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| from torch import nn as nn | |
| from torch.nn import functional as F | |
| from torch.nn.utils import spectral_norm | |
| from basicsr.utils.registry import ARCH_REGISTRY | |
| 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 | |
| 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 | |