import torch
import torchvision.models as models
from torch import nn
import torch.nn.functional as F
from utils.fmodule import FModule

# class Model(FModule):
#     def __init__(self, num_classes=1000, ): # TODO 这里需要定义
#         super(Model, self).__init__()
#         self.input_require_shape = [3, -1, -1]
#         self.model = models.vgg16()
#         self.feature = torch.nn.Sequential(self.model.features,
#                                            self.model.avgpool,
#                                            torch.nn.Flatten(),
#                                            *list(self.model.classifier.children())[:-1])
#         self.classifier = self.model.classifier[-1]
#
#     def features_extras(self, x):
#         x = self.feature(x)
#         return x
#
#     def classifier_head(self, x):
#         return self.classifier(x)
#
#     def forward(self, x):
#         x = self.features_extras(x)
#         x = self.classifier_head(x)
#         return x

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


__all__ = [
    'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
    'vgg19_bn', 'vgg19',
]


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',
}
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 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)

class Model(FModule):

    def __init__(self, num_classes=1000):
        super().__init__()
        self.input_require_shape = [3, -1, -1]
        self.features = make_layers(cfg['D'])
        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),
        )

        self._initialize_weights()

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

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

    def _initialize_weights(self):
        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))
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.size(1)
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()




# def vgg11(pretrained=False, model_root=None, **kwargs):
#     """VGG 11-layer model (configuration "A")"""
#     model = VGG(make_layers(cfg['A']), **kwargs)
#     if pretrained:
#         model.load_state_dict(model_zoo.load_url(model_urls['vgg11'], model_root))
#     return model
#
#
# def vgg11_bn(**kwargs):
#     """VGG 11-layer model (configuration "A") with batch normalization"""
#     kwargs.pop('model_root', None)
#     return VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
#
#
# def vgg13(pretrained=False, model_root=None, **kwargs):
#     """VGG 13-layer model (configuration "B")"""
#     model = VGG(make_layers(cfg['B']), **kwargs)
#     if pretrained:
#         model.load_state_dict(model_zoo.load_url(model_urls['vgg13'], model_root))
#     return model
#
#
# def vgg13_bn(**kwargs):
#     """VGG 13-layer model (configuration "B") with batch normalization"""
#     kwargs.pop('model_root', None)
#     return VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)
#
#
# def vgg16(pretrained=False, model_root=None, **kwargs):
#     """VGG 16-layer model (configuration "D")"""
#     model = VGG(make_layers(cfg['D']), **kwargs)
#     if pretrained:
#         model.load_state_dict(model_zoo.load_url(model_urls['vgg16'], model_root))
#     return model
#
#
# def vgg16_bn(**kwargs):
#     """VGG 16-layer model (configuration "D") with batch normalization"""
#     kwargs.pop('model_root', None)
#     return VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
#
#
# def vgg19(pretrained=False, model_root=None, **kwargs):
#     """VGG 19-layer model (configuration "E")"""
#     model = VGG(make_layers(cfg['E']), **kwargs)
#     if pretrained:
#         model.load_state_dict(model_zoo.load_url(model_urls['vgg19'], model_root))
#     return model
#
#
# def vgg19_bn(**kwargs):
#     """VGG 19-layer model (configuration 'E') with batch normalization"""
#     kwargs.pop('model_root', None)
#     return VGG(make_layers(cfg['E'], batch_norm=True), **kwargs)