main.py

#!/usr/bin/env python3
""" 
    ImageNet Training Script adopted for Refiner-ViT
"""
import argparse
import time
import yaml
import os
import logging
from collections import OrderedDict
from contextlib import suppress
from datetime import datetime
import numpy as np
try:
    import tensorflow as tf
except ImportError: 
    pass
    
import torch
import torch.nn as nn
import torchvision.utils
from torch.nn.parallel import DistributedDataParallel as NativeDDP

from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
from timm.models import create_model, resume_checkpoint, load_checkpoint, convert_splitbn_model, model_parameters
from timm.utils import *
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
from timm.optim import create_optimizer
from timm.scheduler import create_scheduler
from timm.utils import ApexScaler, NativeScaler

import models
from data import create_relabel_target, RelabelMixup, FastCollateRelabelMixup, create_relabel_loader, create_relabel_dataset
from loss import RelabelCrossEntropy,RelabelPooledCrossEntropy, RelabelSoftTargetCrossEntropy, SoftTargetCrossEntropyCosReg
from utils import load_for_transfer_learning,ApexScaler_SAM
from optim import SAM_ascent, SAM_descent

try:
    from apex import amp
    from apex.parallel import DistributedDataParallel as ApexDDP
    from apex.parallel import convert_syncbn_model
    has_apex = True
except ImportError:
    has_apex = False

has_native_amp = False
try:
    if getattr(torch.cuda.amp, 'autocast') is not None:
        has_native_amp = True
except AttributeError:
    pass

torch.backends.cudnn.benchmark = True
_logger = logging.getLogger('train')

# The first arg parser parses out only the --config argument, this argument is used to
# load a yaml file containing key-values that override the defaults for the main parser below
config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
                    help='YAML config file specifying default arguments')


parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')

# Dataset / Model parameters
parser.add_argument('data_dir', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--dataset', '-d', metavar='NAME', default='',
                    help='dataset type (default: ImageFolder/ImageTar if empty)')
parser.add_argument('--train-split', metavar='NAME', default='train',
                    help='dataset train split (default: train)')
parser.add_argument('--val-split', metavar='NAME', default='validation',
                    help='dataset validation split (default: validation)')
parser.add_argument('--model', default='resnet101', type=str, metavar='MODEL',
                    help='Name of model to train (default: "countception"')
parser.add_argument('--pretrained', action='store_true', default=False,
                    help='Start with pretrained version of specified network (if avail)')
parser.add_argument('--eval_first', action='store_true', default=False,
                    help='evaluate the model before training')
parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
                    help='Initialize model from this checkpoint (default: none)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='Resume full model and optimizer state from checkpoint (default: none)')
parser.add_argument('--no-resume-opt', action='store_true', default=False,
                    help='prevent resume of optimizer state when resuming model')
parser.add_argument('--num-classes', type=int, default=None, metavar='N',
                    help='number of label classes (Model default if None)')
parser.add_argument('--gp', default=None, type=str, metavar='POOL',
                    help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
parser.add_argument('--img-size', type=int, default=None, metavar='N',
                    help='Image patch size (default: None => model default)')
parser.add_argument('--input-size', default=None, nargs=3, type=int,
                    metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
parser.add_argument('--crop-pct', default=None, type=float,
                    metavar='N', help='Input image center crop percent (for validation only)')
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                    help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
                    help='Override std deviation of of dataset')
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
                    help='Image resize interpolation type (overrides model)')
parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
                    help='input batch size for training (default: 32)')
parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N',
                    help='ratio of validation batch size to training batch size (default: 1)')

# Optimizer parameters
parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
                    help='Optimizer (default: "sgd"')
parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
                    help='Optimizer Epsilon (default: None, use opt default)')
parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
                    help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                    help='Optimizer momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=0.0001,
                    help='weight decay (default: 0.0001)')
parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
                    help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--clip-mode', type=str, default='norm',
                    help='Gradient clipping mode. One of ("norm", "value", "agc")')


