from collections import OrderedDict
import torch.nn as nn


cfg = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512,'M']

class VGG(nn.Module):
    def __init__(self, num_classes=10):
        super(VGG, self).__init__()
        self.features = self._make_layers(cfg)
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )

    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
        return nn.Sequential(*layers)


class BeeVGG(nn.Module):
    def __init__(self, honeysource, num_classes=10):
        super(BeeVGG, self).__init__()
        self.honeysource = honeysource
        self.features = self._make_layers(cfg)
        self.classifier = nn.Sequential(
            nn.Linear(int(512 * honeysource[len(honeysource)-1] / 10) * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )

    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
        index = 0
        Mlayers = 0
        for x_index, x in enumerate(cfg):
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
                Mlayers += 1
            else:
                x = int(x * self.honeysource[x_index - Mlayers] / 10)
                if x == 0:
                    x = 1
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                           nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        return nn.Sequential(*layers)

'''
def test():
    honey = [5,6,1,2,3,4,5,6,7,3,1,1,1]
    model = BeeVGG(honey)
    print(model)

test()
'''



