import torch import torch.nn as nn import torchvision 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