text: """
  Copied with modification from the one and only:
  https://github.com/pytorch/examples/blob/master/imagenet/main.py
  """
  
  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 torch_xla.core.xla_model as xm
  import torch_xla.distributed.xla_multiprocessing as xmp
  import torch_xla.distributed.parallel_loader as xpl
  import torch_xla.utils as xlu
  import torchvision.transforms as transforms
  import torchvision.datasets as datasets
  
  from max_deeplab.backbone import MaXDeepLabSImageNet
  
  parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
  parser.add_argument('data', metavar='DIR',
                      help='path to dataset')
  parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                      help='number of data loading workers (default: 4)')
  parser.add_argument('-t', '--tpu', action='store_true',
                      help='use TPU accelerator')
  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('--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')
  
  best_acc1 = 0
  
  
  def main():
      args = parser.parse_args()
  
      if args.seed is not None:
          random.seed(args.seed)
          torch.manual_seed(args.seed)
          if args.tpu:
              xm.set_rng_state(args.seed)
          else:
              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.device_id is not None:
          warnings.warn('You have chosen a specific accelerator device. 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
  
      num_workers = len(xm.get_xla_supported_devices('TPU')) if args.tpu else torch.cuda.device_count()
      if args.multiprocessing_distributed:
          # Since we have num_workers processes per node, the total world_size
          # needs to be adjusted accordingly
          args.world_size = num_workers * args.world_size
          # Use torch.multiprocessing.spawn to launch distributed processes: the
          # main_worker process function
          spawn_fn = xmp.spawn if args.tpu else mp.spawn
          spawn_fn(main_worker, nprocs=num_workers, args=(num_workers, args))
      else:
          # Simply call main_worker function
          main_worker(args.device_id, num_workers, args)
  
  
  def main_worker(worker_id, num_workers, args):
      global best_acc1
      args.dev_id = worker_id
  
      if args.dev_id is not None:
          print("Use GPU: {} for training".format(args.dev_id))
  
      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 * num_workers + worker_id
          dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                  world_size=args.world_size, rank=args.rank)
      # create model
      model = MaXDeepLabSImageNet()
  
      if not (args.tpu or 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.dev_id is not None:
              if args.tpu:
                  model.to(xm.xla_device(args.dev_id))
              else:
                  torch.cuda.set_device(args.dev_id)
                  model.cuda(args.dev_id)
              # 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 / num_workers)
              args.workers = int((args.workers + num_workers - 1) / num_workers)
              model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.dev_id])
          else:
              assert not args.tpu, 'Distributed training on TPU requires multiprocessing'
              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.dev_id is not None:
          if args.tpu:
              model.to(xm.xla_device(args.dev_id))
          else:
              torch.cuda.set_device(args.dev_id)
              model = model.cuda(args.dev_id)
      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)
          if args.tpu:
              model = model.to(xm.xla_device())
          else:
              model = model.cuda()
  
      # define loss function (criterion) and optimizer
      criterion = nn.CrossEntropyLoss()
      if args.tpu:
          criterion = criterion.to(xm.xla_device(args.dev_id))
      else:
          criterion = criterion.cuda(args.dev_id)
  
      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.dev_id is None:
                  checkpoint = torch.load(args.resume)
                  if args.tpu:
                      checkpoint = checkpoint.to(xm.xla_device(args.dev_id))
              else:
                  # Map model to be loaded to specified single gpu.
                  loc = xm.xla_device(args.dev_id) if args.tpu else 'cuda:{}'.format(args.dev_id)
                  checkpoint = torch.load(args.resume, map_location=loc)
              args.start_epoch = checkpoint['epoch']
              best_acc1 = checkpoint['best_acc1']
              if args.dev_id is not None:
                  # best_acc1 may be from a checkpoint from a different GPU
                  best_acc1 = best_acc1.to(xm.xla_device(args.dev_id) if args.tpu else args.dev_id)
              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))
      if args.profile:
          if args.tpu:
              pass
          else:
              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)
      if args.tpu:
          train_loader = pl.MpDeviceLoader(train_loader, device)
  
      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.tpu:
          val_loader = pl.MpDeviceLoader(train_loader, device)
  
      if args.evaluate:
          validate(val_loader, model, criterion, args)
          return
  
      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
          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 % num_workers == 0):
              save_checkpoint({
                  'epoch': epoch + 1,
                  'arch': args.arch,
                  'state_dict': model.state_dict(),
                  'best_acc1': best_acc1,
                  'optimizer' : optimizer.state_dict(),
              }, is_best, use_tpu=args.tpu)
  
  
  def train(train_loader, model, criterion, optimizer, 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), tpu=args.tpu)
  
      # 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.dev_id is not None and not args.tpu:
              images = images.cuda(args.dev_id, non_blocking=True)
          if torch.cuda.is_available():
              target = target.cuda(args.dev_id, 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()
          loss.backward()
          if args.tpu:
              xm.optimizer_step(optimizer)
          else:
              optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if i % args.print_freq == 0:
              # wait for all devices if using TPU
              if args.tpu:
                  xm.add_step_closure(progress.display, args=(i,))
              else:
                  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: ', tpu=args.tpu)
  
      # switch to evaluate mode
      model.eval()
  
      with torch.no_grad():
          end = time.time()
          for i, (images, target) in enumerate(val_loader):
              if args.dev_id is not None and not args.tpu:
                  images = images.cuda(args.dev_id, non_blocking=True)
              if torch.cuda.is_available():
                  target = target.cuda(args.dev_id, 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:
                  # wait for all devices if using TPU
                  if args.tpu:
                      xm.add_step_closure(progress.display, args=(i,))
                  else:
                      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', use_tpu=False):
      if use_tpu:
          xm.save(state, filename)
      else:
          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="", tpu=False):
          self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
          self.meters = meters
          self.prefix = prefix
          self.tpu = tpu
  
      def display(self, batch):
          entries = [self.prefix + self.batch_fmtstr.format(batch)]
          entries += [str(meter) for meter in self.meters]
          if self.tpu:
              xm.master_print('\t'.join(entriess))
          else:
              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()
  