model/discriminator.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm

class DiscriminatorVGG(nn.Module):
    def __init__(self, in_ch=3, image_size=128, d=64):
        super(DiscriminatorVGG, self).__init__()
        self.feature_map_size = image_size // 32
        self.d = d

        self.features = nn.Sequential(
            nn.Conv2d(in_ch, d, kernel_size=3, stride=1, padding=1),  # input is 3 x 128 x 128
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d, d, kernel_size=3, stride=2, padding=1, bias=False),  # state size. 64 x 64 x 64
            nn.BatchNorm2d(d),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d, d*2, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(d*2),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d*2, d*2, kernel_size=3, stride=2, padding=1, bias=False),  # state size. 128 x 32 x 32
            nn.BatchNorm2d(d*2),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d*2, d*4, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(d*4),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d*4, d*4, kernel_size=3, stride=2, padding=1, bias=False),  # state size. 256 x 16 x 16
            nn.BatchNorm2d(d*4),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d*4, d*8, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(d*8),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d*8, d*8, kernel_size=3, stride=2, padding=1, bias=False),  # state size. 512 x 8 x 8
            nn.BatchNorm2d(d*8),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d*8, d*8, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(d*8),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),

            nn.Conv2d(d*8, d*8, kernel_size=3, stride=2, padding=1, bias=False),  # state size. 512 x 4 x 4
            nn.BatchNorm2d(d*8),
            nn.LeakyReLU(negative_slope=0.2, inplace=True)
        )

        self.classifier = nn.Sequential(
            nn.Linear((self.d*8) * self.feature_map_size * self.feature_map_size, 100),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),
            nn.Linear(100, 1)
        )
    
    def forward(self, x):
        out = self.features(x)
        out = torch.flatten(out, 1)
        out = self.classifier(out)

        return out


class UNetDiscriminator(nn.Module):
    def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
        super(UNetDiscriminator, self).__init__()
        self.skip_connection = skip_connection
        norm = spectral_norm
        self.num_in_ch = num_in_ch
        self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)

        self.conv1 = norm(nn.Conv2d(num_feat, num_feat*2, kernel_size=4, stride=2, padding=1, bias=False))
        self.conv2 = norm(nn.Conv2d(num_feat*2, num_feat*4, kernel_size=4, stride=2, padding=1, bias=False))
        self.conv3 = norm(nn.Conv2d(num_feat*4, num_feat*8, kernel_size=4, stride=2, padding=1, bias=False))

        # upsample
        self.conv4 = norm(nn.Conv2d(num_feat*8, num_feat*4, kernel_size=3, stride=1, padding=1, bias=False))
        self.conv5 = norm(nn.Conv2d(num_feat*4, num_feat*2, kernel_size=3, stride=1, padding=1, bias=False))
        self.conv6 = norm(nn.Conv2d(num_feat*2, num_feat, kernel_size=3, stride=1, padding=1, bias=False))

        # extra
        self.conv7 = norm(nn.Conv2d(num_feat, num_feat, kernel_size=3, stride=1, padding=1, bias=False))
        self.conv8 = norm(nn.Conv2d(num_feat, num_feat, kernel_size=3, stride=1, padding=1, bias=False))

        self.conv9 = nn.Conv2d(num_feat, 1, kernel_size=3, stride=1, padding=1)

        self.avg_pool = nn.AdaptiveAvgPool2d(1)
    
    def forward(self, x):
        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')
        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')
        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')
        x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)

        if self.skip_connection:
            x6 = x6 + x0

        # extra
        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)
        out = self.avg_pool(out)
        out = torch.flatten(out, 1)
        return out