text: # Copyright (c) Meta Platforms, Inc. and affiliates.
  # All rights reserved.
  
  # This source code is licensed under the license found in the
  # LICENSE file in the root directory of this source tree.
  
  # --------------------------------------------------------
  # BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
  # Github source: https://github.com/microsoft/unilm/tree/master/beit
  # Copyright (c) 2021 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # By Hangbo Bao
  # Based on timm, DINO and DeiT code bases
  # 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
  import os
  
  from pathlib import Path
  
  from timm.data.mixup import Mixup
  from timm.models import create_model
  from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
  from timm.utils import ModelEma
  from optim_factory import create_optimizer, get_parameter_groups, LayerDecayValueAssigner
  
  from datasets import build_dataset
  from engine_for_finetuning import train_one_epoch, evaluate
  from utils import NativeScalerWithGradNormCount as NativeScaler
  import utils
  from scipy import interpolate
  import modeling_finetune
  
  
  def get_args_parser():
      parser = argparse.ArgumentParser('BEiT fine-tuning and evaluation script for image classification', add_help=False)
      parser.add_argument('--batch_size', default=64, type=int)
      parser.add_argument('--epochs', default=30, type=int)
      parser.add_argument('--update_freq', default=1, type=int)
      parser.add_argument('--save_ckpt_freq', default=5, type=int)
  
      # Model parameters
      parser.add_argument('--model', default='deit_base_patch16_224', type=str, metavar='MODEL',
                          help='Name of model to train')
      parser.add_argument('--rel_pos_bias', action='store_true')
      parser.add_argument('--disable_rel_pos_bias', action='store_false', dest='rel_pos_bias')
      parser.set_defaults(rel_pos_bias=True)
      parser.add_argument('--abs_pos_emb', action='store_true')
      parser.set_defaults(abs_pos_emb=False)
      parser.add_argument('--layer_scale_init_value', default=0.1, type=float, 
                          help="0.1 for base, 1e-5 for large. set 0 to disable layer scale")
  
      parser.add_argument('--input_size', default=224, type=int,
                          help='images input size')
  
      parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                          help='Dropout rate (default: 0.)')
      parser.add_argument('--attn_drop_rate', type=float, default=0.0, metavar='PCT',
                          help='Attention dropout rate (default: 0.)')
      parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
                          help='Drop path rate (default: 0.1)')
  
      parser.add_argument('--disable_eval_during_finetuning', action='store_true', default=False)
  
      parser.add_argument('--model_ema', action='store_true', default=False)
      parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
      parser.add_argument('--model_ema_force_cpu', action='store_true', default=False, help='')
  
      # Optimizer 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=5e-4, metavar='LR',
                          help='learning rate (default: 5e-4)')
      parser.add_argument('--layer_decay', type=float, default=0.9)
  
      parser.add_argument('--warmup_lr', type=float, default=1e-6, metavar='LR',
                          help='warmup learning rate (default: 1e-6)')
      parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
                          help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
  
      parser.add_argument('--warmup_epochs', type=int, default=5, 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', action='store_true', default=False,
                          help='Do not random erase first (clean) augmentation split')
  
      # * Mixup params
      parser.add_argument('--mixup', type=float, default=0,
                          help='mixup alpha, mixup enabled if > 0.')
      parser.add_argument('--cutmix', type=float, default=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('--model_key', default='model|module', type=str)
      parser.add_argument('--model_prefix', default='', type=str)
      parser.add_argument('--init_scale', default=0.001, type=float)
      parser.add_argument('--use_mean_pooling', action='store_true')
      parser.set_defaults(use_mean_pooling=True)
      parser.add_argument('--use_cls', action='store_false', dest='use_mean_pooling')
      parser.add_argument('--disable_weight_decay_on_rel_pos_bias', action='store_true', default=False)
  
      # Dataset parameters
      parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', 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=0, type=int,
                          help='number of the classification types')
      parser.add_argument('--imagenet_default_mean_and_std', default=False, action='store_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', action='store_true')
      parser.add_argument('--no_auto_resume', action='store_false', dest='auto_resume')
      parser.set_defaults(auto_resume=True)
  
      parser.add_argument('--save_ckpt', action='store_true')
      parser.add_argument('--no_save_ckpt', action='store_false', dest='save_ckpt')
      parser.set_defaults(save_ckpt=True)
  
      parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                          help='start epoch')
      parser.add_argument('--eval', action='store_true',
                          help='Perform evaluation only')
      parser.add_argument('--dist_eval', action='store_true', default=False,
                          help='Enabling distributed evaluation')
      parser.add_argument('--num_workers', default=10, type=int)
      parser.add_argument('--pin_mem', action='store_true',
                          help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
      parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
      parser.set_defaults(pin_mem=True)
  
