ZFNet-CIFAR10/Classification-normal/scripts/model.py

import torch
import torch.nn as nn

class ZFNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ZFNet, self).__init__()
        self.features = nn.Sequential(
            # conv1
            nn.Conv2d(3, 96, kernel_size=7, stride=2, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),
            
            # conv2
            nn.Conv2d(96, 256, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),
            
            # conv3
            nn.Conv2d(256, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            
            # conv4
            nn.Conv2d(384, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            
            # conv5
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )
        
        self.classifier = nn.Sequential(
            nn.Linear(256 * 2 * 2, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x


    def feature(self, x):
        return self.features(x)

    def prediction(self, x):
        x = x.view(x.size(0), -1)
        return self.classifier(x)