text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.autograd import Variable
  from layers import *
  from data import voc, coco, coco_ssn
  import os
  
  
  class SSD(nn.Module):
      """Single Shot Multibox Architecture
      The network is composed of a base VGG network followed by the
      added multibox conv layers.  Each multibox layer branches into
          1) conv2d for class conf scores
          2) conv2d for localization predictions
          3) associated priorbox layer to produce default bounding
             boxes specific to the layer's feature map size.
      See: https://arxiv.org/pdf/1512.02325.pdf for more details.
  
      Args:
          phase: (string) Can be "test" or "train"
          size: input image size
          base: VGG16 layers for input, size of either 300 or 500
          extras: extra layers that feed to multibox loc and conf layers
          head: "multibox head" consists of loc and conf conv layers
      """
  
      def __init__(self, phase, size, base, extras, head, num_classes):
          super(SSD, self).__init__()
          self.phase = phase
          self.num_classes = num_classes
          self.cfg = coco_ssn # (coco, voc)[num_classes == 21]
          self.priorbox = PriorBox(self.cfg)
          self.priors = Variable(self.priorbox.forward(), volatile=True)
          self.size = size
  
          # SSD network
          self.vgg = nn.ModuleList(base)
          # Layer learns to scale the l2 normalized features from conv4_3
          self.L2Norm = L2Norm(512, 20)
          self.extras = nn.ModuleList(extras)
  
          self.loc = nn.ModuleList(head[0])
          self.conf = nn.ModuleList(head[1])
  
          if phase == 'test':
              self.softmax = nn.Softmax(dim=-1)
              self.detect = Detect(num_classes, 0, 3, 0.01, 0.45)
  
      def forward(self, x):
          """Applies network layers and ops on input image(s) x.
  
          Args:
              x: input image or batch of images. Shape: [batch,3,300,300].
  
          Return:
              Depending on phase:
              test:
                  Variable(tensor) of output class label predictions,
                  confidence score, and corresponding location predictions for
                  each object detected. Shape: [batch,topk,7]
  
              train:
                  list of concat outputs from:
                      1: confidence layers, Shape: [batch*num_priors,num_classes]
                      2: localization layers, Shape: [batch,num_priors*4]
                      3: priorbox layers, Shape: [2,num_priors*4]
          """
          sources = list()
          loc = list()
          conf = list()
  
          # apply vgg up to conv4_3 relu
          for k in range(23):
              x = self.vgg[k](x)
  
          s = self.L2Norm(x)
          sources.append(s)
  
          # apply vgg up to fc7
          for k in range(23, len(self.vgg)):
              x = self.vgg[k](x)
          sources.append(x)
  
          # apply extra layers and cache source layer outputs
          for k, v in enumerate(self.extras):
              x = F.relu(v(x), inplace=True)
              if k % 2 == 1:
                  sources.append(x)
  
          # apply multibox head to source layers
          for (x, l, c) in zip(sources, self.loc, self.conf):
              loc.append(l(x).permute(0, 2, 3, 1).contiguous())
              conf.append(c(x).permute(0, 2, 3, 1).contiguous())
  
          loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
          conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
          if self.phase == "test":
              output = self.detect(
                  loc.view(loc.size(0), -1, 8),                   # loc preds
                  self.softmax(conf.view(conf.size(0), -1,
                               self.num_classes)),                # conf preds
                  self.priors.type(type(x.data))                  # default boxes
              )
          else:
              output = (
                  loc.view(loc.size(0), -1, 8),
                  conf.view(conf.size(0), -1, self.num_classes),
                  self.priors
              )
          return output
  
      def load_weights(self, base_file):
          other, ext = os.path.splitext(base_file)
          if ext == '.pkl' or '.pth':
              print('Loading weights into state dict...')
              self.load_state_dict(torch.load(base_file,
                                   map_location=lambda storage, loc: storage))
              print('Finished!')
          else:
              print('Sorry only .pth and .pkl files supported.')
  
  
  # This function is derived from torchvision VGG make_layers()
  # https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
  def vgg(cfg, i, batch_norm=False):
      layers = []
      in_channels = i
      for v in cfg:
          if v == 'M':
              layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
          elif v == 'C':
              layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
          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
      pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
      conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
      conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
      layers += [pool5, conv6,
                 nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
      return layers
  
  
  def add_extras(cfg, i, batch_norm=False):
      # Extra layers added to VGG for feature scaling
      layers = []
      in_channels = i
      flag = False
      for k, v in enumerate(cfg):
          if in_channels != 'S':
              if v == 'S':
                  layers += [nn.Conv2d(in_channels, cfg[k + 1],
                             kernel_size=(1, 3)[flag], stride=2, padding=1)]
              else:
                  layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
              flag = not flag
          in_channels = v
      return layers
  
  
  def multibox(vgg, extra_layers, cfg, num_classes):
      loc_layers = []
      conf_layers = []
      vgg_source = [21, -2]
      for k, v in enumerate(vgg_source):
          loc_layers += [nn.Conv2d(vgg[v].out_channels,
                                   cfg[k] * 8, kernel_size=3, padding=1)]
          conf_layers += [nn.Conv2d(vgg[v].out_channels,
                          cfg[k] * num_classes, kernel_size=3, padding=1)]
      for k, v in enumerate(extra_layers[1::2], 2):
          loc_layers += [nn.Conv2d(v.out_channels, cfg[k]
                                   * 8, kernel_size=3, padding=1)]
          conf_layers += [nn.Conv2d(v.out_channels, cfg[k]
                                    * num_classes, kernel_size=3, padding=1)]
      return vgg, extra_layers, (loc_layers, conf_layers)
  
  
  base = {
      '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
              512, 512, 512],
      '512': [],
  }
  extras = {
      '300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
      '512': [],
  }
  mbox = {
      '300': [4, 6, 6, 6, 4, 4],  # number of boxes per feature map location
      '512': [],
  }
  
  
  def build_ssd(phase, size=300, num_classes=21):
      if phase != "test" and phase != "train":
          print("ERROR: Phase: " + phase + " not recognized")
          return
      if size != 300:
          print("ERROR: You specified size " + repr(size) + ". However, " +
                "currently only SSD300 (size=300) is supported!")
          return
      base_, extras_, head_ = multibox(vgg(base[str(size)], 3),
                                       add_extras(extras[str(size)], 1024),
                                       mbox[str(size)], num_classes)
      return SSD(phase, size, base_, extras_, head_, num_classes)
  