text: import torch.nn as nn
  import torchvision.transforms as transforms
  from .binarized_modules import  BinarizeLinear,BinarizeConv2d
  
  __all__ = ['alexnet_binary']
  
  class AlexNetOWT_BN(nn.Module):
  
      def __init__(self, num_classes=1000):
          super(AlexNetOWT_BN, self).__init__()
          self.ratioInfl=3
          self.features = nn.Sequential(
              BinarizeConv2d(3, int(64*self.ratioInfl), kernel_size=11, stride=4, padding=2),
              nn.MaxPool2d(kernel_size=3, stride=2),
              nn.BatchNorm2d(int(64*self.ratioInfl)),
              nn.Hardtanh(inplace=True),
              BinarizeConv2d(int(64*self.ratioInfl), int(192*self.ratioInfl), kernel_size=5, padding=2),
              nn.MaxPool2d(kernel_size=3, stride=2),
              nn.BatchNorm2d(int(192*self.ratioInfl)),
              nn.Hardtanh(inplace=True),
  
              BinarizeConv2d(int(192*self.ratioInfl), int(384*self.ratioInfl), kernel_size=3, padding=1),
              nn.BatchNorm2d(int(384*self.ratioInfl)),
              nn.Hardtanh(inplace=True),
  
              BinarizeConv2d(int(384*self.ratioInfl), int(256*self.ratioInfl), kernel_size=3, padding=1),
              nn.BatchNorm2d(int(256*self.ratioInfl)),
              nn.Hardtanh(inplace=True),
  
              BinarizeConv2d(int(256*self.ratioInfl), 256, kernel_size=3, padding=1),
              nn.MaxPool2d(kernel_size=3, stride=2),
              nn.BatchNorm2d(256),
              nn.Hardtanh(inplace=True)
  
          )
          self.classifier = nn.Sequential(
              BinarizeLinear(256 * 6 * 6, 4096),
              nn.BatchNorm1d(4096),
              nn.Hardtanh(inplace=True),
              #nn.Dropout(0.5),
              BinarizeLinear(4096, 4096),
              nn.BatchNorm1d(4096),
              nn.Hardtanh(inplace=True),
              #nn.Dropout(0.5),
              BinarizeLinear(4096, num_classes),
              nn.BatchNorm1d(1000),
              nn.LogSoftmax()
          )
  
          #self.regime = {
          #    0: {'optimizer': 'SGD', 'lr': 1e-2,
          #        'weight_decay': 5e-4, 'momentum': 0.9},
          #    10: {'lr': 5e-3},
          #    15: {'lr': 1e-3, 'weight_decay': 0},
          #    20: {'lr': 5e-4},
          #    25: {'lr': 1e-4}
          #}
          self.regime = {
              0: {'optimizer': 'Adam', 'lr': 5e-3},
              20: {'lr': 1e-3},
              30: {'lr': 5e-4},
              35: {'lr': 1e-4},
              40: {'lr': 1e-5}
          }
          normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                           std=[0.229, 0.224, 0.225])
          self.input_transform = {
              'train': transforms.Compose([
                  transforms.Scale(256),
                  transforms.RandomCrop(224),
                  transforms.RandomHorizontalFlip(),
                  transforms.ToTensor(),
                  normalize
              ]),
              'eval': transforms.Compose([
                  transforms.Scale(256),
                  transforms.CenterCrop(224),
                  transforms.ToTensor(),
                  normalize
              ])
          }
  
      def forward(self, x):
          x = self.features(x)
          x = x.view(-1, 256 * 6 * 6)
          x = self.classifier(x)
          return x
  
  
  def alexnet_binary(**kwargs):
      num_classes = getattr(kwargs, 'num_classes', 1000)
      return AlexNetOWT_BN(num_classes)
  