text: # Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs (https://arxiv.org/abs/2203.06717)
  # Github source: https://github.com/DingXiaoH/RepLKNet-pytorch
  # Licensed under The MIT License [see LICENSE for details]
  # Based on ConvNeXt, timm, DINO and DeiT code bases
  # https://github.com/facebookresearch/ConvNeXt
  # https://github.com/rwightman/pytorch-image-models/tree/master/timm
  # https://github.com/facebookresearch/deit/
  # https://github.com/facebookresearch/dino
  # --------------------------------------------------------'
  import argparse
  import datetime
  import numpy as np
  import time
  import torch
  import torch.backends.cudnn as cudnn
  import json
  from pathlib import Path
  from timm.data.mixup import Mixup
  from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
  from timm.utils import ModelEma
  from optim_factory import create_optimizer, RepLKNetLayerDecayValueAssigner
  from datasets import build_dataset
  from engine import train_one_epoch, evaluate
  from utils import NativeScalerWithGradNormCount as NativeScaler
  import utils
  from replknet import *
  
  
  def str2bool(v):
      """
      Converts string to bool type; enables command line 
      arguments in the format of '--arg1 true --arg2 false'
      """
      if isinstance(v, bool):
          return v
      if v.lower() in ('yes', 'true', 't', 'y', '1'):
          return True
      elif v.lower() in ('no', 'false', 'f', 'n', '0'):
          return False
      else:
          raise argparse.ArgumentTypeError('Boolean value expected.')
  
  def get_args_parser():
      parser = argparse.ArgumentParser('Training script for image classification', add_help=False)
      parser.add_argument('--batch_size', default=64, type=int,
                          help='Per GPU batch size')
      parser.add_argument('--epochs', default=300, type=int)
      parser.add_argument('--update_freq', default=1, type=int,
                          help='gradient accumulation steps')
  
      # Model parameters
      parser.add_argument('--model', default='RepLKNet-31B', type=str, metavar='MODEL',
                          help='Name of model to train')
      parser.add_argument('--drop_path', type=float, default=0, metavar='PCT',
                          help='Drop path rate (default: 0.1)')
      parser.add_argument('--input_size', default=224, type=int,
                          help='image input size')
      parser.add_argument('--test_size', default=-1, type=int,
                          help='test input size')
  
      # EMA related parameters
      parser.add_argument('--model_ema', type=str2bool, default=False)
      parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
      parser.add_argument('--model_ema_force_cpu', type=str2bool, default=False, help='')
      parser.add_argument('--model_ema_eval', type=str2bool, default=False, help='Using ema to eval during training.')
  
      # Optimization parameters
      parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
                          help='Optimizer (default: "adamw"')
      parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON',
                          help='Optimizer Epsilon (default: 1e-8)')
      parser.add_argument('--opt_betas', default=None, type=float, nargs='+', metavar='BETA',
                          help='Optimizer Betas (default: None, use opt default)')
      parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
                          help='Clip gradient norm (default: None, no clipping)')
      parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                          help='SGD momentum (default: 0.9)')
      parser.add_argument('--weight_decay', type=float, default=0.05,
                          help='weight decay (default: 0.05)')
      parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the
          weight decay. We use a cosine schedule for WD and using a larger decay by
          the end of training improves performance for ViTs.""")
  
      parser.add_argument('--lr', type=float, default=4e-3, metavar='LR',
                          help='learning rate (default: 4e-3), with total batch size 4096')
      parser.add_argument('--layer_decay', type=float, default=1.0)
      parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
                          help='lower lr bound for cyclic schedulers that hit 0 (1e-6)')
      parser.add_argument('--warmup_epochs', type=int, default=20, metavar='N',
                          help='epochs to warmup LR, if scheduler supports')
      parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
                          help='num of steps to warmup LR, will overload warmup_epochs if set > 0')
  
      # Augmentation parameters
      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='rand-m9-mstd0.5-inc1', metavar='NAME',
                          help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'),
      parser.add_argument('--smoothing', type=float, default=0.1,
                          help='Label smoothing (default: 0.1)')
      parser.add_argument('--train_interpolation', type=str, default='bicubic',
                          help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
  
      # Evaluation parameters
      parser.add_argument('--crop_pct', type=float, default=None)
  
      # * Random Erase params
      parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
                          help='Random erase prob (default: 0.25)')
      parser.add_argument('--remode', type=str, default='pixel',
                          help='Random erase mode (default: "pixel")')
      parser.add_argument('--recount', type=int, default=1,
                          help='Random erase count (default: 1)')
      parser.add_argument('--resplit', type=str2bool, default=False,
                          help='Do not random erase first (clean) augmentation split')
  
