code: 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
  
  import logging
  from logger import set_logging_config
  import models
  from bop import Bop
  
  from models.seed_conv import SeedConv2d
  from models.masked_psg_seed_conv import PredictiveSeedConv2d
  import pruners
  from generator import masked_parameters
  from prune import prune_loop
  
  
  print = print
  
  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('-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=256, 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('--optimizer', default='SGD', type=str,
                      help='choose among [`SGD`, `BOP`, `Counter`]')
  parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                      metavar='LR', help='initial learning rate', dest='lr')
  parser.add_argument('--savedir', default='results', type=str,
                      help='root dir to save exp checkpoints and logs')
  parser.add_argument('--exp-name', default='SeedNet', type=str,
                      help='path to location to save logs and checkpoints')
  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=100, 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 nodes for distributed training')
  parser.add_argument('--rank', default=-1, type=int,
                      help='node rank for distributed training')
  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='nccl', type=str,
                      help='distributed backend')
  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.')
  parser.add_argument('--multiprocessing-distributed', action='store_true',
                      help='Use multi-processing distributed training to launch '
                           'N processes per node, which has N GPUs. This is the '
                           'fastest way to use PyTorch for either single node or '
                           'multi node data parallel training')
  # SeedNet options
  parser.add_argument('--sign-grouped-dim', default="", type=str,
                      help='dimensions that will be grouped for sign parameters')
  parser.add_argument('--init-method', default='standard', type=str,
                      help='initialization method for conv weights')
  parser.add_argument('--hidden-act', type=str, default='standard',
                      help='choose among [`pruning`, `flipping`, `ternery`, `none`]')
  parser.add_argument('--scaling-input', action='store_true',
                      help='whether scale the input in SeedNet models')
  # BOP options
  parser.add_argument('--ar', type=float,
                      help='list of layer-wise inital adaptivity rates in BOP')
  parser.add_argument('--tau', type=float,
                      help='list of layer-wise thresholds in BOP')
  parser.add_argument('--ar-decay-freq', type=int, default=100,
                      help='freqency to decay the ar hyperparameter in BOP')
  parser.add_argument('--ar-decay-ratio', type=float, default=0.1,
                      help='decay ratio when decay ar')
  # PSG options
  parser.add_argument('--psg-no-backward', action='store_true',
                      help='Do predictive gradient calculation in backward')
  parser.add_argument('--msb-bits', type=int, default=4,
                      help='MSB bits for the input')
  parser.add_argument('--msb-bits-weight', type=int, default=4,
                      help='MSB bits for the weight')
  parser.add_argument('--msb-bits-grad', type=int, default=8,
                      help='MSB bits for the grad')
  parser.add_argument('--psg-threshold', type=float, default=0.0,
                      help='Threshold used in PSG')
  parser.add_argument('--psg-sparsify', action='store_true',
                      help='Sparsify by ignoring small gradients')
  parser.add_argument('--psg-no-take-sign', action='store_true',
                      help='Do not take sign for PSG')
  # Pruning options
  parser.add_argument('--pruner', type=str, default=None, choices=['Mag', 'SNIP', 'GraSP', 'SynFlow'],
                      help='pruning strategy')
  parser.add_argument('--prune-epoch', type=int, default=0,
                      help='epoch number to finish sparsifying by')
  parser.add_argument('--prune-ratio', type=float, default=1.0,
                      help='fraction of non-zero parameters after pruning')
  parser.add_argument('--prune-iters', type=int, default=1,
                      help='number of iterations for scoring (should be 1 for Mag, SNIP, and GraSP)')
  parser.add_argument('--prune-batch-size', type=int, default=256,
                      help='size of sample mini-batch for pruning methods')
  parser.add_argument('--prune-schedule', type=str, default='exponential', choices=['linear', 'exponential'],
                      help='scheduling method for iterative pruning (SynFlow)')
  parser.add_argument('--prune-scope', type=str, default='global', choices=['global', 'local'],
                      help='masking scope')
  parser.add_argument('--prune-shots', type=int, default=1,
                      help='number of shots for pruning')
  
  best_acc1 = 0
  
  
  def main():
      args = parser.parse_args()
  
