model.py

import torch
import torch.nn as nn
import torch.nn.functional as F


class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)


    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        return out
    
dropout_value = 0.01
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # Prep Layer
        self.convblock01 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), padding=1, bias=False),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.Dropout(dropout_value))


        # Layer 1
        self.convblock11 = nn.Sequential(
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=1,  bias=False),
            nn.MaxPool2d((2,2)),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Dropout(dropout_value)
            )

        self.residual11 = ResidualBlock(in_channels = 128, out_channels = 128)


        # Layer 2
        self.convblock21 = nn.Sequential(
            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), padding=1,  bias=False),
            nn.MaxPool2d((2,2)),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Dropout(dropout_value)
            )


        # Layer 3
        self.convblock31 = nn.Sequential(
            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), padding=1,  bias=False),
            nn.MaxPool2d((2,2)),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Dropout(dropout_value)
            )

        self.residual31 = ResidualBlock(in_channels = 512, out_channels = 512)

        self.pool = nn.MaxPool2d((4,4))

        ## Fully Connected Layer
        self.fc = nn.Linear(512, 10)

    def forward(self, x):
        x1 = self.convblock01(x)
        x2 = self.convblock11(x1)
        x3 = x2 + self.residual11(x2)
        x4 = self.convblock21(x3)
        x5 = self.convblock31(x4)
        x6 = x5 + self.residual31(x5)
        x = self.pool(x6)
        x = x.view(-1, 512)
        x = self.fc(x)
        return x