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batchrenorm.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from torch.autograd import Variable
class BatchReNorm1d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, rmax=3.0, dmax=5.0):
super(BatchReNorm1d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.affine = affine
self.rmax = rmax
self.dmax = dmax
if self.affine:
self.weight = Parameter(torch.Tensor(num_features))
self.bias = Parameter(torch.Tensor(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.register_buffer('r', torch.ones(num_features))
self.register_buffer('d', torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
self.running_mean.zero_()
self.running_var.fill_(1)
self.r.fill_(1)
self.d.zero_()
if self.affine:
self.weight.data.uniform_()
self.bias.data.zero_()
def forward(self, input):
n = input.size()[0]
if self.training:
mean = torch.mean(input, dim=0)
sum = torch.sum((input - mean.expand_as(input))**2, dim=0)
invstd = 1./torch.sqrt(sum/n + self.eps)
unbiased_var = sum/(n - 1)
self.r = torch.clamp(torch.sqrt(unbiased_var).data / torch.sqrt(self.running_var),
1./self.rmax, self.rmax)
self.d = torch.clamp((mean.data - self.running_mean)/ torch.sqrt(self.running_var),
-self.dmax, self.dmax)
r = Variable(self.r, requires_grad=False).expand_as(input)
d = Variable(self.d, requires_grad=False).expand_as(input)
input_normalized = (input - mean.expand_as(input)) * invstd.expand_as(input)
input_normalized = input_normalized*r + d
self.running_mean += self.momentum * (mean.data - self.running_mean)
self.running_var += self.momentum * (unbiased_var.data - self.running_var)
if not self.affine:
return input_normalized
output = input_normalized * self.weight.expand_as(input)
output += self.bias.unsqueeze(0).expand_as(input)
return output
else:
mean = Variable(self.running_mean).expand_as(input)
invstd = 1./ torch.sqrt(Variable(self.running_var).expand_as(input) + self.eps)
input_normalized = (input - mean.expand_as(input)) * invstd.expand_as(input)
if not self.affine:
return input_normalized
output = input_normalized * self.weight.expand_as(input)
output += self.bias.unsqueeze(0).expand_as(input)
return output
def __repr__(self):
return ('{name}({num_features}, eps={eps}, momentum={momentum},'
'affine={affine}, rmax={rmax}, dmax={dmax})'
.format(name=self.__class__.__name__, **self.__dict__))
class BatchReNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, rmax=3.0, dmax=5.0):
super(BatchReNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.affine = affine
self.rmax = rmax
self.dmax = dmax
if self.affine:
self.weight = Parameter(torch.Tensor(num_features))
self.bias = Parameter(torch.Tensor(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.register_buffer('r', torch.ones(num_features))
self.register_buffer('d', torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
self.running_mean.zero_()
self.running_var.fill_(1)
self.r.fill_(1)
self.d.zero_()
if self.affine:
self.weight.data.uniform_()
self.bias.data.zero_()
def forward(self, input):
n = input.size()[0]
if self.training:
mean = torch.mean(input, dim=0)
sum = torch.sum((input - mean.expand_as(input))**2, dim=0)
invstd = 1./torch.sqrt(sum/n + self.eps)
unbiased_var = sum/(n - 1)
self.r = torch.clamp(torch.sqrt(unbiased_var).data / torch.sqrt(self.running_var).view(-1, 1, 1).expand_as(mean),
1./self.rmax, self.rmax)
self.d = torch.clamp((mean.data - self.running_mean.view(-1, 1, 1).expand_as(mean))/ torch.sqrt(self.running_var).view(-1, 1, 1).expand_as(mean),
-self.dmax, self.dmax)
r = Variable(self.r, requires_grad=False).expand_as(input)
d = Variable(self.d, requires_grad=False).expand_as(input)
input_normalized = (input - mean.expand_as(input)) * invstd.expand_as(input)
input_normalized = input_normalized*r + d
self.running_mean += torch.mean((self.momentum * (mean.data - self.running_mean.view(-1, 1, 1).expand_as(mean))).view(self.running_mean.size(0), -1), dim=1)
self.running_var += torch.mean((self.momentum * (unbiased_var.data - self.running_var.view(-1, 1, 1).expand_as(mean))).view(self.running_var.size(0), -1), dim=1)
if not self.affine:
return input_normalized
output = input_normalized * self.weight.view(-1, 1, 1).expand_as(input)
output += self.bias.view(-1, 1, 1).expand_as(input)
return output
else:
mean = Variable(self.running_mean).view(-1, 1, 1).expand_as(input)
invstd = 1./ torch.sqrt(Variable(self.running_var).view(-1, 1, 1).expand_as(input) + self.eps)
input_normalized = (input - mean.expand_as(input)) * invstd.expand_as(input)
if not self.affine:
return input_normalized
output = input_normalized * self.weight.view(-1, 1, 1).expand_as(input)
output += self.bias.view(-1, 1, 1).expand_as(input)
return output
def __repr__(self):
return ('{name}({num_features}, eps={eps}, momentum={momentum},'
'affine={affine}, rmax={rmax}, dmax={dmax})'
.format(name=self.__class__.__name__, **self.__dict__))