316 lines
14 KiB
Python
316 lines
14 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torchvision
|
|
from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class Discriminator_VGG_128(nn.Module):
|
|
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
|
|
def __init__(self, in_nc, nf, input_img_factor=1, extra_conv=False):
|
|
super(Discriminator_VGG_128, self).__init__()
|
|
# [64, 128, 128]
|
|
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
|
|
self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
|
|
self.bn0_1 = nn.BatchNorm2d(nf, affine=True)
|
|
# [64, 64, 64]
|
|
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
|
self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
|
self.bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
# [128, 32, 32]
|
|
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
|
|
self.bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
|
self.bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
# [256, 16, 16]
|
|
self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
|
|
self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
# [512, 8, 8]
|
|
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
|
|
self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
final_nf = nf * 8
|
|
|
|
self.extra_conv = extra_conv
|
|
if self.extra_conv:
|
|
self.conv5_0 = nn.Conv2d(nf * 8, nf * 16, 3, 1, 1, bias=False)
|
|
self.bn5_0 = nn.BatchNorm2d(nf * 16, affine=True)
|
|
self.conv5_1 = nn.Conv2d(nf * 16, nf * 16, 4, 2, 1, bias=False)
|
|
self.bn5_1 = nn.BatchNorm2d(nf * 16, affine=True)
|
|
input_img_factor = input_img_factor // 2
|
|
final_nf = nf * 16
|
|
|
|
self.linear1 = nn.Linear(final_nf * 4 * input_img_factor * 4 * input_img_factor, 100)
|
|
self.linear2 = nn.Linear(100, 1)
|
|
|
|
# activation function
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
|
|
def forward(self, x):
|
|
fea = self.lrelu(self.conv0_0(x))
|
|
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
|
|
|
|
#fea = torch.cat([fea, skip_med], dim=1)
|
|
fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
|
|
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
|
|
|
|
#fea = torch.cat([fea, skip_lo], dim=1)
|
|
fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
|
|
fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
|
|
|
|
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
|
|
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
|
|
|
|
fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
|
|
fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
|
|
|
|
if self.extra_conv:
|
|
fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
|
|
fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
|
|
|
|
fea = fea.contiguous().view(fea.size(0), -1)
|
|
fea = self.lrelu(self.linear1(fea))
|
|
out = self.linear2(fea)
|
|
return out
|
|
|
|
|
|
class Discriminator_VGG_PixLoss(nn.Module):
|
|
def __init__(self, in_nc, nf):
|
|
super(Discriminator_VGG_PixLoss, self).__init__()
|
|
# [64, 128, 128]
|
|
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
|
|
self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
|
|
self.bn0_1 = nn.GroupNorm(8, nf, affine=True)
|
|
# [64, 64, 64]
|
|
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
|
self.bn1_0 = nn.GroupNorm(8, nf * 2, affine=True)
|
|
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
|
self.bn1_1 = nn.GroupNorm(8, nf * 2, affine=True)
|
|
# [128, 32, 32]
|
|
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
|
|
self.bn2_0 = nn.GroupNorm(8, nf * 4, affine=True)
|
|
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
|
self.bn2_1 = nn.GroupNorm(8, nf * 4, affine=True)
|
|
# [256, 16, 16]
|
|
self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
|
|
self.bn3_0 = nn.GroupNorm(8, nf * 8, affine=True)
|
|
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn3_1 = nn.GroupNorm(8, nf * 8, affine=True)
|
|
# [512, 8, 8]
|
|
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
|
|
self.bn4_0 = nn.GroupNorm(8, nf * 8, affine=True)
|
|
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
|
|
|
|
self.reduce_1 = ConvGnLelu(nf * 8, nf * 4, bias=False)
|
|
self.pix_loss_collapse = ConvGnLelu(nf * 4, 1, bias=False, norm=False, activation=False)