# Learning rate schedule parameters
parser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER',
                    help='LR scheduler (default: "step"')

parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
                    help='warmup learning rate (default: 0.0001)')
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('--start-epoch', default=None, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
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=3, 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 & regularization parameters
parser.add_argument('--no-aug', action='store_true', default=False,
                    help='Disable all training augmentation, override other train aug args')
parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
                    help='Random resize scale (default: 0.08 1.0)')
parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
                    help='Random resize aspect ratio (default: 0.75 1.33)')
parser.add_argument('--hflip', type=float, default=0.5,
                    help='Horizontal flip training aug probability')
parser.add_argument('--vflip', type=float, default=0.,
                    help='Vertical flip training aug probability')
parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                    help='Color jitter factor (default: 0.4)')
parser.add_argument('--aug-splits', type=int, default=0,
                    help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
parser.add_argument('--jsd', action='store_true', default=False,
                    help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                    help='Random erase prob (default: 0.)')
parser.add_argument('--remode', type=str, default='const',
                    help='Random erase mode (default: "const")')
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')
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"')
parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
                    help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
parser.add_argument('--smoothing', type=float, default=0.1,
                    help='Label smoothing (default: 0.1)')
parser.add_argument('--train-interpolation', type=str, default='random',
                    help='Training interpolation (random, bilinear, bicubic default: "random")')


# Model Exponential Moving Average
parser.add_argument('--model-ema-force-cpu', action='store_true', default=False,
                    help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
parser.add_argument('--model-ema-decay', type=float, default=0.9998,
                    help='decay factor for model weights moving average (default: 0.9998)')

# Misc
parser.add_argument('--seed', type=int, default=42, metavar='S',
                    help='random seed (default: 42)')
parser.add_argument('--log-interval', type=int, default=50, metavar='N',
                    help='how many batches to wait before logging training status')
parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
                    help='how many batches to wait before writing recovery checkpoint')
parser.add_argument('--checkpoint-hist', type=int, default=10, metavar='N',
                    help='number of checkpoints to keep (default: 10)')
parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
                    help='how many training processes to use (default: 1)')
parser.add_argument('--save-images', action='store_true', default=False,
                    help='save images of input bathes every log interval for debugging')
parser.add_argument('--amp', action='store_true', default=False,
                    help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
parser.add_argument('--apex-amp', action='store_true', default=False,
                    help='Use NVIDIA Apex AMP mixed precision')
parser.add_argument('--native-amp', action='store_true', default=False,
                    help='Use Native Torch AMP mixed precision')
parser.add_argument('--channels-last', action='store_true', default=False,
                    help='Use channels_last memory layout')
parser.add_argument('--pin-mem', action='store_true', default=False,
                    help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no-prefetcher', action='store_true', default=False,
                    help='disable fast prefetcher')
parser.add_argument('--output', default='', type=str, metavar='PATH',
                    help='path to output folder (default: none, current dir)')
parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
                    help='Best metric (default: "top1"')
parser.add_argument('--tta', type=int, default=0, metavar='N',
                    help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
parser.add_argument("--local_rank", default=0, type=int)
parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
                    help='use the multi-epochs-loader to save time at the beginning of every epoch')
parser.add_argument('--torchscript', dest='torchscript', action='store_true',
                    help='convert model torchscript for inference')


parser.add_argument('--annealing-weight', action='store_true', default=False,
                    help='Use annealing for dense weight')
parser.add_argument('--cls-weight', type=float, default=1.0,
                    help='Cls token prediction loss multiplier (default: 1.0)')

parser.add_argument('--dense-avg-pool', action='store_true', default=False,
                    help='Avg pool dense to single prediction and compute loss')

# Save and backup

parser.add_argument('--backup', default='', type=str, metavar='PATH',
                    help='path to backup folder (default: none), only args.csv, summary.csv and model_best.pth.tar will be backup')

