import math
import torch.nn as nn


__all__ = ['VGG', 'cvgg11_bn', 'cvgg13_bn', 'cvgg16_bn', 'cvgg19_bn']


class VGG(nn.Module):
    '''
    VGG model
    '''
    def __init__(self, features, num_classes=10):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(512, 512),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(512, 512),
            nn.ReLU(True),
            nn.Linear(512, num_classes),
        )
         # Initialize weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
                m.bias.data.zero_()


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


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfg = {
    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M',
          512, 512, 512, 512, 'M'],
}


def cvgg11_bn(num_classes, batch_norm=False):
    return VGG(make_layers(cfg['A'], batch_norm=batch_norm), num_classes=num_classes)


def cvgg13_bn(num_classes, batch_norm=False):
    return VGG(make_layers(cfg['B'], batch_norm=batch_norm), num_classes=num_classes)


def cvgg16_bn(num_classes, batch_norm=False):
    return VGG(make_layers(cfg['D'], batch_norm=batch_norm), num_classes=num_classes)


def cvgg19_bn(num_classes, batch_norm=False):
    return VGG(make_layers(cfg['E'], batch_norm=batch_norm), num_classes=num_classes)


if __name__ == "__main__":
    import torch

    inter_feature = []
    def hook(module, input, output):
        inter_feature.append(output.clone().detach())

    input = torch.randn(2, 3, 32, 32)
    model = cvgg19_bn(10, batch_norm=True)
    print(model)
    output = model(input)
    print(output)


