code: def main():
      utils.setup_default_logging()
      args, args_text = _parse_args()
  
      if torch.cuda.is_available():
          torch.backends.cuda.matmul.allow_tf32 = True
          torch.backends.cudnn.benchmark = True
  
      args.prefetcher = not args.no_prefetcher
      args.grad_accum_steps = max(1, args.grad_accum_steps)
      device = utils.init_distributed_device(args)
      if args.distributed:
          _logger.info(
              "Training in distributed mode with multiple processes, 1 device per process."
              f"Process {args.rank}, total {args.world_size}, device {args.device}."
          )
      else:
          _logger.info(
              f"Training with a single process on 1 device ({args.device})."
          )
      assert args.rank >= 0
  
      # resolve AMP arguments based on PyTorch / Apex availability
      use_amp = None
      amp_dtype = torch.float16
      if args.amp:
          if args.amp_impl == "apex":
              assert (
                  has_apex
              ), "AMP impl specified as APEX but APEX is not installed."
              use_amp = "apex"
              assert args.amp_dtype == "float16"
          else:
              assert (
                  has_native_amp
              ), "Please update PyTorch to a version with native AMP (or use APEX)."
              use_amp = "native"
              assert args.amp_dtype in ("float16", "bfloat16")
          if args.amp_dtype == "bfloat16":
              amp_dtype = torch.bfloat16
  
      utils.random_seed(args.seed, args.rank)
  
      if args.fuser:
          utils.set_jit_fuser(args.fuser)
      if args.fast_norm:
          set_fast_norm()
  
      in_chans = 3
      if args.in_chans is not None:
          in_chans = args.in_chans
      elif args.input_size is not None:
          in_chans = args.input_size[0]
  
      factory_kwargs = {}
      if args.pretrained_path:
          # merge with pretrained_cfg of model, 'file' has priority over 'url' and 'hf_hub'.
          factory_kwargs["pretrained_cfg_overlay"] = dict(
              file=args.pretrained_path,
              num_classes=-1,  # force head adaptation
          )
  
      model = create_model(
          args.model,
          pretrained=args.pretrained,
          in_chans=in_chans,
          num_classes=args.num_classes,
          drop_rate=args.drop,
          drop_path_rate=args.drop_path,
          drop_block_rate=args.drop_block,
          global_pool=args.gp,
          bn_momentum=args.bn_momentum,
          bn_eps=args.bn_eps,
          scriptable=args.torchscript,
          checkpoint_path=args.initial_checkpoint,
          **factory_kwargs,
          **args.model_kwargs,
      )
      if args.head_init_scale is not None:
          with torch.no_grad():
              model.get_classifier().weight.mul_(args.head_init_scale)
              model.get_classifier().bias.mul_(args.head_init_scale)
      if args.head_init_bias is not None:
          nn.init.constant_(
              model.get_classifier().bias, args.head_init_bias
          )
  
      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.grad_checkpointing:
          model.set_grad_checkpointing(enable=True)
  
      if utils.is_primary(args):
          _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=utils.is_primary(args)
      )
  
      # 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.to(device=device)
      if args.channels_last:
          model.to(memory_format=torch.channels_last)
  
      # setup synchronized BatchNorm for distributed training
      if args.distributed and args.sync_bn:
          args.dist_bn = ""  # disable dist_bn when sync BN active
          assert not args.split_bn
          if has_apex and use_amp == "apex":
              # Apex SyncBN used with Apex AMP
              # WARNING this won't currently work with models using BatchNormAct2d
              model = convert_syncbn_model(model)
          else:
              model = convert_sync_batchnorm(model)
          if utils.is_primary(args):
              _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 args.torchcompile
          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)
  
      if not args.lr:
          global_batch_size = (
              args.batch_size * args.world_size * args.grad_accum_steps
          )
          batch_ratio = global_batch_size / args.lr_base_size
          if not args.lr_base_scale:
              on = args.opt.lower()
              args.lr_base_scale = (
                  "sqrt"
                  if any([o in on for o in ("ada", "lamb")])
                  else "linear"
              )
          if args.lr_base_scale == "sqrt":
              batch_ratio = batch_ratio**0.5
          args.lr = args.lr_base * batch_ratio
          if utils.is_primary(args):
              _logger.info(
                  f"Learning rate ({args.lr}) calculated from base learning rate ({args.lr_base}) "
                  f"and effective global batch size ({global_batch_size}) with {args.lr_base_scale} scaling."
              )
  
      optimizer = create_optimizer_v2(
          model,
          **optimizer_kwargs(cfg=args),
          **args.opt_kwargs,
      )
  