# Finetune
parser.add_argument('--finetune', default='', type=str, metavar='PATH',
                    help='path to checkpoint file (default: none)')

parser.add_argument('--sam-rho', type=float, default=0.0,
                    help='if not 0.0, enable SAM with rho=sam-rho')
parser.add_argument('--sam-ratio', type=float, default=1.0,
                    help='the ratio of forward input in the first step of SAM')


def _parse_args():
    # Do we have a config file to parse?
    args_config, remaining = config_parser.parse_known_args()
    if args_config.config:
        with open(args_config.config, 'r') as f:
            cfg = yaml.safe_load(f)
            parser.set_defaults(**cfg)

    # The main arg parser parses the rest of the args, the usual
    # defaults will have been overridden if config file specified.
    args = parser.parse_args(remaining)

    # Cache the args as a text string to save them in the output dir later
    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
    return args, args_text

def backup(output_dir, backup_dir):
    if not output_dir.split('/')[-2]=='train':
        _logger.info("unable to save due to file mismatch")
        return
    f = output_dir.split('/')[-1]
    backup_exp_dir = os.path.join(backup_dir, f)
    if tf.io.gfile.exists(backup_exp_dir):
        save_items=['summary.csv','model_best.pth.tar']
        sources=[os.path.join(output_dir,item) for item in save_items]
        targets=[os.path.join(backup_exp_dir,item) for item in save_items]
        _logger.info("backup summary and best models")

        for i in range(len(sources)):
            if tf.io.gfile.exists(sources[i]):
                tf.io.gfile.copy(sources[i], targets[i],overwrite=True)
    else:
        tf.io.gfile.makedirs(backup_exp_dir)
        src=os.path.join(output_dir,'args.yaml')
        tgt=os.path.join(backup_exp_dir,'args.yaml')
        tf.io.gfile.copy(src, tgt,overwrite=True)

def main():
    setup_default_logging()
    args, args_text = _parse_args()

    args.prefetcher = not args.no_prefetcher
    args.cos_reg = args.cos_reg_component > 0
    args.distributed = False
    if 'WORLD_SIZE' in os.environ:
        args.distributed = int(os.environ['WORLD_SIZE']) > 1
    args.device = 'cuda:0'
    args.world_size = 1
    args.rank = 0  # global rank
    if args.distributed:
        args.device = 'cuda:%d' % args.local_rank
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(backend='nccl', init_method='env://')
        args.world_size = torch.distributed.get_world_size()
        args.rank = torch.distributed.get_rank()
        _logger.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
                     % (args.rank, args.world_size))
    else:
        _logger.info('Training with a single process on 1 GPUs.')
    assert args.rank >= 0

    # resolve AMP arguments based on PyTorch / Apex availability
    use_amp = None
    if args.amp:
        # `--amp` chooses native amp before apex (APEX ver not actively maintained)
        if has_native_amp:
            args.native_amp = True
        elif has_apex:
            args.apex_amp = True
    if args.apex_amp and has_apex:
        use_amp = 'apex'
    elif args.native_amp and has_native_amp:
        use_amp = 'native'
    elif args.apex_amp or args.native_amp:
        _logger.warning("Neither APEX or native Torch AMP is available, using float32. "
                        "Install NVIDA apex or upgrade to PyTorch 1.6")

    torch.manual_seed(args.seed + args.rank)
    np.random.seed(args.seed + args.rank)
    model = create_model(
        args.model,
        pretrained=args.pretrained,
        num_classes=args.num_classes,
        drop_rate=args.drop,
        drop_connect_rate=args.drop_connect,  # DEPRECATED, use drop_path
        drop_path_rate=args.drop_path,
        drop_block_rate=args.drop_block,
        global_pool=args.gp,
        bn_tf=args.bn_tf,
        bn_momentum=args.bn_momentum,
        bn_eps=args.bn_eps,
        scriptable=args.torchscript,
        checkpoint_path=args.initial_checkpoint,
        img_size=args.img_size,
        cos_reg=args.cos_reg,
        return_dense=args.return_dense,
        mix_token=args.mix_token
        )
    if args.num_classes is None:
        assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
        args.num_classes = model.num_classes  # FIXME handle model default vs config num_classes more elegantly
    
