import torch
import numbers
from torch.nn.parameter import Parameter
from .module import Module
from ._functions import CrossMapLRN2d as _cross_map_lrn2d
from .. import functional as F
from .. import init
from torch import Tensor, Size
from typing import Union, List
[docs]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']
size: int
alpha: float
beta: float
k: float
def __init__(self, size: int, alpha: float = 1e-4, beta: float = 0.75, k: float = 1.) -> None:
super(LocalResponseNorm, self).__init__()
self.size = size
self.alpha = alpha
self.beta = beta
self.k = k
def forward(self, input: Tensor) -> Tensor:
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):
size: int
alpha: float
beta: float
k: float
def __init__(self, size: int, alpha: float = 1e-4, beta: float = 0.75, k: float = 1) -> None:
super(CrossMapLRN2d, self).__init__()
self.size = size
self.alpha = alpha
self.beta = beta
self.k = k
def forward(self, input: Tensor) -> Tensor:
return _cross_map_lrn2d.apply(input, self.size, self.alpha, self.beta,
self.k)
def extra_repr(self) -> str:
return '{size}, alpha={alpha}, beta={beta}, k={k}'.format(**self.__dict__)
_shape_t = Union[int, List[int], Size]
class LayerNorm(Module):
r"""Applies Layer Normalization over a mini-batch of inputs as described in
the paper `Layer Normalization <https://arxiv.org/abs/1607.06450>`__
.. 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``.
The standard-deviation is calculated via the biased estimator, equivalent to
`torch.var(input, unbiased=False)`.
.. 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)
"""
__constants__ = ['normalized_shape', 'eps', 'elementwise_affine']
normalized_shape: _shape_t
eps: float
elementwise_affine: bool
def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True) -> None:
super(LayerNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
self.normalized_shape = tuple(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) -> None:
if self.elementwise_affine:
init.ones_(self.weight)
init.zeros_(self.bias)
def forward(self, input: Tensor) -> Tensor:
return F.layer_norm(
input, self.normalized_shape, self.weight, self.bias, self.eps)
def extra_repr(self) -> Tensor:
return '{normalized_shape}, eps={eps}, ' \
'elementwise_affine={elementwise_affine}'.format(**self.__dict__)
class GroupNorm(Module):
r"""Applies Group Normalization over a mini-batch of inputs as described in
the paper `Group Normalization <https://arxiv.org/abs/1803.08494>`__
.. 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 vectors of size :attr:`num_channels` if
:attr:`affine` is ``True``.
The standard-deviation is calculated via the biased estimator, equivalent to
`torch.var(input, unbiased=False)`.
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, C, *)` where :math:`C=\text{num\_channels}`
- Output: :math:`(N, C, *)` (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)
"""
__constants__ = ['num_groups', 'num_channels', 'eps', 'affine']
num_groups: int
num_channels: int
eps: float
affine: bool
def __init__(self, num_groups: int, num_channels: int, eps: float = 1e-5, affine: bool = True) -> None:
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) -> None:
if self.affine:
init.ones_(self.weight)
init.zeros_(self.bias)
def forward(self, input: Tensor) -> Tensor:
return F.group_norm(
input, self.num_groups, self.weight, self.bias, self.eps)
def extra_repr(self) -> str:
return '{num_groups}, {num_channels}, eps={eps}, ' \
'affine={affine}'.format(**self.__dict__)
# TODO: ContrastiveNorm2d
# TODO: DivisiveNorm2d
# TODO: SubtractiveNorm2d
