models_psloss.py

import torch
import torch.nn as nn

class DnCNN(nn.Module):
    def __init__(self, in_channels, out_channels, num_of_layers=17):
        super(DnCNN, self).__init__()
        kernel_size = 3
        padding = 1
        features = 64
        layers = []
        layers.append(nn.Conv2d(in_channels=in_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=out_channels, kernel_size=kernel_size, padding=padding, bias=False))
        self.dncnn = nn.Sequential(*layers)
        self._initialize_weights()

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
                #nn.init.dirac_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)

    def forward(self, x):
        residual = self.dncnn(x)
        return residual