|
|
|
|
# Pyramid network: upsample with residuals and produce losses at multiple resolutions.
|
|
self.up3_decimate = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=True, activation=False)
|
|
self.up3_converge = ConvGnLelu(nf * 16, nf * 8, kernel_size=3, bias=False)
|
|
self.up3_proc = ConvGnLelu(nf * 8, nf * 8, bias=False)
|
|
self.up3_reduce = ConvGnLelu(nf * 8, nf * 4, bias=False)
|
|
self.up3_pix = ConvGnLelu(nf * 4, 1, bias=False, norm=False, activation=False)
|
|
|
|
self.up2_decimate = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, bias=True, activation=False)
|
|
self.up2_converge = ConvGnLelu(nf * 8, nf * 4, kernel_size=3, bias=False)
|
|
self.up2_proc = ConvGnLelu(nf * 4, nf * 4, bias=False)
|
|
self.up2_reduce = ConvGnLelu(nf * 4, nf * 2, bias=False)
|
|
self.up2_pix = ConvGnLelu(nf * 2, 1, bias=False, norm=False, activation=False)
|
|
|
|
# activation function
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
|
|
def forward(self, x, flatten=True):
|
|
fea0 = self.lrelu(self.conv0_0(x))
|
|
fea0 = self.lrelu(self.bn0_1(self.conv0_1(fea0)))
|
|
|
|
fea1 = self.lrelu(self.bn1_0(self.conv1_0(fea0)))
|
|
fea1 = self.lrelu(self.bn1_1(self.conv1_1(fea1)))
|
|
|
|
fea2 = self.lrelu(self.bn2_0(self.conv2_0(fea1)))
|
|
fea2 = self.lrelu(self.bn2_1(self.conv2_1(fea2)))
|
|
|
|
fea3 = self.lrelu(self.bn3_0(self.conv3_0(fea2)))
|
|
fea3 = self.lrelu(self.bn3_1(self.conv3_1(fea3)))
|
|
|
|
fea4 = self.lrelu(self.bn4_0(self.conv4_0(fea3)))
|
|
fea4 = self.lrelu(self.bn4_1(self.conv4_1(fea4)))
|
|
|
|
loss = self.reduce_1(fea4)
|
|
# "Weight" all losses the same by interpolating them to the highest dimension.
|
|
loss = self.pix_loss_collapse(loss)
|
|
loss = F.interpolate(loss, scale_factor=4, mode="nearest")
|
|
|
|
# And the pyramid network!
|
|
dec3 = self.up3_decimate(F.interpolate(fea4, scale_factor=2, mode="nearest"))
|
|
dec3 = torch.cat([dec3, fea3], dim=1)
|
|
dec3 = self.up3_converge(dec3)
|
|
dec3 = self.up3_proc(dec3)
|
|
loss3 = self.up3_reduce(dec3)
|
|
loss3 = self.up3_pix(loss3)
|
|
loss3 = F.interpolate(loss3, scale_factor=2, mode="nearest")
|
|
|
|
dec2 = self.up2_decimate(F.interpolate(dec3, scale_factor=2, mode="nearest"))
|
|
dec2 = torch.cat([dec2, fea2], dim=1)
|
|
dec2 = self.up2_converge(dec2)
|
|
dec2 = self.up2_proc(dec2)
|
|
dec2 = self.up2_reduce(dec2)
|
|
loss2 = self.up2_pix(dec2)
