compress_deit.py

'''
 * Copyright (c) 2023, Dachuan Shi.
 * Copyright (c) 2015-present, Facebook, Inc.
 * All rights reserved.
 * For full license text, see LICENSE.txt file in the repo root
 * By Dachuan Shi
'''
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 import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.scheduler import create_scheduler
# from timm.optim import create_optimizer
from deit.optim_factory import create_optimizer #
from timm.utils import NativeScaler, get_state_dict, ModelEma
from timm.utils import get_state_dict, ModelEma

from deit.datasets import build_dataset
from deit.engine import train_one_epoch, evaluate, compress, print_compression_statistics, prune_if_compressed
from deit.losses import DistillationLoss
from deit.samplers import RASampler
from deit.augment import new_data_aug_generator

import utils

from fvcore.nn import FlopCountAnalysis, flop_count_str, flop_count_table

# from petrel_client.client import Client
# from petrel_client.utils.data import DataLoader

from torch.utils.data import DataLoader
torch.utils.data._utils.worker.HAS_NUMPY = False

def get_args_parser():
    parser = argparse.ArgumentParser('DeiT training and evaluation script', add_help=False)
    parser.add_argument('--batch-size', default=64, type=int)
    parser.add_argument('--epochs', default=300, type=int)
    parser.add_argument('--bce-loss', action='store_true')
    parser.add_argument('--unscale-lr', action='store_true')

    # Model parameters
    parser.add_argument('--model', default='deit_base_patch16_224', type=str, metavar='MODEL',
                        help='Name of model to train')
    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('--drop-path', type=float, default=0.1, metavar='PCT',
                        help='Drop path rate (default: 0.1)')

    parser.add_argument('--model-ema', action='store_true')
    parser.add_argument('--no-model-ema', action='store_false', dest='model_ema')
    parser.set_defaults(model_ema=True)
    parser.add_argument('--model-ema-decay', type=float, default=0.99996, 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)')
    # Learning rate schedule parameters
    parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                        help='LR scheduler (default: "cosine"')
    parser.add_argument('--lr', type=float, default=5e-4, metavar='LR',
                        help='learning rate (default: 5e-4)')
    parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                        help='learning rate noise on/off epoch percentages')
    parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                        help='learning rate noise limit percent (default: 0.67)')
    parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                        help='learning rate noise std-dev (default: 1.0)')
    parser.add_argument('--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-5, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')

    parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
                        help='epoch interval to decay LR')
    parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
                        help='epochs to warmup LR, if scheduler supports')
    parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
                        help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
    parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
                        help='patience epochs for Plateau LR scheduler (default: 10')
    parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                        help='LR decay rate (default: 0.1)')

    # Augmentation parameters
    parser.add_argument('--color-jitter', type=float, default=0.3, metavar='PCT',
                        help='Color jitter factor (default: 0.3)')
    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")')

    parser.add_argument('--repeated-aug', action='store_true')
    parser.add_argument('--no-repeated-aug', action='store_false', dest='repeated_aug')
    parser.set_defaults(repeated_aug=True)
    
    parser.add_argument('--train-mode', action='store_true')
    parser.add_argument('--no-train-mode', action='store_false', dest='train_mode')
    parser.set_defaults(train_mode=True)
    
    parser.add_argument('--ThreeAugment', action='store_true') #3augment
    
    parser.add_argument('--src', action='store_true') #simple random crop
    
    # * 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=0.8,
                        help='mixup alpha, mixup enabled if > 0. (default: 0.8)')
    parser.add_argument('--cutmix', type=float, default=1.0,
                        help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)')
    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"')

    # Distillation parameters
    parser.add_argument('--teacher-model', default='regnety_160', type=str, metavar='MODEL',
                        help='Name of teacher model to train (default: "regnety_160"')
    parser.add_argument('--teacher-path', type=str, default='')
    parser.add_argument('--distillation-type', default='none', choices=['none', 'soft', 'hard'], type=str, help="")
    parser.add_argument('--distillation-alpha', default=0.5, type=float, help="")
    parser.add_argument('--distillation-tau', default=1.0, type=float, help="")

