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import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaptiveInstanceNorm1d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1):
super(AdaptiveInstanceNorm1d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.weight = None
self.bias = None
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
def forward(self, x, direct_weighting=False, no_std=False):
assert self.weight is not None and \
self.bias is not None, "Please assign AdaIN weight first"
# (bs, nfeats, nframe) <= (nframe, bs, nfeats)
x = x.permute(1,2,0)
b, c = x.size(0), x.size(1) # batch size & channels
running_mean = self.running_mean.repeat(b)
running_var = self.running_var.repeat(b)
# self.weight = torch.ones_like(self.weight)
if direct_weighting:
x_reshaped = x.contiguous().view(b * c)
if no_std:
out = x_reshaped + self.bias
else:
out = x_reshaped.mul(self.weight) + self.bias
out = out.view(b, c, *x.size()[2:])
else:
x_reshaped = x.contiguous().view(1, b * c, *x.size()[2:])
out = F.batch_norm(
x_reshaped, running_mean, running_var, self.weight, self.bias,
True, self.momentum, self.eps)
out = out.view(b, c, *x.size()[2:])
# (nframe, bs, nfeats) <= (bs, nfeats, nframe)
out = out.permute(2,0,1)
return out
def __repr__(self):
return self.__class__.__name__ + '(' + str(self.num_features) + ')'
def assign_adain_params(adain_params, model):
# assign the adain_params to the AdaIN layers in model
for m in model.modules():
if m.__class__.__name__ == "AdaptiveInstanceNorm1d":
mean = adain_params[: , : m.num_features]
std = adain_params[: , m.num_features: 2 * m.num_features]
m.bias = mean.contiguous().view(-1)
m.weight = std.contiguous().view(-1)
if adain_params.size(1) > 2 * m.num_features:
adain_params = adain_params[: , 2 * m.num_features:]
def get_num_adain_params(model):
# return the number of AdaIN parameters needed by the model
num_adain_params = 0
for m in model.modules():
if m.__class__.__name__ == "AdaptiveInstanceNorm1d":
num_adain_params += 2 * m.num_features
return num_adain_params
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