text: from data import *
  from utils.augmentations import SSDAugmentation
  from layers.modules import MultiBoxLoss
  from ssd import build_ssd
  import os
  import sys
  import time
  import torch
  from torch.autograd import Variable
  import torch.nn as nn
  import torch.optim as optim
  import torch.backends.cudnn as cudnn
  import torch.nn.init as init
  import torch.utils.data as data
  import numpy as np
  import argparse
  
  
  def str2bool(v):
      return v.lower() in ("yes", "true", "t", "1")
  
  
  parser = argparse.ArgumentParser(
      description='Single Shot MultiBox Detector Training With Pytorch')
  train_set = parser.add_mutually_exclusive_group()
  parser.add_argument('--dataset', default='VOC', choices=['VOC', 'COCO'],
                      type=str, help='VOC or COCO')
  parser.add_argument('--dataset_root', default=VOC_ROOT,
                      help='Dataset root directory path')
  parser.add_argument('--basenet', default='vgg16_reducedfc.pth',
                      help='Pretrained base model')
  parser.add_argument('--batch_size', default=8, type=int,
                      help='Batch size for training')
  parser.add_argument('--resume', default=None, type=str,
                      help='Checkpoint state_dict file to resume training from')
  parser.add_argument('--start_iter', default=0, type=int,
                      help='Resume training at this iter')
  parser.add_argument('--num_workers', default=4, type=int,
                      help='Number of workers used in dataloading')
  parser.add_argument('--cuda', default=True, type=str2bool,
                      help='Use CUDA to train model')
  parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float,
                      help='initial learning rate')
  parser.add_argument('--momentum', default=0.9, type=float,
                      help='Momentum value for optim')
  parser.add_argument('--weight_decay', default=5e-4, type=float,
                      help='Weight decay for SGD')
  parser.add_argument('--gamma', default=0.1, type=float,
                      help='Gamma update for SGD')
  parser.add_argument('--visdom', default=False, type=str2bool,
                      help='Use visdom for loss visualization')
  parser.add_argument('--save_folder', default='weights/',
                      help='Directory for saving checkpoint models')
  args = parser.parse_args()
  
  
  if torch.cuda.is_available():
      if args.cuda:
          torch.set_default_tensor_type('torch.cuda.FloatTensor')
      if not args.cuda:
          print("WARNING: It looks like you have a CUDA device, but aren't " +
                "using CUDA.\nRun with --cuda for optimal training speed.")
          torch.set_default_tensor_type('torch.FloatTensor')
  else:
      torch.set_default_tensor_type('torch.FloatTensor')
  
  if not os.path.exists(args.save_folder):
      os.mkdir(args.save_folder)
  
  
  def train():
      if args.dataset == 'COCO':
          if args.dataset_root == VOC_ROOT:
              if not os.path.exists(COCO_ROOT):
                  parser.error('Must specify dataset_root if specifying dataset')
              print("WARNING: Using default COCO dataset_root because " +
                    "--dataset_root was not specified.")
              args.dataset_root = COCO_ROOT
          cfg = coco
          dataset = COCODetection(root=args.dataset_root,
                                  transform=SSDAugmentation(cfg['min_dim'],
                                                            MEANS))
      elif args.dataset == 'VOC':
          # if args.dataset_root == COCO_ROOT:
          #     parser.error('Must specify dataset if specifying dataset_root')
          cfg = voc
          dataset = VOCDetection(root=args.dataset_root,
                                 transform=SSDAugmentation(cfg['min_dim'],
                                                           MEANS))
  
      if args.visdom:
          import visdom
          viz = visdom.Visdom()
  
      ssd_net = build_ssd('train', cfg['min_dim'], cfg['num_classes'])
      net = ssd_net
  
      if args.cuda:
          net = torch.nn.DataParallel(ssd_net)
          cudnn.benchmark = True
  
      if args.resume:
          print('Resuming training, loading {}...'.format(args.resume))
          ssd_net.load_weights(args.resume)
      else:
          vgg_weights = torch.load(args.save_folder + args.basenet)
          print('Loading base network...')
          ssd_net.vgg.load_state_dict(vgg_weights)
  
      if args.cuda:
          net = net.cuda()
  
      if not args.resume:
          print('Initializing weights...')
          # initialize newly added layers' weights with xavier method
          ssd_net.extras.apply(weights_init)
          ssd_net.loc.apply(weights_init)
          ssd_net.conf.apply(weights_init)
  
      optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum,
                            weight_decay=args.weight_decay)
      criterion = MultiBoxLoss(cfg['num_classes'], 0.5, True, 0, True, 3, 0.5,
                               False, args.cuda)
  
      net.train()
      # loss counters
      loc_loss = 0
      conf_loss = 0
      epoch = 0
      print('Loading the dataset...')
  
