text: import math
  import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  import sys
  import numpy as np
  
  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 torch.nn.functional as F
  import torchvision
  
  from utils import AverageMeter, save_checkpoint, accuracy
  from loss import BeliefMatchingLoss
  
  parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
  parser.add_argument('-a', '--arch', metavar='ARCH', default='fixup_resnet50')
  parser.add_argument('-j', '--workers', default=4, 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('--data', default='temp',
                      help='path to dataset')
  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('--base_lr', default=0.1, type=float,
                      metavar='base_lr', help='base learning rate (default=0.1)', dest='base_lr')
  parser.add_argument('--save-dir', dest='save_dir',
                      help='The directory used to save the trained models',
                      default='save_temp', type=str)
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                      help='momentum')
  parser.add_argument('--wd', '--weight-decay', default=0.0001, 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='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')
  parser.add_argument('--coeff', default=1e-2, type=float, help='Coefficient to KL term in BM loss')
  parser.add_argument('--prior', default=1.0, type=float, help='Dirichlet prior parameter')
  
  
  
  import os
  os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5,6,7'
      
  
  best_acc1 = 0
  best_acc5 = 0
  args = parser.parse_args()
  base_learning_rate = args.base_lr 
  
  if args.coeff == -1:
      name = '{}-softmax'.format(args.arch)
  else:
      name = '{}-bm-coeff-{}-prior{}'.format(args.arch, args.coeff, args.prior)
  
  def main():
      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
      global best_acc5
      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
      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))
          if args.arch == 'ResNext50':
              model = torchvision.models.resnext50_32x4d(pretrained=False)
          elif args.arch == 'ResNext101':
              model = torchvision.models.resnext101_32x8d(pretrained=False, progress=True)
          else:
              raise NotImplementedError
              
              
          with torch.no_grad():
              model.fc.bias = torch.nn.parameter.Parameter(model.fc.bias + math.log(args.prior))
      
  
      if 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)
              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()
  
      if args.coeff == -1:
          criterion = nn.CrossEntropyLoss()
      else:
          criterion =  BeliefMatchingLoss(args.coeff, args.prior) 
      
      parameters_bias = [p[1] for p in model.named_parameters() if 'bias' in p[0]]
      parameters_scale = [p[1] for p in model.named_parameters() if 'scale' in p[0]]
      parameters_others = [p[1] for p in model.named_parameters() if not ('bias' in p[0] or 'scale' in p[0])]
      optimizer = torch.optim.SGD(
          [{'params': parameters_bias, 'lr': args.base_lr/10.},
          {'params': parameters_scale, 'lr': args.base_lr/10.},
          {'params': parameters_others}],
          lr=base_learning_rate,
          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,
          ]))
  
      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
      
      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, acc5 = validate(val_loader, model, criterion, args)
          
          # remember best acc@1 and save checkpoint
          is_best = acc1 > best_acc1
          if is_best:
              best_acc1 = max(acc1, best_acc1)
              best_acc5 = max(acc5, best_acc5)
  
          if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                  and args.rank % ngpus_per_node == 0):
              save_checkpoint({
                  'epoch': epoch + 1,
                  'state_dict': model.state_dict(),
                  'best_acc1': best_acc1,
                  'best_acc5': best_acc5,
                  'optimizer' : optimizer.state_dict(),
              }, is_best, filename=os.path.join(args.save_dir, 'checkpoint_{}.th'.format(name)))
              
  
  def train(train_loader, model, criterion, optimizer, epoch, args):
      batch_time = AverageMeter()
      data_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to train mode
      model.train()
  
      end = time.time()
      for i, (inputs, targets) in enumerate(train_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          inputs = inputs.cuda(args.gpu, non_blocking=True)
          targets = targets.cuda(args.gpu, non_blocking=True)
          
          targets = targets - 1
          
          # compute output
          output = model(inputs)
          train_loss = criterion(output, targets)
          
          # measure accuracy and record loss
          acc1, acc5 = accuracy(output, targets, topk=(1, 5))
          losses.update(train_loss.item(), inputs.size(0))
          top1.update(acc1[0], inputs.size(0))
          top5.update(acc5[0], inputs.size(0))
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
          train_loss.backward()
  #         if args.arch == 'ResNet50':
          for p in model.parameters():
              nn.utils.clip_grad_norm_(p, 1.)
          optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if i % args.print_freq == 0:
              print('Epoch: [{0}][{1}/{2}]\t'
                    'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                    'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                    'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                    'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                    'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                     epoch, i, len(train_loader), batch_time=batch_time,
                     data_time=data_time, loss=losses, top1=top1, top5=top5))
  
  
  def validate(val_loader, model, criterion, args):
      batch_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to evaluate mode
      model.eval()
  
      with torch.no_grad():
          end = time.time()
          for i, (input, target) in enumerate(val_loader):
              if args.gpu is not None:
                  input = input.cuda(args.gpu, non_blocking=True)
              target = target.cuda(args.gpu, non_blocking=True)
  
              # compute output
              output = model(input)
              loss = criterion(output, target)
  
              # measure accuracy and record loss
              acc1, acc5 = accuracy(output, target, topk=(1, 5))
              top1.update(acc1[0].cpu().item(), input.size(0))
              top5.update(acc5[0].cpu().item(), input.size(0))
  
              # measure elapsed time
              batch_time.update(time.time() - end)
              end = time.time()
  
              if i % args.print_freq == 0:
                  print('Test: [{0}/{1}]\t'
                        'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                        'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                        'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                        'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                         i, len(val_loader), batch_time=batch_time, loss=losses,
                         top1=top1, top5=top5))
  
          print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
                .format(top1=top1, top5=top5))
  
      return top1.avg, top5.avg
  
  
  def adjust_learning_rate(optimizer, epoch, args):
      if epoch == 0:
          for i, param_group in enumerate(optimizer.param_groups):
              lr = base_learning_rate * 0.1
              param_group['lr'] = lr
      else:
          for i, param_group in enumerate(optimizer.param_groups):
              lr = base_learning_rate * (0.1 ** (epoch // 30))
              param_group['lr'] = lr
  
  
  if __name__ == '__main__':
      main()
      
      
  
  