hunk.code_window: torch>=1.4.0
   torchvision>=0.5.0
   pyyaml
   wandb
  
hunk.labels: keep keep replace replace
hunk.after_edit: pyyaml
  
hunk.file_path: requirements.txt
hunk.type: replace
file: #!/usr/bin/env python3
  """ ImageNet Training Script
  
  This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
  training results with some of the latest networks and training techniques. It favours canonical PyTorch
  and standard Python style over trying to be able to 'do it all.' That said, it offers quite a few speed
  and training result improvements over the usual PyTorch example scripts. Repurpose as you see fit.
  
  This script was started from an early version of the PyTorch ImageNet example
  (https://github.com/pytorch/examples/tree/master/imagenet)
  
  NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples
  (https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
  
  Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
  """
  import argparse
  import time
  import yaml
  import os
  import logging
  from collections import OrderedDict
  from contextlib import suppress
  from datetime import datetime
  
  import wandb 
  
  import torch
  import torch.nn as nn
  import torchvision.utils
  from torch.nn.parallel import DistributedDataParallel as NativeDDP
  
  from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
  from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint,\
      convert_splitbn_model, model_parameters
  from timm.utils import *
  from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
  from timm.optim import create_optimizer_v2, optimizer_kwargs
  from timm.scheduler import create_scheduler
  from timm.utils import ApexScaler, NativeScaler
  
  try:
      from apex import amp
      from apex.parallel import DistributedDataParallel as ApexDDP
      from apex.parallel import convert_syncbn_model
      has_apex = True
  except ImportError:
      has_apex = False
  
  has_native_amp = False
  try:
      if getattr(torch.cuda.amp, 'autocast') is not None:
          has_native_amp = True
  except AttributeError:
      pass
  
  torch.backends.cudnn.benchmark = True
  _logger = logging.getLogger('train')
  
  # The first arg parser parses out only the --config argument, this argument is used to
  # load a yaml file containing key-values that override the defaults for the main parser below
  config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
  parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
                      help='YAML config file specifying default arguments')
  
  
  parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
  
  # Dataset / Model parameters
  parser.add_argument('data_dir', metavar='DIR',
                      help='path to dataset')
  parser.add_argument('--dataset', '-d', metavar='NAME', default='',
                      help='dataset type (default: ImageFolder/ImageTar if empty)')
  parser.add_argument('--train-split', metavar='NAME', default='train',
                      help='dataset train split (default: train)')
  parser.add_argument('--val-split', metavar='NAME', default='validation',
                      help='dataset validation split (default: validation)')
  parser.add_argument('--model', default='resnet101', type=str, metavar='MODEL',
                      help='Name of model to train (default: "countception"')
  parser.add_argument('--pretrained', action='store_true', default=False,
                      help='Start with pretrained version of specified network (if avail)')
  parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
                      help='Initialize model from this checkpoint (default: none)')
  parser.add_argument('--resume', default='', type=str, metavar='PATH',
                      help='Resume full model and optimizer state from checkpoint (default: none)')
  parser.add_argument('--no-resume-opt', action='store_true', default=False,
                      help='prevent resume of optimizer state when resuming model')
  parser.add_argument('--num-classes', type=int, default=None, metavar='N',
                      help='number of label classes (Model default if None)')
  parser.add_argument('--gp', default=None, type=str, metavar='POOL',
                      help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
  parser.add_argument('--img-size', type=int, default=None, metavar='N',
                      help='Image patch size (default: None => model default)')
  parser.add_argument('--input-size', default=None, nargs=3, type=int,
                      metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
  parser.add_argument('--crop-pct', default=None, type=float,
                      metavar='N', help='Input image center crop percent (for validation only)')
  parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                      help='Override mean pixel value of dataset')
  parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
                      help='Override std deviation of of dataset')
  parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
                      help='Image resize interpolation type (overrides model)')
  parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
                      help='input batch size for training (default: 32)')
  parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N',
                      help='ratio of validation batch size to training batch size (default: 1)')
  
