from torch import nn as nn from torch.nn import functional as F from torch.nn.utils import spectral_norm from r_basicsr.utils.registry import ARCH_REGISTRY @ARCH_REGISTRY.register() class VGGStyleDiscriminator(nn.Module): """VGG style discriminator with input size 128 x 128 or 256 x 256. It is used to train SRGAN, ESRGAN, and VideoGAN. Args: num_in_ch (int): Channel number of inputs. Default: 3. num_feat (int): Channel number of base intermediate features.Default: 64. """ def __init__(self, num_in_ch, num_feat, input_size=128): super(VGGStyleDiscriminator, self).__init__() self.input_size = input_size assert self.input_size == 128 or self.input_size == 256, ( f'input size must be 128 or 256, but received {input_size}') self.conv0_0 = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True) self.conv0_1 = nn.Conv2d(num_feat, num_feat, 4, 2, 1, bias=False) self.bn0_1 = nn.BatchNorm2d(num_feat, affine=True) self.conv1_0 = nn.Conv2d(num_feat, num_feat * 2, 3, 1, 1, bias=False) self.bn1_0 = nn.BatchNorm2d(num_feat * 2, affine=True) self.conv1_1 = nn.Conv2d(num_feat * 2, num_feat * 2, 4, 2, 1, bias=False) self.bn1_1 = nn.BatchNorm2d(num_feat * 2, affine=True) self.conv2_0 = nn.Conv2d(num_feat * 2, num_feat * 4, 3, 1, 1, bias=False) self.bn2_0 = nn.BatchNorm2d(num_feat * 4, affine=True) self.conv2_1 = nn.Conv2d(num_feat * 4, num_feat * 4, 4, 2, 1, bias=False) self.bn2_1 = nn.BatchNorm2d(num_feat * 4, affine=True) self.conv3_0 = nn.Conv2d(num_feat * 4, num_feat * 8, 3, 1, 1, bias=False) self.bn3_0 = nn.BatchNorm2d(num_feat * 8, affine=True) self.conv3_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) self.bn3_1 = nn.BatchNorm2d(num_feat * 8, affine=True) self.conv4_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False) self.bn4_0 = nn.BatchNorm2d(num_feat * 8, affine=True) self.conv4_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) self.bn4_1 = nn.BatchNorm2d(num_feat * 8, affine=True) if self.input_size == 256: self.conv5_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False) self.bn5_0 = nn.BatchNorm2d(num_feat * 8, affine=True) self.conv5_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) self.bn5_1 = nn.BatchNorm2d(num_feat * 8, affine=True) self.linear1 = nn.Linear(num_feat * 8 * 4 * 4, 100) self.linear2 = nn.Linear(100, 1) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): assert x.size(2) == self.input_size, (f'Input size must be identical to input_size, but received {x.size()}.') feat = self.lrelu(self.conv0_0(x)) feat = self.lrelu(self.bn0_1(self.conv0_1(feat))) # output spatial size: /2 feat = self.lrelu(self.bn1_0(self.conv1_0(feat))) feat = self.lrelu(self.bn1_1(self.conv1_1(feat))) # output spatial size: /4 feat = self.lrelu(self.bn2_0(self.conv2_0(feat))) feat = self.lrelu(self.bn2_1(self.conv2_1(feat))) # output spatial size: /8 feat = self.lrelu(self.bn3_0(self.conv3_0(feat))) feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: /16 feat = self.lrelu(self.bn4_0(self.conv4_0(feat))) feat = self.lrelu(self.bn4_1(self.conv4_1(feat))) # output spatial size: /32 if self.input_size == 256: feat = self.lrelu(self.bn5_0(self.conv5_0(feat))) feat = self.lrelu(self.bn5_1(self.conv5_1(feat))) # output spatial size: / 64 # spatial size: (4, 4) feat = feat.view(feat.size(0), -1) feat = self.lrelu(self.linear1(feat)) out = self.linear2(feat) return out @ARCH_REGISTRY.register(suffix='basicsr') class UNetDiscriminatorSN(nn.Module): """Defines a U-Net discriminator with spectral normalization (SN) It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. Arg: num_in_ch (int): Channel number of inputs. Default: 3. num_feat (int): Channel number of base intermediate features. Default: 64. skip_connection (bool): Whether to use skip connections between U-Net. Default: True. """ def __init__(self, num_in_ch, num_feat=64, skip_connection=True): super(UNetDiscriminatorSN, self).__init__() self.skip_connection = skip_connection norm = spectral_norm # the first convolution self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1) # downsample self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False)) self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False)) self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False)) # upsample self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False)) self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False)) self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False)) # extra convolutions self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1) def forward(self, x): # downsample x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True) x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True) x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True) x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True) # upsample x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False) x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True) if self.skip_connection: x4 = x4 + x2 x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False) x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True) if self.skip_connection: x5 = x5 + x1 x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False) x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True) if self.skip_connection: x6 = x6 + x0 # extra convolutions out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True) out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True) out = self.conv9(out) return out