# -*- coding: utf-8 -*-
from typing import Tuple, Union
from ..functional import local_response_norm
from .module import Module
[docs]class LocalResponseNorm(Module):
r"""
Apply local response normalization to the input tensor.
Args:
kernel_size: the size of the kernel to apply LRN on.
k: hyperparameter k. The default vaule is 2.0.
alpha: hyperparameter alpha. The default value is 1e-4.
beta: hyperparameter beta. The default value is 0.75.
Example:
>>> import numpy as np
>>> inp = Tensor(np.arange(25, dtype=np.float32).reshape(1,1,5,5))
>>> GT = np.array([[[[ 0., 0.999925, 1.9994003, 2.9979765, 3.9952066],
... [ 4.9906454, 5.983851, 6.974385, 7.961814, 8.945709 ],
... [ 9.925651, 10.90122, 11.872011, 12.837625, 13.7976675],
... [14.751757, 15.699524, 16.640602, 17.574642, 18.501305 ],
... [19.420258, 20.331186, 21.233786, 22.127764, 23.012836 ]]]])
>>> op = M.LocalResponseNorm(kernel_size=3, k=1.0, alpha=1e-4, beta=0.75)
>>> out = op(inp)
>>> np.testing.assert_allclose(GT, out.numpy(), rtol=1e-6, atol=1e-6)
"""
def __init__(
self,
kernel_size: int = 5,
k: float = 2.0,
alpha: float = 1e-4,
beta: float = 0.75,
**kwargs
):
super(LocalResponseNorm, self).__init__(**kwargs)
self.kernel_size = kernel_size
self.k = k
self.alpha = alpha
self.beta = beta
def forward(self, inp):
return local_response_norm(inp, self.kernel_size, self.k, self.alpha, self.beta)