}
if type(data) is str:
data = data.encode("utf-8")
request = Request(uri, data, headers)
# A lot of things can happen, let them know which one.
try:
response = urlopen(request)
except HTTPError as err:
if err.code == 400:
msg = "One or more headers were specified incorrectly or conflict with another header."
elif err.code == 401:
msg = "The cloud service did not present a valid authentication ticket."
elif err.code == 403:
msg = "The cloud service is not authorized to send a notification to this URI."
elif err.code == 404:
msg = "The channel URI is not valid or is not recognized by WNS."
elif err.code == 405:
msg = "Invalid method. Only POST or DELETE is allowed."
elif err.code == 406:
msg = "The cloud service exceeded its throttle limit"
elif err.code == 410:
msg = "The channel expired."
elif err.code == 413:
msg = "The notification payload exceeds the 500 byte limit."
elif err.code == 500:
msg = "An internal failure caused notification delivery to fail."
elif err.code == 503:
msg = "The server is currently unavailable."
else:
raise err
raise WNSNotificationResponseError("HTTP %i: %s" % (err.code, msg))
return response.read().decode("utf-8") | python | def _wns_send(uri, data, wns_type="wns/toast", application_id=None):
access_token = _wns_authenticate(application_id=application_id)
content_type = "text/xml"
if wns_type == "wns/raw":
content_type = "application/octet-stream"
headers = {
# content_type is "text/xml" (toast/badge/tile) | "application/octet-stream" (raw)
"Content-Type": content_type,
"Authorization": "Bearer %s" % (access_token),
"X-WNS-Type": wns_type, # wns/toast | wns/badge | wns/tile | wns/raw
}
if type(data) is str:
data = data.encode("utf-8")
request = Request(uri, data, headers)
# A lot of things can happen, let them know which one.
try:
response = urlopen(request)
except HTTPError as err:
if err.code == 400:
msg = "One or more headers were specified incorrectly or conflict with another header."
elif err.code == 401:
msg = "The cloud service did not present a valid authentication ticket."
elif err.code == 403:
msg = "The cloud service is not authorized to send a notification to this URI."
elif err.code == 404:
msg = "The channel URI is not valid or is not recognized by WNS."
elif err.code == 405:
msg = "Invalid method. Only POST or DELETE is allowed."
elif err.code == 406:
msg = "The cloud service exceeded its throttle limit"
elif err.code == 410:
msg = "The channel expired."
elif err.code == 413:
msg = "The notification payload exceeds the 500 byte limit."
elif err.code == 500:
msg = "An internal failure caused notification delivery to fail."
elif err.code == 503:
msg = "The server is currently unavailable."
else:
raise err
raise WNSNotificationResponseError("HTTP %i: %s" % (err.code, msg))
return response.read().decode("utf-8") | [
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242,748 | 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
:param data: dict: The notification data to be converted to an xml tree.
{
"text": ["Title text", "Message Text", "Another message!"],
"image": ["src1", "src2"],
}
:return: str
"""
root = ET.Element("toast")
visual = ET.SubElement(root, "visual")
binding = ET.SubElement(visual, "binding")
binding.attrib["template"] = kwargs.pop("template", "ToastText01")
if "text" in data:
for count, item in enumerate(data["text"], start=1):
elem = ET.SubElement(binding, "text")
elem.text = item
elem.attrib["id"] = str(count)
if "image" in data:
for count, item in enumerate(data["image"], start=1):
elem = ET.SubElement(binding, "img")
elem.attrib["src"] = item
elem.attrib["id"] = str(count)
return ET.tostring(root) | python | def _wns_prepare_toast(data, **kwargs):
root = ET.Element("toast")
visual = ET.SubElement(root, "visual")
binding = ET.SubElement(visual, "binding")
binding.attrib["template"] = kwargs.pop("template", "ToastText01")
if "text" in data:
for count, item in enumerate(data["text"], start=1):
elem = ET.SubElement(binding, "text")
elem.text = item
elem.attrib["id"] = str(count)
if "image" in data:
for count, item in enumerate(data["image"], start=1):
elem = ET.SubElement(binding, "img")
elem.attrib["src"] = item
elem.attrib["id"] = str(count)
return ET.tostring(root) | [
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242,749 | 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.
:param xml_data: dict: A dictionary containing data to be converted to an xml tree.
:param raw_data: str: Data to be sent via a `raw` notification.
"""
res = []
if uri_list:
for uri in uri_list:
r = wns_send_message(
uri=uri, message=message, xml_data=xml_data,
raw_data=raw_data, application_id=application_id, **kwargs
)
res.append(r)
return res | python | def wns_send_bulk_message(
uri_list, message=None, xml_data=None, raw_data=None, application_id=None, **kwargs
):
res = []
if uri_list:
for uri in uri_list:
r = wns_send_message(
uri=uri, message=message, xml_data=xml_data,
raw_data=raw_data, application_id=application_id, **kwargs
)
res.append(r)
return res | [
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242,750 | 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.:
{"example": {
"attrs": {
"key1": "value1",
...
},
...
}}
"""
for key, value in sub_dict.items():
if isinstance(value, list):
for repeated_element in value:
sub_element = ET.SubElement(parent, key)
_add_element_attrs(sub_element, repeated_element.get("attrs", {}))
children = repeated_element.get("children", None)
if isinstance(children, dict):
_add_sub_elements_from_dict(sub_element, children)
elif isinstance(children, str):
sub_element.text = children
else:
sub_element = ET.SubElement(parent, key)
_add_element_attrs(sub_element, value.get("attrs", {}))
children = value.get("children", None)
if isinstance(children, dict):
_add_sub_elements_from_dict(sub_element, children)
elif isinstance(children, str):
sub_element.text = children | python | def _add_sub_elements_from_dict(parent, sub_dict):
for key, value in sub_dict.items():
if isinstance(value, list):
for repeated_element in value:
sub_element = ET.SubElement(parent, key)
_add_element_attrs(sub_element, repeated_element.get("attrs", {}))
children = repeated_element.get("children", None)
if isinstance(children, dict):
_add_sub_elements_from_dict(sub_element, children)
elif isinstance(children, str):
sub_element.text = children
else:
sub_element = ET.SubElement(parent, key)
_add_element_attrs(sub_element, value.get("attrs", {}))
children = value.get("children", None)
if isinstance(children, dict):
_add_sub_elements_from_dict(sub_element, children)
elif isinstance(children, str):
sub_element.text = children | [
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: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.:
{"example": {
"attrs": {
"key1": "value1",
...
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...