      epoch_size = len(dataset) // args.batch_size
      print('Training SSD on:', dataset.name)
      print('Using the specified args:')
      print(args)
  
      step_index = 0
  
      if args.visdom:
          vis_title = 'SSD.PyTorch on ' + dataset.name
          vis_legend = ['Loc Loss', 'Conf Loss', 'Total Loss']
          iter_plot = create_vis_plot('Iteration', 'Loss', vis_title, vis_legend)
          epoch_plot = create_vis_plot('Epoch', 'Loss', vis_title, vis_legend)
  
      data_loader = data.DataLoader(dataset, args.batch_size,
                                    num_workers=args.num_workers,
                                    shuffle=True, collate_fn=detection_collate,
                                    pin_memory=True)
      # create batch iterator
      batch_iterator = iter(data_loader)
      for iteration in range(args.start_iter, cfg['max_iter']):
          if args.visdom and iteration != 0 and (iteration % epoch_size == 0):
              update_vis_plot(epoch, loc_loss, conf_loss, epoch_plot, None,
                              'append', epoch_size)
              # reset epoch loss counters
              loc_loss = 0
              conf_loss = 0
              epoch += 1
  
          if iteration in cfg['lr_steps']:
              step_index += 1
              adjust_learning_rate(optimizer, args.gamma, step_index)
  
          try:
              # load train data
              images, targets = next(batch_iterator)
          except StopIteration:
              batch_iterator = iter(data_loader)
              images, targets = next(batch_iterator)
  
          if args.cuda:
              images = Variable(images.cuda())
              targets = [Variable(ann.cuda(), volatile=True) for ann in targets]
          else:
              images = Variable(images)
              targets = [Variable(ann, volatile=True) for ann in targets]
          # forward
          t0 = time.time()
          out = net(images)
          # backprop
          optimizer.zero_grad()
          loss_l, loss_c = criterion(out, targets)
          loss = loss_l + loss_c
          loss.backward()
          optimizer.step()
          t1 = time.time()
          loc_loss += loss_l.item()
          conf_loss += loss_c.item()
  
          if iteration % 10 == 0:
              print('timer: %.4f sec.' % (t1 - t0))
              print('iter ' + repr(iteration) + ' || Loss: %.4f ||' % (loss.item()), end=' ')
  
          if args.visdom:
              update_vis_plot(iteration, loss_l.item(), loss_c.item(),
                              iter_plot, epoch_plot, 'append')
  
          if iteration != 0 and iteration % 5000 == 0:
              print('Saving state, iter:', iteration)
              # torch.save(ssd_net.state_dict(), 'weights/ssd300_COCO_' +
              #            repr(iteration) + '.pth')
              torch.save(ssd_net.state_dict(), args.save_folder + repr(iteration) + args.dataset + '.pth')
      torch.save(ssd_net.state_dict(),
                 args.save_folder + '' + args.dataset + '.pth')
  
  
  def adjust_learning_rate(optimizer, gamma, step):
      """Sets the learning rate to the initial LR decayed by 10 at every
          specified step
      # Adapted from PyTorch Imagenet example:
      # https://github.com/pytorch/examples/blob/master/imagenet/main.py
      """
      lr = args.lr * (gamma ** (step))
      for param_group in optimizer.param_groups:
          param_group['lr'] = lr
  
  
  def xavier(param):
      init.xavier_uniform(param)
  
  
  def weights_init(m):
      if isinstance(m, nn.Conv2d):
          xavier(m.weight.data)
          m.bias.data.zero_()
  
  
  def create_vis_plot(_xlabel, _ylabel, _title, _legend):
      return viz.line(
          X=torch.zeros((1,)).cpu(),
          Y=torch.zeros((1, 3)).cpu(),
          opts=dict(
              xlabel=_xlabel,
              ylabel=_ylabel,
              title=_title,
              legend=_legend
          )
      )
  
  
  def update_vis_plot(iteration, loc, conf, window1, window2, update_type,
                      epoch_size=1):
      viz.line(
          X=torch.ones((1, 3)).cpu() * iteration,
          Y=torch.Tensor([loc, conf, loc + conf]).unsqueeze(0).cpu() / epoch_size,
          win=window1,
          update=update_type
      )
      # initialize epoch plot on first iteration
      if iteration == 0:
          viz.line(
              X=torch.zeros((1, 3)).cpu(),
              Y=torch.Tensor([loc, conf, loc + conf]).unsqueeze(0).cpu(),
              win=window2,
              update=True
          )
  
  
  if __name__ == '__main__':
      train()
  