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import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm
### single layers
def conv2d(*args, **kwargs):
return spectral_norm(nn.Conv2d(*args, **kwargs))
def convTranspose2d(*args, **kwargs):
return spectral_norm(nn.ConvTranspose2d(*args, **kwargs))
def embedding(*args, **kwargs):
return spectral_norm(nn.Embedding(*args, **kwargs))
def linear(*args, **kwargs):
return spectral_norm(nn.Linear(*args, **kwargs))
def NormLayer(c, mode='batch'):
if mode == 'group':
return nn.GroupNorm(c//2, c)
elif mode == 'batch':
return nn.BatchNorm2d(c)
### Activations
class GLU(nn.Module):
def forward(self, x):
nc = x.size(1)
assert nc % 2 == 0, 'channels dont divide 2!'
nc = int(nc/2)
return x[:, :nc] * torch.sigmoid(x[:, nc:])
class Swish(nn.Module):
def forward(self, feat):
return feat * torch.sigmoid(feat)
### Upblocks
class InitLayer(nn.Module):
def __init__(self, nz, channel, sz=4):
super().__init__()
self.init = nn.Sequential(
convTranspose2d(nz, channel*2, sz, 1, 0, bias=False),
NormLayer(channel*2),
GLU(),
)
def forward(self, noise):
noise = noise.view(noise.shape[0], -1, 1, 1)
return self.init(noise)
def UpBlockSmall(in_planes, out_planes):
block = nn.Sequential(
nn.Upsample(scale_factor=2, mode='nearest'),
conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
NormLayer(out_planes*2), GLU())
return block
class UpBlockSmallCond(nn.Module):
def __init__(self, in_planes, out_planes, z_dim):
super().__init__()
self.in_planes = in_planes
self.out_planes = out_planes
self.up = nn.Upsample(scale_factor=2, mode='nearest')
self.conv = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
self.bn = which_bn(2*out_planes)
self.act = GLU()
def forward(self, x, c):
x = self.up(x)
x = self.conv(x)
x = self.bn(x, c)
x = self.act(x)
return x
def UpBlockBig(in_planes, out_planes):
block = nn.Sequential(
nn.Upsample(scale_factor=2, mode='nearest'),
conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
NoiseInjection(),
NormLayer(out_planes*2), GLU(),
conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False),
NoiseInjection(),
NormLayer(out_planes*2), GLU()
)
return block
class UpBlockBigCond(nn.Module):
def __init__(self, in_planes, out_planes, z_dim):
super().__init__()
self.in_planes = in_planes
self.out_planes = out_planes
self.up = nn.Upsample(scale_factor=2, mode='nearest')
self.conv1 = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
self.conv2 = conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False)
which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
self.bn1 = which_bn(2*out_planes)
self.bn2 = which_bn(2*out_planes)
self.act = GLU()
self.noise = NoiseInjection()
def forward(self, x, c):
# block 1
x = self.up(x)
x = self.conv1(x)
x = self.noise(x)
x = self.bn1(x, c)
x = self.act(x)
# block 2
x = self.conv2(x)
x = self.noise(x)
x = self.bn2(x, c)
x = self.act(x)
return x
class SEBlock(nn.Module):
def __init__(self, ch_in, ch_out):
super().__init__()
self.main = nn.Sequential(
nn.AdaptiveAvgPool2d(4),
conv2d(ch_in, ch_out, 4, 1, 0, bias=False),
Swish(),
conv2d(ch_out, ch_out, 1, 1, 0, bias=False),
nn.Sigmoid(),
)
def forward(self, feat_small, feat_big):
return feat_big * self.main(feat_small)
### Downblocks
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=False):
super(SeparableConv2d, self).__init__()
self.depthwise = conv2d(in_channels, in_channels, kernel_size=kernel_size,
groups=in_channels, bias=bias, padding=1)
self.pointwise = conv2d(in_channels, out_channels,
kernel_size=1, bias=bias)
def forward(self, x):
out = self.depthwise(x)
out = self.pointwise(out)
return out
class DownBlock(nn.Module):
def __init__(self, in_planes, out_planes, separable=False):
super().__init__()
if not separable:
self.main = nn.Sequential(
conv2d(in_planes, out_planes, 4, 2, 1),
NormLayer(out_planes),
nn.LeakyReLU(0.2, inplace=True),
)
else:
self.main = nn.Sequential(
SeparableConv2d(in_planes, out_planes, 3),
NormLayer(out_planes),
nn.LeakyReLU(0.2, inplace=True),
nn.AvgPool2d(2, 2),
)
def forward(self, feat):
return self.main(feat)
class DownBlockPatch(nn.Module):
def __init__(self, in_planes, out_planes, separable=False):
super().__init__()
self.main = nn.Sequential(
DownBlock(in_planes, out_planes, separable),
conv2d(out_planes, out_planes, 1, 1, 0, bias=False),
NormLayer(out_planes),
nn.LeakyReLU(0.2, inplace=True),
)
def forward(self, feat):
return self.main(feat)
### CSM
class ResidualConvUnit(nn.Module):
def __init__(self, cin, activation, bn):
super().__init__()
self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1, bias=True)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
return self.skip_add.add(self.conv(x), x)
class FeatureFusionBlock(nn.Module):
def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, lowest=False):
super().__init__()
self.deconv = deconv
self.align_corners = align_corners
self.expand = expand
out_features = features
if self.expand==True:
out_features = features//2
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, *xs):
output = xs[0]
if len(xs) == 2:
output = self.skip_add.add(output, xs[1])
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
)
output = self.out_conv(output)
return output
### Misc
class NoiseInjection(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1), requires_grad=True)
def forward(self, feat, noise=None):
if noise is None:
batch, _, height, width = feat.shape
noise = torch.randn(batch, 1, height, width).to(feat.device)
return feat + self.weight * noise
class CCBN(nn.Module):
''' conditional batchnorm '''
def __init__(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1):
super().__init__()
self.output_size, self.input_size = output_size, input_size
# Prepare gain and bias layers
self.gain = which_linear(input_size, output_size)
self.bias = which_linear(input_size, output_size)
# epsilon to avoid dividing by 0
self.eps = eps
# Momentum
self.momentum = momentum
self.register_buffer('stored_mean', torch.zeros(output_size))
self.register_buffer('stored_var', torch.ones(output_size))
def forward(self, x, y):
# Calculate class-conditional gains and biases
gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1)
bias = self.bias(y).view(y.size(0), -1, 1, 1)
out = F.batch_norm(x, self.stored_mean, self.stored_var, None, None,
self.training, 0.1, self.eps)
return out * gain + bias
class Interpolate(nn.Module):
"""Interpolation module."""
def __init__(self, size, mode='bilinear', align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): interpolation mode
"""
super(Interpolate, self).__init__()
self.interp = nn.functional.interpolate
self.size = size
self.mode = mode
self.align_corners = align_corners
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: interpolated data
"""
x = self.interp(
x,
size=self.size,
mode=self.mode,
align_corners=self.align_corners,
)
return x
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