    # * Finetuning params
    parser.add_argument('--finetune', default='', help='finetune from checkpoint')
    parser.add_argument('--attn-only', action='store_true') 
    
    # Dataset parameters
    parser.add_argument('--data-path', default='/datasets01/imagenet_full_size/061417/', type=str,
                        help='dataset path')
    parser.add_argument('--data-set', default='IMNET', choices=['CIFAR', 'IMNET', 'INAT', 'INAT19'],
                        type=str, help='Image Net dataset path')
    parser.add_argument('--inat-category', default='name',
                        choices=['kingdom', 'phylum', 'class', 'order', 'supercategory', 'family', 'genus', 'name'],
                        type=str, help='semantic granularity')

    parser.add_argument('--output_dir', default='',
                        help='path where to save, empty for no saving')
    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('--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('--eval-crop-ratio', default=0.875, type=float, help="Crop ratio for evaluation")
    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',
                        help='')
    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('--dist_url', default='env://', help='url used to set up distributed training')

    # compression parameters
    parser.add_argument('--w_sp_attn', default=4.8e-3, type=float, help='regularization coefficient for attn')
    parser.add_argument('--w_sp_mlp', default=2e-4, type=float, help='regularization coefficient for mlp')
    parser.add_argument('--p', default=0.5, type=float, help='total compression ratio')
    parser.add_argument('--interval', default=100, type=int, help='interval of updating compression mask')
    parser.add_argument('--lr-search', type=float, default=5e-4, help='search learning rate (default: 5e-4)')
    parser.add_argument('--epochs-search', default=60, type=int)
    parser.add_argument('--use_ceph', action='store_true')  

    return parser


def main(args, client):
    utils.init_distributed_mode(args)

    print(args)

    if args.distillation_type != 'none' and args.finetune and not args.eval:
        raise NotImplementedError("Finetuning with distillation not yet supported")

    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, client=client)
    dataset_val, _ = build_dataset(is_train=False, args=args, client=client)

    if True:  # args.distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()
        if args.repeated_aug:
            sampler_train = RASampler(
                dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
            )
        else:
            sampler_train = torch.utils.data.DistributedSampler(
                dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
            )
        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)


    data_loader_train = DataLoader(
        dataset_train, sampler=sampler_train,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        pin_memory=args.pin_mem,
        drop_last=True,
        prefetch_factor=4, 
        persistent_workers=True,
    )
    if args.ThreeAugment:
        data_loader_train.dataset.transform = new_data_aug_generator(args)

    data_loader_val = 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,
        prefetch_factor=4, 
        persistent_workers=True,
    )

    mixup_fn = None
    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
    if mixup_active:
        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)
    
    if not (args.eval or args.dist_eval):
        print(f"Creating model for searching: {args.model}")
        search_model = create_model(
            args.model,
            pretrained=False,
            num_classes=args.nb_classes,
            drop_rate=args.drop,
            drop_path_rate=args.drop_path,
            drop_block_rate=None,
            img_size=args.input_size,
            search=True,
        )

                        
        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')

            checkpoint_model = checkpoint['model']
            state_dict = search_model.state_dict()
            for k in ['head.weight', 'head.bias', 'head_dist.weight', 'head_dist.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]

            # interpolate position embedding
            pos_embed_checkpoint = checkpoint_model['pos_embed']
            embedding_size = pos_embed_checkpoint.shape[-1]
            num_patches = search_model.patch_embed.num_patches
            num_extra_tokens = search_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
            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

            msg = search_model.load_state_dict(checkpoint_model, strict=False)
            print("missing keys: ", msg.missing_keys)

                
        search_model.to(device)

        search_model_ema = None
        if args.model_ema:
            # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
            search_model_ema = ModelEma(
                search_model,
                decay=args.model_ema_decay,
                device='cpu' if args.model_ema_force_cpu else '',
                resume='')