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242,751 | 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 in attrs.items():
elem.attrib[attr] = value
return elem | python | def _add_element_attrs(elem, attrs):
for attr, value in attrs.items():
elem.attrib[attr] = value
return elem | [
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242,752 | 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_spec: string
:param username: Username to use for login
:type username: string
:param password: Password to use for login
:type password: string
:return: True on successful authentication, False otherwise
"""
warnings.warn(
"shouldn't use this function anymore ! use connect which handles"
"handles authentication directly.",
DeprecationWarning
)
scheme = "http"
if not host_spec:
u = urlparse(self.endpoint)
host_spec = u.netloc
if u.scheme == "wss":
scheme = "https"
if self.username:
username = self.username
if self.password:
password = self.password
auth = Authenticate(host_spec, scheme=scheme,
username=username, password=password)
try:
auth.login()
cookie = 'authtok={}'.format(auth.authtok)
if self.cookies:
self.cookies.append(cookie)
else:
self.cookies = [cookie, ]
return True
except Exception:
return False | python | def login(self, host_spec="", username="", password=""):
warnings.warn(
"shouldn't use this function anymore ! use connect which handles"
"handles authentication directly.",
DeprecationWarning
)
scheme = "http"
if not host_spec:
u = urlparse(self.endpoint)
host_spec = u.netloc
if u.scheme == "wss":
scheme = "https"
if self.username:
username = self.username
if self.password:
password = self.password
auth = Authenticate(host_spec, scheme=scheme,
username=username, password=password)
try:
auth.login()
cookie = 'authtok={}'.format(auth.authtok)
if self.cookies:
self.cookies.append(cookie)
else:
self.cookies = [cookie, ]
return True
except Exception:
return False | [
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:param host_spec: Host IP and port (e.g. 192.168.10.1:8082)
:type host_spec: string
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:type username: string
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242,753 | 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 = kwargs.pop('return_child', False)
if return_child:
return self.buildozer.cmd_expect(command, **kwargs)
else:
kwargs['get_stdout'] = kwargs.get('get_stdout', True)
return self.buildozer.cmd(command, **kwargs) | python | def _sdkmanager(self, *args, **kwargs):
# 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 = kwargs.pop('return_child', False)
if return_child:
return self.buildozer.cmd_expect(command, **kwargs)
else:
kwargs['get_stdout'] = kwargs.get('get_stdout', True)
return self.buildozer.cmd(command, **kwargs) | [
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242,754 | 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,
'platform-tools')
if not os.path.exists(platform_tools_dir):
return None
data_file = os.path.join(platform_tools_dir, 'source.properties')
if not os.path.exists(data_file):
return None
with open(data_file, 'r') as fileh:
lines = fileh.readlines()
for line in lines:
if line.startswith('Pkg.Revision='):
break
else:
self.buildozer.error('Read {} but found no Pkg.Revision'.format(data_file))
# Don't actually exit, in case the build env is
# okay. Something else will fault if it's important.
return None
revision = line.split('=')[1].strip()
return revision | python | def _android_get_installed_platform_tools_version(self):
platform_tools_dir = os.path.join(
self.android_sdk_dir,
'platform-tools')
if not os.path.exists(platform_tools_dir):
return None
data_file = os.path.join(platform_tools_dir, 'source.properties')
if not os.path.exists(data_file):
return None
with open(data_file, 'r') as fileh:
lines = fileh.readlines()
for line in lines:
if line.startswith('Pkg.Revision='):
break
else:
self.buildozer.error('Read {} but found no Pkg.Revision'.format(data_file))
# Don't actually exit, in case the build env is
# okay. Something else will fault if it's important.
return None
revision = line.split('=')[1].strip()
return revision | [
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242,755 | 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(
'app', 'android.accept_sdk_license', False)
if auto_accept_license:
# `SIGPIPE` is not being reported somehow, but `EPIPE` is.
# This leads to a stderr "Broken pipe" message which is harmless,
# but doesn't look good on terminal, hence redirecting to /dev/null
yes_command = 'yes 2>/dev/null'
command = '{} | {} --licenses'.format(
yes_command, self.sdkmanager_path)
self.buildozer.cmd(command, cwd=self.android_sdk_dir)
self._sdkmanager(*sdkmanager_commands) | python | def _android_update_sdk(self, *sdkmanager_commands):
auto_accept_license = self.buildozer.config.getbooldefault(
'app', 'android.accept_sdk_license', False)
if auto_accept_license:
# `SIGPIPE` is not being reported somehow, but `EPIPE` is.
# This leads to a stderr "Broken pipe" message which is harmless,
# but doesn't look good on terminal, hence redirecting to /dev/null
yes_command = 'yes 2>/dev/null'
command = '{} | {} --licenses'.format(
yes_command, self.sdkmanager_path)
self.buildozer.cmd(command, cwd=self.android_sdk_dir)
self._sdkmanager(*sdkmanager_commands) | [
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242,756 | 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(
"app", "android.logcat_filters", "", section_sep=":", split_char=" ")
filters = " ".join(filters)
self.buildozer.environ['ANDROID_SERIAL'] = serial[0]
self.buildozer.cmd('{adb} logcat {filters}'.format(adb=self.adb_cmd,
filters=filters),
cwd=self.buildozer.global_platform_dir,
show_output=True)
self.buildozer.environ.pop('ANDROID_SERIAL', None) | python | 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(
"app", "android.logcat_filters", "", section_sep=":", split_char=" ")
filters = " ".join(filters)
self.buildozer.environ['ANDROID_SERIAL'] = serial[0]
self.buildozer.cmd('{adb} logcat {filters}'.format(adb=self.adb_cmd,
filters=filters),
cwd=self.buildozer.global_platform_dir,
show_output=True)
self.buildozer.environ.pop('ANDROID_SERIAL', None) | [
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242,757 | 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.spec` for the keys:
{repo}_dir
{repo}_url
{repo}_branch
and use them to determine the source location for a git checkout.
If a `platform` is specified, {platform}.{repo} will be used
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`{repo}_dir` specifies a custom checkout location
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None. Otherwise, `{repo}_url` and `{repo}_branch` will be
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If no keys are present, the kwargs will be used to create
a sensible default URL and branch.
:Parameters:
`repo`: str (required)
name of repository to fetch. Used both for buildozer
keys ({platform}.{repo}_dir|_url|_branch) and in building
default git URL
`branch`: str (default 'master')
Specific branch to retrieve if none specified in
buildozer.spec.
`owner`: str
owner of repo.
`platform`: str or None
platform prefix to use when retrieving `buildozer.spec`
keys. If specified, key names will be {platform}.{repo}
instead of just {repo}
`squash_hyphen`: boolean
if True, change '-' to '_' when looking for
keys in buildozer.spec. This lets us keep backwards
compatibility with old buildozer.spec files
`url_format`: format string
Used to construct default git URL.
can use {repo} {owner} and {branch} if needed.
:Returns:
A Tuple (path, url, branch) where
`path`
Path to a custom git checkout. If specified,
both `url` and `branch` will be None
`url`
URL of git repository from where code should be
checked-out
`branch`
branch name (or tag) that should be used for the
check-out.
"""
if squash_hyphen:
key = repo.replace('-', '_')
else:
key = repo
if platform:
key = "{}.{}".format(platform, key)
config = self.buildozer.config
path = config.getdefault('app', '{}_dir'.format(key), None)
if path is not None:
path = join(self.buildozer.root_dir, path)
url = None
branch = None
else:
branch = config.getdefault('app', '{}_branch'.format(key), branch)
default_url = url_format.format(owner=owner, repo=repo, branch=branch)
url = config.getdefault('app', '{}_url'.format(key), default_url)
if branch != 'master':
url = "--branch {} {}".format(branch, url)
return path, url, branch | python | def path_or_git_url(self, repo, owner='kivy', branch='master',
url_format='https://github.com/{owner}/{repo}.git',
platform=None,
squash_hyphen=True):
if squash_hyphen:
key = repo.replace('-', '_')
else:
key = repo
if platform:
key = "{}.{}".format(platform, key)
config = self.buildozer.config
path = config.getdefault('app', '{}_dir'.format(key), None)
if path is not None:
path = join(self.buildozer.root_dir, path)
url = None
branch = None
else:
branch = config.getdefault('app', '{}_branch'.format(key), branch)
default_url = url_format.format(owner=owner, repo=repo, branch=branch)
url = config.getdefault('app', '{}_url'.format(key), default_url)
if branch != 'master':
url = "--branch {} {}".format(branch, url)
return path, url, branch | [
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{repo}_branch
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If a `platform` is specified, {platform}.{repo} will be used
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:Parameters:
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name of repository to fetch. Used both for buildozer
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`branch`: str (default 'master')
Specific branch to retrieve if none specified in
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`owner`: str
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platform prefix to use when retrieving `buildozer.spec`
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`squash_hyphen`: boolean
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Used to construct default git URL.
can use {repo} {owner} and {branch} if needed.
