text: import csv
  
  import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  
  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.multiprocessing as mp
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms as transforms
  import torchvision.datasets as datasets
  import torchvision.models as models
  
  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', default='/home/zhangzhi/Data/exports/ImageNet2012', help='path to dataset')
  parser.add_argument('-a',
                      '--arch',
                      metavar='ARCH',
                      default='resnet18',
                      choices=model_names,
                      help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet18)')
  parser.add_argument('-j',
                      '--workers',
                      default=4,
                      type=int,
                      metavar='N',
                      help='number of data loading workers (default: 4)')
  parser.add_argument('--epochs', default=90, 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=3200,
                      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=0.1,
                      type=float,
                      metavar='LR',
                      help='initial learning rate',
                      dest='lr')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum')
  parser.add_argument('--wd',
                      '--weight-decay',
                      default=1e-4,
                      type=float,
                      metavar='W',
                      help='weight decay (default: 1e-4)',
                      dest='weight_decay')
  parser.add_argument('-p', '--print-freq', default=10, type=int, metavar='N', help='print frequency (default: 10)')
  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. ')
  
  
  def reduce_mean(tensor, nprocs):
      rt = tensor.clone()
      dist.all_reduce(rt, op=dist.ReduceOp.SUM)
      rt /= nprocs
      return rt
  
  
  def main():
      args = parser.parse_args()
      args.nprocs = torch.cuda.device_count()
  
      mp.spawn(main_worker, nprocs=args.nprocs, args=(args.nprocs, args))
  
  
  def main_worker(local_rank, nprocs, args):
      args.local_rank = local_rank
      
      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.')
  
      best_acc1 = .0
  
      dist.init_process_group(backend='nccl', init_method='tcp://127.0.0.1:23456', world_size=args.nprocs, rank=local_rank)
      # create model
      if args.pretrained:
          print("=> using pre-trained model '{}'".format(args.arch))
          model = models.__dict__[args.arch](pretrained=True)
      else:
          print("=> creating model '{}'".format(args.arch))
          model = models.__dict__[args.arch]()
  
      torch.cuda.set_device(local_rank)
      model.cuda(local_rank)
      # When using a single GPU per process and per
      # DistributedDataParallel, we need to divide the batch size
      # ourselves based on the total number of GPUs we have
      args.batch_size = int(args.batch_size / args.nprocs)
      model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank])
  
      # define loss function (criterion) and optimizer
      criterion = nn.CrossEntropyLoss().cuda(local_rank)
  
      optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
  
      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,
          ]))
      train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
      train_loader = torch.utils.data.DataLoader(train_dataset,
                                                 batch_size=args.batch_size,
                                                 num_workers=2,
                                                 pin_memory=True,
                                                 sampler=train_sampler)
  
      val_dataset = datasets.ImageFolder(
          valdir,
          transforms.Compose([
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              normalize,
          ]))
      val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
      val_loader = torch.utils.data.DataLoader(val_dataset,
                                               batch_size=args.batch_size,
                                               num_workers=2,
                                               pin_memory=True,
                                               sampler=val_sampler)
  
      if args.evaluate:
          validate(val_loader, model, criterion, local_rank, args)
          return
  
      for epoch in range(args.start_epoch, args.epochs):
  
          train_sampler.set_epoch(epoch)
          val_sampler.set_epoch(epoch)
  
          adjust_learning_rate(optimizer, epoch, args)
  
          # train for one epoch
          train(train_loader, model, criterion, optimizer, epoch, local_rank, args)
  
          # evaluate on validation set
          acc1 = validate(val_loader, model, criterion, local_rank, args)
  
          # remember best acc@1 and save checkpoint
          is_best = acc1 > best_acc1
          best_acc1 = max(acc1, best_acc1)
  
          if args.local_rank == 0:
              save_checkpoint(
                  {
                      'epoch': epoch + 1,
                      'arch': args.arch,
                      'state_dict': model.module.state_dict(),
                      'best_acc1': best_acc1,
                  }, is_best)
  
  
  def train(train_loader, model, criterion, optimizer, epoch, local_rank, 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()
  
      end = time.time()
      for i, (images, target) in enumerate(train_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          images = images.cuda(local_rank, non_blocking=True)
          target = target.cuda(local_rank, 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))
  
          torch.distributed.barrier()
  
          reduced_loss = reduce_mean(loss, args.nprocs)
          reduced_acc1 = reduce_mean(acc1, args.nprocs)
          reduced_acc5 = reduce_mean(acc5, args.nprocs)
  
          losses.update(reduced_loss.item(), images.size(0))
          top1.update(reduced_acc1.item(), images.size(0))
          top5.update(reduced_acc5.item(), 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, local_rank, 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):
              images = images.cuda(local_rank, non_blocking=True)
              target = target.cuda(local_rank, 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))
  
              torch.distributed.barrier()
  
              reduced_loss = reduce_mean(loss, args.nprocs)
              reduced_acc1 = reduce_mean(acc1, args.nprocs)
              reduced_acc5 = reduce_mean(acc5, args.nprocs)
  
              losses.update(reduced_loss.item(), images.size(0))
              top1.update(reduced_acc1.item(), images.size(0))
              top5.update(reduced_acc5.item(), 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, filename='checkpoint.pth.tar'):
      torch.save(state, filename)
      if is_best:
          shutil.copyfile(filename, 'model_best.pth.tar')
  
  
  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 adjust_learning_rate(optimizer, epoch, args):
      """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 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()