model.py

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

class ConvNet(nn.Module):
    """LeNet"""
    def __init__(self, num_classes):
        super(ConvNet, self).__init__()
        self.conv1_1 = nn.Conv2d(1, 32, 5, stride=1, padding=2)
        self.prelu1_1 = nn.PReLU()
        self.conv1_2 = nn.Conv2d(32, 32, 5, stride=1, padding=2)
        self.prelu1_2 = nn.PReLU()
        
        self.conv2_1 = nn.Conv2d(32, 64, 5, stride=1, padding=2)
        self.prelu2_1 = nn.PReLU()
        self.conv2_2 = nn.Conv2d(64, 64, 5, stride=1, padding=2)
        self.prelu2_2 = nn.PReLU()
        
        self.conv3_1 = nn.Conv2d(64, 128, 5, stride=1, padding=2)
        self.prelu3_1 = nn.PReLU()
        self.conv3_2 = nn.Conv2d(128, 128, 5, stride=1, padding=2)
        self.prelu3_2 = nn.PReLU()
        
        self.fc1 = nn.Linear(128*3*3, 2)
        self.prelu_fc1 = nn.PReLU()
        self.fc2 = nn.Linear(2, num_classes)

    def forward(self, x):
        x = self.prelu1_1(self.conv1_1(x))
        x = self.prelu1_2(self.conv1_2(x))
        x = F.max_pool2d(x, 2)
        
        x = self.prelu2_1(self.conv2_1(x))
        x = self.prelu2_2(self.conv2_2(x))
        x = F.max_pool2d(x, 2)
        
        x = self.prelu3_1(self.conv3_1(x))
        x = self.prelu3_2(self.conv3_2(x))
        x = F.max_pool2d(x, 2)
        
        x = x.view(-1, 128*3*3)
        x = self.prelu_fc1(self.fc1(x))
        y = self.fc2(x)

        return y,x

if __name__=="__main__":
    x=torch.randn(2,1,28,28)
    net=ConvNet()
    net(x)