:Returns:
A Tuple (path, url, branch) where
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Path to a custom git checkout. If specified,
both `url` and `branch` will be None
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URL of git repository from where code should be
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`branch`
branch name (or tag) that should be used for the
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242,758 | 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)
directory name.
:Parameters:
**kwargs:
Any valid arguments for :meth:`path_or_git_url`
:Returns:
fully qualified path to updated git repo
"""
cmd = self.buildozer.cmd
install_dir = join(self.buildozer.platform_dir, repo)
custom_dir, clone_url, clone_branch = self.path_or_git_url(repo, **kwargs)
if not self.buildozer.file_exists(install_dir):
if custom_dir:
cmd('mkdir -p "{}"'.format(install_dir))
cmd('cp -a "{}"/* "{}"/'.format(custom_dir, install_dir))
else:
cmd('git clone {}'.format(clone_url),
cwd=self.buildozer.platform_dir)
elif self.platform_update:
if custom_dir:
cmd('cp -a "{}"/* "{}"/'.format(custom_dir, install_dir))
else:
cmd('git clean -dxf', cwd=install_dir)
cmd('git pull origin {}'.format(clone_branch), cwd=install_dir)
return install_dir | python | def install_or_update_repo(self, repo, **kwargs):
cmd = self.buildozer.cmd
install_dir = join(self.buildozer.platform_dir, repo)
custom_dir, clone_url, clone_branch = self.path_or_git_url(repo, **kwargs)
if not self.buildozer.file_exists(install_dir):
if custom_dir:
cmd('mkdir -p "{}"'.format(install_dir))
cmd('cp -a "{}"/* "{}"/'.format(custom_dir, install_dir))
else:
cmd('git clone {}'.format(clone_url),
cwd=self.buildozer.platform_dir)
elif self.platform_update:
if custom_dir:
cmd('cp -a "{}"/* "{}"/'.format(custom_dir, install_dir))
else:
cmd('git clean -dxf', cwd=install_dir)
cmd('git pull origin {}'.format(clone_branch), cwd=install_dir)
return install_dir | [
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242,759 | kivy/buildozer | buildozer/__init__.py | set_config_token_from_env | def set_config_token_from_env(section, token, config):
'''Given a config section and token, checks for an appropriate
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 replaced by underscores.
Returns True if the environment variable exists and was used, or
False otherwise.
'''
env_var_name = ''.join([section.upper(), '_',
token.upper().replace('.', '_')])
env_var = os.environ.get(env_var_name)
if env_var is None:
return False
config.set(section, token, env_var)
return True | python | def set_config_token_from_env(section, token, config):
'''Given a config section and token, checks for an appropriate
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 replaced by underscores.
Returns True if the environment variable exists and was used, or
False otherwise.
'''
env_var_name = ''.join([section.upper(), '_',
token.upper().replace('.', '_')])
env_var = os.environ.get(env_var_name)
if env_var is None:
return False
config.set(section, token, env_var)
return True | [
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242,760 | 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_requirements()
self.info('Install platform')
self.target.install_platform()
self.info('Check application requirements')
self.check_application_requirements()
self.info('Check garden requirements')
self.check_garden_requirements()
self.info('Compile platform')
self.target.compile_platform()
# flag to prevent multiple build
self.target._build_prepared = True | 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_requirements()
self.info('Install platform')
self.target.install_platform()
self.info('Check application requirements')
self.check_application_requirements()
self.info('Check garden requirements')
self.check_garden_requirements()
self.info('Compile platform')
self.target.compile_platform()
# flag to prevent multiple build
self.target._build_prepared = True | [
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242,761 | 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'))
if hasattr(self.target, '_build_done'):
return
# increment the build number
self.build_id = int(self.state.get('cache.build_id', '0')) + 1
self.state['cache.build_id'] = str(self.build_id)
self.info('Build the application #{}'.format(self.build_id))
self.build_application()
self.info('Package the application')
self.target.build_package()
# flag to prevent multiple build
self.target._build_done = True | 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'))
if hasattr(self.target, '_build_done'):
return
# increment the build number
self.build_id = int(self.state.get('cache.build_id', '0')) + 1
self.state['cache.build_id'] = str(self.build_id)
self.info('Build the application #{}'.format(self.build_id))
self.build_application()
self.info('Package the application')
self.target.build_package()
# flag to prevent multiple build
self.target._build_done = True | [
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242,762 | kivy/buildozer | buildozer/__init__.py | Buildozer.log_env | def log_env(self, level, env):
"""dump env into debug logger in readable format"""
self.log(level, "ENVIRONMENT:")
for k, v in env.items():
self.log(level, " {} = {}".format(k, pformat(v))) | python | def log_env(self, level, env):
self.log(level, "ENVIRONMENT:")
for k, v in env.items():
self.log(level, " {} = {}".format(k, pformat(v))) | [
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242,763 | kivy/buildozer | buildozer/__init__.py | Buildozer.check_configuration_tokens | def check_configuration_tokens(self):
'''Ensure the spec file is 'correct'.
'''
self.info('Check configuration tokens')
self.migrate_configuration_tokens()
get = self.config.getdefault
errors = []
adderror = errors.append
if not get('app', 'title', ''):
adderror('[app] "title" is missing')
if not get('app', 'source.dir', ''):
adderror('[app] "source.dir" is missing')
package_name = get('app', 'package.name', '')
if not package_name:
adderror('[app] "package.name" is missing')
elif package_name[0] in map(str, range(10)):
adderror('[app] "package.name" may not start with a number.')
version = get('app', 'version', '')
version_regex = get('app', 'version.regex', '')
if not version and not version_regex:
adderror('[app] One of "version" or "version.regex" must be set')
if version and version_regex:
adderror('[app] Conflict between "version" and "version.regex"'
', only one can be used.')
if version_regex and not get('app', 'version.filename', ''):
adderror('[app] "version.filename" is missing'
', required by "version.regex"')
orientation = get('app', 'orientation', 'landscape')
if orientation not in ('landscape', 'portrait', 'all', 'sensorLandscape'):
adderror('[app] "orientation" have an invalid value')
if errors:
self.error('{0} error(s) found in the buildozer.spec'.format(
len(errors)))
for error in errors:
print(error)
exit(1) | python | def check_configuration_tokens(self):
'''Ensure the spec file is 'correct'.
'''
self.info('Check configuration tokens')
self.migrate_configuration_tokens()
get = self.config.getdefault
errors = []
adderror = errors.append
if not get('app', 'title', ''):
adderror('[app] "title" is missing')
if not get('app', 'source.dir', ''):
adderror('[app] "source.dir" is missing')
package_name = get('app', 'package.name', '')
if not package_name:
adderror('[app] "package.name" is missing')
elif package_name[0] in map(str, range(10)):
adderror('[app] "package.name" may not start with a number.')
version = get('app', 'version', '')
version_regex = get('app', 'version.regex', '')
if not version and not version_regex:
adderror('[app] One of "version" or "version.regex" must be set')
if version and version_regex:
adderror('[app] Conflict between "version" and "version.regex"'
', only one can be used.')
if version_regex and not get('app', 'version.filename', ''):
adderror('[app] "version.filename" is missing'
', required by "version.regex"')
orientation = get('app', 'orientation', 'landscape')
if orientation not in ('landscape', 'portrait', 'all', 'sensorLandscape'):
adderror('[app] "orientation" have an invalid value')
if errors:
self.error('{0} error(s) found in the buildozer.spec'.format(
len(errors)))
for error in errors:
print(error)
exit(1) | [
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242,764 | kivy/buildozer | buildozer/__init__.py | Buildozer.check_application_requirements | def check_application_requirements(self):
'''Ensure the application requirements are all available and ready to be
packaged as well.
