model.py

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

class LeNet(nn.Module):
    def __init__(self, num_classes=10, **kwargs):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=5)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=5)
        self.fc1 = nn.Linear(128 * 5 * 5, 1024)
        self.fc2 = nn.Linear(1024, 1024)
        self.last_layer = nn.Linear(1024, num_classes)

    def forward(self, x, stop="full"):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)

        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x =  F.relu(x)
        x = self.last_layer(x)
        return x