        #### Count parameters and FLOPs ####
        search_model.eval()
        with torch.no_grad():
            flop = FlopCountAnalysis(search_model, torch.randn(1, 3, 224, 224).to(device))
            print(flop_count_table(flop, max_depth=7, show_param_shapes=True))
            print("Total", flop.total() / 1e9)
        search_model.train() 
        #######################################
        search_model_without_ddp = search_model
        if args.distributed:
            search_model = torch.nn.parallel.DistributedDataParallel(search_model, device_ids=[args.gpu])
            search_model_without_ddp = search_model.module

        if not args.unscale_lr:
            linear_scaled_lr_search = args.lr_search * args.batch_size * utils.get_world_size() / 512.0
            args.lr_search = linear_scaled_lr_search
        optimizer = create_optimizer(args, search_model_without_ddp, search=True)
        loss_scaler = NativeScaler()

        criterion = LabelSmoothingCrossEntropy()

        if mixup_active:
            # smoothing is handled with mixup label transform
            criterion = SoftTargetCrossEntropy()
        elif args.smoothing:
            criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
        else:
            criterion = torch.nn.CrossEntropyLoss()
            
        if args.bce_loss:
            criterion = torch.nn.BCEWithLogitsLoss()
            
        teacher_model = None
        if args.distillation_type != 'none':
            assert args.teacher_path, 'need to specify teacher-path when using distillation'
            print(f"Creating teacher model: {args.teacher_model}")
            teacher_model = create_model(
                args.teacher_model,
                pretrained=False,
                num_classes=args.nb_classes,
                global_pool='avg',
            )
            if args.teacher_path.startswith('https'):
                checkpoint = torch.hub.load_state_dict_from_url(
                    args.teacher_path, map_location='cpu', check_hash=True)
            else:
                checkpoint = torch.load(args.teacher_path, map_location='cpu')
            teacher_model.load_state_dict(checkpoint['model'])
            teacher_model.to(device)
            teacher_model.eval()

        # wrap the criterion in our custom DistillationLoss, which
        # just dispatches to the original criterion if args.distillation_type is 'none'
        criterion = DistillationLoss(
            criterion, teacher_model, args.distillation_type, args.distillation_alpha, args.distillation_tau
        )

        output_dir = Path(args.output_dir)


        print(f"Start searching for {args.epochs_search} epochs")
        start_time = time.time()
        for epoch in range(args.start_epoch, args.epochs_search):
            if args.distributed:
                data_loader_train.sampler.set_epoch(epoch)

            train_stats = train_one_epoch(
                search_model, criterion, data_loader_train,
                optimizer, device, epoch, loss_scaler,
                args.clip_grad, search_model_ema, mixup_fn,
                set_training_mode=args.train_mode,  # keep in eval mode for deit finetuning / train mode for training and deit III finetuning
                args = args,
                search = True,
            )

        print_compression_statistics(search_model)
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        print('Searching time {}'.format(total_time_str))

        ########################################################################

        print(f"Creating model for retrain: {args.model}")
        model = create_model(
            args.model,
            pretrained=False,
            num_classes=args.nb_classes,
            drop_rate=args.drop,
            drop_path_rate=args.drop_path,
            drop_block_rate=None,
            img_size=args.input_size,
        )
        msg = model.load_state_dict(search_model_without_ddp.state_dict(), strict=False)
        print("missing keys: ", msg.missing_keys)
        compress(model, search_model_without_ddp)    
    else:
        print(f"Creating model for evaluation: {args.model}")
        model = create_model(
            args.model,
            pretrained=False,
            num_classes=args.nb_classes,
            drop_rate=args.drop,
            drop_path_rate=args.drop_path,
            drop_block_rate=None,
            img_size=args.input_size,
        )
        prune_if_compressed(model, client, args.resume)

    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='')

