content: import argparse
  import datetime
  from operator import is_
  import numpy as np
  import time
  import torch
  import torch.backends.cudnn as cudnn
  
  import json
  import os
  from functools import partial
  from pathlib import Path
  from collections import OrderedDict
  
  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_ava_dataset, BatchCollator, build_kinetics_dataset, build_ak_dataset
  from engine_for_finetuning import train_one_epoch, validation_one_epoch, final_test, merge
  from utils import NativeScalerWithGradNormCount as NativeScaler
  from utils import multiple_samples_collate
  import utils
  import modeling_finetune
  from data.transforms import build_transforms
  
  
  def get_args():
      parser = argparse.ArgumentParser('VideoMAE fine-tuning and evaluation script for video 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=100, type=int)  #
      parser.add_argument('--val_freq', default=2, type=int)  #
      parser.add_argument('--enable_randaug', action='store_true', default=False)  #
      parser.add_argument('--aug_type',default='strong', type=str, choices=['strong','default','default_w_cutout','default_wo_affine'])  #
      # Model parameters
      parser.add_argument('--model', default='vit_base_patch16_224', type=str, metavar='MODEL',
                          help='Name of model to train')
      parser.add_argument('--tubelet_size', type=int, default=2)
      parser.add_argument('--input_size', default=224, type=int,
                          help='videos 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='')
      parser.add_argument('--head_type',type=str, default='linear', choices=['linear', 'acar'])
  
      # Optimizer parameters
      parser.add_argument('--accumulation_step',type=int,default=1)
      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=1e-3, metavar='LR',
                          help='learning rate (default: 1e-3)')
      parser.add_argument('--layer_decay', type=float, default=0.75)
  
      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=2, 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',  # no use
                          help='Color jitter factor (default: 0.4)')
      parser.add_argument('--num_sample', type=int, default=2,  # 1
                          help='Repeated_aug (default: 2)')
      parser.add_argument('--aa', type=str, default='rand-m7-n4-mstd0.5-inc1', metavar='NAME',  # no use
                          help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m7-n4-mstd0.5-inc1)'),
      parser.add_argument('--smoothing', type=float, default=0.1,  # 0.
                          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, no use
      parser.add_argument('--crop_pct', type=float, default=None)
      parser.add_argument('--short_side_size', type=int, default=224)
      parser.add_argument('--test_num_segment', type=int, default=5)
      parser.add_argument('--test_num_crop', type=int, default=3)
  
      # Random Erase params, no use
      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, no use
      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('--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)  # head init
      parser.add_argument('--use_mean_pooling', action='store_true')  # cls_token
      parser.set_defaults(use_mean_pooling=True)
      parser.add_argument('--use_cls', action='store_false', dest='use_mean_pooling')
  
      # Dataset parameters
      parser.add_argument('--data_path', default='/path/to/list_kinetics-400', 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=80, type=int,  #
                          help='number of the classification types')
      parser.add_argument('--imagenet_default_mean_and_std', default=True, action='store_true')
      parser.add_argument('--num_segments', type=int, default=1)
      parser.add_argument('--num_frames', type=int, default=16)
      parser.add_argument('--sampling_rate', type=int, default=4)
      parser.add_argument('--data_set', default='ava',
                          choices=['ava','ava-kinetics'],
                          type=str, help='dataset')  #
      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('--eval_kinetics',action='store_true',default=False)
      parser.add_argument('--enable_deepspeed', action='store_true', default=False)
  
      known_args, _ = parser.parse_known_args()
  
      if known_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'")
              exit(0)
      else:
          ds_init = None
  
      return parser.parse_args(), ds_init
  
  
  def main(args, ds_init):
      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)
  
      cudnn.benchmark = True
  
      transform_train = build_transforms(is_train=True,args=args)
      transform_val = build_transforms(is_train=False,args=args)
  
      if args.data_set == 'ava':
          dataset_train = build_ava_dataset(is_train=True, transforms=transform_train)
      else:
          dataset_train = build_ak_dataset(is_train=True, transforms=transform_train)
  
      if args.eval_kinetics:
          dataset_val = build_kinetics_dataset(is_train=False, transforms=transform_val)
      else:
          dataset_val = build_ava_dataset(is_train=False, transforms=transform_val)
  