  # Optimizer parameters
  parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
                      help='Optimizer (default: "sgd"')
  parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
                      help='Optimizer Epsilon (default: None, use opt default)')
  parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
                      help='Optimizer Betas (default: None, use opt default)')
  parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                      help='Optimizer momentum (default: 0.9)')
  parser.add_argument('--weight-decay', type=float, default=0.0001,
                      help='weight decay (default: 0.0001)')
  parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
                      help='Clip gradient norm (default: None, no clipping)')
  parser.add_argument('--clip-mode', type=str, default='norm',
                      help='Gradient clipping mode. One of ("norm", "value", "agc")')
  
  
  # Learning rate schedule parameters
  parser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER',
                      help='LR scheduler (default: "step"')
  parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                      help='learning rate (default: 0.01)')
  parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                      help='learning rate noise on/off epoch percentages')
  parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                      help='learning rate noise limit percent (default: 0.67)')
  parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                      help='learning rate noise std-dev (default: 1.0)')
  parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
                      help='learning rate cycle len multiplier (default: 1.0)')
  parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
                      help='learning rate cycle limit')
  parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
                      help='warmup learning rate (default: 0.0001)')
  parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                      help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
  parser.add_argument('--epochs', type=int, default=200, metavar='N',
                      help='number of epochs to train (default: 2)')
  parser.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
                      help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
  parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
                      help='manual epoch number (useful on restarts)')
  parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
                      help='epoch interval to decay LR')
  parser.add_argument('--warmup-epochs', type=int, default=3, metavar='N',
                      help='epochs to warmup LR, if scheduler supports')
  parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
                      help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
  parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
                      help='patience epochs for Plateau LR scheduler (default: 10')
  parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                      help='LR decay rate (default: 0.1)')
  
  # Augmentation & regularization parameters
  parser.add_argument('--no-aug', action='store_true', default=False,
                      help='Disable all training augmentation, override other train aug args')
  parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
                      help='Random resize scale (default: 0.08 1.0)')
  parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
                      help='Random resize aspect ratio (default: 0.75 1.33)')
  parser.add_argument('--hflip', type=float, default=0.5,
                      help='Horizontal flip training aug probability')
  parser.add_argument('--vflip', type=float, default=0.,
                      help='Vertical flip training aug probability')
  parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                      help='Color jitter factor (default: 0.4)')
  parser.add_argument('--aa', type=str, default=None, metavar='NAME',
                      help='Use AutoAugment policy. "v0" or "original". (default: None)'),
  parser.add_argument('--aug-splits', type=int, default=0,
                      help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
  parser.add_argument('--jsd', action='store_true', default=False,
                      help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
  parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                      help='Random erase prob (default: 0.)')
  parser.add_argument('--remode', type=str, default='const',
                      help='Random erase mode (default: "const")')
  parser.add_argument('--recount', type=int, default=1,
                      help='Random erase count (default: 1)')
  parser.add_argument('--resplit', action='store_true', default=False,
                      help='Do not random erase first (clean) augmentation split')
  parser.add_argument('--mixup', type=float, default=0.0,
                      help='mixup alpha, mixup enabled if > 0. (default: 0.)')
  parser.add_argument('--cutmix', type=float, default=0.0,
                      help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
  parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
                      help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
  parser.add_argument('--mixup-prob', type=float, default=1.0,
                      help='Probability of performing mixup or cutmix when either/both is enabled')
  parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
                      help='Probability of switching to cutmix when both mixup and cutmix enabled')
  parser.add_argument('--mixup-mode', type=str, default='batch',
                      help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
  parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
                      help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
  parser.add_argument('--smoothing', type=float, default=0.1,
                      help='Label smoothing (default: 0.1)')
  parser.add_argument('--train-interpolation', type=str, default='random',
                      help='Training interpolation (random, bilinear, bicubic default: "random")')
  parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                      help='Dropout rate (default: 0.)')
  parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
                      help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
  parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
                      help='Drop path rate (default: None)')
  parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
                      help='Drop block rate (default: None)')
  
  # Batch norm parameters (only works with gen_efficientnet based models currently)
  parser.add_argument('--bn-tf', action='store_true', default=False,
                      help='Use Tensorflow BatchNorm defaults for models that support it (default: False)')
  parser.add_argument('--bn-momentum', type=float, default=None,
                      help='BatchNorm momentum override (if not None)')
  parser.add_argument('--bn-eps', type=float, default=None,
                      help='BatchNorm epsilon override (if not None)')
  parser.add_argument('--sync-bn', action='store_true',
                      help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
  parser.add_argument('--dist-bn', type=str, default='',
                      help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
  parser.add_argument('--split-bn', action='store_true',
                      help='Enable separate BN layers per augmentation split.')
  