|
|
|
|
# Compress all of the loss values into the batch dimension. The actual loss attached to this output will
|
|
# then know how to handle them.
|
|
combined_losses = torch.cat([loss, loss3, loss2], dim=1)
|
|
return combined_losses.view(-1, 1)
|
|
|
|
def pixgan_parameters(self):
|
|
return 3, 8
|
|
|
|
|
|
class Discriminator_UNet(nn.Module):
|
|
def __init__(self, in_nc, nf):
|
|
super(Discriminator_UNet, self).__init__()
|
|
# [64, 128, 128]
|
|
self.conv0_0 = ConvGnLelu(in_nc, nf, kernel_size=3, bias=True, activation=False)
|
|
self.conv0_1 = ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False)
|
|
# [64, 64, 64]
|
|
self.conv1_0 = ConvGnLelu(nf, nf * 2, kernel_size=3, bias=False)
|
|
self.conv1_1 = ConvGnLelu(nf * 2, nf * 2, kernel_size=3, stride=2, bias=False)
|
|
# [128, 32, 32]
|
|
self.conv2_0 = ConvGnLelu(nf * 2, nf * 4, kernel_size=3, bias=False)
|
|
self.conv2_1 = ConvGnLelu(nf * 4, nf * 4, kernel_size=3, stride=2, bias=False)
|
|
# [256, 16, 16]
|
|
self.conv3_0 = ConvGnLelu(nf * 4, nf * 8, kernel_size=3, bias=False)
|
|
self.conv3_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False)
|
|
# [512, 8, 8]
|
|
self.conv4_0 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=False)
|
|
self.conv4_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False)
|
|
|
|
self.up1 = ExpansionBlock(nf * 8, nf * 8, block=ConvGnLelu)
|
|
self.proc1 = ConvGnLelu(nf * 8, nf * 8, bias=False)
|
|
self.collapse1 = ConvGnLelu(nf * 8, 1, bias=True, norm=False, activation=False)
|
|
|
|
self.up2 = ExpansionBlock(nf * 8, nf * 4, block=ConvGnLelu)
|
|
self.proc2 = ConvGnLelu(nf * 4, nf * 4, bias=False)
|
|
self.collapse2 = ConvGnLelu(nf * 4, 1, bias=True, norm=False, activation=False)
|
|
|
|
self.up3 = ExpansionBlock(nf * 4, nf * 2, block=ConvGnLelu)
|
|
self.proc3 = ConvGnLelu(nf * 2, nf * 2, bias=False)
|
|
self.collapse3 = ConvGnLelu(nf * 2, 1, bias=True, norm=False, activation=False)
|
|
|
|
def forward(self, x, flatten=True):
|
|
fea0 = self.conv0_0(x)
|
|
fea0 = self.conv0_1(fea0)
|
|
|
|
fea1 = self.conv1_0(fea0)
|
|
fea1 = self.conv1_1(fea1)
|
|
|
|
fea2 = self.conv2_0(fea1)
|
|
fea2 = self.conv2_1(fea2)
|
|
|
|
fea3 = self.conv3_0(fea2)
|
|
fea3 = self.conv3_1(fea3)
|
|
|
|
fea4 = self.conv4_0(fea3)
|
|
fea4 = self.conv4_1(fea4)
|
|
|
|
# And the pyramid network!
|
|
u1 = self.up1(fea4, fea3)
|
|
loss1 = self.collapse1(self.proc1(u1))
|
|
u2 = self.up2(u1, fea2)
|
|
loss2 = self.collapse2(self.proc2(u2))
|
|
u3 = self.up3(u2, fea1)
|
|
loss3 = self.collapse3(self.proc3(u3))
|
|
res = loss3.shape[2:]
