From 495ffc4777522e40941753e3b1b79c02f84b25b4 Mon Sep 17 00:00:00 2001 From: Grafting Rayman <156515434+GraftingRayman@users.noreply.github.com> Date: Fri, 17 Jan 2025 11:00:30 +0000 Subject: Add files via upload --- r_basicsr/archs/discriminator_arch.py | 150 ++++++++++++++++++++++++++++++++++ 1 file changed, 150 insertions(+) create mode 100644 r_basicsr/archs/discriminator_arch.py (limited to 'r_basicsr/archs/discriminator_arch.py') diff --git a/r_basicsr/archs/discriminator_arch.py b/r_basicsr/archs/discriminator_arch.py new file mode 100644 index 0000000..2229748 --- /dev/null +++ b/r_basicsr/archs/discriminator_arch.py @@ -0,0 +1,150 @@ +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 -- cgit v1.2.3