import torch.nn as nn
import os
import torch
from functools import reduce
import torch.nn as nn
import math
from torch.autograd import Variable

class VGG(nn.Module):

    def __init__(self, features, num_classes=1000, init_weights=True):
        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),
        )
        if init_weights:
            self._initialize_weights()

    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):
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()


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'],
}


class binary_VGG19bn(nn.Module):
    def __init__(self):
        super(VGG19bn, self).__init__()
        self.num_classes = 1
        self.model =  VGG(make_layers(cfg['E'], batch_norm=True))

        model_list = [nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)] + list(self.model.features.children())[1:]
        self.model.features = nn.Sequential(*model_list)

        self.avgpool = nn.AvgPool2d(3)
        self.last_linear = nn.Linear(512, self.num_classes)
        self.drop1 = nn.Dropout(p = 0.5)
        self.sig = nn.Sigmoid()

    def forward(self, img):
        x = img
        x = self.model(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.drop1(x)
        x = self.last_linear(x)
        x = self.sig(x)
        return x



if __name__ == '__main__':
    test_flag = True
    net = binary_VGG19bn()
    x = Variable(torch.randn(10, 1, 161, 99))
    y = net(x)
    print('output', y.size())