      # 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', action='store_true')
      parser.add_argument('--dist_url', default='env://',
                          help='url used to set up distributed training')
  
      parser.add_argument('--enable_deepspeed', action='store_true', default=False)
  
      return parser
  
  
  def main(args, parser):
      if args.enable_deepspeed:
          try:
              import deepspeed
              from deepspeed import DeepSpeedConfig
              parser = deepspeed.add_config_arguments(parser)
              ds_init = deepspeed.initialize
          except:
              print("Please 'pip install deepspeed==0.4.0'")
              exit(0)
      else:
          ds_init = None
  
      utils.init_distributed_mode(args)
  
      if ds_init is not None:
          utils.create_ds_config(args)
  
      print(args)
  
      device = torch.device(args.device)
  
      # fix the seed for reproducibility
      seed = args.seed + utils.get_rank()
      torch.manual_seed(seed)
      np.random.seed(seed)
      # random.seed(seed)
  
      cudnn.benchmark = True
  
      dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
      if args.disable_eval_during_finetuning:
          dataset_val = None
      else:
          dataset_val, _ = build_dataset(is_train=False, args=args)
  
      if True:  # args.distributed:
          num_tasks = utils.get_world_size()
          global_rank = utils.get_rank()
          sampler_train = torch.utils.data.DistributedSampler(
              dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
          )
          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)
      else:
          sampler_train = torch.utils.data.RandomSampler(dataset_train)
          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)
  
      model = create_model(
          args.model,
          pretrained=False,
          num_classes=args.nb_classes,
          drop_rate=args.drop,
          drop_path_rate=args.drop_path,
          attn_drop_rate=args.attn_drop_rate,
          drop_block_rate=None,
          use_mean_pooling=args.use_mean_pooling,
          init_scale=args.init_scale,
          use_rel_pos_bias=args.rel_pos_bias,
          use_abs_pos_emb=args.abs_pos_emb,
          init_values=args.layer_scale_init_value,
      )
  
      patch_size = model.patch_embed.patch_size
      print("Patch size = %s" % str(patch_size))
      args.window_size = (args.input_size // patch_size[0], args.input_size // patch_size[1])
      args.patch_size = patch_size
  
      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]
  
          if model.use_rel_pos_bias and "rel_pos_bias.relative_position_bias_table" in checkpoint_model:
              print("Expand the shared relative position embedding to each transformer block. ")
              num_layers = model.get_num_layers()
              rel_pos_bias = checkpoint_model["rel_pos_bias.relative_position_bias_table"]
              for i in range(num_layers):
                  checkpoint_model["blocks.%d.attn.relative_position_bias_table" % i] = rel_pos_bias.clone()
  
              checkpoint_model.pop("rel_pos_bias.relative_position_bias_table")
  
          all_keys = list(checkpoint_model.keys())
          for key in all_keys:
              if "relative_position_index" in key:
                  checkpoint_model.pop(key)
  
              if "relative_position_bias_table" in key:
                  rel_pos_bias = checkpoint_model[key]
                  src_num_pos, num_attn_heads = rel_pos_bias.size()
                  dst_num_pos, _ = model.state_dict()[key].size()
                  dst_patch_shape = model.patch_embed.patch_shape
                  if dst_patch_shape[0] != dst_patch_shape[1]:
                      raise NotImplementedError()
                  num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1)
                  src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
                  dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)
                  if src_size != dst_size:
                      print("Position interpolate for %s from %dx%d to %dx%d" % (
                          key, src_size, src_size, dst_size, dst_size))
                      extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
                      rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
  
                      def geometric_progression(a, r, n):
                          return a * (1.0 - r ** n) / (1.0 - r)
  
                      left, right = 1.01, 1.5
                      while right - left > 1e-6:
                          q = (left + right) / 2.0
                          gp = geometric_progression(1, q, src_size // 2)
                          if gp > dst_size // 2:
                              right = q
                          else:
                              left = q
  