      # setup automatic mixed-precision (AMP) loss scaling and op casting
      amp_autocast = suppress  # do nothing
      loss_scaler = None
      if use_amp == "apex":
          assert device.type == "cuda"
          model, optimizer = amp.initialize(
              model, optimizer, opt_level="O1"
          )
          loss_scaler = ApexScaler()
          if utils.is_primary(args):
              _logger.info(
                  "Using NVIDIA APEX AMP. Training in mixed precision."
              )
      elif use_amp == "native":
          try:
              amp_autocast = partial(
                  torch.autocast,
                  device_type=device.type,
                  dtype=amp_dtype,
              )
          except (AttributeError, TypeError):
              # fallback to CUDA only AMP for PyTorch < 1.10
              assert device.type == "cuda"
              amp_autocast = torch.cuda.amp.autocast
          if device.type == "cuda" and amp_dtype == torch.float16:
              # loss scaler only used for float16 (half) dtype, bfloat16 does not need it
              loss_scaler = NativeScaler()
          if utils.is_primary(args):
              _logger.info(
                  "Using native Torch AMP. Training in mixed precision."
              )
      else:
          if utils.is_primary(args):
              _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=utils.is_primary(args),
          )
  
      # 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 DDP wrapper
          model_ema = utils.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 == "apex":
              # Apex DDP preferred unless native amp is activated
              if utils.is_primary(args):
                  _logger.info(
                      "Using NVIDIA APEX DistributedDataParallel."
                  )
              model = ApexDDP(model, delay_allreduce=True)
          else:
              if utils.is_primary(args):
                  _logger.info(
                      "Using native Torch DistributedDataParallel."
                  )
              model = NativeDDP(
                  model,
                  device_ids=[device],
                  broadcast_buffers=not args.no_ddp_bb,
              )
          # NOTE: EMA model does not need to be wrapped by DDP
  
      if args.torchcompile:
          # torch compile should be done after DDP
          assert (
              has_compile
          ), "A version of torch w/ torch.compile() is required for --compile, possibly a nightly."
          model = torch.compile(model, backend=args.torchcompile)
  
      # create the train and eval datasets
      if args.data and not args.data_dir:
          args.data_dir = args.data
      if args.input_img_mode is None:
          input_img_mode = (
              "RGB" if data_config["input_size"][0] == 3 else "L"
          )
      else:
          input_img_mode = args.input_img_mode
  
      dataset_train = create_dataset(
          args.dataset,
          root=args.data_dir,
          split=args.train_split,
          is_training=True,
          class_map=args.class_map,
          download=args.dataset_download,
          batch_size=args.batch_size,
          seed=args.seed,
          repeats=args.epoch_repeats,
          input_img_mode=input_img_mode,
          input_key=args.input_key,
          target_key=args.target_key,
          num_samples=args.train_num_samples,
      )
  
      if args.val_split:
          dataset_eval = create_dataset(
              args.dataset,
              root=args.data_dir,
              split=args.val_split,
              is_training=False,
              class_map=args.class_map,
              download=args.dataset_download,
              batch_size=args.batch_size,
              input_img_mode=input_img_mode,
              input_key=args.input_key,
              target_key=args.target_key,
              num_samples=args.val_num_samples,
          )
  
      # setup mixup / cutmix
      collate_fn = None
      mixup_fn = None
      mixup_active = (
          args.mixup > 0
          or args.cutmix > 0.0
          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,
          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,
          color_jitter_prob=args.color_jitter_prob,
          grayscale_prob=args.grayscale_prob,
          gaussian_blur_prob=args.gaussian_blur_prob,
          auto_augment=args.aa,
          num_aug_repeats=args.aug_repeats,
          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,
          device=device,
          use_prefetcher=args.prefetcher,
          use_multi_epochs_loader=args.use_multi_epochs_loader,
          worker_seeding=args.worker_seeding,
      )
  
      loader_eval = None
      if args.val_split:
          eval_workers = args.workers
          if args.distributed and (
              "tfds" in args.dataset or "wds" in args.dataset
          ):
              # FIXME reduces validation padding issues when using TFDS, WDS w/ workers and distributed training
              eval_workers = min(2, args.workers)
          loader_eval = create_loader(
              dataset_eval,
              input_size=data_config["input_size"],
              batch_size=args.validation_batch_size or args.batch_size,
              is_training=False,
              interpolation=data_config["interpolation"],
              mean=data_config["mean"],
              std=data_config["std"],
              num_workers=eval_workers,
              distributed=args.distributed,
              crop_pct=data_config["crop_pct"],
              pin_memory=args.pin_mem,
              device=device,
              use_prefetcher=args.prefetcher,
          )
  
      # setup loss function
      if args.jsd_loss:
          assert (
              num_aug_splits > 1
          )  # JSD only valid with aug splits set
          train_loss_fn = JsdCrossEntropy(
              num_splits=num_aug_splits, smoothing=args.smoothing
          )
      elif mixup_active:
          # smoothing is handled with mixup target transform which outputs sparse, soft targets
          if args.bce_loss:
              train_loss_fn = BinaryCrossEntropy(
                  target_threshold=args.bce_target_thresh,
                  sum_classes=args.bce_sum,
                  pos_weight=args.bce_pos_weight,
              )
          else:
              train_loss_fn = SoftTargetCrossEntropy()
      elif args.smoothing:
          if args.bce_loss:
              train_loss_fn = BinaryCrossEntropy(
                  smoothing=args.smoothing,
                  target_threshold=args.bce_target_thresh,
                  sum_classes=args.bce_sum,
                  pos_weight=args.bce_pos_weight,
              )
          else:
              train_loss_fn = LabelSmoothingCrossEntropy(
                  smoothing=args.smoothing
              )
      else:
          train_loss_fn = nn.CrossEntropyLoss()
      train_loss_fn = train_loss_fn.to(device=device)
      validate_loss_fn = nn.CrossEntropyLoss().to(device=device)
  