    if args.finetune:
        # import pdb;pdb.set_trace()
        load_for_transfer_learning(model=model,checkpoint_path=args.finetune,use_ema=args.model_ema, strict=False)

    if args.local_rank == 0:
        _logger.info('Model %s created, param count: %d' %
                     (args.model, sum([m.numel() for m in model.parameters()])))

    data_config = resolve_data_config(vars(args), model=model, verbose=args.local_rank == 0)

    # setup augmentation batch splits for contrastive loss or split bn
    num_aug_splits = 0
    if args.aug_splits > 0:
        assert args.aug_splits > 1, 'A split of 1 makes no sense'
        num_aug_splits = args.aug_splits

    # enable split bn (separate bn stats per batch-portion)
    if args.split_bn:
        assert num_aug_splits > 1 or args.resplit
        model = convert_splitbn_model(model, max(num_aug_splits, 2))

    # move model to GPU, enable channels last layout if set
    model.cuda()
    if args.channels_last:
        model = model.to(memory_format=torch.channels_last)

    # setup synchronized BatchNorm for distributed training
    if args.distributed and args.sync_bn:
        assert not args.split_bn
        if has_apex and use_amp != 'native':
            # Apex SyncBN preferred unless native amp is activated
            model = convert_syncbn_model(model)
        else:
            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
        if args.local_rank == 0:
            _logger.info(
                'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using '
                'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.')

    if args.torchscript:
        assert not use_amp == 'apex', 'Cannot use APEX AMP with torchscripted model'
        assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model'
        model = torch.jit.script(model)

    optimizer = create_optimizer(args, model)

    # setup automatic mixed-precision (AMP) loss scaling and op casting
    amp_autocast = suppress  # do nothing
    loss_scaler = None
    optimizers=None
    if args.sam_rho>0:
        assert use_amp=='apex', 'only support apex sam currently'
        loss_scaler = ApexScaler_SAM()
        optimizers = [SAM_ascent(optimizer), SAM_descent(optimizer), optimizer]
        model, optimizers = amp.initialize(model, optimizers, opt_level='O1')
        optimizer = optimizers[2]
        if args.local_rank == 0:
            _logger.info('Using SAM with NVIDIA APEX AMP. Training in mixed precision.')
    else:
        if use_amp == 'apex':
            model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
            loss_scaler = ApexScaler()
            if args.local_rank == 0:
                _logger.info('Using NVIDIA APEX AMP. Training in mixed precision.')
        elif use_amp == 'native':
            amp_autocast = torch.cuda.amp.autocast
            loss_scaler = NativeScaler()
            if args.local_rank == 0:
                _logger.info('Using native Torch AMP. Training in mixed precision.')
        else:
            if args.local_rank == 0:
                _logger.info('AMP not enabled. Training in float32.')

    # optionally resume from a checkpoint
    resume_epoch = None
    if args.resume:
        resume_epoch = resume_checkpoint(
            model, args.resume,
            optimizer=None if args.no_resume_opt else optimizer,
            loss_scaler=None if args.no_resume_opt else loss_scaler,
            log_info=args.local_rank == 0)

    # setup exponential moving average of model weights, SWA could be used here too
    model_ema = None
    if args.model_ema:
        # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
        model_ema = ModelEmaV2(
            model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else None)
        if args.resume:
            load_checkpoint(model_ema.module, args.resume, use_ema=True)

