pytorch_vgg_implementation.py

'''
A from scratch implementation of the VGG architecture.

Video explanation: https://youtu.be/ACmuBbuXn20
Got any questions leave a comment on youtube :)

Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
*    2020-04-05 Initial coding

'''

# Imports
import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions

VGG_types = {
    '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_net(nn.Module):
    def __init__(self, in_channels=3, num_classes=1000):
        super(VGG_net, self).__init__()
        self.in_channels = in_channels
        self.conv_layers = self.create_conv_layers(VGG_types['VGG16'])
        
        self.fcs = nn.Sequential(
            nn.Linear(512*7*7, 4096),
            nn.ReLU(),
            nn.Dropout(p=0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(),
            nn.Dropout(p=0.5),
            nn.Linear(4096, num_classes)
            )
        
    def forward(self, x):
        x = self.conv_layers(x)
        x = x.reshape(x.shape[0], -1)
        x = self.fcs(x)
        return x

    def create_conv_layers(self, architecture):
        layers = []
        in_channels = self.in_channels
        
        for x in architecture:
            if type(x) == int:
                out_channels = x
                
                layers += [nn.Conv2d(in_channels=in_channels,out_channels=out_channels,
                                     kernel_size=(3,3), stride=(1,1), padding=(1,1)),
                           nn.BatchNorm2d(x),
                           nn.ReLU()]
                in_channels = x
            elif x == 'M':
                layers += [nn.MaxPool2d(kernel_size=(2,2), stride=(2,2))]
                
        return nn.Sequential(*layers)

if __name__ == '__main__':
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    model = VGG_net(in_channels=3,num_classes=1000).to(device)
    print(model)
    ## N = 3 (Mini batch size)
    #x = torch.randn(3, 3, 224, 224).to(device)
    #print(model(x).shape)