text: import argparse
  import os
  import numpy as np
  import shutil
  import time
  
  import torch
  import torch.nn as nn
  import torch.nn.parallel
  import torch.backends.cudnn as cudnn
  import torch.distributed as dist
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms as transforms
  import torchvision.datasets as datasets
  import torchvision.models as models
  
  from resnet import resnet34
  
  
  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 ImageNet Training')
  parser.add_argument('data', metavar='DIR',
                      help='path to dataset')
  parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet34',
                      choices=model_names,
                      help='model architecture: ' +
                          ' | '.join(model_names) +
                          ' (default: resnet18)')
  parser.add_argument('-j', '--workers', default=25, type=int, metavar='N',
                      help='number of data loading workers (default: 25)')
  parser.add_argument('--epochs', default=20, type=int, metavar='N',
                      help='number of total epochs to run')
  parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                      help='manual epoch number (useful on restarts)')
  parser.add_argument('-b', '--batch-size', default=256, type=int,
                      metavar='N', help='mini-batch size (default: 256)')
  parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
                      metavar='LR', help='initial learning rate')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                      help='momentum')
  parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
                      metavar='W', help='weight decay (default: 1e-4)')
  parser.add_argument('--print-freq', '-p', default=10, 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('--world-size', default=1, type=int,
                      help='number of distributed processes')
  parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                      help='url used to set up distributed training')
  parser.add_argument('--dist-backend', default='gloo', type=str,
                      help='distributed backend')
  parser.add_argument('--s', type=float, default=0,
                      help='scale sparse rate (default: 0)')
  parser.add_argument('--save', default='.', type=str, metavar='PATH',
                      help='path to save prune model (default: current directory)')
  parser.add_argument('--refine', default='', type=str, metavar='PATH',
                      help='the PATH to pruned model')
  
  best_prec1 = 0
  
  def main():
      global args, best_prec1
      args = parser.parse_args()
  
      args.distributed = args.world_size > 1
  
      if not os.path.exists(args.save):
          os.makedirs(args.save)
  
      if args.distributed:
          dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                  world_size=args.world_size)
  
      if args.refine:
          checkpoint = torch.load(args.refine)
          model = resnet34(cfg=checkpoint['cfg'])
  
      if not args.distributed:
          if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
              model.features = torch.nn.DataParallel(model.features)
              model.cuda()
          else:
              model = torch.nn.DataParallel(model).cuda()
      else:
          model.cuda()
          model = torch.nn.parallel.DistributedDataParallel(model)
  
      if args.refine:
          model.load_state_dict(checkpoint['state_dict'])
  
      # define loss function (criterion) and optimizer
      criterion = nn.CrossEntropyLoss().cuda()
  
      optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                  momentum=args.momentum,
                                  weight_decay=args.weight_decay)
  
      # optionally resume from a checkpoint
      if args.resume:
          if os.path.isfile(args.resume):
              print("=> loading checkpoint '{}'".format(args.resume))
              checkpoint = torch.load(args.resume)
              args.start_epoch = checkpoint['epoch']
              best_prec1 = checkpoint['best_prec1']
              model.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))
  
      cudnn.benchmark = True
  
      # Data loading code
      traindir = os.path.join(args.data, 'train')
      valdir = os.path.join(args.data, 'val')
      normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                       std=[0.229, 0.224, 0.225])
  
      train_dataset = datasets.ImageFolder(
          traindir,
          transforms.Compose([
              transforms.RandomResizedCrop(224),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              normalize,
          ]))
  
      if args.distributed:
          train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
      else:
          train_sampler = None
  
      train_loader = torch.utils.data.DataLoader(
          train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
          num_workers=args.workers, pin_memory=True, sampler=train_sampler)
  
      val_loader = torch.utils.data.DataLoader(
          datasets.ImageFolder(valdir, transforms.Compose([
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              normalize,
          ])),
          batch_size=args.batch_size, shuffle=False,
          num_workers=args.workers, pin_memory=True)
  
      if args.evaluate:
          validate(val_loader, model, criterion)
          return
  
      history_score = np.zeros((args.epochs + 1, 1))
      np.savetxt(os.path.join(args.save, 'record.txt'), history_score, fmt = '%10.5f', delimiter=',')
      for epoch in range(args.start_epoch, args.epochs):
          if args.distributed:
              train_sampler.set_epoch(epoch)
          adjust_learning_rate(optimizer, epoch)
  
          # train for one epoch
          train(train_loader, model, criterion, optimizer, epoch)
  
          # evaluate on validation set
          prec1 = validate(val_loader, model, criterion)
          history_score[epoch] = prec1
          np.savetxt(os.path.join(args.save, 'record.txt'), history_score, fmt = '%10.5f', delimiter=',')
  
          # remember best prec@1 and save checkpoint
          is_best = prec1 > best_prec1
          best_prec1 = max(prec1, best_prec1)
          save_checkpoint({
              'epoch': epoch + 1,
              'arch': args.arch,
              'state_dict': model.state_dict(),
              'best_prec1': best_prec1,
              'optimizer' : optimizer.state_dict(),
          }, is_best, args.save)
  
      history_score[-1] = best_prec1
      np.savetxt(os.path.join(args.save, 'record.txt'), history_score, fmt = '%10.5f', delimiter=',')
  
  def train(train_loader, model, criterion, optimizer, epoch):
      batch_time = AverageMeter()
      data_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to train mode
      model.train()
  
      end = time.time()
      for i, (input, target) in enumerate(train_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          target = target.cuda()
  
          # compute output
          output = model(input)
          loss = criterion(output, target)
  
          # measure accuracy and record loss
          prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
          losses.update(loss.item(), input.size(0))
          top1.update(prec1.item(), input.size(0))
          top5.update(prec5.item(), input.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:
              print('Epoch: [{0}][{1}/{2}]\t'
                    'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                    'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                    'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                    'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                    'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                     epoch, i, len(train_loader), batch_time=batch_time,
                     data_time=data_time, loss=losses, top1=top1, top5=top5))
  
  def validate(val_loader, model, criterion):
      batch_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to evaluate mode
      model.eval()
  
      end = time.time()
      for i, (input, target) in enumerate(val_loader):
          target = target.cuda()
  
          with torch.no_grad():
              # compute output
              output = model(input)
              loss = criterion(output, target)
  
              # measure accuracy and record loss
              prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
              losses.update(loss.item(), input.size(0))
              top1.update(prec1.item(), input.size(0))
              top5.update(prec5.item(), input.size(0))
  
              # measure elapsed time
              batch_time.update(time.time() - end)
              end = time.time()
  
              if i % args.print_freq == 0:
                  print('Test: [{0}/{1}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                      i, len(val_loader), batch_time=batch_time, loss=losses,
                      top1=top1, top5=top5))
  
      print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
            .format(top1=top1, top5=top5))
  
      return top1.avg
  
  def save_checkpoint(state, is_best, filepath):
      torch.save(state, os.path.join(filepath, 'checkpoint.pth.tar'))
      if is_best:
          shutil.copyfile(os.path.join(filepath, 'checkpoint.pth.tar'), os.path.join(filepath, 'model_best.pth.tar'))
  
  class AverageMeter(object):
      """Computes and stores the average and current value"""
      def __init__(self):
          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 adjust_learning_rate(optimizer, epoch):
      """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
      lr = args.lr * (0.1 ** (epoch // 30))
      for param_group in optimizer.param_groups:
          param_group['lr'] = lr
  
  def accuracy(output, target, topk=(1,)):
      """Computes the precision@k for the specified values of k"""
      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()