    # setup distributed training
    if args.distributed:
        if has_apex and use_amp != 'native':
            # Apex DDP preferred unless native amp is activated
            if args.local_rank == 0:
                _logger.info("Using NVIDIA APEX DistributedDataParallel.")
            model = ApexDDP(model, delay_allreduce=True)
        else:
            if args.local_rank == 0:
                _logger.info("Using native Torch DistributedDataParallel.")
            model = NativeDDP(model, device_ids=[args.local_rank])  # can use device str in Torch >= 1.1
        # NOTE: EMA model does not need to be wrapped by DDP

    # setup learning rate schedule and starting epoch
    lr_scheduler, num_epochs = create_scheduler(args, optimizer)
    start_epoch = 0
    if args.start_epoch is not None:
        # a specified start_epoch will always override the resume epoch
        start_epoch = args.start_epoch
    elif resume_epoch is not None:
        start_epoch = resume_epoch
    if lr_scheduler is not None and start_epoch > 0:
        lr_scheduler.step(start_epoch)

    if args.local_rank == 0:
        _logger.info('Scheduled epochs: {}'.format(num_epochs))

    # create the train and eval datasets

    # create relabel dataset
    if args.relabel_data:
        dataset_train = create_relabel_dataset(args.dataset, root=args.data_dir, relabel_root=args.relabel_data)
    else:
        dataset_train = create_dataset(
            args.dataset, root=args.data_dir, split=args.train_split, is_training=True, batch_size=args.batch_size)
    dataset_eval = create_dataset(
        args.dataset, root=args.data_dir, split=args.val_split, is_training=False, batch_size=args.batch_size)

    # setup mixup / cutmix
    collate_fn = None
    mixup_fn = None
    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
    if mixup_active:
        mixup_args = dict(
            mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
            prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
            label_smoothing=args.smoothing, num_classes=args.num_classes)
        # create relabel mixup
        if args.relabel_data:
            mixup_args['label_size']=args.relabel_size
            if args.prefetcher:
                assert not num_aug_splits
                collate_fn = FastCollateRelabelMixup(**mixup_args)
            else:
                mixup_fn = RelabelMixup(**mixup_args)
        else:
            if args.prefetcher:
                assert not num_aug_splits  # collate conflict (need to support deinterleaving in collate mixup)
                collate_fn = FastCollateMixup(**mixup_args)
            else:
                mixup_fn = Mixup(**mixup_args)

    # wrap dataset in AugMix helper
    if num_aug_splits > 1:
        dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)

    # create data loaders w/ augmentation pipeiine
    train_interpolation = args.train_interpolation
    if args.no_aug or not train_interpolation:
        train_interpolation = data_config['interpolation']
    if args.relabel and args.relabel_data:
        use_relabel = True
    else:
        use_relabel = False
    loader_train = create_relabel_loader(
        dataset_train,
        input_size=data_config['input_size'],
        batch_size=args.batch_size,
        is_training=True,
        use_prefetcher=args.prefetcher,
        no_aug=args.no_aug,
        re_prob=args.reprob,
        re_mode=args.remode,
        re_count=args.recount,
        re_split=args.resplit,
        scale=args.scale,
        ratio=args.ratio,
        hflip=args.hflip,
        vflip=args.vflip,
        color_jitter=args.color_jitter,
        auto_augment=args.aa,
        num_aug_splits=num_aug_splits,
        interpolation=train_interpolation,
        mean=data_config['mean'],
        std=data_config['std'],
        num_workers=args.workers,
        distributed=args.distributed,
        collate_fn=collate_fn,
        pin_memory=args.pin_mem,
        use_multi_epochs_loader=args.use_multi_epochs_loader,
        use_relabel=use_relabel
    )

    loader_eval = create_loader(
        dataset_eval,
        input_size=data_config['input_size'],
        batch_size=args.validation_batch_size_multiplier * args.batch_size,
        is_training=False,
        use_prefetcher=args.prefetcher,
        interpolation=data_config['interpolation'],
        mean=data_config['mean'],
        std=data_config['std'],
        num_workers=args.workers,
        distributed=args.distributed,
        crop_pct=data_config['crop_pct'],
        pin_memory=args.pin_mem,
    )