      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.')
  
      if args.dist_url == "env://" and args.world_size == -1:
          args.world_size = int(os.environ["WORLD_SIZE"])
  
      args.distributed = args.world_size > 1 or args.multiprocessing_distributed
  
      ngpus_per_node = torch.cuda.device_count()
      if args.multiprocessing_distributed:
          # Since we have ngpus_per_node processes per node, the total world_size
          # needs to be adjusted accordingly
          args.world_size = ngpus_per_node * args.world_size
          # Use torch.multiprocessing.spawn to launch distributed processes: the
          # main_worker process function
          mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
      else:
          # Simply call main_worker function
          main_worker(args.gpu, ngpus_per_node, args)
  
  
  def main_worker(gpu, ngpus_per_node, args):
      global best_acc1
      args.gpu = gpu
  
      args.savedir = os.path.join(args.savedir, args.exp_name)
      if not os.path.isdir(args.savedir):
          os.makedirs(args.savedir)
      args.logger = set_logging_config(args.savedir)
  
      if args.gpu is not None:
          args.logger.info("Use GPU: {} for training".format(args.gpu))
  
      if args.distributed:
          if args.dist_url == "env://" and args.rank == -1:
              args.rank = int(os.environ["RANK"])
          if args.multiprocessing_distributed:
              # For multiprocessing distributed training, rank needs to be the
              # global rank among all the processes
              args.rank = args.rank * ngpus_per_node + gpu
          dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                  world_size=args.world_size, rank=args.rank)
      # create model
      if args.pretrained:
          args.logger.info("=> using pre-trained model '{}'".format(args.arch))
          model = models.__dict__[args.arch](pretrained=True)
      else:
          args.logger.info("=> creating model '{}'".format(args.arch))
          if args.arch.startswith('seed_resnet'):
              pass
          if args.arch.startswith('psg'):
              model = models.__dict__[args.arch](
                  init_method=args.init_method,
                  predictive_backward = not args.psg_no_backward,
                  msb_bits = args.msb_bits,
                  msb_bits_weight = args.msb_bits_weight,
                  msb_bits_grad = args.msb_bits_grad,
                  threshold = args.psg_threshold,
                  sparsify = args.psg_sparsify,
                  sign = not args.psg_no_take_sign
              )
              temp_arch = args.arch[9:] if 'seed' in args.arch else args.arch[4:]
              model_for_pruning = models.__dict__[temp_arch](init_method=args.init_method)
          else:
              model = models.__dict__[args.arch](init_method=args.init_method)
              model_for_pruning = None
  
      if not torch.cuda.is_available():
          print('using CPU, this will be slow')
      elif args.distributed:
          # For multiprocessing distributed, DistributedDataParallel constructor
          # should always set the single device scope, otherwise,
          # DistributedDataParallel will use all available devices.
          if args.gpu is not None:
              torch.cuda.set_device(args.gpu)
              model.cuda(args.gpu)
              # 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 / ngpus_per_node)
              args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
              model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
              model_without_ddp = model.module
              if model_for_pruning is not None:
                  model_for_pruning.cuda(args.gpu)
                  model_for_pruning = torch.nn.parallel.DistributedDataParallel(model_for_pruning, device_ids=[args.gpu])
          else:
              model.cuda()
              # DistributedDataParallel will divide and allocate batch_size to all
              # available GPUs if device_ids are not set
              model = torch.nn.parallel.DistributedDataParallel(model)
              model_without_ddp = model.module
              if model_for_pruning is not None:
                  model_for_pruning.cuda()
                  model_for_pruning = torch.nn.parallel.DistributedDataParallel(model_for_pruning)
      elif args.gpu is not None:
          torch.cuda.set_device(args.gpu)
          model = model.cuda(args.gpu)
      else:
          # DataParallel will divide and allocate batch_size to all available GPUs
          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()
  