  # Model Exponential Moving Average
  parser.add_argument('--model-ema', action='store_true', default=False,
                      help='Enable tracking moving average of model weights')
  parser.add_argument('--model-ema-force-cpu', action='store_true', default=False,
                      help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
  parser.add_argument('--model-ema-decay', type=float, default=0.9998,
                      help='decay factor for model weights moving average (default: 0.9998)')
  
  # Misc
  parser.add_argument('--seed', type=int, default=42, metavar='S',
                      help='random seed (default: 42)')
  parser.add_argument('--log-interval', type=int, default=50, metavar='N',
                      help='how many batches to wait before logging training status')
  parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
                      help='how many batches to wait before writing recovery checkpoint')
  parser.add_argument('--checkpoint-hist', type=int, default=10, metavar='N',
                      help='number of checkpoints to keep (default: 10)')
  parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
                      help='how many training processes to use (default: 1)')
  parser.add_argument('--save-images', action='store_true', default=False,
                      help='save images of input bathes every log interval for debugging')
  parser.add_argument('--amp', action='store_true', default=False,
                      help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
  parser.add_argument('--apex-amp', action='store_true', default=False,
                      help='Use NVIDIA Apex AMP mixed precision')
  parser.add_argument('--native-amp', action='store_true', default=False,
                      help='Use Native Torch AMP mixed precision')
  parser.add_argument('--channels-last', action='store_true', default=False,
                      help='Use channels_last memory layout')
  parser.add_argument('--pin-mem', action='store_true', default=False,
                      help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
  parser.add_argument('--no-prefetcher', action='store_true', default=False,
                      help='disable fast prefetcher')
  parser.add_argument('--output', default='', type=str, metavar='PATH',
                      help='path to output folder (default: none, current dir)')
  parser.add_argument('--experiment', default='', type=str, metavar='NAME',
                      help='name of train experiment, name of sub-folder for output')
  parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
                      help='Best metric (default: "top1"')
  parser.add_argument('--tta', type=int, default=0, metavar='N',
                      help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
  parser.add_argument("--local_rank", default=0, type=int)
  parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
                      help='use the multi-epochs-loader to save time at the beginning of every epoch')
  parser.add_argument('--torchscript', dest='torchscript', action='store_true',
                      help='convert model torchscript for inference')
  parser.add_argument('--log-wandb', action='store_true', default=False,
                      help='use wandb for training and validation logs')
  
  
  def _parse_args():
      # Do we have a config file to parse?
      args_config, remaining = config_parser.parse_known_args()
      if args_config.config:
          with open(args_config.config, 'r') as f:
              cfg = yaml.safe_load(f)
              parser.set_defaults(**cfg)
  
      # The main arg parser parses the rest of the args, the usual
      # defaults will have been overridden if config file specified.
      args = parser.parse_args(remaining)
  
      # Cache the args as a text string to save them in the output dir later
      args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
      return args, args_text
  
  
  def main():
      setup_default_logging()
      args, args_text = _parse_args()
      
      if args.log_wandb:
          wandb.init(project=args.experiment, config=args)
      
      args.prefetcher = not args.no_prefetcher
      args.distributed = False
      if 'WORLD_SIZE' in os.environ:
          args.distributed = int(os.environ['WORLD_SIZE']) > 1
      args.device = 'cuda:0'
      args.world_size = 1
      args.rank = 0  # global rank
      if args.distributed:
          args.device = 'cuda:%d' % args.local_rank
          torch.cuda.set_device(args.local_rank)
          torch.distributed.init_process_group(backend='nccl', init_method='env://')
          args.world_size = torch.distributed.get_world_size()
          args.rank = torch.distributed.get_rank()
          _logger.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
                       % (args.rank, args.world_size))
      else:
          _logger.info('Training with a single process on 1 GPUs.')
      assert args.rank >= 0
  