    # setup loss function
    if args.jsd:
        assert num_aug_splits > 1  # JSD only valid with aug splits set
        train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda()
    # use relabel loss
    elif args.relabel:
        if args.relabel_size==1:
            # disable dense prediction
            if args.dense_avg_pool:
                train_loss_fn = RelabelPooledCrossEntropy(dense_weight=args.dense_weight,\
                    cls_weight = args.cls_weight, mixup_active = mixup_active, smoothing = args.smoothing).cuda()
            else:
                train_loss_fn = RelabelSoftTargetCrossEntropy(n_comn = args.cos_reg_component).cuda()
        else:
            train_loss_fn = RelabelCrossEntropy(dense_weight=args.dense_weight,\
                cls_weight = args.cls_weight, mixup_active = mixup_active, smoothing = args.smoothing, n_comn = args.cos_reg_component).cuda()
    elif args.cos_reg:
        train_loss_fn = SoftTargetCrossEntropyCosReg().cuda()
    elif mixup_active:
        # smoothing is handled with mixup target transform
        train_loss_fn = SoftTargetCrossEntropy().cuda()
    elif args.smoothing:
        train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda()
    else:
        train_loss_fn = nn.CrossEntropyLoss().cuda()
    validate_loss_fn = nn.CrossEntropyLoss().cuda()
    # import pdb; pdb.set_trace()

    # setup checkpoint saver and eval metric tracking
    eval_metric = args.eval_metric
    best_metric = None
    best_epoch = None
    saver = None
    output_dir = ''
    if args.local_rank == 0:
        output_base = args.output if args.output else './output'
        exp_name = '-'.join([
            datetime.now().strftime("%Y%m%d-%H%M%S"),
            args.model,
            str(data_config['input_size'][-1])
        ])
        output_dir = get_outdir(output_base, 'train', exp_name)
        decreasing = True if eval_metric == 'loss' else False
        saver = CheckpointSaver(
            model=model, optimizer=optimizer, args=args, model_ema=model_ema, amp_scaler=loss_scaler,
            checkpoint_dir=output_dir, recovery_dir=output_dir, decreasing=decreasing, max_history=args.checkpoint_hist)
        with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
            f.write(args_text)

    try:
        for epoch in range(start_epoch, num_epochs):
            if args.distributed and hasattr(loader_train.sampler, 'set_epoch'):
                loader_train.sampler.set_epoch(epoch)
            if args.annealing_weight:
                lrl = [param_group['lr'] for param_group in optimizer.param_groups]
                currtent_lr = sum(lrl) / len(lrl)
                train_loss_fn.dense_weight = args.dense_weight*currtent_lr/args.lr

            if args.eval_first:
                validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast)
            train_metrics = train_one_epoch(
                epoch, model, loader_train, optimizer, train_loss_fn, args,
                lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
                amp_autocast=amp_autocast, loss_scaler=loss_scaler, model_ema=model_ema, mixup_fn=mixup_fn, optimizers=optimizers)

            if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                if args.local_rank == 0:
                    _logger.info("Distributing BatchNorm running means and vars")
                distribute_bn(model, args.world_size, args.dist_bn == 'reduce')

            eval_metrics = validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast)

            if model_ema is not None and not args.model_ema_force_cpu:
                if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                    distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')
                ema_eval_metrics = validate(
                    model_ema.module, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, log_suffix=' (EMA)')
                eval_metrics = ema_eval_metrics

            if lr_scheduler is not None:
                # step LR for next epoch
                lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])

            update_summary(
                epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'),
                write_header=best_metric is None)

            if saver is not None:
                # save proper checkpoint with eval metric
                save_metric = eval_metrics[eval_metric]
                best_metric, best_epoch = saver.save_checkpoint(epoch, metric=save_metric)
            if args.backup and args.local_rank == 0:
                backup(output_dir, args.backup)
    except KeyboardInterrupt:
        pass
    if best_metric is not None:
        _logger.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))


