code: import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  import copy 
  
  import torch
  import torch.nn as nn
  import torch.nn.parallel
  import torch.backends.cudnn as cudnn
  import torch.optim
  import torch.utils.data
  import torchvision.transforms as transforms
  import torchvision.datasets as datasets
  import torchvision.models as models
  
  from pruning_utils import *
  from visda2017 import VisDA17
  
  model_names = sorted(name for name in models.__dict__
      if name.islower() and not name.startswith("__")
      and callable(models.__dict__[name]))
  
  parser = argparse.ArgumentParser(description='PyTorch Visda Training')
  ################################ required settings ################################
  parser.add_argument('data', metavar='DIR',
                      help='path to dataset')
  parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
                      choices=model_names,
                      help='model architecture: ' +
                          ' | '.join(model_names) +
                          ' (default: resnet18)')
  parser.add_argument('--epochs', default=20, type=int, metavar='N',
                      help='number of total epochs to run')
  parser.add_argument('-b', '--batch-size', default=128, type=int,
                      metavar='N',
                      help='mini-batch size (default: 256), this is the total '
                          'batch size of all GPUs on the current node when '
                          'using Data Parallel or Distributed Data Parallel')
  parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
                      metavar='LR', help='initial learning rate', dest='lr')
  
  parser.add_argument('--prune_type', default=None, type=str, help='prune type [lt, pt_trans]')
  parser.add_argument('--pre_weight', default=None, type=str)
  parser.add_argument('--dataset', default='visda17', type=str)
  parser.add_argument('--save_dir', default='results/', type=str)
  parser.add_argument('--percent', default=0.2, type=float, help='pruning rate for each iteration')
  parser.add_argument('--states', default=19, type=int, help='number of iterative pruning states')
  parser.add_argument('--start_state', default=0, type=int, help='number of iterative pruning states')
  
  ################################ other settings ################################
  parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                      help='number of data loading workers (default: 4)')
  parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                      help='manual epoch number (useful on restarts)')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                      help='momentum')
  parser.add_argument('--wd', '--weight-decay', default=5e-4, type=float,
                      metavar='W', help='weight decay (default: 1e-4)',
                      dest='weight_decay')
  parser.add_argument('-p', '--print-freq', default=50, type=int,
                      metavar='N', help='print frequency (default: 10)')
  parser.add_argument('--resume', default='', type=str, metavar='PATH',
                      help='path to latest checkpoint (default: none)')
  parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                      help='evaluate model on validation set')
  parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                      help='use pre-trained model')
  parser.add_argument('--seed', default=None, type=int,
                      help='seed for initializing training. ')
  parser.add_argument('--gpu', default=None, type=int,
                      help='GPU id to use.')
  
  
  best_acc1 = 0
  best_epoch = 0
  
  def main():
      args = parser.parse_args()
  
      os.makedirs(args.save_dir, exist_ok=True)
  
      if args.seed is not None:
          random.seed(args.seed)
          torch.manual_seed(args.seed)
          cudnn.deterministic = True
          warnings.warn('You have chosen to seed training. '
                        'This will turn on the CUDNN deterministic setting, '
                        'which can slow down your training considerably! '
                        'You may see unexpected behavior when restarting '
                        'from checkpoints.')
  
      if args.gpu is not None:
          warnings.warn('You have chosen a specific GPU. This will completely '
                        'disable data parallelism.')
  
      main_worker(args.gpu, args)
  
  
  def main_worker(gpu, args):
      global best_acc1, best_epoch
      args.gpu = gpu
  
      if args.gpu is not None:
          print("Use GPU: {} for training".format(args.gpu))
  
      # create model
      print("=> using model '{}'".format(args.arch))
      model = models.__dict__[args.arch](pretrained=False)
      if_pruned = False
  
      assert args.dataset == 'visda17'
  
      ch = model.fc.in_features
      model.fc = nn.Linear(ch,12)
  
      if args.prune_type=='lt':
          print('using Lottery Tickets setting ')
          initalization = copy.deepcopy(model.state_dict())
          torch.save({'state_dict': initalization}, os.path.join(args.save_dir, 'random_init.pt'))
  
      elif args.prune_type=='pt_trans':
          print('using Pretrain Tickets setting')
          ticket_init_weight = torch.load(args.pre_weight)
          if 'state_dict' in ticket_init_weight.keys():
              ticket_init_weight = ticket_init_weight['state_dict']
  
          all_keys = list(ticket_init_weight.keys())
          for key in all_keys:
              if 'fc.' in key:
                  del ticket_init_weight[key] 
  
          print('layer number', len(ticket_init_weight.keys()))
          for key in ticket_init_weight.keys():
              assert key in model.state_dict().keys()
          model.load_state_dict(ticket_init_weight, strict=False)
          initalization = copy.deepcopy(model.state_dict())
  
      else:
          raise ValueError("Unknown Pruning Type")
  
      print('Mode: Dataparallel')
      model = torch.nn.DataParallel(model).cuda()
  