      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)
          # sampler_test = torch.utils.data.DistributedSampler(
          #     dataset_test, 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
  
      collate_func = BatchCollator(size_divisible=16)
      data_loader_train = torch.utils.data.DataLoader(
          dataset_train, sampler=sampler_train,
          batch_size=args.batch_size,  # 8
          num_workers=args.num_workers,
          pin_memory=args.pin_mem,
          drop_last=True,
          collate_fn=collate_func,
      )
      data_loader_train.num_samples = len(dataset_train)
  
      if dataset_val is not None:
          data_loader_val = torch.utils.data.DataLoader(
              dataset_val, sampler=sampler_val,
              batch_size=4,
              num_workers=args.num_workers,
              pin_memory=args.pin_mem,
              drop_last=False,
              collate_fn=collate_func,
          )
          data_loader_val.num_samples = len(dataset_val)
      else:
          data_loader_val = None
  
      mixup_fn = None
  
      model = create_model(
          args.model,
          pretrained=False,
          num_classes=args.nb_classes,
          all_frames=args.num_frames * args.num_segments,
          tubelet_size=args.tubelet_size,
          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,
          head_type=args.head_type
      )
  
      patch_size = model.patch_embed.patch_size  # 16
      print("Patch size = %s" % str(patch_size))
      # no use
      args.window_size = (args.num_frames // 2, 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()
          try:
              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]
          except KeyError:
              del checkpoint_model['head.weight']
              del checkpoint_model['head.bias']
          all_keys = list(checkpoint_model.keys())
          new_dict = OrderedDict()
          for key in all_keys:
              if key.startswith('backbone.'):
                  new_dict[key[9:]] = checkpoint_model[key]
              elif key.startswith('encoder.'):
                  new_dict[key[8:]] = checkpoint_model[key]
              else:
                  new_dict[key] = checkpoint_model[key]
          checkpoint_model = new_dict
  
          # interpolate position embedding
          if 'pos_embed' in checkpoint_model:
              pos_embed_checkpoint = checkpoint_model['pos_embed']
              embedding_size = pos_embed_checkpoint.shape[-1]  # channel dim
              num_patches = model.patch_embed.num_patches  #
              num_extra_tokens = model.pos_embed.shape[-2] - num_patches  # 0/1
  
              # height (== width) for the checkpoint position embedding 
              orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens) // (
                          args.num_frames // model.patch_embed.tubelet_size)) ** 0.5)
              # height (== width) for the new position embedding
              new_size = int((num_patches // (args.num_frames // model.patch_embed.tubelet_size)) ** 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:]
                  # B, L, C -> BT, H, W, C -> BT, C, H, W
                  pos_tokens = pos_tokens.reshape(-1, args.num_frames // model.patch_embed.tubelet_size, orig_size,
                                                  orig_size, embedding_size)
                  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)
                  # BT, C, H, W -> BT, H, W, C ->  B, T, H, W, C
                  pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1,
                                                                      args.num_frames // model.patch_embed.tubelet_size,
                                                                      new_size, new_size, embedding_size)
                  pos_tokens = pos_tokens.flatten(1, 3)  # B, L, C
                  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.to(device)
  
      model_ema = None
  
      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
      args.lr = args.lr * total_batch_size / 256
      args.min_lr = args.min_lr * total_batch_size / 256
      args.warmup_lr = args.warmup_lr * total_batch_size / 256
      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()
      print("Skip weight decay list: ", skip_weight_decay_list)
  
      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)))
  
      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:
          validation_one_epoch(data_loader_val, model, device, args.output_dir, args.start_epoch, log_writer)
          exit(0)
  
      print(f"Start training for {args.epochs} 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, 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,
          )
          # 1. save ckpt
          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)
          # 2. log
          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")
          # 3. eval
          if data_loader_val is not None and (
                  (epoch + 1) % args.val_freq == 0 or epoch + 1 == args.epochs):
              validation_one_epoch(data_loader_val, model, device, args.output_dir, epoch, log_writer)
      torch.distributed.barrier()
      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__':
      opts, ds_init = get_args()
      if opts.output_dir:
          Path(opts.output_dir).mkdir(parents=True, exist_ok=True)
      main(opts, ds_init)
  