      # * Mixup params
      parser.add_argument('--mixup', type=float, default=0.8,
                          help='mixup alpha, mixup enabled if > 0.')
      parser.add_argument('--cutmix', type=float, default=1.0,
                          help='cutmix alpha, cutmix enabled if > 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"')
  
      # * Finetuning params
      parser.add_argument('--finetune', default='',
                          help='finetune from checkpoint')
      parser.add_argument('--head_init_scale', default=1.0, type=float,
                          help='classifier head initial scale, typically adjusted in fine-tuning')    # Provided by code of ConvNeXt. Not used.
      parser.add_argument('--model_key', default='model|module', type=str,
                          help='which key to load from saved state dict, usually model or model_ema')
      parser.add_argument('--model_prefix', default='', type=str)
  
      # Dataset parameters
      parser.add_argument('--data_path', default='path_to_imagenet', type=str,
                          help='dataset path')
      parser.add_argument('--eval_data_path', default=None, type=str,
                          help='dataset path for evaluation')
      parser.add_argument('--nb_classes', default=1000, type=int,
                          help='number of the classification types')
      parser.add_argument('--imagenet_default_mean_and_std', type=str2bool, default=True)
      parser.add_argument('--data_set', default='IMNET', choices=['CIFAR', 'IMNET', 'image_folder'],
                          type=str, help='ImageNet dataset path')
      parser.add_argument('--output_dir', default='',
                          help='path where to save, empty for no saving')
      parser.add_argument('--log_dir', default=None,
                          help='path where to tensorboard log')
      parser.add_argument('--device', default='cuda',
                          help='device to use for training / testing')
      parser.add_argument('--seed', default=0, type=int)
  
      parser.add_argument('--resume', default='',
                          help='resume from checkpoint')
      parser.add_argument('--auto_resume', type=str2bool, default=True)
      parser.add_argument('--save_ckpt', type=str2bool, default=True)
      parser.add_argument('--save_ckpt_freq', default=20, type=int)
      parser.add_argument('--save_ckpt_num', default=3, type=int)
  
      parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                          help='start epoch')
      parser.add_argument('--eval', type=str2bool, default=False,
                          help='Perform evaluation only')
      parser.add_argument('--with_small_kernel_merged', type=str2bool, default=False,
                          help='Merge small kernels to check the equivalency')
      parser.add_argument('--dist_eval', type=str2bool, default=True,
                          help='Enabling distributed evaluation')
      parser.add_argument('--disable_eval', type=str2bool, default=False,
                          help='Disabling evaluation during training')
      parser.add_argument('--num_workers', default=10, type=int)
      parser.add_argument('--pin_mem', type=str2bool, default=True,
                          help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
  
      # distributed training parameters
      parser.add_argument('--world_size', default=1, type=int,
                          help='number of distributed processes')
      parser.add_argument('--local_rank', default=-1, type=int)
      parser.add_argument('--dist_on_itp', type=str2bool, default=False)
      parser.add_argument('--dist_url', default='env://',
                          help='url used to set up distributed training')
  
      parser.add_argument('--use_amp', type=str2bool, default=False, 
                          help="Use PyTorch's AMP (Automatic Mixed Precision) or not")
      parser.add_argument('--use_checkpoint', type=str2bool, default=False,
                          help="Use PyTorch's torch.util.checkpoint to save memory or not")
  
      return parser
  
  def main(args):
      utils.init_distributed_mode(args)
      print(args)
      device = torch.device(args.device)
      if args.test_size <= 0:
          args.test_size = args.input_size
      print('input size', args.input_size, ' test size ', args.test_size)
  
      # fix the seed for reproducibility
      seed = args.seed + utils.get_rank()
  
      torch.manual_seed(seed)
      np.random.seed(seed)
      cudnn.benchmark = True
  
      dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
      if args.disable_eval:
          args.dist_eval = False
          dataset_val = None
      else:
          dataset_val, _ = build_dataset(is_train=False, args=args)
  
      num_tasks = utils.get_world_size()
      global_rank = utils.get_rank()
  
      print('global rank ', global_rank)
  
      sampler_train = torch.utils.data.DistributedSampler(
          dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True, seed=args.seed,
      )
      print("Sampler_train = %s" % str(sampler_train))
      if args.dist_eval:
          if len(dataset_val) % num_tasks != 0:
              print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
                      'This will slightly alter validation results as extra duplicate entries are added to achieve '
                      'equal num of samples per-process.')
          sampler_val = torch.utils.data.DistributedSampler(
              dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False)
      else:
          sampler_val = torch.utils.data.SequentialSampler(dataset_val)
  
      if global_rank == 0 and args.log_dir is not None:
          os.makedirs(args.log_dir, exist_ok=True)
          log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
      else:
          log_writer = None
  
      data_loader_train = torch.utils.data.DataLoader(
          dataset_train, sampler=sampler_train,
          batch_size=args.batch_size,
          num_workers=args.num_workers,
          pin_memory=args.pin_mem,
          drop_last=True,
      )
  
      if dataset_val is not None:
          data_loader_val = torch.utils.data.DataLoader(
              dataset_val, sampler=sampler_val,
              batch_size=int(1.5 * args.batch_size),
              num_workers=args.num_workers,
              pin_memory=args.pin_mem,
              drop_last=False
          )
      else:
          data_loader_val = None
  
      mixup_fn = None
      mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
      if mixup_active:
          print("Mixup is activated!")
          mixup_fn = Mixup(
              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.nb_classes)
  
