import torch.nn as nn

class SISUDiscriminator(nn.Module):
    def __init__(self):
        super(SISUDiscriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Conv1d(2, 128, kernel_size=3, padding=1),
            #nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(128, 256, kernel_size=3, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(256, 128, kernel_size=3, padding=1),
            #nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(128, 64, kernel_size=3, padding=1),
            #nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(64, 1, kernel_size=3, padding=1),
            #nn.LeakyReLU(0.2, inplace=True),
        )
        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)  # Output size (1,)

    def forward(self, x):
        x = self.model(x)
        x = self.global_avg_pool(x)
        x = x.view(-1, 1)  # Flatten to (batch_size, 1)
        return x