mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
2260649fb6
* Local Response Normalization * Add 1D and 3D LRN * Generalise LRN to higher dims * Use mean instead of sum Specify 'across-channels'
74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
from .module import Module
|
|
from .. import functional as F
|
|
|
|
|
|
class LocalResponseNorm(Module):
|
|
def __init__(self, size, alpha=1e-4, beta=0.75, k=1):
|
|
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)
|
|
>>> 2d_signal = autograd.Variable(torch.randn(32, 5, 24, 24))
|
|
>>> 4d_signal = autograd.Variable(torch.randn(16, 5, 7, 7, 7, 7))
|
|
>>> 2d_output = lrn(2d_signal)
|
|
>>> 4d_output = lrn(4d_signal)
|
|
"""
|
|
super(LocalResponseNorm, self).__init__()
|
|
self.size = size
|
|
self.alpha = alpha
|
|
self.beta = beta
|
|
self.k = k
|
|
|
|
def forward(self, input):
|
|
return F.local_response_norm(input, self.size, self.alpha, self.beta,
|
|
self.k)
|
|
|
|
def __repr__(self):
|
|
return self.__class__.__name__ + '(' \
|
|
+ str(self.size) \
|
|
+ ', alpha=' + str(self.alpha) \
|
|
+ ', beta=' + str(self.beta) \
|
|
+ ', k=' + str(self.k) + ')'
|
|
|
|
|
|
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 __repr__(self):
|
|
return self.__class__.__name__ + '(' \
|
|
+ str(self.size) \
|
|
+ ', alpha=' + str(self.alpha) \
|
|
+ ', beta=' + str(self.beta) \
|
|
+ ', k=' + str(self.k) + ')'
|
|
|
|
|
|
# TODO: ContrastiveNorm2d
|
|
# TODO: DivisiveNorm2d
|
|
# TODO: SubtractiveNorm2d
|