      # resolve AMP arguments based on PyTorch / Apex availability
      use_amp = None
      if args.amp:
          # `--amp` chooses native amp before apex (APEX ver not actively maintained)
          if has_native_amp:
              args.native_amp = True
          elif has_apex:
              args.apex_amp = True
      if args.apex_amp and has_apex:
          use_amp = 'apex'
      elif args.native_amp and has_native_amp:
          use_amp = 'native'
      elif args.apex_amp or args.native_amp:
          _logger.warning("Neither APEX or native Torch AMP is available, using float32. "
                          "Install NVIDA apex or upgrade to PyTorch 1.6")
  
      torch.manual_seed(args.seed + args.rank)
  
      model = create_model(
          args.model,
          pretrained=args.pretrained,
          num_classes=args.num_classes,
          drop_rate=args.drop,
          drop_connect_rate=args.drop_connect,  # DEPRECATED, use drop_path
          drop_path_rate=args.drop_path,
          drop_block_rate=args.drop_block,
          global_pool=args.gp,
          bn_tf=args.bn_tf,
          bn_momentum=args.bn_momentum,
          bn_eps=args.bn_eps,
          scriptable=args.torchscript,
          checkpoint_path=args.initial_checkpoint)
      if args.num_classes is None:
          assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
          args.num_classes = model.num_classes  # FIXME handle model default vs config num_classes more elegantly
  
      if args.local_rank == 0:
          _logger.info(
              f'Model {safe_model_name(args.model)} created, param count:{sum([m.numel() for m in model.parameters()])}')
  
      data_config = resolve_data_config(vars(args), model=model, verbose=args.local_rank == 0)
  
      # setup augmentation batch splits for contrastive loss or split bn
      num_aug_splits = 0
      if args.aug_splits > 0:
          assert args.aug_splits > 1, 'A split of 1 makes no sense'
          num_aug_splits = args.aug_splits
  
      # enable split bn (separate bn stats per batch-portion)
      if args.split_bn:
          assert num_aug_splits > 1 or args.resplit
          model = convert_splitbn_model(model, max(num_aug_splits, 2))
  
      # move model to GPU, enable channels last layout if set
      model.cuda()
      if args.channels_last:
          model = model.to(memory_format=torch.channels_last)
  
      # setup synchronized BatchNorm for distributed training
      if args.distributed and args.sync_bn:
          assert not args.split_bn
          if has_apex and use_amp != 'native':
              # Apex SyncBN preferred unless native amp is activated
              model = convert_syncbn_model(model)
          else:
              model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
          if args.local_rank == 0:
              _logger.info(
                  'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using '
                  'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.')
  
      if args.torchscript:
          assert not use_amp == 'apex', 'Cannot use APEX AMP with torchscripted model'
          assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model'
          model = torch.jit.script(model)
  
      optimizer = create_optimizer_v2(model, **optimizer_kwargs(cfg=args))
  
      # setup automatic mixed-precision (AMP) loss scaling and op casting
      amp_autocast = suppress  # do nothing
      loss_scaler = None
      if use_amp == 'apex':
          model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
          loss_scaler = ApexScaler()
          if args.local_rank == 0:
              _logger.info('Using NVIDIA APEX AMP. Training in mixed precision.')
      elif use_amp == 'native':
          amp_autocast = torch.cuda.amp.autocast
          loss_scaler = NativeScaler()
          if args.local_rank == 0:
              _logger.info('Using native Torch AMP. Training in mixed precision.')
      else:
          if args.local_rank == 0:
              _logger.info('AMP not enabled. Training in float32.')
  