def train_one_epoch(
        epoch, model, loader, optimizer, loss_fn, args,
        lr_scheduler=None, saver=None, output_dir='', amp_autocast=suppress,
        loss_scaler=None, model_ema=None, mixup_fn=None, optimizers = None):

    if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
        if args.prefetcher and loader.mixup_enabled:
            loader.mixup_enabled = False
        elif mixup_fn is not None:
            mixup_fn.mixup_enabled = False

    second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
    batch_time_m = AverageMeter()
    data_time_m = AverageMeter()
    losses_m = AverageMeter()

    model.train()

    end = time.time()
    last_idx = len(loader) - 1
    num_updates = epoch * len(loader)
    for batch_idx, (input, target) in enumerate(loader):
        last_batch = batch_idx == last_idx
        data_time_m.update(time.time() - end)
        if not args.prefetcher:
            input, target = input.cuda(), target.cuda()
            if mixup_fn is not None:
                input, target = mixup_fn(input, target)
            else:
                # handle relabel without mixup
                if args.relabel and args.relabel_data:
                    target=create_relabel_target(target,num_classes=args.num_classes,
                        smoothing=args.smoothing, label_size=args.relabel_size)
                if len(target.shape)==1:
                    target=create_relabel_target(target,num_classes=args.num_classes,
                        smoothing=args.smoothing)
        else:
            if args.relabel and args.relabel_data and not loader.mixup_enabled:
                target=create_relabel_target(target,num_classes=args.num_classes,
                    smoothing=args.smoothing, label_size=args.relabel_size)
            if args.relabel and len(target.shape)==1:
                target=create_relabel_target(target,num_classes=args.num_classes,
                    smoothing=args.smoothing)
        if args.channels_last:
            input = input.contiguous(memory_format=torch.channels_last)
        if args.sam_rho>0:
            # step 1 
            first_step_btz = int(input.shape[0]*args.sam_ratio)
            output = model(input[:first_step_btz])
            loss = loss_fn(output, target[:first_step_btz])
            optimizer.zero_grad()
            loss_scaler(
                loss, optimizers[0],
                clip_grad=args.clip_grad, clip_mode=args.clip_mode,
                parameters=model_parameters(model, exclude_head='agc' in args.clip_mode),
                create_graph=second_order, step=1, rho=args.sam_rho)

            # step 2
            output = model(input)
            loss = loss_fn(output, target)
            optimizer.zero_grad()
            optimizers[1].state=optimizers[0].state
            loss_scaler(
                loss, optimizers[1],
                clip_grad=args.clip_grad, clip_mode=args.clip_mode,
                parameters=model_parameters(model, exclude_head='agc' in args.clip_mode),
                create_graph=second_order, step=2, rho=args.sam_rho)
            if not args.distributed:
                losses_m.update(loss.item(), input.size(0))
        else:
            with amp_autocast():
                if args.cos_reg_component > 0:
                    output, atten_list = model(input)
                    loss = loss_fn(output, target, atten = atten_list)
                else:
                    output = model(input)
                    # import pdb;pdb.set_trace()
                    loss = loss_fn(output, target)

            if not args.distributed:
                losses_m.update(loss.item(), input.size(0))

            optimizer.zero_grad()
            if loss_scaler is not None:
                loss_scaler(
                    loss, optimizer,
                    clip_grad=args.clip_grad, clip_mode=args.clip_mode,
                    parameters=model_parameters(model, exclude_head='agc' in args.clip_mode),
                    create_graph=second_order)
            else:
                loss.backward(create_graph=second_order)
                if args.clip_grad is not None:
                    dispatch_clip_grad(
                        model_parameters(model, exclude_head='agc' in args.clip_mode),
                        value=args.clip_grad, mode=args.clip_mode)
                optimizer.step()

        if model_ema is not None:
            model_ema.update(model)

