text: import torch
  from torch.nn import Conv2d, Sequential, ModuleList, ReLU
  from ..nn.squeezenet import squeezenet1_1
  
  from .ssd import SSD
  from .predictor import Predictor
  from .config import squeezenet_ssd_config as config
  
  
  def SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0):
      """Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d.
      """
      return Sequential(
          Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size,
                 groups=in_channels, stride=stride, padding=padding),
          ReLU(),
          Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1),
      )
  
  
  def create_squeezenet_ssd_lite(num_classes, is_test=False):
      base_net = squeezenet1_1(False).features  # disable dropout layer
  
      source_layer_indexes = [
          12
      ]
      extras = ModuleList([
          Sequential(
              Conv2d(in_channels=512, out_channels=256, kernel_size=1),
              ReLU(),
              SeperableConv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=2),
          ),
          Sequential(
              Conv2d(in_channels=512, out_channels=256, kernel_size=1),
              ReLU(),
              SeperableConv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1),
          ),
          Sequential(
              Conv2d(in_channels=512, out_channels=128, kernel_size=1),
              ReLU(),
              SeperableConv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1),
          ),
          Sequential(
              Conv2d(in_channels=256, out_channels=128, kernel_size=1),
              ReLU(),
              SeperableConv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1),
          ),
          Sequential(
              Conv2d(in_channels=256, out_channels=128, kernel_size=1),
              ReLU(),
              SeperableConv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1)
          )
      ])
  
      regression_headers = ModuleList([
          SeperableConv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1),
          Conv2d(in_channels=256, out_channels=6 * 4, kernel_size=1),
      ])
  
      classification_headers = ModuleList([
          SeperableConv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
          Conv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=1),
      ])
  
      return SSD(num_classes, base_net, source_layer_indexes,
                 extras, classification_headers, regression_headers, is_test=is_test, config=config)
  
  
  def create_squeezenet_ssd_lite_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=None):
      predictor = Predictor(net, config.image_size, config.image_mean,
                            config.image_std,
                            nms_method=nms_method,
                            iou_threshold=config.iou_threshold,
                            candidate_size=candidate_size,
                            sigma=sigma,
                            device=device)
      return predictor
  