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""" Weights normalization modules """
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
def get_var_maybe_avg(namespace, var_name, training, polyak_decay):
""" utility for retrieving polyak averaged params
Update average
"""
v = getattr(namespace, var_name)
v_avg = getattr(namespace, var_name + '_avg')
v_avg -= (1 - polyak_decay) * (v_avg - v.data)
if training:
return v
else:
return v_avg
def get_vars_maybe_avg(namespace, var_names, training, polyak_decay):
""" utility for retrieving polyak averaged params """
vars = []
for vn in var_names:
vars.append(get_var_maybe_avg(
namespace, vn, training, polyak_decay))
return vars
class WeightNormLinear(nn.Linear):
"""
Implementation of "Weight Normalization: A Simple Reparameterization
to Accelerate Training of Deep Neural Networks"
:cite:`DBLP:journals/corr/SalimansK16`
As a reparameterization method, weight normalization is same
as BatchNormalization, but it doesn't depend on minibatch.
NOTE: This is used nowhere in the code at this stage
Vincent Nguyen 05/18/2018
"""
def __init__(self, in_features, out_features,
init_scale=1., polyak_decay=0.9995):
super(WeightNormLinear, self).__init__(
in_features, out_features, bias=True)
self.V = self.weight
self.g = Parameter(torch.Tensor(out_features))
self.b = self.bias
self.register_buffer(
'V_avg', torch.zeros(out_features, in_features))
self.register_buffer('g_avg', torch.zeros(out_features))
self.register_buffer('b_avg', torch.zeros(out_features))
self.init_scale = init_scale
self.polyak_decay = polyak_decay
self.reset_parameters()
def reset_parameters(self):
return
def forward(self, x, init=False):
if init is True:
# out_features * in_features
self.V.data.copy_(torch.randn(self.V.data.size()).type_as(
self.V.data) * 0.05)
# norm is out_features * 1
v_norm = self.V.data / \
self.V.data.norm(2, 1).expand_as(self.V.data)
# batch_size * out_features
x_init = F.linear(x, v_norm).data
# out_features
m_init, v_init = x_init.mean(0).squeeze(
0), x_init.var(0).squeeze(0)
# out_features
scale_init = self.init_scale / \
torch.sqrt(v_init + 1e-10)
self.g.data.copy_(scale_init)
self.b.data.copy_(-m_init * scale_init)
x_init = scale_init.view(1, -1).expand_as(x_init) \
* (x_init - m_init.view(1, -1).expand_as(x_init))
self.V_avg.copy_(self.V.data)
self.g_avg.copy_(self.g.data)
self.b_avg.copy_(self.b.data)
return x_init
else:
v, g, b = get_vars_maybe_avg(self, ['V', 'g', 'b'],
self.training,
polyak_decay=self.polyak_decay)
# batch_size * out_features
x = F.linear(x, v)
scalar = g / torch.norm(v, 2, 1).squeeze(1)
x = scalar.view(1, -1).expand_as(x) * x + \
b.view(1, -1).expand_as(x)
return x
class WeightNormConv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, init_scale=1.,
polyak_decay=0.9995):
super(WeightNormConv2d, self).__init__(in_channels, out_channels,
kernel_size, stride, padding,
dilation, groups)
self.V = self.weight
self.g = Parameter(torch.Tensor(out_channels))
self.b = self.bias
self.register_buffer('V_avg', torch.zeros(self.V.size()))
self.register_buffer('g_avg', torch.zeros(out_channels))
self.register_buffer('b_avg', torch.zeros(out_channels))
self.init_scale = init_scale
self.polyak_decay = polyak_decay
self.reset_parameters()
def reset_parameters(self):
return
def forward(self, x, init=False):
if init is True:
# out_channels, in_channels // groups, * kernel_size
self.V.data.copy_(torch.randn(self.V.data.size()
).type_as(self.V.data) * 0.05)
v_norm = self.V.data / self.V.data.view(self.out_channels, -1)\
.norm(2, 1).view(self.out_channels, *(
[1] * (len(self.kernel_size) + 1))).expand_as(self.V.data)
