models.py

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


class Lenet5(nn.Module):

    def __init__(self):
        super(Lenet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1, stride=1)
        self.relu1 = nn.ReLU(inplace=True)
        self.maxpool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=1)
        self.relu2 = nn.ReLU(inplace=True)
        self.maxpool2 = nn.MaxPool2d(2)
        self.linear1 = nn.Linear(7 * 7 * 64, 200)
        self.relu3 = nn.ReLU(inplace=True)
        self.linear2 = nn.Linear(200, 10)

    def forward(self, x):
        x = self.maxpool1(self.relu1(self.conv1(x)))
        x = self.maxpool2(self.relu2(self.conv2(x)))
        x = x.view(x.size(0), -1)
        x = self.relu3(self.linear1(x))
        x = self.linear2(x)
        return x
    
class Alexnet(nn.Module):

    def __init__(self, num_classes):
        super(Alexnet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=1, padding=0),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=1),
            nn.Conv2d(64, 192, kernel_size=5, padding=0),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=1),
            nn.Conv2d(192, 384, kernel_size=3, padding=0),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=0),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=1),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        x = F.softmax(x, dim=1)
        return  x