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Re-implementation: https://arxiv.org/abs/1911.09737
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
import chainer | |
import chainer.functions as F | |
import chainer.links as L | |
import numpy as np | |
class FRN(chainer.Chain): | |
def __init__(self, in_c): | |
self.eps=1e-6 | |
super(FRN, self).__init__( | |
_gamma=L.Parameter(np.ones((1,in_c,1,1),dtype=np.float32)), | |
_beta=L.Parameter(np.zeros((1,in_c,1,1),dtype=np.float32)), | |
_eps=L.Parameter(np.zeros((1,1,1,1),dtype=np.float32)), | |
) | |
def __call__(self, x): | |
nyu2 = F.mean(x*x,axis=(2,3),keepdims=True) | |
_eps = F.broadcast_to(self.eps+F.absolute(self._eps()),nyu2.shape) | |
x_hat = x/F.broadcast_to(F.sqrt(nyu2+_eps),x.shape) | |
_gamma = F.broadcast_to(self._gamma(),x_hat.shape) | |
_beta = F.broadcast_to(self._beta(),x_hat.shape) | |
h = _gamma*x_hat+_beta | |
return h | |
class TLU(chainer.Chain): | |
def __init__(self, in_c): | |
super(TLU, self).__init__( | |
_tau=L.Parameter(np.zeros((1,in_c,1,1),dtype=np.float32)), | |
) | |
def __call__(self, x): | |
_tau = F.broadcast_to(self._tau(),x.shape) | |
h = F.relu(x-_tau)+_tau | |
return h |
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