text: # Copyright 2020 Lorna Authors. All Rights Reserved.
  # Licensed under the Apache License, Version 2.0 (the "License");
  #   you may not use this file except in compliance with the License.
  #   You may obtain a copy of the License at
  #
  #       http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  """
  Evaluate on ImageNet. Note that at the moment, training is not implemented
  that being said, evaluation is working.
  """
  import argparse
  import os
  import random
  import warnings
  
  import PIL.Image
  import torch
  import torch.backends.cudnn as cudnn
  import torch.distributed as dist
  import torch.multiprocessing as mp
  import torch.nn as nn
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.datasets as datasets
  import torchvision.models as models
  import torchvision.transforms as transforms
  
  from alexnet_pytorch import AlexNet
  from .utils import adjust_learning_rate
  from .utils import save_checkpoint
  from .utils import train
  from .utils import validate
  
  mixed_precision = True
  try:
      from apex import amp
  except:
      mixed_precision = False
      warnings.warn("Warning: Apex tool not install.")
  
  parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
  parser.add_argument('data', metavar='DIR', default='data',
                      help='path to dataset')
  parser.add_argument('-a', '--arch', metavar='ARCH', default='alexnet',
                      help='model architecture (default: alexnet)')
  parser.add_argument('-j', '--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=128, type=int,
                      metavar='N',
                      help='mini-batch size (default: 128), 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=0.01, 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=5e-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('--opt_level', default="O1", type=str,
                      help="Choose which accuracy to train. (default: 'O1')")
  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=0, type=int,
                      help='GPU id to use.')
  parser.add_argument('--num_classes', type=int, default=1000,
                      help="number of dataset category.")
  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(f"Use GPU: {args.gpu} for training!")
  
      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 'alexnet' in args.arch:  # NEW
          if args.pretrained:
              model = AlexNet.from_pretrained(args.arch, args.num_classes)
              print(f"=> using pre-trained model '{args.arch}'")
          else:
              print(f"=> creating model '{args.arch}'")
              model = AlexNet.from_name(args.arch)
      else:
          if args.pretrained:
              print(f"=> using pre-trained model '{args.arch}'")
              model = models.__dict__[args.arch](pretrained=True)
          else:
              print(f"=> creating model '{args.arch}'")
              model = models.__dict__[args.arch]()
  
      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'):
              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)
      if mixed_precision:
          model, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level)
  
      # optionally resume from a checkpoint
      if args.resume:
          if os.path.isfile(args.resume):
              print(f"=> loading checkpoint '{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 = best_acc1.to(args.gpu)
              model.load_state_dict(checkpoint['state_dict'])
              optimizer.load_state_dict(checkpoint['optimizer'])
              amp.load_state_dict(checkpoint['amp'])
              print(f"=> loaded checkpoint '{args.resume}' (epoch {checkpoint['epoch']})")
          else:
              print(f"=> no checkpoint found at '{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,
          transform=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 'alexnet' in args.arch:
          image_size = AlexNet.get_image_size(args.arch)
          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)
      else:
          val_transforms = transforms.Compose([
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              normalize,
          ])
          print('Using image size', 224)
  
      val_loader = torch.utils.data.DataLoader(
          datasets.ImageFolder(valdir, val_transforms),
          batch_size=args.batch_size, shuffle=False,
          num_workers=args.workers, pin_memory=True)
  
      if args.evaluate:
          top1 = validate(val_loader, model, criterion, args)
          with open('res.txt', 'w') as f:
              print(f"Acc@1: {top1}", file=f)
          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 % 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(),
                  'amp': amp.state_dict(),
              }, is_best)
  
  
  if __name__ == '__main__':
      main()
  