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"""Normalization layers used in blocks
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
# weight and bias are dynamically assigned
self.weight = None
self.bias = None
# just dummy buffers, not used
self.register_buffer("running_mean", torch.zeros(num_features))
self.register_buffer("running_var", torch.ones(num_features))
def forward(self, x):
assert (
self.weight is not None and self.bias is not None
), "Please assign weight and bias before calling AdaIN!"
b, c = x.size(0), x.size(1)
running_mean = self.running_mean.repeat(b)
running_var = self.running_var.repeat(b)
# Apply instance norm
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,
)
return out.view(b, c, *x.size()[2:])
def __repr__(self):
return self.__class__.__name__ + "(" + str(self.num_features) + ")"
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-5, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())
self.beta = nn.Parameter(torch.zeros(num_features))
def forward(self, x):
shape = [-1] + [1] * (x.dim() - 1)
# print(x.size())
if x.size(0) == 1:
# These two lines run much faster in pytorch 0.4
# than the two lines listed below.
mean = x.view(-1).mean().view(*shape)
std = x.view(-1).std().view(*shape)
else:
mean = x.view(x.size(0), -1).mean(1).view(*shape)
std = x.view(x.size(0), -1).std(1).view(*shape)
x = (x - mean) / (std + self.eps)
if self.affine:
shape = [1, -1] + [1] * (x.dim() - 2)
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
return x
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
"""
Based on the paper "Spectral Normalization for Generative Adversarial Networks"
by Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida and the
Pytorch implementation:
https://github.com/christiancosgrove/pytorch-spectral-normalization-gan
"""
def __init__(self, module, name="weight", power_iterations=1):
super().__init__()
self.module = module
self.name = name
self.power_iterations = power_iterations
if not self._made_params():
self._make_params()
def _update_u_v(self):
u = getattr(self.module, self.name + "_u")
v = getattr(self.module, self.name + "_v")
w = getattr(self.module, self.name + "_bar")
height = w.data.shape[0]
for _ in range(self.power_iterations):
v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data), u.data))
u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
# sigma = torch.dot(u.data, torch.mv(w.view(height,-1).data, v.data))
sigma = u.dot(w.view(height, -1).mv(v))
setattr(self.module, self.name, w / sigma.expand_as(w))
def _made_params(self):
try:
u = getattr(self.module, self.name + "_u") # noqa: F841
v = getattr(self.module, self.name + "_v") # noqa: F841
w = getattr(self.module, self.name + "_bar") # noqa: F841
return True
except AttributeError:
return False
def _make_params(self):
w = getattr(self.module, self.name)
height = w.data.shape[0]
width = w.view(height, -1).data.shape[1]
u = nn.Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
v = nn.Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
u.data = l2normalize(u.data)
v.data = l2normalize(v.data)
w_bar = nn.Parameter(w.data)
del self.module._parameters[self.name]
self.module.register_parameter(self.name + "_u", u)
self.module.register_parameter(self.name + "_v", v)
self.module.register_parameter(self.name + "_bar", w_bar)
def forward(self, *args):
self._update_u_v()
return self.module.forward(*args)
class SPADE(nn.Module):
def __init__(self, param_free_norm_type, kernel_size, norm_nc, cond_nc):
super().__init__()
if param_free_norm_type == "instance":
self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
# elif param_free_norm_type == "syncbatch":
# self.param_free_norm = SynchronizedBatchNorm2d(norm_nc, affine=False)
elif param_free_norm_type == "batch":
self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False)
else:
raise ValueError(
"%s is not a recognized param-free norm type in SPADE"
% param_free_norm_type
)
# The dimension of the intermediate embedding space. Yes, hardcoded.
nhidden = 128
pw = kernel_size // 2
self.mlp_shared = nn.Sequential(
nn.Conv2d(cond_nc, nhidden, kernel_size=kernel_size, padding=pw), nn.ReLU()
)
self.mlp_gamma = nn.Conv2d(
nhidden, norm_nc, kernel_size=kernel_size, padding=pw
)
self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=kernel_size, padding=pw)
def forward(self, x, segmap):
# Part 1. generate parameter-free normalized activations
normalized = self.param_free_norm(x)
# Part 2. produce scaling and bias conditioned on semantic map
segmap = F.interpolate(segmap, size=x.size()[2:], mode="nearest")
actv = self.mlp_shared(segmap)
gamma = self.mlp_gamma(actv)
beta = self.mlp_beta(actv)
# apply scale and bias
out = normalized * (1 + gamma) + beta
return out
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