  
      #   ================================================== build model ================================ #
      if args.model == 'RepLKNet-31B':
          model = create_RepLKNet31B(drop_path_rate=args.drop_path, num_classes=args.nb_classes, use_checkpoint=args.use_checkpoint,
                                     small_kernel_merged=False)
      elif args.model == 'RepLKNet-31L':
          model = create_RepLKNet31L(drop_path_rate=args.drop_path, num_classes=args.nb_classes,
                                     use_checkpoint=args.use_checkpoint, small_kernel_merged=False)
      elif args.model == 'RepLKNet-XL':
          model = create_RepLKNetXL(drop_path_rate=args.drop_path, num_classes=args.nb_classes,
                                     use_checkpoint=args.use_checkpoint, small_kernel_merged=False)
      else:
          raise ValueError('unsupported model. add it here.')
      #   ================================================== build model ================================ #
  
      model_without_ddp = model
      n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
  
      print("Model = %s" % str(model_without_ddp))
      print('number of params:', n_parameters)
  
      total_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
      num_training_steps_per_epoch = len(dataset_train) // total_batch_size
      print("LR = %.8f" % args.lr)
      print("Batch size = %d" % total_batch_size)
      print("Update frequent = %d" % args.update_freq)
      print("Number of training examples = %d" % len(dataset_train))
      print("Number of training training per epoch = %d" % num_training_steps_per_epoch)
  
      if args.layer_decay < 1.0 or args.layer_decay > 1.0:
          num_layers = 12
          #   set lower learning rate for lower-level layers.
          #   follow the implementation in the code of ConvNeXt and BeiT
          assigner = RepLKNetLayerDecayValueAssigner(list(args.layer_decay ** (num_layers + 1 - i) for i in range(num_layers + 2)))
      else:
          assigner = None
  
      optimizer = create_optimizer(
          args, model_without_ddp, skip_list=None,
          get_num_layer=assigner.get_layer_id if assigner is not None else None,
          get_layer_scale=assigner.get_scale if assigner is not None else None)
  
      if assigner is not None:
          print("Assigned values = %s" % str(assigner.values))
  
      if args.finetune:
          if args.finetune.startswith('https'):
              checkpoint = torch.hub.load_state_dict_from_url(
                  args.finetune, map_location='cpu', check_hash=True)
          else:
              checkpoint = torch.load(args.finetune, map_location='cpu')
  
          print("Load ckpt from %s" % args.finetune)
          checkpoint_model = None
          for model_key in args.model_key.split('|'):
              if model_key in checkpoint:
                  checkpoint_model = checkpoint[model_key]
                  print("Load state_dict by model_key = %s" % model_key)
                  break
          if checkpoint_model is None:
              checkpoint_model = checkpoint
          state_dict = model.state_dict()
          for k in ['head.weight', 'head.bias']:
              if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
                  print(f"Removing key {k} from pretrained checkpoint")
                  del checkpoint_model[k]
          utils.load_state_dict(model, checkpoint_model, prefix=args.model_prefix)
  
      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 = ModelEma(
              model,
              decay=args.model_ema_decay,
              device='cpu' if args.model_ema_force_cpu else '',
              resume='')
          print("Using EMA with decay = %.8f" % args.model_ema_decay)
  
      if args.weight_decay_end is None:
          args.weight_decay_end = args.weight_decay
      wd_schedule_values = utils.cosine_scheduler(
          args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch)
      print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values)))
  
      print('=============================== GPU memory before AUTO LOAD', torch.cuda.memory_allocated())
      loss_scaler = NativeScaler()  # if args.use_amp is False, this won't be used
      utils.auto_load_model(
          args=args, model=model, model_without_ddp=model_without_ddp,
          optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema)
      print('=============================== GPU memory after AUTO LOAD', torch.cuda.memory_allocated())
      model.to(device)
      utils.optimizer_to(optimizer, device)
      print('=============================== GPU memory after moving optim to GPU', torch.cuda.memory_allocated())
      if model_ema is not None and not args.model_ema_force_cpu:
          model_ema.ema.to(device)
      print('=============================== after moving EMA', torch.cuda.memory_allocated())
      if args.distributed:
          model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=False)
          model_without_ddp = model.module
      print("Use Cosine LR scheduler")
      lr_schedule_values = utils.cosine_scheduler(
          args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
          warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
      )
  
