network/decom.py

import torch
import torch.nn as nn
from network.architecture import *

class Decom(nn.Module):
    def __init__(self):
        super().__init__()
        self.decom = nn.Sequential(
            get_conv2d_layer(in_c=3, out_c=32, k=3, s=1, p=1),
            nn.LeakyReLU(0.2, inplace=True),
            get_conv2d_layer(in_c=32, out_c=32, k=3, s=1, p=1),
            nn.LeakyReLU(0.2, inplace=True),
            get_conv2d_layer(in_c=32, out_c=32, k=3, s=1, p=1),
            nn.LeakyReLU(0.2, inplace=True),
            get_conv2d_layer(in_c=32, out_c=4, k=3, s=1, p=1),
            nn.ReLU()
        )

    def forward(self, input):
        output = self.decom(input)
        R = output[:, 0:3, :, :]
        L = output[:, 3:4, :, :]
        return R, L