      # optionally resume from a checkpoint
      if args.resume:
          if os.path.isfile(args.resume):
              print("=> loading checkpoint '{}'".format(args.resume))
              if args.gpu is None:
                  checkpoint = torch.load(args.resume)
              else:
                  # Map model to be loaded to specified single gpu.
                  loc = 'cuda:{}'.format(args.gpu)
                  checkpoint = torch.load(args.resume, map_location=loc)
              args.start_epoch = checkpoint['epoch']
              args.start_state = checkpoint['state']
              best_acc1 = checkpoint['best_acc1']
              if_pruned = checkpoint['if_pruned']
              initalization = checkpoint['init_weight']
  
              if if_pruned:
                  prune_model_custom(model.module, checkpoint['mask'], False)
  
              model.module.load_state_dict(checkpoint['state_dict'])
              optimizer.load_state_dict(checkpoint['optimizer'])
              print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
          else:
              print("=> no checkpoint found at '{}'".format(args.resume))
  
      criterion = nn.CrossEntropyLoss().cuda(args.gpu)
      optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                  momentum=args.momentum,
                                  weight_decay=args.weight_decay)
  
      cudnn.benchmark = True
  
      # Data loading code
      normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                      std=[0.229, 0.224, 0.225])
  
      train_trans = transforms.Compose([
              transforms.RandomResizedCrop(size=224, scale=(0.75, 1.33)),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              normalize,
          ])
  
      val_trans = transforms.Compose([
              transforms.Resize((224, 224)),
              transforms.ToTensor(),
              normalize,
          ])
  
      train_dataset = VisDA17(txt_file=os.path.join(args.data, "train/image_list.txt"), 
                              root_dir=os.path.join(args.data, "train"), transform=train_trans)
      val_dataset = VisDA17(txt_file=os.path.join(args.data, "validation/image_list.txt"), 
                              root_dir=os.path.join(args.data, "validation"), transform=val_trans)
  
      train_loader = torch.utils.data.DataLoader(
          train_dataset, batch_size=args.batch_size, shuffle=True,
          num_workers=args.workers, pin_memory=True)
  
      val_loader = torch.utils.data.DataLoader(
          val_dataset,
          batch_size=args.batch_size, shuffle=False,
          num_workers=args.workers, pin_memory=True)
  
      if args.evaluate:
          validate(val_loader, model, criterion, args)
          return
  
      for prun_iter in range(args.start_state, args.states):
  
          check_sparsity(model.module, False)
          for epoch in range(args.start_epoch, args.epochs):
  
              print(optimizer.state_dict()['param_groups'][0]['lr'])
              # train for one epoch
              train(train_loader, model, criterion, optimizer, epoch, args)
  
              # evaluate on validation set
              acc1 = validate(val_loader, model, criterion, args)
  
              # remember best acc@1 and save checkpoint
              is_best = acc1 > best_acc1
              best_acc1 = max(acc1, best_acc1)
  
              if is_best:
                  best_epoch = epoch+1
  
              if if_pruned:
                  mask_dict = extract_mask(model.state_dict())
              else:
                  mask_dict = None
  
              save_checkpoint({
                  'epoch': epoch + 1,
                  'state': prun_iter,
                  'arch': args.arch,
                  'state_dict': model.module.state_dict(),
                  'mask': mask_dict,
                  'best_acc1': best_acc1,
                  'optimizer' : optimizer.state_dict(),
                  'if_pruned': if_pruned,
                  'init_weight':initalization
              }, is_best, checkpoint=args.save_dir, best_name=str(prun_iter)+'model_best.pth.tar')
  
          check_sparsity(model.module, False)
          print('**best TA = ', best_acc1, 'best epoch = ', best_epoch)
  
          # start pruning 
          print('start pruning model')
          pruning_model(model.module, args.percent, False)
          if_pruned = True
          
          current_mask = extract_mask(model.state_dict())
          remove_prune(model.module, False)
  
          model.module.load_state_dict(initalization)
          best_acc1 = 0 
          best_epoch = 0
          prune_model_custom(model.module, current_mask, False)
          validate(val_loader, model, criterion, args)
          optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                      momentum=args.momentum,
                                      weight_decay=args.weight_decay)
  
  
  def train(train_loader, model, criterion, optimizer, epoch, args):
      batch_time = AverageMeter('Time', ':6.3f')
      data_time = AverageMeter('Data', ':6.3f')
      losses = AverageMeter('Loss', ':.4e')
      top1 = AverageMeter('Acc@1', ':6.2f')
      top5 = AverageMeter('Acc@5', ':6.2f')
      progress = ProgressMeter(
          len(train_loader),
          [batch_time, data_time, losses, top1, top5],
          prefix="Epoch: [{}]".format(epoch))
  
