modularsimplenet2.py

class Unit(nn.Module):
    def __init__(self,in_channels,out_channels):
        super(Unit,self).__init__()
        

        self.conv = nn.Conv2d(in_channels=in_channels,kernel_size=3,out_channels=out_channels,stride=1,padding=1)
        self.bn = nn.BatchNorm2d(num_features=out_channels)
        self.relu = nn.ReLU()

    def forward(self,input):
        output = self.conv(input)
        output = self.bn(output)
        output = self.relu(output)

        return output

class SimpleNet(nn.Module):
    def __init__(self,num_classes=10):
        super(SimpleNet,self).__init__()
        
        #Create 14 layers of the unit with max pooling in between
        self.net = nn.Sequential(
            Unit(in_channels=3,out_channels=32),
            Unit(in_channels=32, out_channels=32),
            Unit(in_channels=32, out_channels=32),

            nn.MaxPool2d(kernel_size=2),

            Unit(in_channels=32, out_channels=64),
            Unit(in_channels=64, out_channels=64),
            Unit(in_channels=64, out_channels=64),
            Unit(in_channels=64, out_channels=64),

            nn.MaxPool2d(kernel_size=2),

            Unit(in_channels=64, out_channels=128),
            Unit(in_channels=128, out_channels=128),
            Unit(in_channels=128, out_channels=128),
            Unit(in_channels=128, out_channels=128),

            nn.MaxPool2d(kernel_size=2),

            Unit(in_channels=128, out_channels=128),
            Unit(in_channels=128, out_channels=128),
            Unit(in_channels=128, out_channels=128),

            nn.AdaptiveAvgPool2d(1)
        )
       

        self.fc = nn.Linear(in_features=128,out_features=num_classes)

    def forward(self, input):
        output = self.net(input)
        output = output.view(-1,128)
        output = self.fc(output)
        return output