      # define loss function (criterion) and optimizer
      criterion = nn.CrossEntropyLoss().cuda(args.gpu)
  
      if args.optimizer == 'SGD':
          parameters = [p for p in model_without_ddp.parameters() if p.requires_grad]
          optimizer = torch.optim.SGD(parameters, args.lr,
                                      momentum=args.momentum,
                                      weight_decay=args.weight_decay)
          bop_optimizer = None
      elif args.optimizer == 'BOP':
          bop_params, non_bop_params = model_without_ddp.get_bop_params(), model_without_ddp.get_non_bop_params()
          bop_param_masks = model_without_ddp.get_bop_param_masks()
          bop_dict = [{'params': bop_params, 'adaptivity_rate': args.ar, 'threshold': args.tau}]
          # optimizer = optim.SGD(non_bop_params, lr=args.lr, momentum=0.9, weight_decay=5e-4)
          optimizer = torch.optim.SGD(non_bop_params, args.lr,
                                      momentum=args.momentum,
                                      weight_decay=args.weight_decay)
          # bop_optimizer = Bop(bop_params, None, ar=args.ar, threshold=args.tau)
          bop_optimizer = Bop(bop_params, None, bop_param_masks, ar=args.ar, threshold=args.tau, device=args.gpu)
          # schedulers = (optim.lr_scheduler.MultiStepLR(non_bop_optimizer, milestones=[80, 120], gamma=0.1),)
  
      # optionally resume from a checkpoint
      if args.resume:
          if os.path.isfile(args.resume):
              args.logger.info("=> 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']
              best_acc1 = checkpoint['best_acc1']
              if args.gpu is not None:
                  # best_acc1 may be from a checkpoint from a different GPU
                  best_acc1 = best_acc1.to(args.gpu)
              model_without_ddp.load_state_dict(checkpoint['state_dict'])
              optimizer.load_state_dict(checkpoint['optimizer'])
              args.logger.info("=> loaded checkpoint '{}' (epoch {})"
                    .format(args.resume, checkpoint['epoch']))
          else:
              args.logger.info("=> 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)
  
      sample_batch_indices = torch.randperm(len(train_dataset))[:100]
      sample_batch = torch.utils.data.Subset(train_dataset, sample_batch_indices)
      pruneloader = torch.utils.data.DataLoader(sample_batch, args.prune_batch_size, shuffle=True, num_workers=4)
  
      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, args)
          return
  
      # Create pruner
      num_classes = 1000
      # if args.pruner:
      #     pruner = pruners.__dict__[args.pruner](masked_parameters(model, False, False, False), num_classes)
  
      seed_convs = list(filter(lambda m: isinstance(m, (SeedConv2d, PredictiveSeedConv2d,)), model.modules()))
      cur_shot = 0
      prune_interval = int(args.prune_epoch / args.prune_shots)
      for epoch in range(args.start_epoch, args.epochs):
          if args.distributed:
              train_sampler.set_epoch(epoch)
          adjust_learning_rate(optimizer, epoch, args)
          if args.optimizer == 'BOP' and (epoch + 1) % args.ar_decay_freq == 0:
              bop_optimizer.decay_ar(args.ar_decay_ratio)
  