      # setup checkpoint saver and eval metric tracking
      eval_metric = (
          args.eval_metric if loader_eval is not None else "loss"
      )
      decreasing_metric = eval_metric == "loss"
      best_metric = None
      best_epoch = None
      saver = None
      output_dir = None
      if utils.is_primary(args):
          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 = utils.get_outdir(
              args.output if args.output else "./output/train", exp_name
          )
          saver = utils.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_metric,
              max_history=args.checkpoint_hist,
          )
          with open(os.path.join(output_dir, "args.yaml"), "w") as f:
              f.write(args_text)
  
      if utils.is_primary(args) and args.log_wandb:
          if has_wandb:
              wandb.init(project=args.experiment, config=args)
          else:
              _logger.warning(
                  "You've requested to log metrics to wandb but package not found. "
                  "Metrics not being logged to wandb, try `pip install wandb`"
              )
  
      # setup learning rate schedule and starting epoch
      updates_per_epoch = (
          len(loader_train) + args.grad_accum_steps - 1
      ) // args.grad_accum_steps
      lr_scheduler, num_epochs = create_scheduler_v2(
          optimizer,
          **scheduler_kwargs(args, decreasing_metric=decreasing_metric),
          updates_per_epoch=updates_per_epoch,
      )
      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:
          if args.sched_on_updates:
              lr_scheduler.step_update(start_epoch * updates_per_epoch)
          else:
              lr_scheduler.step(start_epoch)
  
      if utils.is_primary(args):
          _logger.info(
              f'Scheduled epochs: {num_epochs}. LR stepped per {"epoch" if lr_scheduler.t_in_epochs else "update"}.'
          )
  
      results = []
      try:
          for epoch in range(start_epoch, num_epochs):
              if hasattr(dataset_train, "set_epoch"):
                  dataset_train.set_epoch(epoch)
              elif 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 utils.is_primary(args):
                      _logger.info(
                          "Distributing BatchNorm running means and vars"
                      )
                  utils.distribute_bn(
                      model, args.world_size, args.dist_bn == "reduce"
                  )
  
              if loader_eval is not None:
                  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",
                      ):
                          utils.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
              else:
                  eval_metrics = None
  
              if output_dir is not None:
                  lrs = [
                      param_group["lr"]
                      for param_group in optimizer.param_groups
                  ]
                  utils.update_summary(
                      epoch,
                      train_metrics,
                      eval_metrics,
                      filename=os.path.join(output_dir, "summary.csv"),
                      lr=sum(lrs) / len(lrs),
                      write_header=best_metric is None,
                      log_wandb=args.log_wandb and has_wandb,
                  )
  
              if eval_metrics is not None:
                  latest_metric = eval_metrics[eval_metric]
              else:
                  latest_metric = train_metrics[eval_metric]
  
              if saver is not None:
                  # save proper checkpoint with eval metric
                  best_metric, best_epoch = saver.save_checkpoint(
                      epoch, metric=latest_metric
                  )
  
              if lr_scheduler is not None:
                  # step LR for next epoch
                  lr_scheduler.step(epoch + 1, latest_metric)
  
              results.append(
                  {
                      "epoch": epoch,
                      "train": train_metrics,
                      "validation": eval_metrics,
                  }
              )
  
      except KeyboardInterrupt:
          pass
  
      results = {"all": results}
      if best_metric is not None:
          results["best"] = results["all"][best_epoch - start_epoch]
          _logger.info(
              "*** Best metric: {0} (epoch {1})".format(
                  best_metric, best_epoch
              )
          )
      print(f"--result\n{json.dumps(results, indent=4)}")
  
imports: import argparse
  import json
  import logging
  import os
  import time
  from collections import OrderedDict
  from contextlib import suppress
  from datetime import datetime
  from functools import partial
  import torch
  import torch.nn
  import torchvision.utils
  import yaml
  from torch.nn.parallel import DistributedDataParallel
  from timm import utils
  from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
  from timm.layers import convert_splitbn_model, convert_sync_batchnorm, set_fast_norm
  from timm.loss import JsdCrossEntropy, SoftTargetCrossEntropy, BinaryCrossEntropy, LabelSmoothingCrossEntropy
  from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint, model_parameters
  from timm.optim import create_optimizer_v2, optimizer_kwargs
  from timm.scheduler import create_scheduler_v2, scheduler_kwargs
  from timm.utils import ApexScaler, NativeScaler
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types