torch/nn/modules/normalization.py - platform/external/pytorch - Git at Google

 import torch
 import numbers
 from torch.nn.parameter import Parameter
 from .module import Module
 from .batchnorm import _BatchNorm
 from .. import functional as F
 from .. import init
 from ..._jit_internal import weak_module, weak_script_method


 @weak_module
 class LocalResponseNorm(Module):
     r"""Applies local response normalization over an input signal composed
     of several input planes, where channels occupy the second dimension.
     Applies normalization across channels.

     .. math::
         b_{c} = a_{c}\left(k + \frac{\alpha}{n}
         \sum_{c'=\max(0, c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta}

     Args:
         size: amount of neighbouring channels used for normalization
         alpha: multiplicative factor. Default: 0.0001
         beta: exponent. Default: 0.75
         k: additive factor. Default: 1

     Shape:
         - Input: :math:`(N, C, ...)`
         - Output: :math:`(N, C, ...)` (same shape as input)

     Examples::

         >>> lrn = nn.LocalResponseNorm(2)
         >>> signal_2d = torch.randn(32, 5, 24, 24)
         >>> signal_4d = torch.randn(16, 5, 7, 7, 7, 7)
         >>> output_2d = lrn(signal_2d)
         >>> output_4d = lrn(signal_4d)

     """
     __constants__ = ['size', 'alpha', 'beta', 'k']

     def __init__(self, size, alpha=1e-4, beta=0.75, k=1.):
         super(LocalResponseNorm, self).__init__()
         self.size = size
         self.alpha = alpha
         self.beta = beta
         self.k = k

     @weak_script_method
     def forward(self, input):
         return F.local_response_norm(input, self.size, self.alpha, self.beta,
                                      self.k)

     def extra_repr(self):
         return '{size}, alpha={alpha}, beta={beta}, k={k}'.format(**self.__dict__)


 class CrossMapLRN2d(Module):

     def __init__(self, size, alpha=1e-4, beta=0.75, k=1):
         super(CrossMapLRN2d, self).__init__()
         self.size = size
         self.alpha = alpha
         self.beta = beta
         self.k = k

     def forward(self, input):
         return self._backend.CrossMapLRN2d(self.size, self.alpha, self.beta,
                                            self.k)(input)

     def extra_repr(self):
         return '{size}, alpha={alpha}, beta={beta}, k={k}'.format(**self.__dict__)


 @weak_module
 class LayerNorm(Module):
     r"""Applies Layer Normalization over a mini-batch of inputs as described in
     the paper `Layer Normalization`_ .

     .. math::
         y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

     The mean and standard-deviation are calculated separately over the last
     certain number dimensions which have to be of the shape specified by
     :attr:`normalized_shape`.
     :math:`\gamma` and :math:`\beta` are learnable affine transform parameters of
     :attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``.

     .. note::
         Unlike Batch Normalization and Instance Normalization, which applies
         scalar scale and bias for each entire channel/plane with the
         :attr:`affine` option, Layer Normalization applies per-element scale and
         bias with :attr:`elementwise_affine`.

     This layer uses statistics computed from input data in both training and
     evaluation modes.

     Args:
         normalized_shape (int or list or torch.Size): input shape from an expected input
             of size

             .. math::
                 [* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1]
                     \times \ldots \times \text{normalized\_shape}[-1]]

             If a single integer is used, it is treated as a singleton list, and this module will
             normalize over the last dimension which is expected to be of that specific size.
         eps: a value added to the denominator for numerical stability. Default: 1e-5
         elementwise_affine: a boolean value that when set to ``True``, this module
             has learnable per-element affine parameters initialized to ones (for weights)
             and zeros (for biases). Default: ``True``.

