import torch.nn as nn


class WatermarkEmbeder(nn.Module):

    def __init__(self, pct_dim, wm_dim):
        super(WatermarkEmbeder, self).__init__()

        self.model = nn.Sequential(
            nn.Linear(pct_dim, 256, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(256, wm_dim, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.model(x)