utils/ImageOnly.py

import torch.nn as nn


class Decoder4(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(Decoder4, self).__init__()
        self.fc1 = nn.Linear(input_dim, 256)
        self.batch_norm1 = nn.BatchNorm1d(256)
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(0.5)
        self.fc2 = nn.Linear(256, 128)
        self.batch_norm2 = nn.BatchNorm1d(128)
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(0.5)
        self.fc3 = nn.Linear(128, output_dim)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.fc1(x)
        x = self.batch_norm1(x)
        x = self.relu1(x)
        x = self.dropout1(x)
        x = self.fc2(x)
        x = self.batch_norm2(x)
        x = self.relu2(x)
        x = self.dropout2(x)
        x = self.fc3(x)
        x = self.sigmoid(x)
        return x