     Shape:
         - Input: :math:`(N, *)`
         - Output: :math:`(N, *)` (same shape as input)

     Examples::

         >>> input = torch.randn(20, 5, 10, 10)
         >>> # With Learnable Parameters
         >>> m = nn.LayerNorm(input.size()[1:])
         >>> # Without Learnable Parameters
         >>> m = nn.LayerNorm(input.size()[1:], elementwise_affine=False)
         >>> # Normalize over last two dimensions
         >>> m = nn.LayerNorm([10, 10])
         >>> # Normalize over last dimension of size 10
         >>> m = nn.LayerNorm(10)
         >>> # Activating the module
         >>> output = m(input)

     .. _`Layer Normalization`: https://arxiv.org/abs/1607.06450
     """
     __constants__ = ['normalized_shape', 'weight', 'bias', 'eps']

     def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
         super(LayerNorm, self).__init__()
         if isinstance(normalized_shape, numbers.Integral):
             normalized_shape = (normalized_shape,)
         self.normalized_shape = torch.Size(normalized_shape)
         self.eps = eps
         self.elementwise_affine = elementwise_affine
         if self.elementwise_affine:
             self.weight = Parameter(torch.Tensor(*normalized_shape))
             self.bias = Parameter(torch.Tensor(*normalized_shape))
         else:
             self.register_parameter('weight', None)
             self.register_parameter('bias', None)
         self.reset_parameters()

     def reset_parameters(self):
         if self.elementwise_affine:
             init.ones_(self.weight)
             init.zeros_(self.bias)

     @weak_script_method
     def forward(self, input):
         return F.layer_norm(
             input, self.normalized_shape, self.weight, self.bias, self.eps)

     def extra_repr(self):
         return '{normalized_shape}, eps={eps}, ' \
             'elementwise_affine={elementwise_affine}'.format(**self.__dict__)


 @weak_module
 class GroupNorm(Module):
     r"""Applies Group Normalization over a mini-batch of inputs as described in
     the paper `Group Normalization`_ .

     .. math::
         y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

     The input channels are separated into :attr:`num_groups` groups, each containing
     ``num_channels / num_groups`` channels. The mean and standard-deviation are calculated
     separately over the each group. :math:`\gamma` and :math:`\beta` are learnable
     per-channel affine transform parameter vectorss of size :attr:`num_channels` if
     :attr:`affine` is ``True``.

     This layer uses statistics computed from input data in both training and
     evaluation modes.

     Args:
         num_groups (int): number of groups to separate the channels into
         num_channels (int): number of channels expected in input
         eps: a value added to the denominator for numerical stability. Default: 1e-5
         affine: a boolean value that when set to ``True``, this module
             has learnable per-channel affine parameters initialized to ones (for weights)
             and zeros (for biases). Default: ``True``.

     Shape:
         - Input: :math:`(N, num\_channels, *)`
         - Output: :math:`(N, num\_channels, *)` (same shape as input)

     Examples::

         >>> input = torch.randn(20, 6, 10, 10)
         >>> # Separate 6 channels into 3 groups
         >>> m = nn.GroupNorm(3, 6)
         >>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm)
         >>> m = nn.GroupNorm(6, 6)
         >>> # Put all 6 channels into a single group (equivalent with LayerNorm)
         >>> m = nn.GroupNorm(1, 6)
         >>> # Activating the module
         >>> output = m(input)

     .. _`Group Normalization`: https://arxiv.org/abs/1803.08494
     """
     __constants__ = ['num_groups', 'num_channels', 'eps', 'affine', 'weight',
                      'bias']

     def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
         super(GroupNorm, self).__init__()
         self.num_groups = num_groups
         self.num_channels = num_channels
         self.eps = eps
         self.affine = affine
         if self.affine:
             self.weight = Parameter(torch.Tensor(num_channels))
             self.bias = Parameter(torch.Tensor(num_channels))
         else:
             self.register_parameter('weight', None)
             self.register_parameter('bias', None)
         self.reset_parameters()

     def reset_parameters(self):
         if self.affine:
             init.ones_(self.weight)
             init.zeros_(self.bias)

     @weak_script_method
     def forward(self, input):
         return F.group_norm(
             input, self.num_groups, self.weight, self.bias, self.eps)

     def extra_repr(self):
         return '{num_groups}, {num_channels}, eps={eps}, ' \
             'affine={affine}'.format(**self.__dict__)


 # TODO: ContrastiveNorm2d
 # TODO: DivisiveNorm2d
 # TODO: SubtractiveNorm2d
	import torch
	import numbers
	from torch.nn.parameter import Parameter
	from .module import Module
	from .batchnorm import _BatchNorm
	from .. import functional as F
	from .. import init
	from ..._jit_internal import weak_module, weak_script_method


	@weak_module
	class LocalResponseNorm(Module):
	r"""Applies local response normalization over an input signal composed
	of several input planes, where channels occupy the second dimension.
	Applies normalization across channels.