'''
requirements = self.config.getlist('app', 'requirements', '')
target_available_packages = self.target.get_available_packages()
if target_available_packages is True:
# target handles all packages!
return
# remove all the requirements that the target can compile
onlyname = lambda x: x.split('==')[0] # noqa: E731
requirements = [x for x in requirements if onlyname(x) not in
target_available_packages]
if requirements and hasattr(sys, 'real_prefix'):
e = self.error
e('virtualenv is needed to install pure-Python modules, but')
e('virtualenv does not support nesting, and you are running')
e('buildozer in one. Please run buildozer outside of a')
e('virtualenv instead.')
exit(1)
# did we already installed the libs ?
if (
exists(self.applibs_dir) and
self.state.get('cache.applibs', '') == requirements
):
self.debug('Application requirements already installed, pass')
return
# recreate applibs
self.rmdir(self.applibs_dir)
self.mkdir(self.applibs_dir)
# ok now check the availability of all requirements
for requirement in requirements:
self._install_application_requirement(requirement)
# everything goes as expected, save this state!
self.state['cache.applibs'] = requirements | python | def check_application_requirements(self):
'''Ensure the application requirements are all available and ready to be
packaged as well.
'''
requirements = self.config.getlist('app', 'requirements', '')
target_available_packages = self.target.get_available_packages()
if target_available_packages is True:
# target handles all packages!
return
# remove all the requirements that the target can compile
onlyname = lambda x: x.split('==')[0] # noqa: E731
requirements = [x for x in requirements if onlyname(x) not in
target_available_packages]
if requirements and hasattr(sys, 'real_prefix'):
e = self.error
e('virtualenv is needed to install pure-Python modules, but')
e('virtualenv does not support nesting, and you are running')
e('buildozer in one. Please run buildozer outside of a')
e('virtualenv instead.')
exit(1)
# did we already installed the libs ?
if (
exists(self.applibs_dir) and
self.state.get('cache.applibs', '') == requirements
):
self.debug('Application requirements already installed, pass')
return
# recreate applibs
self.rmdir(self.applibs_dir)
self.mkdir(self.applibs_dir)
# ok now check the availability of all requirements
for requirement in requirements:
self._install_application_requirement(requirement)
# everything goes as expected, save this state!
self.state['cache.applibs'] = requirements | [
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242,765 | kivy/buildozer | buildozer/__init__.py | Buildozer.check_garden_requirements | def check_garden_requirements(self):
'''Ensure required garden packages are available to be included.
'''
garden_requirements = self.config.getlist('app',
'garden_requirements', '')
# have we installed the garden packages?
if exists(self.gardenlibs_dir) and \
self.state.get('cache.gardenlibs', '') == garden_requirements:
self.debug('Garden requirements already installed, pass')
return
# we're going to reinstall all the garden libs.
self.rmdir(self.gardenlibs_dir)
# but if we don't have requirements, or if the user removed everything,
# don't do anything.
if not garden_requirements:
self.state['cache.gardenlibs'] = garden_requirements
return
self._ensure_virtualenv()
self.cmd('pip install Kivy-Garden==0.1.1', env=self.env_venv)
# recreate gardenlibs
self.mkdir(self.gardenlibs_dir)
for requirement in garden_requirements:
self._install_garden_package(requirement)
# save gardenlibs state
self.state['cache.gardenlibs'] = garden_requirements | python | def check_garden_requirements(self):
'''Ensure required garden packages are available to be included.
'''
garden_requirements = self.config.getlist('app',
'garden_requirements', '')
# have we installed the garden packages?
if exists(self.gardenlibs_dir) and \
self.state.get('cache.gardenlibs', '') == garden_requirements:
self.debug('Garden requirements already installed, pass')
return
# we're going to reinstall all the garden libs.
self.rmdir(self.gardenlibs_dir)
# but if we don't have requirements, or if the user removed everything,
# don't do anything.
if not garden_requirements:
self.state['cache.gardenlibs'] = garden_requirements
return
self._ensure_virtualenv()
self.cmd('pip install Kivy-Garden==0.1.1', env=self.env_venv)
# recreate gardenlibs
self.mkdir(self.gardenlibs_dir)
for requirement in garden_requirements:
self._install_garden_package(requirement)
# save gardenlibs state
self.state['cache.gardenlibs'] = garden_requirements | [
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242,766 | kivy/buildozer | buildozer/__init__.py | Buildozer.cmd_init | def cmd_init(self, *args):
'''Create a initial buildozer.spec in the current directory
'''
if exists('buildozer.spec'):
print('ERROR: You already have a buildozer.spec file.')
exit(1)
copyfile(join(dirname(__file__), 'default.spec'), 'buildozer.spec')
print('File buildozer.spec created, ready to customize!') | python | def cmd_init(self, *args):
'''Create a initial buildozer.spec in the current directory
'''
if exists('buildozer.spec'):
print('ERROR: You already have a buildozer.spec file.')
exit(1)
copyfile(join(dirname(__file__), 'default.spec'), 'buildozer.spec')
print('File buildozer.spec created, ready to customize!') | [
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242,767 | kivy/buildozer | buildozer/__init__.py | Buildozer.cmd_distclean | def cmd_distclean(self, *args):
'''Clean the whole Buildozer environment.
'''
print("Warning: Your ndk, sdk and all other cached packages will be"
" removed. Continue? (y/n)")
if sys.stdin.readline().lower()[0] == 'y':
self.info('Clean the global build directory')
if not exists(self.global_buildozer_dir):
return
rmtree(self.global_buildozer_dir) | python | def cmd_distclean(self, *args):
'''Clean the whole Buildozer environment.
'''
print("Warning: Your ndk, sdk and all other cached packages will be"
" removed. Continue? (y/n)")
if sys.stdin.readline().lower()[0] == 'y':
self.info('Clean the global build directory')
if not exists(self.global_buildozer_dir):
return
rmtree(self.global_buildozer_dir) | [
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242,768 | kivy/buildozer | buildozer/__init__.py | Buildozer.cmd_serve | def cmd_serve(self, *args):
'''Serve the bin directory via SimpleHTTPServer
'''
try:
from http.server import SimpleHTTPRequestHandler
from socketserver import TCPServer
except ImportError:
from SimpleHTTPServer import SimpleHTTPRequestHandler
from SocketServer import TCPServer
os.chdir(self.bin_dir)
handler = SimpleHTTPRequestHandler
httpd = TCPServer(("", SIMPLE_HTTP_SERVER_PORT), handler)
print("Serving via HTTP at port {}".format(SIMPLE_HTTP_SERVER_PORT))
print("Press Ctrl+c to quit serving.")
httpd.serve_forever() | python | def cmd_serve(self, *args):
'''Serve the bin directory via SimpleHTTPServer
'''
try:
from http.server import SimpleHTTPRequestHandler
from socketserver import TCPServer
except ImportError:
from SimpleHTTPServer import SimpleHTTPRequestHandler
from SocketServer import TCPServer
os.chdir(self.bin_dir)
handler = SimpleHTTPRequestHandler
httpd = TCPServer(("", SIMPLE_HTTP_SERVER_PORT), handler)
print("Serving via HTTP at port {}".format(SIMPLE_HTTP_SERVER_PORT))
print("Press Ctrl+c to quit serving.")
httpd.serve_forever() | [
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242,769 | kivy/buildozer | buildozer/targets/ios.py | TargetIos.cmd_xcode | def cmd_xcode(self, *args):
'''Open the xcode project.
