text: import argparse
  import builtins
  import os
  import random
  import shutil
  import time
  import warnings
  
  import wandb
  
  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 tensorboard_logger as tb_logger
  
  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='resnet50',
                      choices=model_names,
                      help='model architecture: ' +
                          ' | '.join(model_names) +
                          ' (default: resnet50)')
  parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
                      help='number of data loading workers (default: 32)')
  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=5., type=float,
                      metavar='LR', help='initial learning rate', dest='lr')
  parser.add_argument('--schedule', default=[60, 80], nargs='*', type=int,
                      help='learning rate schedule (when to drop lr by a ratio)')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                      help='momentum')
  parser.add_argument('--wd', '--weight-decay', default=0., type=float,
                      metavar='W', help='weight decay (default: 0.)',
                      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')
  parser.add_argument('--exp-name', default='', type=str,
                              help='experiment name')
  
  parser.add_argument('--pretrained', default='', type=str,
                      help='path to pretrained checkpoint')
  parser.add_argument('--id', type=str, default='')
  
  parser.add_argument('--imagenet-supervised', action='store_true',
                      help='running with model trained on imagenet supervised from pretrained location')
  
  
  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
      
      args.tb_folder = 'Linear_eval/{}_tensorboard'.format(args.id)
      if not os.path.isdir(args.tb_folder):
          os.makedirs(args.tb_folder)
          
      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):
      args.gpu = gpu
      
      if args.gpu is not None:
          print("Use GPU: {} for training".format(args.gpu))
          
      # suppress printing if not master
      # if args.multiprocessing_distributed and args.gpu != 0:
      #     def print_pass(*args):
      #         pass
      #     builtins.print = print_pass
          
      # 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)
  
      # if torch.distributed.get_rank() == 0:
      wandb.init(entity='aai', project='Representation Learning', notes='linear classification from supervised imagenet features', name = "LinearEval - " + args.exp_name)
      wandb.config.update(args)
  
      # create model
      print("=> creating model '{}'".format(args.arch))
      if not args.imagenet_supervised:
          model = models.__dict__[args.arch](num_classes=10) if args.data == 'cifar' else models.__dict__[args.arch]()
      else:
          print('=> loading imagenet pretrained resnet50')
          model = models.__dict__[args.arch](pretrained=True) if args.data == 'cifar' else models.__dict__[args.arch](pretrained=True)
          if args.data == 'cifar':
              model.fc = nn.Linear(in_features=2048, out_features=10, bias=True)
      
      # freeze all layers but the last fc
      for name, param in model.named_parameters():
          if name not in ['fc.weight', 'fc.bias']:
              param.requires_grad = False
  
      # init the fc layer
      model.fc.weight.data.normal_(mean=0.0, std=0.01)
      model.fc.bias.data.zero_()
      
      # if args.gpu==0:
      #     logger = tb_logger.Logger(logdir=args.tb_folder, flush_secs=2)
      # else:
      #     logger = None
          
      # load from pre-trained, before DistributedDataParallel constructor
      if args.pretrained and not args.imagenet_supervised:
          if os.path.isfile(args.pretrained):
              print("=> loading checkpoint '{}'".format(args.pretrained))
              checkpoint = torch.load(args.pretrained, map_location="cpu")
  
              # rename pre-trained keys
              state_dict = checkpoint['state_dict']
  
              # print(state_dict.keys())
              for k in list(state_dict.keys()):
                  # retain only encoder_q up to before the embedding layer
                  # if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
                  #     # remove prefix
                  #     state_dict[k[len("module.encoder_q."):]] = state_dict[k]
                  if k.startswith('encoder_q') and not k.startswith('encoder_q.fc'):
                      # remove prefix
                      state_dict[k[len("encoder_q."):]] = state_dict[k]
                  # delete renamed or unused k
                  del state_dict[k]
  
              args.start_epoch = 0
              msg = model.load_state_dict(state_dict, strict=False)
              assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}
  
              print("=> loaded pre-trained model '{}'".format(args.pretrained))
          else:
              print("=> no checkpoint found at '{}'".format(args.pretrained))
  
      # 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 - 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)
      if 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)
  
      # optimize only the linear classifier
      parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
      assert len(parameters) == 2  # fc.weight, fc.bias
      optimizer = torch.optim.SGD(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']
              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
  
      if args.data == 'cifar':
          print(model)
          print('Training on cifar10...')
          normalize = transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
                                          std=[0.247, 0.243, 0.261])
  
          train_dataset = datasets.CIFAR10('data', train=True, 
              transform=transforms.Compose([
                  transforms.RandomResizedCrop(32), #was 224 - When using a cifar model, the images should be size 32 oops
                  transforms.RandomHorizontalFlip(),
                  transforms.ToTensor(),
                  normalize,
              ]),
              download=True)
          
