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): 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) self.linear1 = nn.Linear(int(nf * 8 * 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, skip_med, skip_lo = 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))) fea = fea.view(fea.size(0), -1) fea = self.lrelu(self.linear1(fea)) out = self.linear2(fea) return out class VGGFeatureExtractor(nn.Module): def __init__(self, feature_layer=34, use_bn=False, use_input_norm=True, device=torch.device('cpu')): super(VGGFeatureExtractor, self).__init__() self.use_input_norm = use_input_norm if use_bn: model = torchvision.models.vgg19_bn(pretrained=True) else: model = torchvision.models.vgg19(pretrained=True) if self.use_input_norm: mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device) # [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1] std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device) # [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1] self.register_buffer('mean', mean) self.register_buffer('std', std) self.features = nn.Sequential(*list(model.features.children())[:(feature_layer + 1)]) # No need to BP to variable for k, v in self.features.named_parameters(): v.requires_grad = False def forward(self, x): # Assume input range is [0, 1] if self.use_input_norm: x = (x - self.mean) / self.std output = self.features(x) return output