train_gps.py

#!/usr/bin/env python3
""" ImageNet Training Script

This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
training results with some of the latest networks and training techniques. It favours canonical PyTorch
and standard Python style over trying to be able to 'do it all.' That said, it offers quite a few speed
and training result improvements over the usual PyTorch example scripts. Repurpose as you see fit.

This script was started from an early version of the PyTorch ImageNet example
(https://github.com/pytorch/examples/tree/master/imagenet)

NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples
(https://github.com/NVIDIA/apex/tree/master/examples/imagenet)

Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
"""
import time
import os
import logging
import numpy as np
from collections import OrderedDict
from contextlib import suppress
from datetime import datetime

import torch
import torch.nn as nn
import torchvision.utils
from torch.nn.parallel import DistributedDataParallel as NativeDDP

from timm.data import resolve_data_config, Mixup, FastCollateMixup, AugMixDataset


from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint,\
    convert_splitbn_model, model_parameters
from timm.utils import *
from timm.loss import *

from timm.scheduler import create_scheduler
from timm.utils import ApexScaler, NativeScaler


from data import create_loader, create_dataset
from optim_factory import create_optimizer_v2, optimizer_kwargs

from models import vision_transformer, swin_transformer, convnext, as_mlp

from utils.cuda import  NativeScaler
import ipdb

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

try:
    import wandb
    has_wandb = True
except ImportError: 
    has_wandb = False

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


from args import _parse_args


from utils.loss_utils import SupConLoss
from utils.pruning import prune_by_percentile_gradient_perCell

import os
import ssl

ssl._create_default_https_context = ssl._create_unverified_context


def create_dataload(args, num_aug_splits, data_config):
    # create the train and eval datasets
    dataset_grad = create_dataset(
        args.dataset, root=args.data_dir, split=args.train_split, is_training=True,
        class_map=args.class_map,
        download=args.dataset_download,
        batch_size=args.batch_size,
        repeats=args.epoch_repeats)
    dataset_train = create_dataset(
        args.dataset, root=args.data_dir, split=args.train_split, is_training=True,
        class_map=args.class_map,
        download=args.dataset_download,
        batch_size=args.batch_size,
        repeats=args.epoch_repeats)
    dataset_eval = create_dataset(
        args.dataset, root=args.data_dir, split=args.val_split, is_training=False,
        class_map=args.class_map,
        download=args.dataset_download,
        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)
        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)
    else:
        print(" NO mixup cutmix")

    # 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']
    
    loader_grad = create_loader(
        dataset_grad,
        input_size=data_config['input_size'],
        batch_size=args.batch_size,
        is_training=True,
        use_prefetcher=args.prefetcher,
        no_aug=args.no_aug,
        simple_aug=args.simple_aug,
        contrast_aug=args.contrast_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_repeats=args.aug_repeats,
        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,
        worker_seeding=args.worker_seeding,
    )
    
    loader_train = create_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,
        simple_aug=args.simple_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_repeats=args.aug_repeats,
        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,
        worker_seeding=args.worker_seeding,
    )

    loader_eval = create_loader(
        dataset_eval,
        input_size=data_config['input_size'],
        batch_size=args.validation_batch_size or args.batch_size,
        is_training=False,
        use_prefetcher=args.prefetcher,
        direct_resize=args.direct_resize,
        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,
    )

    return  loader_grad, loader_train, loader_eval, mixup_active, mixup_fn



def main(args_input = None):
    setup_default_logging()
    args, args_text = _parse_args(args_input)
    if type(args.dataset) ==list and len(args.dataset)==1:
        args.dataset = args.dataset[0]

    args.output = os.path.join(args.output,args.dataset)
    args.run_name = f"{args.dataset}_bz{args.batch_size}_lr{args.lr}_num{args.times_para}" + f"_{args.run_name}"
    args.output = os.path.join(args.output,args.run_name)

    os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id

    if args.log_wandb:
        if has_wandb:
            wandb.init(project=args.experiment, config=args, name=args.run_name)
        else: 
            _logger.warning("You've requested to log metrics to wandb but package not found. "
                            "Metrics not being logged to wandb, try `pip install wandb`")
             
    args.prefetcher = not args.no_prefetcher
    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")

    random_seed(args.seed, args.rank)

    if args.fuser:
        set_jit_fuser(args.fuser)

    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_momentum=args.bn_momentum,
        bn_eps=args.bn_eps,
        scriptable=args.torchscript,
        checkpoint_path=args.initial_checkpoint,
        tuning_mode=args.tuning_mode)

    if args.log_wandb and has_wandb:
        wandb.watch(model, log="all")

    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.grad_checkpointing:
        model.set_grad_checkpointing(enable=True)


    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 == 'apex':
            # 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_v2(model, **optimizer_kwargs(cfg=args))

    if args.local_rank == 0:
        _logger.info(
            f'Model {safe_model_name(args.model)} created, param count:{sum([m.numel() for m in model.parameters()])}')
        _logger.info(f"number of params for requires grad: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")