      # switch to train mode
      model.train()
      wp_steps = len(train_loader)
  
      end = time.time()
      for i, (images, target) in enumerate(train_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          adjust_learning_rate(optimizer, epoch, args, i+1, steps_for_one_epoch=wp_steps)
  
          if args.gpu is not None:
              images = images.cuda(args.gpu, non_blocking=True)
          target = target.cuda(args.gpu, non_blocking=True)
  
          # compute output
          output = model(images)
          loss = criterion(output, target)
  
          # measure accuracy and record loss
          acc1, acc5 = accuracy(output, target, topk=(1, 5))
          losses.update(loss.item(), images.size(0))
          top1.update(acc1[0], images.size(0))
          top5.update(acc5[0], images.size(0))
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if i % args.print_freq == 0:
              progress.display(i)
  
  
  def validate(val_loader, model, criterion, args):
      batch_time = AverageMeter('Time', ':6.3f')
      losses = AverageMeter('Loss', ':.4e')
      top1 = AverageMeter('Acc@1', ':6.2f')
      top5 = AverageMeter('Acc@5', ':6.2f')
      progress = ProgressMeter(
          len(val_loader),
          [batch_time, losses, top1, top5],
          prefix='Test: ')
  
      # switch to evaluate mode
      model.eval()
  
      with torch.no_grad():
          end = time.time()
          for i, (images, target) in enumerate(val_loader):
              if args.gpu is not None:
                  images = images.cuda(args.gpu, non_blocking=True)
              target = target.cuda(args.gpu, non_blocking=True)
  
              # compute output
              output = model(images)
              loss = criterion(output, target)
  
              # measure accuracy and record loss
              acc1, acc5 = accuracy(output, target, topk=(1, 5))
              losses.update(loss.item(), images.size(0))
              top1.update(acc1[0], images.size(0))
              top5.update(acc5[0], images.size(0))
  
              # measure elapsed time
              batch_time.update(time.time() - end)
              end = time.time()
  
              if i % args.print_freq == 0:
                  progress.display(i)
  
          # TODO: this should also be done with the ProgressMeter
          print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
                .format(top1=top1, top5=top5))
  
      return top1.avg
  
  def save_checkpoint(state, is_best, checkpoint, filename='checkpoint.pth.tar', best_name='model_best.pth.tar'):
      filepath = os.path.join(checkpoint, filename)
      torch.save(state, filepath)
      if is_best:
          shutil.copyfile(filepath, os.path.join(checkpoint, best_name))
  
  def adjust_learning_rate(optimizer, epoch, args, iterations, steps_for_one_epoch):
  
      max_lr = args.lr
  
      if epoch < 10:
          lr = max_lr
      else:
          lr = max_lr*0.1
  
      for param_group in optimizer.param_groups:
          param_group['lr'] = lr
  
  class AverageMeter(object):
      """Computes and stores the average and current value"""
      def __init__(self, name, fmt=':f'):
          self.name = name
          self.fmt = fmt
          self.reset()
  
      def reset(self):
          self.val = 0
          self.avg = 0
          self.sum = 0
          self.count = 0
  
      def update(self, val, n=1):
          self.val = val
          self.sum += val * n
          self.count += n
          self.avg = self.sum / self.count
  
      def __str__(self):
          fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
          return fmtstr.format(**self.__dict__)
  
  class ProgressMeter(object):
      def __init__(self, num_batches, meters, prefix=""):
          self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
          self.meters = meters
          self.prefix = prefix
  
      def display(self, batch):
          entries = [self.prefix + self.batch_fmtstr.format(batch)]
          entries += [str(meter) for meter in self.meters]
          print('\t'.join(entries))
  
      def _get_batch_fmtstr(self, num_batches):
          num_digits = len(str(num_batches // 1))
          fmt = '{:' + str(num_digits) + 'd}'
          return '[' + fmt + '/' + fmt.format(num_batches) + ']'
  
  def accuracy(output, target, topk=(1,)):
      """Computes the accuracy over the k top predictions for the specified values of k"""
      with torch.no_grad():
          maxk = max(topk)
          batch_size = target.size(0)
  
          _, pred = output.topk(maxk, 1, True, True)
          pred = pred.t()
          correct = pred.eq(target.view(1, -1).expand_as(pred))
  
          res = []
          for k in topk:
              correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
              res.append(correct_k.mul_(100.0 / batch_size))
          return res
  
  if __name__ == '__main__':
      main()
apis: torch.utils.data.DataLoader torch.nn.Linear torch.load torch.manual_seed torch.save torch.no_grad torch.nn.CrossEntropyLoss torch.nn.DataParallel