'''
app_name = self.buildozer.namify(self.buildozer.config.get('app',
'package.name'))
app_name = app_name.lower()
ios_dir = ios_dir = join(self.buildozer.platform_dir, 'kivy-ios')
self.buildozer.cmd('open {}.xcodeproj'.format(
app_name), cwd=join(ios_dir, '{}-ios'.format(app_name))) | python | def cmd_xcode(self, *args):
'''Open the xcode project.
'''
app_name = self.buildozer.namify(self.buildozer.config.get('app',
'package.name'))
app_name = app_name.lower()
ios_dir = ios_dir = join(self.buildozer.platform_dir, 'kivy-ios')
self.buildozer.cmd('open {}.xcodeproj'.format(
app_name), cwd=join(ios_dir, '{}-ios'.format(app_name))) | [
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242,770 | kivy/buildozer | buildozer/targets/ios.py | TargetIos.cmd_list_identities | def cmd_list_identities(self, *args):
'''List the available identities to use for signing.
'''
identities = self._get_available_identities()
print('Available identities:')
for x in identities:
print(' - {}'.format(x)) | python | def cmd_list_identities(self, *args):
'''List the available identities to use for signing.
'''
identities = self._get_available_identities()
print('Available identities:')
for x in identities:
print(' - {}'.format(x)) | [
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242,771 | kevin1024/vcrpy | vcr/cassette.py | CassetteContextDecorator._handle_generator | def _handle_generator(self, fn):
"""Wraps a generator so that we're inside the cassette context for the
duration of the generator.
"""
with self as cassette:
coroutine = fn(cassette)
# We don't need to catch StopIteration. The caller (Tornado's
# gen.coroutine, for example) will handle that.
to_yield = next(coroutine)
while True:
try:
to_send = yield to_yield
except Exception:
to_yield = coroutine.throw(*sys.exc_info())
else:
try:
to_yield = coroutine.send(to_send)
except StopIteration:
break | python | def _handle_generator(self, fn):
with self as cassette:
coroutine = fn(cassette)
# We don't need to catch StopIteration. The caller (Tornado's
# gen.coroutine, for example) will handle that.
to_yield = next(coroutine)
while True:
try:
to_send = yield to_yield
except Exception:
to_yield = coroutine.throw(*sys.exc_info())
else:
try:
to_yield = coroutine.send(to_send)
except StopIteration:
break | [
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242,772 | kevin1024/vcrpy | vcr/cassette.py | Cassette.append | def append(self, request, response):
"""Add a request, response pair to this cassette"""
request = self._before_record_request(request)
if not request:
return
# Deepcopy is here because mutation of `response` will corrupt the
# real response.
response = copy.deepcopy(response)
response = self._before_record_response(response)
if response is None:
return
self.data.append((request, response))
self.dirty = True | python | def append(self, request, response):
request = self._before_record_request(request)
if not request:
return
# Deepcopy is here because mutation of `response` will corrupt the
# real response.
response = copy.deepcopy(response)
response = self._before_record_response(response)
if response is None:
return
self.data.append((request, response))
self.dirty = True | [
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242,773 | kevin1024/vcrpy | vcr/cassette.py | Cassette._responses | def _responses(self, request):
"""
internal API, returns an iterator with all responses matching
the request.
"""
request = self._before_record_request(request)
for index, (stored_request, response) in enumerate(self.data):
if requests_match(request, stored_request, self._match_on):
yield index, response | python | def _responses(self, request):
request = self._before_record_request(request)
for index, (stored_request, response) in enumerate(self.data):
if requests_match(request, stored_request, self._match_on):
yield index, response | [
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242,774 | kevin1024/vcrpy | vcr/cassette.py | Cassette.play_response | def play_response(self, request):
"""
Get the response corresponding to a request, but only if it
hasn't been played back before, and mark it as played
"""
for index, response in self._responses(request):
if self.play_counts[index] == 0:
self.play_counts[index] += 1
return response
# The cassette doesn't contain the request asked for.
raise UnhandledHTTPRequestError(
"The cassette (%r) doesn't contain the request (%r) asked for"
% (self._path, request)
) | python | def play_response(self, request):
for index, response in self._responses(request):
if self.play_counts[index] == 0:
self.play_counts[index] += 1
return response
# The cassette doesn't contain the request asked for.
raise UnhandledHTTPRequestError(
"The cassette (%r) doesn't contain the request (%r) asked for"
% (self._path, request)
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242,775 | kevin1024/vcrpy | vcr/cassette.py | Cassette.responses_of | def responses_of(self, request):
"""
Find the responses corresponding to a request.
This function isn't actually used by VCR internally, but is
provided as an external API.
"""
responses = [response for index, response in self._responses(request)]
if responses:
return responses
# The cassette doesn't contain the request asked for.
raise UnhandledHTTPRequestError(
"The cassette (%r) doesn't contain the request (%r) asked for"
% (self._path, request)
) | python | def responses_of(self, request):
responses = [response for index, response in self._responses(request)]
if responses:
return responses
# The cassette doesn't contain the request asked for.
raise UnhandledHTTPRequestError(
"The cassette (%r) doesn't contain the request (%r) asked for"
% (self._path, request)
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242,776 | kevin1024/vcrpy | vcr/stubs/__init__.py | parse_headers | def parse_headers(header_list):
"""
Convert headers from our serialized dict with lists for keys to a
HTTPMessage
"""
header_string = b""
for key, values in header_list.items():
for v in values:
header_string += \
key.encode('utf-8') + b":" + v.encode('utf-8') + b"\r\n"
return compat.get_httpmessage(header_string) | python | def parse_headers(header_list):
header_string = b""
for key, values in header_list.items():
for v in values:
header_string += \
key.encode('utf-8') + b":" + v.encode('utf-8') + b"\r\n"
return compat.get_httpmessage(header_string) | [
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242,777 | kevin1024/vcrpy | vcr/stubs/__init__.py | VCRConnection._uri | def _uri(self, url):
"""Returns request absolute URI"""
if url and not url.startswith('/'):
# Then this must be a proxy request.
return url
uri = "{0}://{1}{2}{3}".format(
self._protocol,
self.real_connection.host,
self._port_postfix(),
url,
)
return uri | python | def _uri(self, url):
if url and not url.startswith('/'):
# Then this must be a proxy request.
return url
uri = "{0}://{1}{2}{3}".format(
self._protocol,
self.real_connection.host,
self._port_postfix(),
url,
)
return uri | [
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242,778 | kevin1024/vcrpy | vcr/stubs/__init__.py | VCRConnection._url | def _url(self, uri):
"""Returns request selector url from absolute URI"""
prefix = "{}://{}{}".format(
self._protocol,
self.real_connection.host,
self._port_postfix(),
)
return uri.replace(prefix, '', 1) | python | def _url(self, uri):
prefix = "{}://{}{}".format(
self._protocol,
self.real_connection.host,
self._port_postfix(),
)
return uri.replace(prefix, '', 1) | [
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242,779 | kevin1024/vcrpy | vcr/stubs/__init__.py | VCRConnection.request | def request(self, method, url, body=None, headers=None, *args, **kwargs):
'''Persist the request metadata in self._vcr_request'''
self._vcr_request = Request(
method=method,
uri=self._uri(url),
body=body,
headers=headers or {}
)
log.debug('Got {}'.format(self._vcr_request))
