lenet_cifar.py

from timm.models.registry import register_model
from torch import nn
from torch.nn import functional as F


# Define a simple CNN model
class LeNet(nn.Module):
    def __init__(self, num_classes: int = 10):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, kernel_size=5, stride=1, padding=2)  # 28*28->32*32-->28*28
        self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
        self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2)
        self.flatten1 = nn.Flatten()
        self.fc1 = nn.Linear(24 * 24, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, num_classes)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool1(x)
        x = F.relu(self.conv2(x))
        x = self.pool2(x)
        x = self.flatten1(x)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


@register_model
def lenet_cifar(num_classes: int) -> nn.Module:
    return LeNet(num_classes=num_classes)