        torch.cuda.synchronize()
        num_updates += 1
        batch_time_m.update(time.time() - end)
        if last_batch or batch_idx % args.log_interval == 0:
            lrl = [param_group['lr'] for param_group in optimizer.param_groups]
            lr = sum(lrl) / len(lrl)

            if args.distributed:
                reduced_loss = reduce_tensor(loss.data, args.world_size)
                losses_m.update(reduced_loss.item(), input.size(0))

            if args.local_rank == 0:
                _logger.info(
                    'Train: {} [{:>4d}/{} ({:>3.0f}%)]  '
                    'Loss: {loss.val:>9.6f} ({loss.avg:>6.4f})  '
                    'Time: {batch_time.val:.3f}s, {rate:>7.2f}/s  '
                    '({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  '
                    'LR: {lr:.3e}  '
                    'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format(
                        epoch,
                        batch_idx, len(loader),
                        100. * batch_idx / last_idx,
                        loss=losses_m,
                        batch_time=batch_time_m,
                        rate=input.size(0) * args.world_size / batch_time_m.val,
                        rate_avg=input.size(0) * args.world_size / batch_time_m.avg,
                        lr=lr,
                        data_time=data_time_m))

                if args.save_images and output_dir:
                    torchvision.utils.save_image(
                        input,
                        os.path.join(output_dir, 'train-batch-%d.jpg' % batch_idx),
                        padding=0,
                        normalize=True)

        if saver is not None and args.recovery_interval and (
                last_batch or (batch_idx + 1) % args.recovery_interval == 0):
            saver.save_recovery(epoch, batch_idx=batch_idx)

        if lr_scheduler is not None:
            lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg)

        end = time.time()
        # end for

    if hasattr(optimizer, 'sync_lookahead'):
        optimizer.sync_lookahead()

    return OrderedDict([('loss', losses_m.avg)])


def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=''):
    batch_time_m = AverageMeter()
    losses_m = AverageMeter()
    top1_m = AverageMeter()
    top5_m = AverageMeter()

    model.eval()

    end = time.time()
    last_idx = len(loader) - 1
    with torch.no_grad():
        for batch_idx, (input, target) in enumerate(loader):
            last_batch = batch_idx == last_idx
            if not args.prefetcher:
                input = input.cuda()
                target = target.cuda()
            if args.channels_last:
                input = input.contiguous(memory_format=torch.channels_last)

            with amp_autocast():
                output = model(input)
            if isinstance(output, (tuple, list)):
                output = output[0]
                if args.cls_weight==0:
                    output=output[1].mean(1)

            # augmentation reduction
            reduce_factor = args.tta
            if reduce_factor > 1:
                output = output.unfold(0, reduce_factor, reduce_factor).mean(dim=2)
                target = target[0:target.size(0):reduce_factor]

            loss = loss_fn(output, target)
            acc1, acc5 = accuracy(output, target, topk=(1, 5))

            if args.distributed:
                reduced_loss = reduce_tensor(loss.data, args.world_size)
                acc1 = reduce_tensor(acc1, args.world_size)
                acc5 = reduce_tensor(acc5, args.world_size)
            else:
                reduced_loss = loss.data

            torch.cuda.synchronize()

            losses_m.update(reduced_loss.item(), input.size(0))
            top1_m.update(acc1.item(), output.size(0))
            top5_m.update(acc5.item(), output.size(0))

            batch_time_m.update(time.time() - end)
            end = time.time()
            if args.local_rank == 0 and (last_batch or batch_idx % args.log_interval == 0):
                log_name = 'Test' + log_suffix
                _logger.info(
                    '{0}: [{1:>4d}/{2}]  '
                    'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  '
                    'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '
                    'Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  '
                    'Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(
                        log_name, batch_idx, last_idx, batch_time=batch_time_m,
                        loss=losses_m, top1=top1_m, top5=top5_m))

    metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])

    return metrics


if __name__ == '__main__':
    main()