	.. math::
	b_{c} = a_{c}\left(k + \frac{\alpha}{n}
	\sum_{c'=\max(0, c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta}

	Args:
	size: amount of neighbouring channels used for normalization
	alpha: multiplicative factor. Default: 0.0001
	beta: exponent. Default: 0.75
	k: additive factor. Default: 1

	Shape:
	- Input: :math:`(N, C, ...)`
	- Output: :math:`(N, C, ...)` (same shape as input)

	Examples::

	>>> lrn = nn.LocalResponseNorm(2)
	>>> signal_2d = torch.randn(32, 5, 24, 24)
	>>> signal_4d = torch.randn(16, 5, 7, 7, 7, 7)
	>>> output_2d = lrn(signal_2d)
	>>> output_4d = lrn(signal_4d)

	"""
	__constants__ = ['size', 'alpha', 'beta', 'k']

	def __init__(self, size, alpha=1e-4, beta=0.75, k=1.):
	super(LocalResponseNorm, self).__init__()
	self.size = size
	self.alpha = alpha
	self.beta = beta
	self.k = k

	@weak_script_method
	def forward(self, input):
	return F.local_response_norm(input, self.size, self.alpha, self.beta,
	self.k)

	def extra_repr(self):
	return '{size}, alpha={alpha}, beta={beta}, k={k}'.format(**self.__dict__)


	class CrossMapLRN2d(Module):

	def __init__(self, size, alpha=1e-4, beta=0.75, k=1):
	super(CrossMapLRN2d, self).__init__()
	self.size = size
	self.alpha = alpha
	self.beta = beta
	self.k = k

	def forward(self, input):
	return self._backend.CrossMapLRN2d(self.size, self.alpha, self.beta,
	self.k)(input)

	def extra_repr(self):
	return '{size}, alpha={alpha}, beta={beta}, k={k}'.format(**self.__dict__)


	@weak_module
	class LayerNorm(Module):
	r"""Applies Layer Normalization over a mini-batch of inputs as described in
	the paper `Layer Normalization`_ .

	.. math::
	y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

	The mean and standard-deviation are calculated separately over the last
	certain number dimensions which have to be of the shape specified by
	:attr:`normalized_shape`.
	:math:`\gamma` and :math:`\beta` are learnable affine transform parameters of
	:attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``.

	.. note::
	Unlike Batch Normalization and Instance Normalization, which applies
	scalar scale and bias for each entire channel/plane with the
	:attr:`affine` option, Layer Normalization applies per-element scale and
	bias with :attr:`elementwise_affine`.

	This layer uses statistics computed from input data in both training and
	evaluation modes.

	Args:
	normalized_shape (int or list or torch.Size): input shape from an expected input
	of size

	.. math::
	[* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1]
	\times \ldots \times \text{normalized\_shape}[-1]]

	If a single integer is used, it is treated as a singleton list, and this module will
	normalize over the last dimension which is expected to be of that specific size.
	eps: a value added to the denominator for numerical stability. Default: 1e-5
	elementwise_affine: a boolean value that when set to ``True``, this module
	has learnable per-element affine parameters initialized to ones (for weights)
	and zeros (for biases). Default: ``True``.