      # optionally resume from a checkpoint
      resume_epoch = None
      if args.resume:
          resume_epoch = resume_checkpoint(
              model, args.resume,
              optimizer=None if args.no_resume_opt else optimizer,
              loss_scaler=None if args.no_resume_opt else loss_scaler,
              log_info=args.local_rank == 0)
  
      # setup exponential moving average of model weights, SWA could be used here too
      model_ema = None
      if args.model_ema:
          # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
          model_ema = ModelEmaV2(
              model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else None)
          if args.resume:
              load_checkpoint(model_ema.module, args.resume, use_ema=True)
  
      # setup distributed training
      if args.distributed:
          if has_apex and use_amp != 'native':
              # Apex DDP preferred unless native amp is activated
              if args.local_rank == 0:
                  _logger.info("Using NVIDIA APEX DistributedDataParallel.")
              model = ApexDDP(model, delay_allreduce=True)
          else:
              if args.local_rank == 0:
                  _logger.info("Using native Torch DistributedDataParallel.")
              model = NativeDDP(model, device_ids=[args.local_rank])  # can use device str in Torch >= 1.1
          # NOTE: EMA model does not need to be wrapped by DDP
  
      # setup learning rate schedule and starting epoch
      lr_scheduler, num_epochs = create_scheduler(args, optimizer)
      start_epoch = 0
      if args.start_epoch is not None:
          # a specified start_epoch will always override the resume epoch
          start_epoch = args.start_epoch
      elif resume_epoch is not None:
          start_epoch = resume_epoch
      if lr_scheduler is not None and start_epoch > 0:
          lr_scheduler.step(start_epoch)
  
      if args.local_rank == 0:
          _logger.info('Scheduled epochs: {}'.format(num_epochs))
  
      # create the train and eval datasets
      dataset_train = create_dataset(
          args.dataset,
          root=args.data_dir, split=args.train_split, is_training=True,
          batch_size=args.batch_size, repeats=args.epoch_repeats)
      dataset_eval = create_dataset(
          args.dataset, root=args.data_dir, split=args.val_split, is_training=False, batch_size=args.batch_size)
  
      # setup mixup / cutmix
      collate_fn = None
      mixup_fn = None
      mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
      if mixup_active:
          mixup_args = dict(
              mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
              prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
              label_smoothing=args.smoothing, num_classes=args.num_classes)
          if args.prefetcher:
              assert not num_aug_splits  # collate conflict (need to support deinterleaving in collate mixup)
              collate_fn = FastCollateMixup(**mixup_args)
          else:
              mixup_fn = Mixup(**mixup_args)
  
      # wrap dataset in AugMix helper
      if num_aug_splits > 1:
          dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)
  
      # create data loaders w/ augmentation pipeiine
      train_interpolation = args.train_interpolation
      if args.no_aug or not train_interpolation:
          train_interpolation = data_config['interpolation']
      loader_train = create_loader(
          dataset_train,
          input_size=data_config['input_size'],
          batch_size=args.batch_size,
          is_training=True,
          use_prefetcher=args.prefetcher,
          no_aug=args.no_aug,
          re_prob=args.reprob,
          re_mode=args.remode,
          re_count=args.recount,
          re_split=args.resplit,
          scale=args.scale,
          ratio=args.ratio,
          hflip=args.hflip,
          vflip=args.vflip,
          color_jitter=args.color_jitter,
          auto_augment=args.aa,
          num_aug_splits=num_aug_splits,
          interpolation=train_interpolation,
          mean=data_config['mean'],
          std=data_config['std'],
          num_workers=args.workers,
          distributed=args.distributed,
          collate_fn=collate_fn,
          pin_memory=args.pin_mem,
          use_multi_epochs_loader=args.use_multi_epochs_loader
      )
  
      loader_eval = create_loader(
          dataset_eval,
          input_size=data_config['input_size'],
          batch_size=args.validation_batch_size_multiplier * args.batch_size,
          is_training=False,
          use_prefetcher=args.prefetcher,
          interpolation=data_config['interpolation'],
          mean=data_config['mean'],
          std=data_config['std'],
          num_workers=args.workers,
          distributed=args.distributed,
          crop_pct=data_config['crop_pct'],
          pin_memory=args.pin_mem,
      )
  
      # setup loss function
      if args.jsd:
          assert num_aug_splits > 1  # JSD only valid with aug splits set
          train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda()
      elif mixup_active:
          # smoothing is handled with mixup target transform
          train_loss_fn = SoftTargetCrossEntropy().cuda()
      elif args.smoothing:
          train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda()
      else:
          train_loss_fn = nn.CrossEntropyLoss().cuda()
      validate_loss_fn = nn.CrossEntropyLoss().cuda()
  
