# 导入需要的包
import torch.nn as nn


class SimpleNet(nn.Module):
    def __init__(self, num_classes=10):
        super(SimpleNet, self).__init__()

        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()

        self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()

        self.pool = nn.MaxPool2d(kernel_size=2)

        self.conv3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
        self.relu3 = nn.ReLU()

        self.conv4 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=3, stride=1, padding=1)
        self.relu4 = nn.ReLU()

        self.fc = nn.Linear(in_features=16 * 16 * 24, out_features=num_classes)

    def forward(self, input):
        output = self.conv1(input)
        output = self.relu1(output)

        output = self.conv2(output)
        output = self.relu2(output)

        output = self.pool(output)

        output = self.conv3(output)
        output = self.relu3(output)

        output = self.conv4(output)
        output = self.relu4(output)

        output = output.view(-1, 16 * 16 * 24)

        output = self.fc(output)

        return output


class S1(nn.Module):
    def __init__(self):
        super(S1, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        return self.conv1(x)