models/vdsr.py

# models/vdsr.py
import torch.nn as nn
from math import sqrt

class Conv_ReLU_Block(nn.Module):
    def __init__(self):
        super(Conv_ReLU_Block, self).__init__()
        self.conv = nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        
    def forward(self, x):
        return self.relu(self.conv(x))

class VDSR(nn.Module):
    def __init__(self):
        super(VDSR, self).__init__()
        self.residual_layer = self.make_layer(Conv_ReLU_Block, 18)
        self.input = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
        self.output = nn.Conv2d(64, 1, kernel_size=3, padding=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        
        # Initialize weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, sqrt(2. / n))
                
    def make_layer(self, block, num_of_layer):
        layers = []
        for _ in range(num_of_layer):
            layers.append(block())
        return nn.Sequential(*layers)

    def forward(self, x):
        residual = x
        out = self.relu(self.input(x))
        out = self.residual_layer(out)
        out = self.output(out)
        return out + residual