      # setup checkpoint saver and eval metric tracking
      eval_metric = args.eval_metric
      best_metric = None
      best_epoch = None
      saver = None
      output_dir = ''
      if args.local_rank == 0:
          if args.experiment:
              exp_name = args.experiment
          else:
              exp_name = '-'.join([
                  datetime.now().strftime("%Y%m%d-%H%M%S"),
                  safe_model_name(args.model),
                  str(data_config['input_size'][-1])
              ])
          output_dir = get_outdir(args.output if args.output else './output/train', exp_name)
          decreasing = True if eval_metric == 'loss' else False
          saver = CheckpointSaver(
              model=model, optimizer=optimizer, args=args, model_ema=model_ema, amp_scaler=loss_scaler,
              checkpoint_dir=output_dir, recovery_dir=output_dir, decreasing=decreasing, max_history=args.checkpoint_hist)
          with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
              f.write(args_text)
  
      try:
          for epoch in range(start_epoch, num_epochs):
              if args.distributed and hasattr(loader_train.sampler, 'set_epoch'):
                  loader_train.sampler.set_epoch(epoch)
  
              train_metrics = train_one_epoch(
                  epoch, model, loader_train, optimizer, train_loss_fn, args,
                  lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
                  amp_autocast=amp_autocast, loss_scaler=loss_scaler, model_ema=model_ema, mixup_fn=mixup_fn)
  
              if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                  if args.local_rank == 0:
                      _logger.info("Distributing BatchNorm running means and vars")
                  distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
  
              eval_metrics = validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast)
  
              if model_ema is not None and not args.model_ema_force_cpu:
                  if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                      distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')
                  ema_eval_metrics = validate(
                      model_ema.module, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, log_suffix=' (EMA)')
                  eval_metrics = ema_eval_metrics
  
              if lr_scheduler is not None:
                  # step LR for next epoch
                  lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])
  
              update_summary(
                  epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'),
                  write_header=best_metric is None, log_wandb=args.log_wandb)
  
              if saver is not None:
                  # save proper checkpoint with eval metric
                  save_metric = eval_metrics[eval_metric]
                  best_metric, best_epoch = saver.save_checkpoint(epoch, metric=save_metric)
  
      except KeyboardInterrupt:
          pass
      if best_metric is not None:
          _logger.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))
  
  
  def train_one_epoch(
          epoch, model, loader, optimizer, loss_fn, args,
          lr_scheduler=None, saver=None, output_dir='', amp_autocast=suppress,
          loss_scaler=None, model_ema=None, mixup_fn=None):
  
      if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
          if args.prefetcher and loader.mixup_enabled:
              loader.mixup_enabled = False
          elif mixup_fn is not None:
              mixup_fn.mixup_enabled = False
  
      second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
      batch_time_m = AverageMeter()
      data_time_m = AverageMeter()
      losses_m = AverageMeter()
  
      model.train()
  
      end = time.time()
      last_idx = len(loader) - 1
      num_updates = epoch * len(loader)
      for batch_idx, (input, target) in enumerate(loader):
          last_batch = batch_idx == last_idx
          data_time_m.update(time.time() - end)
          if not args.prefetcher:
              input, target = input.cuda(), target.cuda()
              if mixup_fn is not None:
                  input, target = mixup_fn(input, target)
          if args.channels_last:
              input = input.contiguous(memory_format=torch.channels_last)
  
          with amp_autocast():
              output = model(input)
              loss = loss_fn(output, target)
  
          if not args.distributed:
              losses_m.update(loss.item(), input.size(0))
  
          optimizer.zero_grad()
          if loss_scaler is not None:
              loss_scaler(
                  loss, optimizer,
                  clip_grad=args.clip_grad, clip_mode=args.clip_mode,
                  parameters=model_parameters(model, exclude_head='agc' in args.clip_mode),
                  create_graph=second_order)
          else:
              loss.backward(create_graph=second_order)
              if args.clip_grad is not None:
                  dispatch_clip_grad(
                      model_parameters(model, exclude_head='agc' in args.clip_mode),
                      value=args.clip_grad, mode=args.clip_mode)
              optimizer.step()
  
