import torch.nn as nn

class SISUDiscriminator(nn.Module):
    def __init__(self):
        super(SISUDiscriminator, self).__init__()
        layers = 32
        self.model = nn.Sequential(
            nn.Conv1d(1, layers, kernel_size=5, stride=2, padding=2),
            nn.BatchNorm1d(layers),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(layers, layers * 2, kernel_size=5, stride=2, padding=2),
            nn.BatchNorm1d(layers * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(layers * 2, layers * 4, kernel_size=5, stride=2, padding=2),
            nn.BatchNorm1d(layers * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(layers * 4, layers * 8, kernel_size=5, stride=2, padding=2),
            nn.BatchNorm1d(layers * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(layers * 8, 1, kernel_size=3, padding=1),
        )
        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.model(x)
        x = self.global_avg_pool(x)
        x = x.view(-1, 1)
        x = self.sigmoid(x)
        return x