  
  
          val_dataset = datasets.CIFAR10('data', train=False, 
              transform=transforms.Compose([
                  transforms.Resize(32), #was 256
                  transforms.CenterCrop(32), #was 224
                  transforms.ToTensor(),
                  normalize,
              ]),
              download=True)
      else:
  
          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,
              ]))
  
          val_dataset = datasets.ImageFolder(valdir, transforms.Compose([
                  transforms.Resize(256),
                  transforms.CenterCrop(224),
                  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(
          val_dataset,
          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 = validate(val_loader, model, criterion, args, epoch)
  
          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(),
                  'optimizer' : optimizer.state_dict(),
              })
              if epoch == args.start_epoch and not args.imagenet_supervised:
                  sanity_check(model.state_dict(), args.pretrained)
  
  
  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))
  
      num_iters = len(train_loader)
  
      """
      Switch to eval mode:
      Under the protocol of linear classification on frozen features/models,
      it is not legitimate to change any part of the pre-trained model.
      BatchNorm in train mode may revise running mean/std (even if it receives
      no gradient), which are part of the model parameters too.
      """
      model.eval()
  
      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)
          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()
          loss.backward()
          optimizer.step()
  
          # if torch.distributed.get_rank() == 0:
          # wandb.log({'Linear - Train Loss': loss.cpu().item(), 
          #                     'Linear - Train Acc@1': acc1.cpu().item(),
          #                     'Linear - Train Acc@5': acc5.cpu().item()})
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if i % args.print_freq == 0:
              progress.display(i)
              
      wandb.log({'Linear - Train Loss': losses.avg, 
                              'Linear - Train Acc@1': top1.avg,
                              'Linear - Train Acc@5': top5.avg})
  
  
  def validate(val_loader, model, criterion, args, epoch):
      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: ')
  
      num_iters = len(val_loader)
  
      # 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)
              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))
  
              # if torch.distributed.get_rank() == 0:
              #     
              # wandb.log({'Linear - Val. Loss': loss.cpu().item(), 
              #                 'Linear - Val. Acc@1': acc1.cpu().item(),
              #                 'Linear - Val. Acc@5': acc5.cpu().item()})
  
              # 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))
          
          wandb.log({'Linear - Val. Loss': losses.avg, 
                  'Linear - Val. Acc@1': top1.avg,
                  'Linear - Val. Acc@5': top5.avg})
  
      # if args.gpu==0:    
      #     logger.log_value('test_acc', top1.avg, epoch)
      #     logger.log_value('test_acc5', top5.avg, epoch)
      return top1.avg
  
  
  def save_checkpoint(state, filename='checkpoint.pth.tar'):
      torch.save(state, filename)
  
  
  def sanity_check(state_dict, pretrained_weights):
      """
      Linear classifier should not change any weights other than the linear layer.
      This sanity check asserts nothing wrong happens (e.g., BN stats updated).
      """
      print("=> loading '{}' for sanity check".format(pretrained_weights))
      checkpoint = torch.load(pretrained_weights, map_location="cpu")
      state_dict_pre = checkpoint['state_dict']
  
      for k in list(state_dict.keys()):
          # only ignore fc layer
          if 'fc.weight' in k or 'fc.bias' in k:
              continue
          # name in pretrained model
          # k_pre = 'module.encoder_q.' + k[len('module.'):] \
          #     if k.startswith('module.') else 'module.encoder_q.' + k
          k_pre = 'encoder_q.' + k[len(''):] \
              if k.startswith('module.') else 'encoder_q.' + k
  
          assert ((state_dict[k].cpu() == state_dict_pre[k_pre]).all()), \
              '{} is changed in linear classifier training.'.format(k)
  
      print("=> sanity check passed.")
      
      # state_dict_pre[k_pre]).all()), \
      #         '{} is changed in linear classifier training.'.format(k)
  
      # print("=> sanity check passed.")
  
  
  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):
      """Decay the learning rate based on schedule"""
      lr = args.lr
      for milestone in args.schedule:
          lr *= 0.1 if epoch >= milestone else 1.
      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].view(-1).float().sum(0, keepdim=True)
              res.append(correct_k.mul_(100.0 / batch_size))
          return res
  
  
  if __name__ == '__main__':
      main()