    #### Count parameters and FLOPs ####
    model.eval()
    with torch.no_grad():
        flop = FlopCountAnalysis(model, torch.randn(1, 3, 224, 224).to(device))
        print(flop_count_table(flop, max_depth=7, show_param_shapes=True))
        print("Total", flop.total() / 1e9)
    model.train() 
    #######################################
    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    if args.eval or args.dist_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}%")
        return

    if not args.unscale_lr:
        linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size() / 512.0
        args.lr = linear_scaled_lr
    optimizer = create_optimizer(args, model_without_ddp)
    loss_scaler = NativeScaler()

    lr_scheduler, _ = create_scheduler(args, optimizer)

    criterion = LabelSmoothingCrossEntropy()

    if mixup_active:
        # smoothing is handled with mixup label transform
        criterion = SoftTargetCrossEntropy()
    elif args.smoothing:
        criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
    else:
        criterion = torch.nn.CrossEntropyLoss()
        
    if args.bce_loss:
        criterion = torch.nn.BCEWithLogitsLoss()
        
    teacher_model = None
    if args.distillation_type != 'none':
        assert args.teacher_path, 'need to specify teacher-path when using distillation'
        print(f"Creating teacher model: {args.teacher_model}")
        teacher_model = create_model(
            args.teacher_model,
            pretrained=False,
            num_classes=args.nb_classes,
            global_pool='avg',
        )
        if args.teacher_path.startswith('https'):
            checkpoint = torch.hub.load_state_dict_from_url(
                args.teacher_path, map_location='cpu', check_hash=True)
        else:
            checkpoint = torch.load(args.teacher_path, map_location='cpu')
        teacher_model.load_state_dict(checkpoint['model'])
        teacher_model.to(device)
        teacher_model.eval()

    # wrap the criterion in our custom DistillationLoss, which
    # just dispatches to the original criterion if args.distillation_type is 'none'
    criterion = DistillationLoss(
        criterion, teacher_model, args.distillation_type, args.distillation_alpha, args.distillation_tau
    )

    output_dir = Path(args.output_dir)


    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)

        train_stats = train_one_epoch(
            model, criterion, data_loader_train,
            optimizer, device, epoch, loss_scaler,
            args.clip_grad, model_ema, mixup_fn,
            set_training_mode=args.train_mode,  # keep in eval mode for deit finetuning / train mode for training and deit III finetuning
            args = args,
        )

        lr_scheduler.step(epoch)
        if args.output_dir:
            checkpoint_paths = [output_dir / 'checkpoint.pth']
            for checkpoint_path in checkpoint_paths:
                utils.save_on_master({
                    'model': model_without_ddp.state_dict(),
                    # 'optimizer': optimizer.state_dict(),
                    # 'lr_scheduler': lr_scheduler.state_dict(),
                    # 'epoch': epoch,
                    # 'model_ema': get_state_dict(model_ema),
                    # 'scaler': loss_scaler.state_dict(),
                    # 'args': args,
                }, checkpoint_path)
             

        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:
                checkpoint_paths = [output_dir / 'best_checkpoint.pth']
                for checkpoint_path in checkpoint_paths:
                    utils.save_on_master({
                        'model': model_without_ddp.state_dict(),
                        # 'optimizer': optimizer.state_dict(),
                        # 'lr_scheduler': lr_scheduler.state_dict(),
                        # 'epoch': epoch,
                        # 'model_ema': get_state_dict(model_ema),
                        # 'scaler': loss_scaler.state_dict(),
                        # 'args': args,
                    }, checkpoint_path)
            
        print(f'Max accuracy: {max_accuracy:.2f}%')

        log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                **{f'test_{k}': v for k, v in test_stats.items()},
                'retrain_epoch': epoch}
        
        if args.output_dir and utils.is_main_process():
            with (output_dir / "log.txt").open("a") 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('DeiT training and evaluation script', parents=[get_args_parser()])
    args = parser.parse_args()
    client = None
    if args.use_ceph:
        client = Client('~/petreloss.conf', enable_mc=True)
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    main(args, client=client)