text: import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  import importlib
  import warnings
  import numpy as np
  warnings.filterwarnings('ignore')
  
  import torch
  import torch.fft
  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 torchvision.utils as vutils
  from torch.autograd import Variable
  
  from datasets.APR import APRecombination
  import datasets.augmentations as augmentations
  
  augmentations.IMAGE_SIZE = 224
  
  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', default='./data',)
  parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                      help='model architecture: ' +
                          ' | '.join(model_names) +
                          ' (default: resnet18)')
  parser.add_argument('-j', '--workers', default=100, type=int, metavar='N',
                      help='number of data loading workers (default: 4)')
  parser.add_argument('--epochs', default=100, 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('--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('--lam', default=1.0, type=float, metavar='M',
                      help='lam')
  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=1000, 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('--conf', dest='conf', action='store_true',
                      help='use conf')
  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://127.0.0.1:8001', 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')
  
  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
  
      if args.gpu is not None:
          print("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
      print('create model:', args.arch)
      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]()
  
      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])
          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)
      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)
  
      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))
              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.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.RandomApply([APRecombination(img_size=224, aug=augmentations)], p=1.0),
          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, args)
          return
      
      is_best, best_acc1 = False, 0
      for epoch in range(args.start_epoch, args.epochs):
          if args.distributed:
              train_sampler.set_epoch(epoch)
          adjust_learning_rate(optimizer, epoch, args)
  
          if epoch == args.start_epoch and args.pretrained:
              acc1 = validate(val_loader, model, criterion, args)
  
          # 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 not args.multiprocessing_distributed or (args.multiprocessing_distributed
                  and args.rank % ngpus_per_node == 0):
              print('Saved!!!')
              save_checkpoint({
                  'epoch': epoch + 1,
                  'arch': args.arch,
                  'state_dict': model.state_dict(),
                  'best_acc1': best_acc1,
                  'optimizer' : optimizer.state_dict(),
              }, is_best, args=args)
              is_best = False
  
  def mix_data(x, use_cuda=True):
      '''Returns mixed inputs, pairs of targets, and lambda'''
      
      p = random.uniform(0, 1)
  
      if p > 0.5:
          return x
  
      batch_size = x.size()[0]
      if use_cuda:
          index = torch.randperm(batch_size).cuda()
      else:
          index = torch.randperm(batch_size)
  
      fft_1 = torch.fft.fftn(x, dim=(1,2,3))
      abs_1, angle_1 = torch.abs(fft_1), torch.angle(fft_1)
  
      fft_2 = torch.fft.fftn(x[index, :], dim=(1,2,3))
      abs_2, angle_2 = torch.abs(fft_2), torch.angle(fft_2)
  
      fft_1 = abs_2*torch.exp((1j) * angle_1)
  
      mixed_x = torch.fft.ifftn(fft_1, dim=(1,2,3)).float()
  
      return mixed_x
  
  def train(train_loader, model, criterion, optimizer, epoch, args):
  
      losses = AverageMeter('Loss', ':.4e')
      top1 = AverageMeter('Acc@1', ':6.2f')
      top5 = AverageMeter('Acc@5', ':6.2f')
      progress = ProgressMeter(
          len(train_loader),
          [losses, top1, top5],
          prefix="Epoch: [{}]".format(epoch))
  
      # switch to train mode
      model.train()
  
      end = time.time()
      for i, (images, targets) in enumerate(train_loader):
  
          if args.gpu is not None:
              images = images.cuda(args.gpu, non_blocking=True)
          if torch.cuda.is_available():
              targets = targets.cuda(args.gpu, non_blocking=True)
  
          print(i, end='\r')
  
          images_mix = mix_data(images)
          images_mix = Variable(images_mix)
  
          batch_size = images.size(0)
          images = torch.cat([images, images_mix], 0)
  
          with torch.set_grad_enabled(True):
              # compute output
              output = model(images)
              loss = criterion(output[:batch_size], targets) + criterion(output[batch_size:], targets)
  
              # compute gradient and do SGD step
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
  
          # measure accuracy and record loss
          acc1, acc5 = accuracy(output[:batch_size], targets, 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) 
  
  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 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='checkpoints/checkpoint.pth.tar', args=None):
      filename = 'checkpoints/{}_{}_checkpoint.pth.tar'.format(args.arch, args.conf)
      torch.save(state, filename)
      if is_best:
          shutil.copyfile(filename, 'checkpoints/{}_{}_best.pth.tar'.format(args.arch, args.conf))
  
  
  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].reshape(-1).float().sum(0, keepdim=True)
              res.append(correct_k.mul_(100.0 / batch_size))
          return res
  
  
  if __name__ == '__main__':
      main()