model.py

import torch.nn as nn

# Denoising Convolutional Neural Network from DnCNN paper
class DnCNN(nn.Module):
    def __init__(self, channels, num_of_layers=17, kernel_size=3, padding=1, features=64):
        super(DnCNN, self).__init__()
        layers = []
        layers.append(nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False))
        layers.append(nn.ReLU(inplace=True))
        for _ in range(num_of_layers - 2):
            layers.append(nn.Conv2d(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False))
            layers.append(nn.BatchNorm2d(features))
            layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(in_channels=features, out_channels=channels, kernel_size=kernel_size, padding=padding, bias=False))
        self.dncnn = nn.Sequential(*layers)
    def forward(self, x):
        out = self.dncnn(x)
        return x - out