#!/usr/bin/env python
# coding=utf-8
import numpy
import torch.nn as nn
from torch.autograd import Variable

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)
        out = out.view(out.size(0),-1)
        out = self.classifier(out)
        return 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)