# Note: The request may not actually be finished at this point, so
# I'm not sending the actual request until getresponse(). This
# allows me to compare the entire length of the response to see if it
# exists in the cassette.
self._sock = VCRFakeSocket() | python | def request(self, method, url, body=None, headers=None, *args, **kwargs):
'''Persist the request metadata in self._vcr_request'''
self._vcr_request = Request(
method=method,
uri=self._uri(url),
body=body,
headers=headers or {}
)
log.debug('Got {}'.format(self._vcr_request))
# Note: The request may not actually be finished at this point, so
# I'm not sending the actual request until getresponse(). This
# allows me to compare the entire length of the response to see if it
# exists in the cassette.
self._sock = VCRFakeSocket() | [
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242,780 | kevin1024/vcrpy | vcr/stubs/__init__.py | VCRConnection.getresponse | def getresponse(self, _=False, **kwargs):
'''Retrieve the response'''
# Check to see if the cassette has a response for this request. If so,
# then return it
if self.cassette.can_play_response_for(self._vcr_request):
log.info(
"Playing response for {} from cassette".format(
self._vcr_request
)
)
response = self.cassette.play_response(self._vcr_request)
return VCRHTTPResponse(response)
else:
if self.cassette.write_protected and self.cassette.filter_request(
self._vcr_request
):
raise CannotOverwriteExistingCassetteException(
"No match for the request (%r) was found. "
"Can't overwrite existing cassette (%r) in "
"your current record mode (%r)."
% (self._vcr_request, self.cassette._path,
self.cassette.record_mode)
)
# Otherwise, we should send the request, then get the response
# and return it.
log.info(
"{} not in cassette, sending to real server".format(
self._vcr_request
)
)
# This is imported here to avoid circular import.
# TODO(@IvanMalison): Refactor to allow normal import.
from vcr.patch import force_reset
with force_reset():
self.real_connection.request(
method=self._vcr_request.method,
url=self._url(self._vcr_request.uri),
body=self._vcr_request.body,
headers=self._vcr_request.headers,
)
# get the response
response = self.real_connection.getresponse()
# put the response into the cassette
response = {
'status': {
'code': response.status,
'message': response.reason
},
'headers': serialize_headers(response),
'body': {'string': response.read()},
}
self.cassette.append(self._vcr_request, response)
return VCRHTTPResponse(response) | python | def getresponse(self, _=False, **kwargs):
'''Retrieve the response'''
# Check to see if the cassette has a response for this request. If so,
# then return it
if self.cassette.can_play_response_for(self._vcr_request):
log.info(
"Playing response for {} from cassette".format(
self._vcr_request
)
)
response = self.cassette.play_response(self._vcr_request)
return VCRHTTPResponse(response)
else:
if self.cassette.write_protected and self.cassette.filter_request(
self._vcr_request
):
raise CannotOverwriteExistingCassetteException(
"No match for the request (%r) was found. "
"Can't overwrite existing cassette (%r) in "
"your current record mode (%r)."
% (self._vcr_request, self.cassette._path,
self.cassette.record_mode)
)
# Otherwise, we should send the request, then get the response
# and return it.
log.info(
"{} not in cassette, sending to real server".format(
self._vcr_request
)
)
# This is imported here to avoid circular import.
# TODO(@IvanMalison): Refactor to allow normal import.
from vcr.patch import force_reset
with force_reset():
self.real_connection.request(
method=self._vcr_request.method,
url=self._url(self._vcr_request.uri),
body=self._vcr_request.body,
headers=self._vcr_request.headers,
)
# get the response
response = self.real_connection.getresponse()
# put the response into the cassette
response = {
'status': {
'code': response.status,
'message': response.reason
},
'headers': serialize_headers(response),
'body': {'string': response.read()},
}
self.cassette.append(self._vcr_request, response)
return VCRHTTPResponse(response) | [
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242,781 | kevin1024/vcrpy | vcr/stubs/__init__.py | VCRConnection.connect | def connect(self, *args, **kwargs):
"""
httplib2 uses this. Connects to the server I'm assuming.
Only pass to the baseclass if we don't have a recorded response
and are not write-protected.
"""
if hasattr(self, '_vcr_request') and \
self.cassette.can_play_response_for(self._vcr_request):
# We already have a response we are going to play, don't
# actually connect
return
if self.cassette.write_protected:
# Cassette is write-protected, don't actually connect
return
from vcr.patch import force_reset
with force_reset():
return self.real_connection.connect(*args, **kwargs)
self._sock = VCRFakeSocket() | python | def connect(self, *args, **kwargs):
if hasattr(self, '_vcr_request') and \
self.cassette.can_play_response_for(self._vcr_request):
# We already have a response we are going to play, don't
# actually connect
return
if self.cassette.write_protected:
# Cassette is write-protected, don't actually connect
return
from vcr.patch import force_reset
with force_reset():
return self.real_connection.connect(*args, **kwargs)
self._sock = VCRFakeSocket() | [
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242,782 | kevin1024/vcrpy | vcr/patch.py | CassettePatcherBuilder._recursively_apply_get_cassette_subclass | def _recursively_apply_get_cassette_subclass(self, replacement_dict_or_obj):
"""One of the subtleties of this class is that it does not directly
replace HTTPSConnection with `VCRRequestsHTTPSConnection`, but a
subclass of the aforementioned class that has the `cassette`
class attribute assigned to `self._cassette`. This behavior is
necessary to properly support nested cassette contexts.
This function exists to ensure that we use the same class
object (reference) to patch everything that replaces
VCRRequestHTTP[S]Connection, but that we can talk about
patching them with the raw references instead, and without
worrying about exactly where the subclass with the relevant
value for `cassette` is first created.
The function is recursive because it looks in to dictionaries
and replaces class values at any depth with the subclass
described in the previous paragraph.
"""
if isinstance(replacement_dict_or_obj, dict):
for key, replacement_obj in replacement_dict_or_obj.items():
replacement_obj = self._recursively_apply_get_cassette_subclass(
replacement_obj)
replacement_dict_or_obj[key] = replacement_obj
return replacement_dict_or_obj
if hasattr(replacement_dict_or_obj, 'cassette'):
replacement_dict_or_obj = self._get_cassette_subclass(
replacement_dict_or_obj)
return replacement_dict_or_obj | python | def _recursively_apply_get_cassette_subclass(self, replacement_dict_or_obj):
if isinstance(replacement_dict_or_obj, dict):
for key, replacement_obj in replacement_dict_or_obj.items():
replacement_obj = self._recursively_apply_get_cassette_subclass(
replacement_obj)
replacement_dict_or_obj[key] = replacement_obj
return replacement_dict_or_obj
if hasattr(replacement_dict_or_obj, 'cassette'):
replacement_dict_or_obj = self._get_cassette_subclass(
replacement_dict_or_obj)
return replacement_dict_or_obj | [
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242,783 | pymupdf/PyMuPDF | fitz/fitz.py | _Widget_fontdict | def _Widget_fontdict():
"""Turns the above font definitions into a dictionary. Assumes certain line breaks and spaces.