|
|
|
|
# Compress all of the loss values into the batch dimension. The actual loss attached to this output will
|
|
# then know how to handle them.
|
|
combined_losses = torch.cat([F.interpolate(loss1, scale_factor=4),
|
|
F.interpolate(loss2, scale_factor=2),
|
|
F.interpolate(loss3, scale_factor=1)], dim=1)
|
|
return combined_losses.view(-1, 1)
|
|
|
|
def pixgan_parameters(self):
|
|
return 3, 4
|
|
|
|
|
|
class Discriminator_UNet_FeaOut(nn.Module):
|
|
def __init__(self, in_nc, nf, feature_mode=False):
|
|
super(Discriminator_UNet_FeaOut, self).__init__()
|
|
# [64, 128, 128]
|
|
self.conv0_0 = ConvGnLelu(in_nc, nf, kernel_size=3, bias=True, activation=False)
|
|
self.conv0_1 = ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False)
|
|
# [64, 64, 64]
|
|
self.conv1_0 = ConvGnLelu(nf, nf * 2, kernel_size=3, bias=False)
|
|
self.conv1_1 = ConvGnLelu(nf * 2, nf * 2, kernel_size=3, stride=2, bias=False)
|
|
# [128, 32, 32]
|
|
self.conv2_0 = ConvGnLelu(nf * 2, nf * 4, kernel_size=3, bias=False)
|
|
self.conv2_1 = ConvGnLelu(nf * 4, nf * 4, kernel_size=3, stride=2, bias=False)
|
|
# [256, 16, 16]
|
|
self.conv3_0 = ConvGnLelu(nf * 4, nf * 8, kernel_size=3, bias=False)
|
|
self.conv3_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False)
|
|
# [512, 8, 8]
|
|
self.conv4_0 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=False)
|
|
self.conv4_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False)
|
|
|
|
self.up1 = ExpansionBlock(nf * 8, nf * 8, block=ConvGnLelu)
|
|
self.proc1 = ConvGnLelu(nf * 8, nf * 8, bias=False)
|
|
self.collapse1 = ConvGnLelu(nf * 8, 1, bias=True, norm=False, activation=False)
|
|
|
|
self.up2 = ExpansionBlock(nf * 8, nf * 4, block=ConvGnLelu)
|
|
self.proc2 = ConvGnLelu(nf * 4, nf * 4, bias=False)
|
|
self.collapse2 = ConvGnLelu(nf * 4, 1, bias=True, norm=False, activation=False)
|
|
|
|
self.up3 = ExpansionBlock(nf * 4, nf * 2, block=ConvGnLelu)
|
|
self.proc3 = ConvGnLelu(nf * 2, nf * 2, bias=False)
|
|
self.collapse3 = ConvGnLelu(nf * 2, 1, bias=True, norm=False, activation=False)
|
|
|
|
self.feature_mode = feature_mode
|
|
|
|
def forward(self, x, output_feature_vector=False):
|
|
fea0 = self.conv0_0(x)
|
|
fea0 = self.conv0_1(fea0)
|
|
|
|
fea1 = self.conv1_0(fea0)
|
|
fea1 = self.conv1_1(fea1)
|
|
|
|
fea2 = self.conv2_0(fea1)
|
|
fea2 = self.conv2_1(fea2)
|
|
|
|
fea3 = self.conv3_0(fea2)
|
|
fea3 = self.conv3_1(fea3)
|
|
|
|
feat = self.conv4_0(fea3)
|
|
fea4 = self.conv4_1(feat)
|
|
|
|
# And the pyramid network!
|
|
u1 = self.up1(fea4, fea3)
|
|
loss1 = self.collapse1(self.proc1(u1))
|
|
u2 = self.up2(u1, fea2)
|
|
loss2 = self.collapse2(self.proc2(u2))
|
|
u3 = self.up3(u2, fea1)
|
|
loss3 = self.collapse3(self.proc3(u3))
|
|
res = loss3.shape[2:]
|
|
|
|
if self.feature_mode:
|
|
combined_losses = F.interpolate(loss1, scale_factor=4)
|
|
else:
|
|
# Compress all of the loss values into the batch dimension. The actual loss attached to this output will
|
|
# then know how to handle them.
|
|
combined_losses = torch.cat([F.interpolate(loss1, scale_factor=4),
|
|
F.interpolate(loss2, scale_factor=2),
|
|
F.interpolate(loss3, scale_factor=1)], dim=1)
|
|
if output_feature_vector:
|
|
return combined_losses.view(-1, 1), feat
|
|
else:
|
|
return combined_losses.view(-1, 1)
|
|
|
|
def pixgan_parameters(self):
|
|
if self.feature_mode:
|
|
return 1, 4
|
|
else:
|
|
return 3, 4 |