      if mixup_fn is not None:
          # smoothing is handled with mixup label transform
          criterion = SoftTargetCrossEntropy()
      elif args.smoothing > 0.:
          criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
      else:
          criterion = torch.nn.CrossEntropyLoss()
  
      print("criterion = %s" % str(criterion))
  
      if args.eval:
          print(f"Eval only mode")
          if args.with_small_kernel_merged and hasattr(model_without_ddp, 'structural_reparam'):
              model_without_ddp.structural_reparam()
          test_stats = evaluate(data_loader_val, model, device, use_amp=args.use_amp)
          print(f"Accuracy of the network on {len(dataset_val)} test images: {test_stats['acc1']:.5f}%")
          return
  
      max_accuracy = 0.0
      if args.model_ema and args.model_ema_eval:
          max_accuracy_ema = 0.0
  
      print('=============================== GPU memory before training', torch.cuda.memory_allocated())
  
      print("Start training for %d epochs" % args.epochs)
      start_time = time.time()
      for epoch in range(args.start_epoch, args.epochs):
          if args.distributed:
              data_loader_train.sampler.set_epoch(epoch)
          if log_writer is not None:
              log_writer.set_step(epoch * num_training_steps_per_epoch * args.update_freq)
          train_stats = train_one_epoch(
              model, criterion, data_loader_train, optimizer,
              device, epoch, loss_scaler, args.clip_grad, model_ema, mixup_fn,
              log_writer=log_writer, start_steps=epoch * num_training_steps_per_epoch,
              lr_schedule_values=lr_schedule_values, wd_schedule_values=wd_schedule_values,
              num_training_steps_per_epoch=num_training_steps_per_epoch, update_freq=args.update_freq,
              use_amp=args.use_amp
          )
          if args.output_dir and args.save_ckpt:
              if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
                  utils.save_model(
                      args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                      loss_scaler=loss_scaler, epoch=epoch, model_ema=model_ema)
              utils.save_model(
                  args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                  loss_scaler=loss_scaler, epoch=epoch, model_ema=model_ema, is_latest=True)
          if data_loader_val is not None:
              test_stats = evaluate(data_loader_val, model, device, use_amp=args.use_amp)
              print(f"Accuracy of the model on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
              if max_accuracy < test_stats["acc1"]:
                  max_accuracy = test_stats["acc1"]
                  if args.output_dir and args.save_ckpt:
                      utils.save_model(
                          args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                          loss_scaler=loss_scaler, epoch="best", model_ema=model_ema)
              print(f'Max accuracy: {max_accuracy:.2f}%')
  
              if log_writer is not None:
                  log_writer.update(test_acc1=test_stats['acc1'], head="perf", step=epoch)
                  log_writer.update(test_acc5=test_stats['acc5'], head="perf", step=epoch)
                  log_writer.update(test_loss=test_stats['loss'], head="perf", step=epoch)
  
              log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                           **{f'test_{k}': v for k, v in test_stats.items()},
                           'epoch': epoch,
                           'n_parameters': n_parameters}
  
              # repeat testing routines for EMA, if ema eval is turned on
              if args.model_ema and args.model_ema_eval:
                  test_stats_ema = evaluate(data_loader_val, model_ema.ema, device, use_amp=args.use_amp)
                  print(f"Accuracy of the model EMA on {len(dataset_val)} test images: {test_stats_ema['acc1']:.1f}%")
                  if max_accuracy_ema < test_stats_ema["acc1"]:
                      max_accuracy_ema = test_stats_ema["acc1"]
                      if args.output_dir and args.save_ckpt:
                          utils.save_model(
                              args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                              loss_scaler=loss_scaler, epoch="best-ema", model_ema=model_ema)
                      print(f'Max EMA accuracy: {max_accuracy_ema:.2f}%')
                  if log_writer is not None:
                      log_writer.update(test_acc1_ema=test_stats_ema['acc1'], head="perf", step=epoch)
                  log_stats.update({**{f'test_{k}_ema': v for k, v in test_stats_ema.items()}})
          else:
              log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                           'epoch': epoch,
                           'n_parameters': n_parameters}
  
          if args.output_dir and utils.is_main_process():
              if log_writer is not None:
                  log_writer.flush()
              with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
                  f.write(json.dumps(log_stats) + "\n")
  
      total_time = time.time() - start_time
      total_time_str = str(datetime.timedelta(seconds=int(total_time)))
      print('Training time {}'.format(total_time_str))
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser('Training script for image classification', parents=[get_args_parser()])
      args = parser.parse_args()
      if args.output_dir:
          Path(args.output_dir).mkdir(parents=True, exist_ok=True)
      main(args)