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import torch
import torch.nn as nn
import math
from attacks.CGNC.models.attention import SpatialTransformer
# To control feature map in generator
ngf = 64
def snconv2d(eps=1e-12, **kwargs):
return nn.utils.spectral_norm(nn.Conv2d(**kwargs), eps=eps)
def snlinear(eps=1e-12, **kwargs):
return nn.utils.spectral_norm(nn.Linear(**kwargs), eps=eps)
def get_gaussian_kernel(kernel_size=3, pad=2, sigma=1, channels=3):
x_coord = torch.arange(kernel_size)
x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
y_grid = x_grid.t()
xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
mean = (kernel_size - 1) / 2.
variance = sigma ** 2.
gaussian_kernel = (1. / (2. * math.pi * variance)) * \
torch.exp(
-torch.sum((xy_grid - mean) ** 2., dim=-1) / \
(2 * variance)
)
gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size)
gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1)
gaussian_filter = torch.nn.Conv2d(in_channels=channels, out_channels=channels,
kernel_size=kernel_size, groups=channels, padding=kernel_size - pad, bias=False)
gaussian_filter.weight.data = gaussian_kernel
gaussian_filter.weight.requires_grad = False
return gaussian_filter
class CrossAttenGenerator(nn.Module):
def __init__(self, inception=False, device='cuda', num_head=1, nz=16, loc=[1, 1, 1], context_dim=512):
'''
:param inception: if True crop layer will be added to go from 3x300x300 to 3x299x299.
'''
super(CrossAttenGenerator, self).__init__()
self.inception = inception
self.device = device
self.snlinear = snlinear(in_features=512, out_features=nz, bias=False)
self.loc = loc
self.block1 = nn.Sequential(
nn.ReflectionPad2d(3),
nn.Conv2d(3 + nz * self.loc[0], ngf, kernel_size=7, padding=0, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True)
)
self.block2 = nn.Sequential(
nn.Conv2d(ngf + nz * self.loc[1], ngf * 2, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True)
)
self.block3 = nn.Sequential(
nn.Conv2d(ngf * 2 + nz * self.loc[2], ngf * 4, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True)
)
self.resblock1 = ResidualBlock(ngf * 4)
self.resblock2 = ResidualBlock(ngf * 4)
self.cross_att2 = SpatialTransformer(ngf * 4, num_head, ngf * 4 // num_head, depth=1, context_dim=context_dim)
self.resblock3 = ResidualBlock(ngf * 4)
self.resblock4 = ResidualBlock(ngf * 4)
self.cross_att4 = SpatialTransformer(ngf * 4, num_head, ngf * 4 // num_head, depth=1, context_dim=context_dim)
self.resblock5 = ResidualBlock(ngf * 4)
self.resblock6 = ResidualBlock(ngf * 4)
self.upsampl1 = nn.Sequential(
nn.ConvTranspose2d(ngf * 4, ngf * 2, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True)
)
self.upsampl2 = nn.Sequential(
nn.ConvTranspose2d(ngf * 2, ngf, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True)
)
self.blockf = nn.Sequential(
nn.ReflectionPad2d(3),
nn.Conv2d(ngf, 3, kernel_size=7, padding=0)
)
self.crop = nn.ConstantPad2d((0, -1, -1, 0), 0)
self.alf_layer = get_gaussian_kernel(kernel_size=3, pad=2, sigma=1)
def forward(self, input, cond, eps=16):
text_cond = cond.unsqueeze(1).to(torch.float)
z_cond = self.snlinear(cond.float())
# loc 0
z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), input.size(2), input.size(3))
x = self.block1(torch.cat((input, z_img), dim=1))
# loc 1
z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), x.size(2), x.size(3))
x = self.block2(torch.cat((x, z_img), dim=1)) if self.loc[1] else self.block2(x)
# loc 2
z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), x.size(2), x.size(3))
x = self.block3(torch.cat((x, z_img), dim=1)) if self.loc[2] else self.block3(x)
x = self.resblock1(x)
x = self.resblock2(x)
x = self.cross_att2(x, text_cond)
x = self.resblock3(x)
x = self.resblock4(x)
x = self.cross_att4(x, text_cond)
x = self.resblock5(x)
x = self.resblock6(x)
x = self.upsampl1(x)
x = self.upsampl2(x)
x = self.blockf(x)
if self.inception:
x = self.crop(x)
x = torch.tanh(x)
x = self.alf_layer(x)
return x * eps
class ResidualBlock(nn.Module):
def __init__(self, num_filters):
super(ResidualBlock, self).__init__()
self.block = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(in_channels=num_filters, out_channels=num_filters, kernel_size=3, stride=1, padding=0,
bias=False),
nn.BatchNorm2d(num_filters),
nn.ReLU(True),
nn.Dropout(0.5),
nn.ReflectionPad2d(1),
nn.Conv2d(in_channels=num_filters, out_channels=num_filters, kernel_size=3, stride=1, padding=0,
bias=False),
nn.BatchNorm2d(num_filters)
)
def forward(self, x):
residual = self.block(x)
return x + residual
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