/
switchable_norm.py
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/
switchable_norm.py
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# https://github.com/switchablenorms/Switchable-Normalization
"""Switchable Normalization Implementation"""
import torch
import torch.nn as nn
class SwitchNorm1d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.997, using_moving_average=True):
super(SwitchNorm1d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.weight = nn.Parameter(torch.ones(1, num_features))
self.bias = nn.Parameter(torch.zeros(1, num_features))
self.mean_weight = nn.Parameter(torch.ones(2))
self.var_weight = nn.Parameter(torch.ones(2))
self.register_buffer('running_mean', torch.zeros(1, num_features))
self.register_buffer('running_var', torch.zeros(1, num_features))
self.reset_parameters()
def reset_parameters(self):
self.running_mean.zero_()
self.running_var.zero_()
self.weight.data.fill_(1)
self.bias.data.zero_()
def _check_input_dim(self, input):
if input.dim() != 2:
raise ValueError('expected 2D input (got {}D input)'
.format(input.dim()))
def forward(self, x):
self._check_input_dim(x)
mean_ln = x.mean(1, keepdim=True)
var_ln = x.var(1, keepdim=True)
if self.training:
mean_bn = x.mean(0, keepdim=True)
var_bn = x.var(0, keepdim=True)
if self.using_moving_average:
self.running_mean.mul_(self.momentum)
self.running_mean.add_((1 - self.momentum) * mean_bn.data)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * var_bn.data)
else:
self.running_mean.add_(mean_bn.data)
self.running_var.add_(mean_bn.data ** 2 + var_bn.data)
else:
mean_bn = torch.autograd.Variable(self.running_mean)
var_bn = torch.autograd.Variable(self.running_var)
softmax = nn.Softmax(0)
mean_weight = softmax(self.mean_weight)
var_weight = softmax(self.var_weight)
mean = mean_weight[0] * mean_ln + mean_weight[1] * mean_bn
var = var_weight[0] * var_ln + var_weight[1] * var_bn
x = (x - mean) / (var + self.eps).sqrt()
return x * self.weight + self.bias
class SwitchNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.997, using_moving_average=True, using_bn=True,
last_gamma=False):
super(SwitchNorm2d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.using_bn = using_bn
self.last_gamma = last_gamma
self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1))
if self.using_bn:
self.mean_weight = nn.Parameter(torch.ones(3))
self.var_weight = nn.Parameter(torch.ones(3))
else:
self.mean_weight = nn.Parameter(torch.ones(2))
self.var_weight = nn.Parameter(torch.ones(2))
if self.using_bn:
self.register_buffer('running_mean', torch.zeros(1, num_features, 1))
self.register_buffer('running_var', torch.zeros(1, num_features, 1))
self.reset_parameters()
def reset_parameters(self):
if self.using_bn:
self.running_mean.zero_()
self.running_var.zero_()
if self.last_gamma:
self.weight.data.fill_(0)
else:
self.weight.data.fill_(1)
self.bias.data.zero_()
def _check_input_dim(self, input):
if input.dim() != 4:
raise ValueError('expected 4D input (got {}D input)'
.format(input.dim()))
def forward(self, x):
self._check_input_dim(x)
N, C, H, W = x.size()
x = x.view(N, C, -1)
mean_in = x.mean(-1, keepdim=True)
var_in = x.var(-1, keepdim=True)
mean_ln = mean_in.mean(1, keepdim=True)
temp = var_in + mean_in ** 2
var_ln = temp.mean(1, keepdim=True) - mean_ln ** 2
if self.using_bn:
if self.training:
mean_bn = mean_in.mean(0, keepdim=True)
var_bn = temp.mean(0, keepdim=True) - mean_bn ** 2
if self.using_moving_average:
self.running_mean.mul_(self.momentum)
self.running_mean.add_((1 - self.momentum) * mean_bn.data)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * var_bn.data)
else:
self.running_mean.add_(mean_bn.data)
self.running_var.add_(mean_bn.data ** 2 + var_bn.data)
else:
mean_bn = torch.autograd.Variable(self.running_mean)
var_bn = torch.autograd.Variable(self.running_var)
softmax = nn.Softmax(0)
mean_weight = softmax(self.mean_weight)
var_weight = softmax(self.var_weight)
if self.using_bn:
mean = mean_weight[0] * mean_in + mean_weight[1] * mean_ln + mean_weight[2] * mean_bn
var = var_weight[0] * var_in + var_weight[1] * var_ln + var_weight[2] * var_bn
else:
mean = mean_weight[0] * mean_in + mean_weight[1] * mean_ln
var = var_weight[0] * var_in + var_weight[1] * var_ln
x = (x-mean) / (var+self.eps).sqrt()
x = x.view(N, C, H, W)
return x * self.weight + self.bias
class SwitchNorm3d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.997, using_moving_average=True, using_bn=True,
last_gamma=False):
super(SwitchNorm3d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.using_bn = using_bn
self.last_gamma = last_gamma
self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1, 1))
if self.using_bn:
self.mean_weight = nn.Parameter(torch.ones(3))
self.var_weight = nn.Parameter(torch.ones(3))
else:
self.mean_weight = nn.Parameter(torch.ones(2))
self.var_weight = nn.Parameter(torch.ones(2))
if self.using_bn:
self.register_buffer('running_mean', torch.zeros(1, num_features, 1))
self.register_buffer('running_var', torch.zeros(1, num_features, 1))
self.reset_parameters()
def reset_parameters(self):
if self.using_bn:
self.running_mean.zero_()
self.running_var.zero_()
if self.last_gamma:
self.weight.data.fill_(0)
else:
self.weight.data.fill_(1)
self.bias.data.zero_()
def _check_input_dim(self, input):
if input.dim() != 5:
raise ValueError('expected 5D input (got {}D input)'
.format(input.dim()))
def forward(self, x):
self._check_input_dim(x)
N, C, D, H, W = x.size()
x = x.view(N, C, -1)
mean_in = x.mean(-1, keepdim=True)
var_in = x.var(-1, keepdim=True)
mean_ln = mean_in.mean(1, keepdim=True)
temp = var_in + mean_in ** 2
var_ln = temp.mean(1, keepdim=True) - mean_ln ** 2
if self.using_bn:
if self.training:
mean_bn = mean_in.mean(0, keepdim=True)
var_bn = temp.mean(0, keepdim=True) - mean_bn ** 2
if self.using_moving_average:
self.running_mean.mul_(self.momentum)
self.running_mean.add_((1 - self.momentum) * mean_bn.data)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * var_bn.data)
else:
self.running_mean.add_(mean_bn.data)
self.running_var.add_(mean_bn.data ** 2 + var_bn.data)
else:
mean_bn = torch.autograd.Variable(self.running_mean)
var_bn = torch.autograd.Variable(self.running_var)
softmax = nn.Softmax(0)
mean_weight = softmax(self.mean_weight)
var_weight = softmax(self.var_weight)
if self.using_bn:
mean = mean_weight[0] * mean_in + mean_weight[1] * mean_ln + mean_weight[2] * mean_bn
var = var_weight[0] * var_in + var_weight[1] * var_ln + var_weight[2] * var_bn
else:
mean = mean_weight[0] * mean_in + mean_weight[1] * mean_ln
var = var_weight[0] * var_in + var_weight[1] * var_ln
x = (x - mean) / (var + self.eps).sqrt()
x = x.view(N, C, D, H, W)
return x * self.weight + self.bias