model.py

# -*- coding: utf-8 -*-
"""
Created on Tue Feb  2 21:10:36 2021

@author: Xi Yu, Shujian Yu
"""

import torch
import torch.nn as nn


cfg = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}


class VGG(nn.Module):
    def __init__(self, vgg_name):
        super(VGG, self).__init__()
        self.features = self._make_layers(cfg[vgg_name])
        self.classifier = nn.Linear(512, 10)

    def forward(self, x):
        out = self.features(x)
        z = out.view(out.size(0), -1)
        out = self.classifier(z)
        return z, out

    def _make_layers(self, cfg):
        layers = []
        in_channels = 3
        for x in cfg:
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                           nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)


def test():
    net = VGG('VGG11')
    x = torch.randn(2,3,32,32)
    y = net(x)
    print(y.size())
    
class MLP(nn.Module):
    def __init__(self, num_classes):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(784, 1024)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(1024, 1024)
        self.fc3 = nn.Linear(1024, 256)
        self.fc4 = nn.Linear(256,num_classes)



    def forward(self, x):

        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.relu(out)
        encoder = self.fc3(out)
        output = self.fc4(encoder)
        return encoder, output