	Shape:
	- Input: :math:`(N, *)`
	- Output: :math:`(N, *)` (same shape as input)

	Examples::

	>>> input = torch.randn(20, 5, 10, 10)
	>>> # With Learnable Parameters
	>>> m = nn.LayerNorm(input.size()[1:])
	>>> # Without Learnable Parameters
	>>> m = nn.LayerNorm(input.size()[1:], elementwise_affine=False)
	>>> # Normalize over last two dimensions
	>>> m = nn.LayerNorm([10, 10])
	>>> # Normalize over last dimension of size 10
	>>> m = nn.LayerNorm(10)
	>>> # Activating the module
	>>> output = m(input)

	.. _`Layer Normalization`: https://arxiv.org/abs/1607.06450
	"""
	__constants__ = ['normalized_shape', 'weight', 'bias', 'eps']

	def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
	super(LayerNorm, self).__init__()
	if isinstance(normalized_shape, numbers.Integral):
	normalized_shape = (normalized_shape,)
	self.normalized_shape = torch.Size(normalized_shape)
	self.eps = eps
	self.elementwise_affine = elementwise_affine
	if self.elementwise_affine:
	self.weight = Parameter(torch.Tensor(*normalized_shape))
	self.bias = Parameter(torch.Tensor(*normalized_shape))
	else:
	self.register_parameter('weight', None)
	self.register_parameter('bias', None)
	self.reset_parameters()

	def reset_parameters(self):
	if self.elementwise_affine:
	init.ones_(self.weight)
	init.zeros_(self.bias)

	@weak_script_method
	def forward(self, input):
	return F.layer_norm(
	input, self.normalized_shape, self.weight, self.bias, self.eps)

	def extra_repr(self):
	return '{normalized_shape}, eps={eps}, ' \
	'elementwise_affine={elementwise_affine}'.format(**self.__dict__)


	@weak_module
	class GroupNorm(Module):
	r"""Applies Group Normalization over a mini-batch of inputs as described in
	the paper `Group Normalization`_ .

	.. math::
	y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

	The input channels are separated into :attr:`num_groups` groups, each containing
	``num_channels / num_groups`` channels. The mean and standard-deviation are calculated
	separately over the each group. :math:`\gamma` and :math:`\beta` are learnable
	per-channel affine transform parameter vectorss of size :attr:`num_channels` if
	:attr:`affine` is ``True``.

	This layer uses statistics computed from input data in both training and
	evaluation modes.

	Args:
	num_groups (int): number of groups to separate the channels into
	num_channels (int): number of channels expected in input
	eps: a value added to the denominator for numerical stability. Default: 1e-5
	affine: a boolean value that when set to ``True``, this module
	has learnable per-channel affine parameters initialized to ones (for weights)
	and zeros (for biases). Default: ``True``.

	Shape:
	- Input: :math:`(N, num\_channels, *)`
	- Output: :math:`(N, num\_channels, *)` (same shape as input)

	Examples::

	>>> input = torch.randn(20, 6, 10, 10)
	>>> # Separate 6 channels into 3 groups
	>>> m = nn.GroupNorm(3, 6)
	>>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm)
	>>> m = nn.GroupNorm(6, 6)
	>>> # Put all 6 channels into a single group (equivalent with LayerNorm)
	>>> m = nn.GroupNorm(1, 6)
	>>> # Activating the module
	>>> output = m(input)

	.. _`Group Normalization`: https://arxiv.org/abs/1803.08494
	"""
	__constants__ = ['num_groups', 'num_channels', 'eps', 'affine', 'weight',
	'bias']

	def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
	super(GroupNorm, self).__init__()
	self.num_groups = num_groups
	self.num_channels = num_channels
	self.eps = eps
	self.affine = affine
	if self.affine:
	self.weight = Parameter(torch.Tensor(num_channels))
	self.bias = Parameter(torch.Tensor(num_channels))
	else:
	self.register_parameter('weight', None)
	self.register_parameter('bias', None)
	self.reset_parameters()

	def reset_parameters(self):
	if self.affine:
	init.ones_(self.weight)
	init.zeros_(self.bias)

	@weak_script_method
	def forward(self, input):
	return F.group_norm(
	input, self.num_groups, self.weight, self.bias, self.eps)

	def extra_repr(self):
	return '{num_groups}, {num_channels}, eps={eps}, ' \
	'affine={affine}'.format(**self.__dict__)


	# TODO: ContrastiveNorm2d
	# TODO: DivisiveNorm2d
	# TODO: SubtractiveNorm2d