import torch import torch.nn as nn import torchvision from models.archs.arch_util import ConvBnLelu, ConvGnLelu 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): x = x[0] 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, gn=False, lelu=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, lelu=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, gn=False, lelu=False) self.up2_decimate = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, bias=True, lelu=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, gn=False, lelu=False) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x, flatten=True): x = x[0] 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) loss2 = self.up2_reduce(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)