                      # if q > 1.090307:
                      #     q = 1.090307
  
                      dis = []
                      cur = 1
                      for i in range(src_size // 2):
                          dis.append(cur)
                          cur += q ** (i + 1)
  
                      r_ids = [-_ for _ in reversed(dis)]
  
                      x = r_ids + [0] + dis
                      y = r_ids + [0] + dis
  
                      t = dst_size // 2.0
                      dx = np.arange(-t, t + 0.1, 1.0)
                      dy = np.arange(-t, t + 0.1, 1.0)
  
                      print("Original positions = %s" % str(x))
                      print("Target positions = %s" % str(dx))
  
                      all_rel_pos_bias = []
  
                      for i in range(num_attn_heads):
                          z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()
                          f = interpolate.interp2d(x, y, z, kind='cubic')
                          all_rel_pos_bias.append(
                              torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))
  
                      rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
  
                      new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)
                      checkpoint_model[key] = new_rel_pos_bias
  
          # interpolate position embedding
          if 'pos_embed' in checkpoint_model:
              pos_embed_checkpoint = checkpoint_model['pos_embed']
              embedding_size = pos_embed_checkpoint.shape[-1]
              num_patches = model.patch_embed.num_patches
              num_extra_tokens = model.pos_embed.shape[-2] - num_patches
              # height (== width) for the checkpoint position embedding
              orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
              # height (== width) for the new position embedding
              new_size = int(num_patches ** 0.5)
              # class_token and dist_token are kept unchanged
              if orig_size != new_size:
                  print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
                  extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
                  # only the position tokens are interpolated
                  pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
                  pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
                  pos_tokens = torch.nn.functional.interpolate(
                      pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
                  pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
                  new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
                  checkpoint_model['pos_embed'] = new_pos_embed
  
          utils.load_state_dict(model, checkpoint_model, prefix=args.model_prefix)
          # model.load_state_dict(checkpoint_model, strict=False)
  
      model.to(device)
  
      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)
  
      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)
  
      num_layers = model_without_ddp.get_num_layers()
      if args.layer_decay < 1.0:
          assigner = LayerDecayValueAssigner(list(args.layer_decay ** (num_layers + 1 - i) for i in range(num_layers + 2)))
      else:
          assigner = None
  
      if assigner is not None:
          print("Assigned values = %s" % str(assigner.values))
  
      skip_weight_decay_list = model.no_weight_decay()
      if args.disable_weight_decay_on_rel_pos_bias:
          for i in range(num_layers):
              skip_weight_decay_list.add("blocks.%d.attn.relative_position_bias_table" % i)
  
      if args.enable_deepspeed:
          loss_scaler = None
          optimizer_params = get_parameter_groups(
              model, args.weight_decay, skip_weight_decay_list,
              assigner.get_layer_id if assigner is not None else None,
              assigner.get_scale if assigner is not None else None)
          model, optimizer, _, _ = ds_init(
              args=args, model=model, model_parameters=optimizer_params, dist_init_required=not args.distributed,
          )
  
          print("model.gradient_accumulation_steps() = %d" % model.gradient_accumulation_steps())
          assert model.gradient_accumulation_steps() == args.update_freq
      else:
          if args.distributed:
              model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
              model_without_ddp = model.module
  
          optimizer = create_optimizer(
              args, model_without_ddp, skip_list=skip_weight_decay_list,
              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)
          loss_scaler = NativeScaler()
  
      print("Use step level 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 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)))
  
      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))
  
      utils.auto_load_model(
          args=args, model=model, model_without_ddp=model_without_ddp,
          optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema)
  
      if args.eval:
          test_stats = evaluate(data_loader_val, model, device)
          print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
          exit(0)
  
      print(f"Start training for {args.epochs} epochs")
      start_time = time.time()
      max_accuracy = 0.0
      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,
          )
          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)
          if data_loader_val is not None:
              test_stats = evaluate(data_loader_val, model, device)
              print(f"Accuracy of the network 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}
          else:
              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}
  
          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 = get_args_parser()
      args, _ = parser.parse_known_args()
      if args.output_dir:
          Path(args.output_dir).mkdir(parents=True, exist_ok=True)
      main(args, parser)
  