"""
flist = Widget_fontobjects[2:-2].splitlines()
fdict = {}
for f in flist:
k, v = f.split(" ")
fdict[k[1:]] = v
return fdict | python | def _Widget_fontdict():
flist = Widget_fontobjects[2:-2].splitlines()
fdict = {}
for f in flist:
k, v = f.split(" ")
fdict[k[1:]] = v
return fdict | [
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242,784 | pymupdf/PyMuPDF | fitz/fitz.py | getTextlength | def getTextlength(text, fontname="helv", fontsize=11, encoding=0):
"""Calculate length of a string for a given built-in font.
Args:
fontname: name of the font.
fontsize: size of font in points.
encoding: encoding to use (0=Latin, 1=Greek, 2=Cyrillic).
Returns:
(float) length of text.
"""
fontname = fontname.lower()
basename = Base14_fontdict.get(fontname, None)
glyphs = None
if basename == "Symbol":
glyphs = symbol_glyphs
if basename == "ZapfDingbats":
glyphs = zapf_glyphs
if glyphs is not None:
w = sum([glyphs[ord(c)][1] if ord(c)<256 else glyphs[183][1] for c in text])
return w * fontsize
if fontname in Base14_fontdict.keys():
return TOOLS.measure_string(text, Base14_fontdict[fontname], fontsize, encoding)
if fontname in ["china-t", "china-s",
"china-ts", "china-ss",
"japan", "japan-s",
"korea", "korea-s"]:
return len(text) * fontsize
raise ValueError("Font '%s' is unsupported" % fontname) | python | def getTextlength(text, fontname="helv", fontsize=11, encoding=0):
fontname = fontname.lower()
basename = Base14_fontdict.get(fontname, None)
glyphs = None
if basename == "Symbol":
glyphs = symbol_glyphs
if basename == "ZapfDingbats":
glyphs = zapf_glyphs
if glyphs is not None:
w = sum([glyphs[ord(c)][1] if ord(c)<256 else glyphs[183][1] for c in text])
return w * fontsize
if fontname in Base14_fontdict.keys():
return TOOLS.measure_string(text, Base14_fontdict[fontname], fontsize, encoding)
if fontname in ["china-t", "china-s",
"china-ts", "china-ss",
"japan", "japan-s",
"korea", "korea-s"]:
return len(text) * fontsize
raise ValueError("Font '%s' is unsupported" % fontname) | [
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242,785 | pymupdf/PyMuPDF | fitz/fitz.py | getPDFstr | def getPDFstr(s):
""" Return a PDF string depending on its coding.
Notes:
If only ascii then "(original)" is returned, else if only 8 bit chars
then "(original)" with interspersed octal strings \nnn is returned,
else a string "<FEFF[hexstring]>" is returned, where [hexstring] is the
UTF-16BE encoding of the original.
"""
if not bool(s):
return "()"
def make_utf16be(s):
r = hexlify(bytearray([254, 255]) + bytearray(s, "UTF-16BE"))
t = r if fitz_py2 else r.decode()
return "<" + t + ">" # brackets indicate hex
# following either returns original string with mixed-in
# octal numbers \nnn if outside ASCII range, or:
# exits with utf-16be BOM version of the string
r = ""
for c in s:
oc = ord(c)
if oc > 255: # shortcut if beyond code range
return make_utf16be(s)
if oc > 31 and oc < 127:
if c in ("(", ")", "\\"):
r += "\\"
r += c
continue
if oc > 127:
r += "\\" + oct(oc)[-3:]
continue
if oc < 8 or oc > 13 or oc == 11 or c == 127:
r += "\\267" # indicate unsupported char
continue
if oc == 8:
r += "\\b"
elif oc == 9:
r += "\\t"
elif oc == 10:
r += "\\n"
elif oc == 12:
r += "\\f"
elif oc == 13:
r += "\\r"
return "(" + r + ")" | python | def getPDFstr(s):
if not bool(s):
return "()"
def make_utf16be(s):
r = hexlify(bytearray([254, 255]) + bytearray(s, "UTF-16BE"))
t = r if fitz_py2 else r.decode()
return "<" + t + ">" # brackets indicate hex
# following either returns original string with mixed-in
# octal numbers \nnn if outside ASCII range, or:
# exits with utf-16be BOM version of the string
r = ""
for c in s:
oc = ord(c)
if oc > 255: # shortcut if beyond code range
return make_utf16be(s)
if oc > 31 and oc < 127:
if c in ("(", ")", "\\"):
r += "\\"
r += c
continue
if oc > 127:
r += "\\" + oct(oc)[-3:]
continue
if oc < 8 or oc > 13 or oc == 11 or c == 127:
r += "\\267" # indicate unsupported char
continue
if oc == 8:
r += "\\b"
elif oc == 9:
r += "\\t"
elif oc == 10:
r += "\\n"
elif oc == 12:
r += "\\f"
elif oc == 13:
r += "\\r"
return "(" + r + ")" | [
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242,786 | pymupdf/PyMuPDF | fitz/fitz.py | CheckFont | def CheckFont(page, fontname):
"""Return an entry in the page's font list if reference name matches.
"""
for f in page.getFontList():
if f[4] == fontname:
return f
if f[3].lower() == fontname.lower():
return f
return None | python | def CheckFont(page, fontname):
for f in page.getFontList():
if f[4] == fontname:
return f
if f[3].lower() == fontname.lower():
return f
return None | [
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242,787 | pymupdf/PyMuPDF | fitz/fitz.py | Matrix.invert | def invert(self, src=None):
"""Calculate the inverted matrix. Return 0 if successful and replace
current one. Else return 1 and do nothing.
"""
if src is None:
dst = TOOLS._invert_matrix(self)
else:
dst = TOOLS._invert_matrix(src)
if dst[0] == 1:
return 1
self.a, self.b, self.c, self.d, self.e, self.f = dst[1]
return 0 | python | def invert(self, src=None):
if src is None:
dst = TOOLS._invert_matrix(self)
else:
dst = TOOLS._invert_matrix(src)
if dst[0] == 1:
return 1
self.a, self.b, self.c, self.d, self.e, self.f = dst[1]
return 0 | [
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242,788 | pymupdf/PyMuPDF | fitz/fitz.py | Matrix.preTranslate | def preTranslate(self, tx, ty):
"""Calculate pre translation and replace current matrix."""
self.e += tx * self.a + ty * self.c
self.f += tx * self.b + ty * self.d
return self | python | def preTranslate(self, tx, ty):
self.e += tx * self.a + ty * self.c
self.f += tx * self.b + ty * self.d
return self | [
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242,789 | pymupdf/PyMuPDF | fitz/fitz.py | Matrix.preScale | def preScale(self, sx, sy):
"""Calculate pre scaling and replace current matrix."""
self.a *= sx
self.b *= sx
self.c *= sy
self.d *= sy
return self | python | def preScale(self, sx, sy):
self.a *= sx
self.b *= sx
self.c *= sy
self.d *= sy
return self | [
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242,790 | pymupdf/PyMuPDF | fitz/fitz.py | Matrix.preShear | def preShear(self, h, v):
"""Calculate pre shearing and replace current matrix."""
a, b = self.a, self.b
self.a += v * self.c
self.b += v * self.d
self.c += h * a
self.d += h * b
return self | python | def preShear(self, h, v):
a, b = self.a, self.b
self.a += v * self.c
self.b += v * self.d
self.c += h * a
self.d += h * b
return self | [
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242,791 | pymupdf/PyMuPDF | fitz/fitz.py | Matrix.preRotate | def preRotate(self, theta):
"""Calculate pre rotation and replace current matrix."""