          if model_ema is not None:
              model_ema.update(model)
  
          torch.cuda.synchronize()
          num_updates += 1
          batch_time_m.update(time.time() - end)
          if last_batch or batch_idx % args.log_interval == 0:
              lrl = [param_group['lr'] for param_group in optimizer.param_groups]
              lr = sum(lrl) / len(lrl)
  
              if args.distributed:
                  reduced_loss = reduce_tensor(loss.data, args.world_size)
                  losses_m.update(reduced_loss.item(), input.size(0))
  
              if args.local_rank == 0:
                  _logger.info(
                      'Train: {} [{:>4d}/{} ({:>3.0f}%)]  '
                      'Loss: {loss.val:>9.6f} ({loss.avg:>6.4f})  '
                      'Time: {batch_time.val:.3f}s, {rate:>7.2f}/s  '
                      '({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  '
                      'LR: {lr:.3e}  '
                      'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format(
                          epoch,
                          batch_idx, len(loader),
                          100. * batch_idx / last_idx,
                          loss=losses_m,
                          batch_time=batch_time_m,
                          rate=input.size(0) * args.world_size / batch_time_m.val,
                          rate_avg=input.size(0) * args.world_size / batch_time_m.avg,
                          lr=lr,
                          data_time=data_time_m))
  
                  if args.save_images and output_dir:
                      torchvision.utils.save_image(
                          input,
                          os.path.join(output_dir, 'train-batch-%d.jpg' % batch_idx),
                          padding=0,
                          normalize=True)
  
          if saver is not None and args.recovery_interval and (
                  last_batch or (batch_idx + 1) % args.recovery_interval == 0):
              saver.save_recovery(epoch, batch_idx=batch_idx)
  
          if lr_scheduler is not None:
              lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg)
  
          end = time.time()
          # end for
  
      if hasattr(optimizer, 'sync_lookahead'):
          optimizer.sync_lookahead()
  
      return OrderedDict([('loss', losses_m.avg)])
  
  
  def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=''):
      batch_time_m = AverageMeter()
      losses_m = AverageMeter()
      top1_m = AverageMeter()
      top5_m = AverageMeter()
  
      model.eval()
  
      end = time.time()
      last_idx = len(loader) - 1
      with torch.no_grad():
          for batch_idx, (input, target) in enumerate(loader):
              last_batch = batch_idx == last_idx
              if not args.prefetcher:
                  input = input.cuda()
                  target = target.cuda()
              if args.channels_last:
                  input = input.contiguous(memory_format=torch.channels_last)
  
              with amp_autocast():
                  output = model(input)
              if isinstance(output, (tuple, list)):
                  output = output[0]
  
              # augmentation reduction
              reduce_factor = args.tta
              if reduce_factor > 1:
                  output = output.unfold(0, reduce_factor, reduce_factor).mean(dim=2)
                  target = target[0:target.size(0):reduce_factor]
  
              loss = loss_fn(output, target)
              acc1, acc5 = accuracy(output, target, topk=(1, 5))
  
              if args.distributed:
                  reduced_loss = reduce_tensor(loss.data, args.world_size)
                  acc1 = reduce_tensor(acc1, args.world_size)
                  acc5 = reduce_tensor(acc5, args.world_size)
              else:
                  reduced_loss = loss.data
  
              torch.cuda.synchronize()
  
              losses_m.update(reduced_loss.item(), input.size(0))
              top1_m.update(acc1.item(), output.size(0))
              top5_m.update(acc5.item(), output.size(0))
  
              batch_time_m.update(time.time() - end)
              end = time.time()
              if args.local_rank == 0 and (last_batch or batch_idx % args.log_interval == 0):
                  log_name = 'Test' + log_suffix
                  _logger.info(
                      '{0}: [{1:>4d}/{2}]  '
                      'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  '
                      'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '
                      'Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  '
                      'Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(
                          log_name, batch_idx, last_idx, batch_time=batch_time_m,
                          loss=losses_m, top1=top1_m, top5=top5_m))
  
      metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])
  
      return metrics
  
  
  if __name__ == '__main__':
      main()
  