          # Enable gradients for pruning in SeedNet
          for seed_conv in seed_convs:
              seed_conv.enable_weight_grad()
          if args.pruner and epoch == (cur_shot + 1) * prune_interval and cur_shot < args.prune_shots:
              target_sparsity = 1 - (1 - args.prune_ratio) * (cur_shot + 1) / args.prune_shots
              if args.arch.lower().startswith('psg'):
                  model_for_pruning.load_state_dict(model.state_dict(), strict=False)
                  # pruner = pruners.__dict__[args.pruner](masked_parameters(model_for_pruning, False, False, False), num_classes)
                  # prune_loop(model_for_pruning, criterion, pruner, pruneloader, num_classes, args.gpu, target_sparsity,
                  #            args.prune_schedule, args.prune_scope, args.prune_iters)
                  prune_loop(model_for_pruning, criterion, args.pruner,
                             pruneloader, num_classes, args.gpu, target_sparsity,
                             args.prune_schedule, args.prune_scope, args.prune_iters,
                             prune_bias=False, prune_batchnorm=False, prune_residual=False,
                             weight_flips=None, score_threshold=None)
                  model.load_state_dict(model_for_pruning.state_dict(), strict=False)
              else:
                  # prune_loop(model, criterion, pruner, pruneloader, num_classes, args.gpu, target_sparsity,
                  #            args.prune_schedule, args.prune_scope, args.prune_iters)
                  prune_loop(model, criterion, args.pruner,
                             pruneloader, num_classes, args.gpu, target_sparsity,
                             args.prune_schedule, args.prune_scope, args.prune_iters,
                             prune_bias=False, prune_batchnorm=False, prune_residual=False,
                             weight_flips=None, score_threshold=None)
              # Really copy the mask to the model
              # with torch.no_grad():
              #     pruned_masks = [m for m, _ in pruner.masked_parameters]
              #     model_masks  = [m for m, _ in masked_parameters(model, False, False, False)]
              #     for model_mask, pruned_mask in zip(model_masks, pruned_masks):
              #         model_mask.copy_(pruned_mask.data.detach().clone())
              # Disable gradients when resuming training for SeedNet
              for seed_conv in seed_convs:
                  seed_conv.disable_weight_grad()
              cur_shot += 1
  
          # train for one epoch
          train(train_loader, model, criterion, optimizer, epoch, args, bop_optimizer=bop_optimizer)
  
          # evaluate on validation set
          acc1, acc5 = validate(val_loader, model, criterion, args)
          if args.gpu == 0:
              args.logger.info('epoch {} \t Top-1 acc {} \t Top-5 acc {}'.format(epoch + 1, acc1, acc5))
  
          # remember best acc@1 and save checkpoint
          is_best = acc1 > best_acc1
          best_acc1 = max(acc1, best_acc1)
          args.logger.info(f'Max accuracy: {best_acc1}')
          best_acc1_acc5 = acc5
  
          if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                  and args.rank % ngpus_per_node == 0):
              save_checkpoint({
                  'epoch': epoch + 1,
                  'arch': args.arch,
                  'state_dict': model_without_ddp.state_dict(),
                  'best_acc1': best_acc1,
                  'acc5': best_acc1_acc5,
                  'optimizer' : optimizer.state_dict(),
              }, is_best)
  
      args.logger.info('best Top-1 acc {} \t corresponding Top-5 acc {}'.format(best_acc1, best_acc1_acc5))
  
  
  def train(train_loader, model, criterion, optimizer, epoch, args, bop_optimizer=None):
      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)
  
          if args.gpu is not None:
              images = images.cuda(args.gpu, non_blocking=True)
          if torch.cuda.is_available():
              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()
          if bop_optimizer is not None:
              bop_optimizer.zero_grad()
          loss.backward()
          optimizer.step()
          if bop_optimizer is not None:
              bop_optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if args.gpu == 0 and 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)
              if torch.cuda.is_available():
                  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 args.gpu == 0 and i % args.print_freq == 0:
                  progress.display(i)
  
          # TODO: this should also be done with the ProgressMeter
          top1.synchronize()
          top5.synchronize()
          # args.logger.info(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
          #       .format(top1=top1, top5=top5))
  
      return top1.avg, top5.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 synchronize(self):
          """
          Warning: does not synchronize `val`
          """
          t = torch.tensor([self.sum, self.count], dtype=torch.float64, device='cuda')
          dist.barrier()
          dist.all_reduce(t)
          t = t.tolist()
          self.sum = float(t[0])
          self.count = int(t[1])
          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].reshape(-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.no_grad torch.cuda.is_available torch.distributed.init_process_group torch.save torch.cuda.device_count torch.nn.DataParallel torch.cuda.set_device torch.optim.SGD torch.multiprocessing.spawn torch.load torch.utils.data.Subset torch.manual_seed torch.tensor torch.distributed.barrier torch.nn.parallel.DistributedDataParallel torch.distributed.all_reduce torch.utils.data.distributed.DistributedSampler torch.nn.CrossEntropyLoss