while theta < 0: theta += 360
while theta >= 360: theta -= 360
epsilon = 1e-5
if abs(0 - theta) < epsilon:
pass
elif abs(90.0 - theta) < epsilon:
a = self.a
b = self.b
self.a = self.c
self.b = self.d
self.c = -a
self.d = -b
elif abs(180.0 - theta) < epsilon:
self.a = -self.a
self.b = -self.b
self.c = -self.c
self.d = -self.d
elif abs(270.0 - theta) < epsilon:
a = self.a
b = self.b
self.a = -self.c
self.b = -self.d
self.c = a
self.d = b
else:
rad = math.radians(theta)
s = round(math.sin(rad), 12)
c = round(math.cos(rad), 12)
a = self.a
b = self.b
self.a = c * a + s * self.c
self.b = c * b + s * self.d
self.c =-s * a + c * self.c
self.d =-s * b + c * self.d
return self | python | def preRotate(self, theta):
while theta < 0: theta += 360
while theta >= 360: theta -= 360
epsilon = 1e-5
if abs(0 - theta) < epsilon:
pass
elif abs(90.0 - theta) < epsilon:
a = self.a
b = self.b
self.a = self.c
self.b = self.d
self.c = -a
self.d = -b
elif abs(180.0 - theta) < epsilon:
self.a = -self.a
self.b = -self.b
self.c = -self.c
self.d = -self.d
elif abs(270.0 - theta) < epsilon:
a = self.a
b = self.b
self.a = -self.c
self.b = -self.d
self.c = a
self.d = b
else:
rad = math.radians(theta)
s = round(math.sin(rad), 12)
c = round(math.cos(rad), 12)
a = self.a
b = self.b
self.a = c * a + s * self.c
self.b = c * b + s * self.d
self.c =-s * a + c * self.c
self.d =-s * b + c * self.d
return self | [
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242,792 | pymupdf/PyMuPDF | fitz/fitz.py | Matrix.concat | def concat(self, one, two):
"""Multiply two matrices and replace current one."""
if not len(one) == len(two) == 6:
raise ValueError("bad sequ. length")
self.a, self.b, self.c, self.d, self.e, self.f = TOOLS._concat_matrix(one, two)
return self | python | def concat(self, one, two):
if not len(one) == len(two) == 6:
raise ValueError("bad sequ. length")
self.a, self.b, self.c, self.d, self.e, self.f = TOOLS._concat_matrix(one, two)
return self | [
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242,793 | pymupdf/PyMuPDF | fitz/fitz.py | Point.transform | def transform(self, m):
"""Replace point by its transformation with matrix-like m."""
if len(m) != 6:
raise ValueError("bad sequ. length")
self.x, self.y = TOOLS._transform_point(self, m)
return self | python | def transform(self, m):
if len(m) != 6:
raise ValueError("bad sequ. length")
self.x, self.y = TOOLS._transform_point(self, m)
return self | [
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242,794 | pymupdf/PyMuPDF | fitz/fitz.py | Point.unit | def unit(self):
"""Return unit vector of a point."""
s = self.x * self.x + self.y * self.y
if s < 1e-5:
return Point(0,0)
s = math.sqrt(s)
return Point(self.x / s, self.y / s) | python | def unit(self):
s = self.x * self.x + self.y * self.y
if s < 1e-5:
return Point(0,0)
s = math.sqrt(s)
return Point(self.x / s, self.y / s) | [
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242,795 | pymupdf/PyMuPDF | fitz/fitz.py | Point.abs_unit | def abs_unit(self):
"""Return unit vector of a point with positive coordinates."""
s = self.x * self.x + self.y * self.y
if s < 1e-5:
return Point(0,0)
s = math.sqrt(s)
return Point(abs(self.x) / s, abs(self.y) / s) | python | def abs_unit(self):
s = self.x * self.x + self.y * self.y
if s < 1e-5:
return Point(0,0)
s = math.sqrt(s)
return Point(abs(self.x) / s, abs(self.y) / s) | [
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242,796 | pymupdf/PyMuPDF | fitz/fitz.py | Point.distance_to | def distance_to(self, *args):
"""Return the distance to a rectangle or another point."""
if not len(args) > 0:
raise ValueError("at least one parameter must be given")
x = args[0]
if len(args) > 1:
unit = args[1]
else:
unit = "px"
u = {"px": (1.,1.), "in": (1.,72.), "cm": (2.54, 72.),
"mm": (25.4, 72.)}
f = u[unit][0] / u[unit][1]
if type(x) is Point:
return abs(self - x) * f
# from here on, x is a rectangle
# as a safeguard, make a finite copy of it
r = Rect(x.top_left, x.top_left)
r = r | x.bottom_right
if self in r:
return 0.0
if self.x > r.x1:
if self.y >= r.y1:
return self.distance_to(r.bottom_right, unit)
elif self.y <= r.y0:
return self.distance_to(r.top_right, unit)
else:
return (self.x - r.x1) * f
elif r.x0 <= self.x <= r.x1:
if self.y >= r.y1:
return (self.y - r.y1) * f
else:
return (r.y0 - self.y) * f
else:
if self.y >= r.y1:
return self.distance_to(r.bottom_left, unit)
elif self.y <= r.y0:
return self.distance_to(r.top_left, unit)
else:
return (r.x0 - self.x) * f | python | def distance_to(self, *args):
if not len(args) > 0:
raise ValueError("at least one parameter must be given")
x = args[0]
if len(args) > 1:
unit = args[1]
else:
unit = "px"
u = {"px": (1.,1.), "in": (1.,72.), "cm": (2.54, 72.),
"mm": (25.4, 72.)}
f = u[unit][0] / u[unit][1]
if type(x) is Point:
return abs(self - x) * f
# from here on, x is a rectangle
# as a safeguard, make a finite copy of it
r = Rect(x.top_left, x.top_left)
r = r | x.bottom_right
if self in r:
return 0.0
if self.x > r.x1:
if self.y >= r.y1:
return self.distance_to(r.bottom_right, unit)
elif self.y <= r.y0:
return self.distance_to(r.top_right, unit)
else:
return (self.x - r.x1) * f
elif r.x0 <= self.x <= r.x1:
if self.y >= r.y1:
return (self.y - r.y1) * f
else:
return (r.y0 - self.y) * f
else:
if self.y >= r.y1:
return self.distance_to(r.bottom_left, unit)
elif self.y <= r.y0:
return self.distance_to(r.top_left, unit)
else:
return (r.x0 - self.x) * f | [
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242,797 | pymupdf/PyMuPDF | fitz/fitz.py | Rect.normalize | def normalize(self):
"""Replace rectangle with its finite version."""
if self.x1 < self.x0:
self.x0, self.x1 = self.x1, self.x0
if self.y1 < self.y0:
self.y0, self.y1 = self.y1, self.y0
return self | python | def normalize(self):
if self.x1 < self.x0:
self.x0, self.x1 = self.x1, self.x0
if self.y1 < self.y0:
self.y0, self.y1 = self.y1, self.y0
return self | [
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242,798 | pymupdf/PyMuPDF | fitz/fitz.py | Rect.isEmpty | def isEmpty(self):
"""Check if rectangle area is empty."""
return self.x0 == self.x1 or self.y0 == self.y1 | python | def isEmpty(self):
return self.x0 == self.x1 or self.y0 == self.y1 | [
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242,799 | pymupdf/PyMuPDF | fitz/fitz.py | Rect.isInfinite | def isInfinite(self):
"""Check if rectangle is infinite."""
return self.x0 > self.x1 or self.y0 > self.y1 | python | def isInfinite(self):
return self.x0 > self.x1 or self.y0 > self.y1 | [
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