import torch.nn as nn
import torch.utils.model_zoo as model_zoo

# need to be abandoned,only use the feature of origin net to construct loss function
model_urls = {
    'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
    'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
    'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
    'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
    'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
    'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
    'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
}


class VGG(nn.Module):
    def __init__(self, features, num_classes=1000):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )

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

    def get_feature(self, layer_type, layer_num, layer_index, input):
        types = ['conv', 'ReLU']
        type = types.index(layer_type)
        num = 1
        index = 1
        is_return = 0
        x = input
        for i, layer in enumerate(self.modules()):
            if i <= 1:
                continue
            if isinstance(layer, nn.Conv2d):
                if type == 0:
                    is_return += 1
                x = layer(x)
                is_return += (layer_num == num) + (layer_index == index)
                if is_return == 3:
                    return x
                else:
                    is_return = 0
            if isinstance(layer, nn.ReLU):
                if type == 1:
                    is_return += 1
                x = layer(x)
                is_return += (layer_num == num) + (layer_index == index)
                if is_return == 3:
                    return x
                else:
                    is_return = 0
                index += 1
            if isinstance(layer, nn.MaxPool2d):
                x = layer(x)
                num += 1
                index = 1


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 modify_vgg():
    model = VGG(make_layers(cfg['E']))
    model.load_state_dict(model_zoo.load_url(model_urls['vgg19']))
    return model
