text: """
  Evaluate on ImageNet. Note that at the moment, training is not implemented (I am working on it).
  that being said, evaluation is working.
  """
  
  import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  import PIL
  import math
  
  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
  
  from torch.cuda.amp import autocast
  from torch.cuda.amp import GradScaler
  
  # import horovod
  try:
      import horovod.torch as hvd
  except ModuleNotFoundError:
      print('horovod is not installed')
  
  import sys
  sys.path.append('./')
  
  from efficientnet_pytorch import EfficientNet
  
  
  ## DDP
  # python -m torch.distributed.launch --nproc_per_node=1 examples/imagenet/main.py --data /data/image/ -a efficientnet-b0 --multiprocessing-distributed
  ## horovod
  # horovodrun -np 4 python examples/imagenet/main.py --data /data/image/ -a efficientnet-b0 --use-horovod
  
  # from torch.utils.tensorboard import SummaryWriter
  # writer = SummaryWriter('runs/imagenet_DDP')
  
  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',
                      help='model architecture (default: resnet18)')
  parser.add_argument('-j', '--n-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('--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('--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='env://', 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('--image_size', default=224, type=int,
                      help='image size')
  parser.add_argument('--advprop', default=False, action='store_true',
                      help='use advprop or not')
  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')
  
  parser.add_argument('--local_rank', default=-1, type=int, help='local rank')
  parser.add_argument('--use-horovod', default=False, action='store_true', help='use horovod')
  parser.add_argument('--mixed-precision', default=False, action='store_true', help='use mixed procision')
  parser.add_argument('--fp16_allreduce', default=False, action='store_true', help='horovod fp16_allreduce')
  
  best_acc1 = 0
  os.environ['MASTER_ADDR'] = '127.0.0.1'
  os.environ['MASTER_PORT'] = '29500'
  
  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 and args.multiprocessing_distributed:
          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
         
  
          os.environ.setdefault("NCCL_SOCKET_IFNAME", "^lo,docker")
          print(os.environ.get("NCCL_SOCKET_IFNAME"))
  
  
          print(args.dist_backend, args.dist_url, args.world_size, args.rank)
          dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                  world_size=args.world_size, rank=args.rank)
      # create model
      if 'efficientnet' in args.arch:  # NEW
          if args.pretrained:
              model = EfficientNet.from_pretrained(args.arch, advprop=args.advprop)
              print("=> using pre-trained model '{}'".format(args.arch))
          else:
              print("=> creating model '{}'".format(args.arch))
              model = EfficientNet.from_name(args.arch)
  
      else:
          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]()
  
      optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
  
      if args.use_horovod:
          print('use horovod')
          hvd.init()
          torch.cuda.set_device(hvd.local_rank())
          model = model.cuda(args.gpu)
          
          args.batch_size = int(args.batch_size / ngpus_per_node)
          args.n_workers = int(args.n_workers / ngpus_per_node)
          print(args.n_workers)
          compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
          # compression = hvd.Compression.none
          optimizer = hvd.DistributedOptimizer(optimizer,
                                               named_parameters=model.named_parameters(),
                                               compression=compression)
          hvd.broadcast_parameters(model.state_dict(), root_rank=0)
          hvd.broadcast_optimizer_state(optimizer, root_rank=0)
  
      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.n_workers = int(args.n_workers / ngpus_per_node)
              print(args.n_workers)
              print('use DDP')
              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:
              print('use DP')
              model = model.cuda()
              model = torch.nn.DataParallel(model).cuda()
  
      # define loss function (criterion) and optimizer
      criterion = nn.CrossEntropyLoss().cuda(args.gpu)
  
      # 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_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')
      if args.advprop:
          normalize = transforms.Lambda(lambda img: img * 2.0 - 1.0)
      else:
          normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                           std=[0.229, 0.224, 0.225])
  
      if 'efficientnet' in args.arch:
          image_size = EfficientNet.get_image_size(args.arch)
      else:
          image_size = args.image_size
  
  
      train_dataset = datasets.ImageFolder(
          traindir,
          transforms.Compose([
              transforms.RandomResizedCrop(image_size),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              normalize,
          ]))
  
      if args.use_horovod:
          train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
                                                                          num_replicas=hvd.size(),
                                                                          rank=hvd.rank())
      elif 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.n_workers, pin_memory=True, sampler=train_sampler)
  
      val_transforms = transforms.Compose([
          transforms.Resize(image_size, interpolation=PIL.Image.BICUBIC),
          transforms.CenterCrop(image_size),
          transforms.ToTensor(),
          normalize,
      ])
      print('Using image size', image_size)
  
      val_loader = torch.utils.data.DataLoader(
          datasets.ImageFolder(valdir, val_transforms),
          batch_size=args.batch_size, shuffle=False,
          num_workers=args.n_workers, pin_memory=True)
  
      if args.evaluate:
          res = validate(val_loader, model, criterion, args)
          with open('res.txt', 'w') as f:
              print(res, file=f)
          return
      
      scaler = GradScaler()
  
      for epoch in range(args.start_epoch, args.epochs):
          if args.distributed:
              train_sampler.set_epoch(epoch)
          adjust_learning_rate(optimizer, epoch, args)
  
          # train for one epoch
          start_time = time.time()
          train(train_loader, model, criterion, optimizer, scaler, epoch, args)
          end_time = time.time()
          with open('benchmarks_1209.txt', 'a') as f:
              f.write('\texecution time per single epoch: ' + str(end_time - start_time) + '\n')
          exit()
  
          # evaluate on validation set
          acc1 = validate(val_loader, model, criterion, scaler, 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):
              save_checkpoint({
                  'epoch': epoch + 1,
                  'arch': args.arch,
                  'state_dict': model.state_dict(),
                  'best_acc1': best_acc1,
                  'optimizer' : optimizer.state_dict(),
              }, is_best)
  
  
  def train(train_loader, model, criterion, optimizer, scaler, 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()
  
      end = time.time()
      for i, data in enumerate(train_loader):
          images, target = data
          # measure data loading time
          data_time.update(time.time() - end)
  
          if args.gpu is not None:
              images = images.cuda(args.gpu, non_blocking=True)
              target = target.cuda(args.gpu, non_blocking=True)
          else:
              images, target = images.cuda(), target.cuda()
          
          optimizer.zero_grad()
  
          # compute output
          if args.mixed_precision:
              with autocast():
                  output = model(images, use_amp=True)
                  loss = criterion(output, target)
          else:
              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
  
          if args.mixed_precision:
              scaler.scale(loss).backward()
          else:
              loss.backward()
  
          if args.use_horovod:
              optimizer.synchronize()
              with optimizer.skip_synchronize():
                  if args.mixed_precision:
                      scaler.step(optimizer)
                      scaler.update()
                  else:
                      optimizer.step()
  
          else:
              if args.mixed_precision:
                  scaler.step(optimizer)
                  scaler.update()
              else:
                  optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if i % args.print_freq == 0:
              progress.print(i)
      
  
  def validate(val_loader, model, criterion, scaler, 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: ')
  
      # print('len_val_loader:', len(val_loader))
      # switch to evaluate mode
      model.eval()
  
      with torch.no_grad():
          end = time.time()
          for i, data in enumerate(val_loader):
              images, target = data
  
              if args.gpu is not None:
                  images = images.cuda(args.gpu, non_blocking=True)
              else:
                  images = images.cuda(non_blocking=True)
              target = target.cuda(args.gpu, non_blocking=True)
  
              # compute output
              if args.mixed_precision:
                  with autocast():
                      output = model(images, use_amp=True)
                      loss = criterion(output, target)
              else:
                  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.print(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 print(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()
  