x_init = F.conv2d(x, v_norm, None, self.stride,
self.padding, self.dilation, self.groups).data
t_x_init = x_init.transpose(0, 1).contiguous().view(
self.out_channels, -1)
m_init, v_init = t_x_init.mean(1).squeeze(
1), t_x_init.var(1).squeeze(1)
# out_features
scale_init = self.init_scale / \
torch.sqrt(v_init + 1e-10)
self.g.data.copy_(scale_init)
self.b.data.copy_(-m_init * scale_init)
scale_init_shape = scale_init.view(
1, self.out_channels, *([1] * (len(x_init.size()) - 2)))
m_init_shape = m_init.view(
1, self.out_channels, *([1] * (len(x_init.size()) - 2)))
x_init = scale_init_shape.expand_as(
x_init) * (x_init - m_init_shape.expand_as(x_init))
self.V_avg.copy_(self.V.data)
self.g_avg.copy_(self.g.data)
self.b_avg.copy_(self.b.data)
return x_init
else:
v, g, b = get_vars_maybe_avg(
self, ['V', 'g', 'b'], self.training,
polyak_decay=self.polyak_decay)
scalar = torch.norm(v.view(self.out_channels, -1), 2, 1)
if len(scalar.size()) == 2:
scalar = g / scalar.squeeze(1)
else:
scalar = g / scalar
w = scalar.view(self.out_channels, *
([1] * (len(v.size()) - 1))).expand_as(v) * v
x = F.conv2d(x, w, b, self.stride,
self.padding, self.dilation, self.groups)
return x
# This is used nowhere in the code at the moment (Vincent Nguyen 05/18/2018)
class WeightNormConvTranspose2d(nn.ConvTranspose2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, init_scale=1.,
polyak_decay=0.9995):
super(WeightNormConvTranspose2d, self).__init__(
in_channels, out_channels,
kernel_size, stride,
padding, output_padding,
groups)
# in_channels, out_channels, *kernel_size
self.V = self.weight
self.g = Parameter(torch.Tensor(out_channels))
self.b = self.bias
self.register_buffer('V_avg', torch.zeros(self.V.size()))
self.register_buffer('g_avg', torch.zeros(out_channels))
self.register_buffer('b_avg', torch.zeros(out_channels))
self.init_scale = init_scale
self.polyak_decay = polyak_decay
self.reset_parameters()
def reset_parameters(self):
return
def forward(self, x, init=False):
if init is True:
# in_channels, out_channels, *kernel_size
self.V.data.copy_(torch.randn(self.V.data.size()).type_as(
self.V.data) * 0.05)
v_norm = self.V.data / self.V.data.transpose(0, 1).contiguous() \
.view(self.out_channels, -1).norm(2, 1).view(
self.in_channels, self.out_channels,
*([1] * len(self.kernel_size))).expand_as(self.V.data)
x_init = F.conv_transpose2d(
x, v_norm, None, self.stride,
self.padding, self.output_padding, self.groups).data
# self.out_channels, 1
t_x_init = x_init.tranpose(0, 1).contiguous().view(
self.out_channels, -1)
# out_features
m_init, v_init = t_x_init.mean(1).squeeze(
1), t_x_init.var(1).squeeze(1)
# out_features
scale_init = self.init_scale / \
torch.sqrt(v_init + 1e-10)
self.g.data.copy_(scale_init)
self.b.data.copy_(-m_init * scale_init)
scale_init_shape = scale_init.view(
1, self.out_channels, *([1] * (len(x_init.size()) - 2)))
m_init_shape = m_init.view(
1, self.out_channels, *([1] * (len(x_init.size()) - 2)))
x_init = scale_init_shape.expand_as(x_init)\
* (x_init - m_init_shape.expand_as(x_init))
self.V_avg.copy_(self.V.data)
self.g_avg.copy_(self.g.data)
self.b_avg.copy_(self.b.data)
return x_init
else:
v, g, b = get_vars_maybe_avg(
self, ['V', 'g', 'b'], self.training,
polyak_decay=self.polyak_decay)
scalar = g / \
torch.norm(v.transpose(0, 1).contiguous().view(
self.out_channels, -1), 2, 1).squeeze(1)
w = scalar.view(self.in_channels, self.out_channels,
*([1] * (len(v.size()) - 2))).expand_as(v) * v
x = F.conv_transpose2d(x, w, b, self.stride,
self.padding, self.output_padding,
self.groups)
return x
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