    # setup automatic mixed-precision (AMP) loss scaling and op casting
    amp_autocast = suppress  # do nothing
    loss_scaler = None
    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 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 == 'apex':
            # 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], broadcast_buffers=not args.no_ddp_bb)
        # 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))

    loader_grad, loader_train, loader_eval, mixup_active, mixup_fn = create_dataload(args, num_aug_splits, data_config)


    # setup loss function
    if args.jsd_loss:
        assert num_aug_splits > 1  # JSD only valid with aug splits set
        train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing)
    elif mixup_active:
        # smoothing is handled with mixup target transform which outputs sparse, soft targets
        if args.bce_loss:
            train_loss_fn = BinaryCrossEntropy(target_threshold=args.bce_target_thresh)
        else:
            train_loss_fn = SoftTargetCrossEntropy()
    elif args.smoothing:
        if args.bce_loss:
            train_loss_fn = BinaryCrossEntropy(smoothing=args.smoothing, target_threshold=args.bce_target_thresh)
        else:
            train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
    else:
        train_loss_fn = nn.CrossEntropyLoss()
        print("8888888888888888888888888888888888 using  CrossEntropyLoss 88888888888888888888888888888888888888888")

    train_loss_fn = train_loss_fn.cuda()
    train_loss_co = SupConLoss().cuda()
    validate_loss_fn = nn.CrossEntropyLoss().cuda()
        
    # setup checkpoint saver and eval metric tracking
    eval_metric = args.eval_metric
    best_metric = None
    best_metric_original = 0
    best_metric_ema = 0
    best_epoch = None
    saver = None
    output_dir = None
    if args.rank == 0:
        if args.experiment:
            exp_name = args.experiment
        else:
            exp_name = '-'.join([
                datetime.now().strftime("%Y%m%d-%H%M%S"),
                safe_model_name(args.model),
                str(data_config['input_size'][-1])
            ])
        output_dir = get_outdir(args.output if args.output else './output/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)
        # saver = None

    if args.evaluate:
        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 saver is not None:
            # save proper checkpoint with eval metric
            save_metric = eval_metrics[eval_metric]
            best_metric, best_epoch = saver.save_checkpoint(start_epoch, metric=save_metric)

        return


    if args.pruning:
        if args.pruning_method == 'gradient_perCell':
            if args.contrastive:
                calculate_gradient(model, loader_grad, optimizer, train_loss_co, args, amp_autocast=amp_autocast)
            else:
                calculate_gradient(model, loader_grad, optimizer, train_loss_fn, args, amp_autocast=amp_autocast)
            mask = prune_by_percentile_gradient_perCell(model, args.times_para)
    else:
        mask = None


    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)

            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, mask=mask, tuning_mode=args.tuning_mode)

            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 output_dir is not None:
                update_summary(
                    epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'),
                    write_header=best_metric is None, log_wandb=args.log_wandb and has_wandb)

            if best_metric_original < eval_metrics["top1"]:
                best_metric_original = eval_metrics["top1"]
            best_metric_dic_original = OrderedDict(epoch=epoch)
            best_metric_dic_original.update([('best_eval_acc_top_1_original', best_metric_original)])
            if args.log_wandb and has_wandb:
                wandb.log(best_metric_dic_original)

            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

                temp_ema_eval_metrics = OrderedDict(epoch=epoch)
                temp_ema_eval_metrics.update([('eval_' + k + '_ema', v) for k, v in ema_eval_metrics.items()])

                if args.log_wandb and has_wandb:
                    wandb.log(temp_ema_eval_metrics)

                if best_metric_ema < ema_eval_metrics["top1"]:
                    best_metric_ema = ema_eval_metrics["top1"]
                best_metric_dic_ema = OrderedDict(epoch=epoch)
                best_metric_dic_ema.update([('best_eval_acc_top_1_ema', best_metric_ema)])
                if args.log_wandb and has_wandb:
                    wandb.log(best_metric_dic_ema)

            if lr_scheduler is not None:
                # step LR for next epoch
                if model_ema is not None and not args.model_ema_force_cpu:
                    lr_scheduler.step(epoch + 1, ema_eval_metrics[eval_metric])
                else:
                    lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])



            if saver is not None:
                # save proper checkpoint with eval metric
                if model_ema is not None and not args.model_ema_force_cpu:
                    save_metric = ema_eval_metrics[eval_metric]
                else:
                    save_metric = eval_metrics[eval_metric]
                best_metric, best_epoch = saver.save_checkpoint(epoch, metric=save_metric)



    except KeyboardInterrupt:
        pass
    if best_metric is not None:
        _logger.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))

    if args.log_wandb and has_wandb:
        wandb.finish()


def calculate_gradient(model, loader, optimizer, loss_fn, args, amp_autocast=suppress, log_suffix=''):
    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()
    optimizer.zero_grad()

    end = time.time()
    last_idx = len(loader) - 1


    for batch_idx, (input, target) in enumerate(loader):
        last_batch = batch_idx == last_idx
        data_time_m.update(time.time() - end)
        if args.contrastive:
            input = torch.cat([input[0], input[1]], dim=0)
        bsz = target.shape[0]
        if not args.prefetcher:
            input, target = input.cuda(), target.cuda()
        if args.channels_last:
            input = input.contiguous(memory_format=torch.channels_last)

        with amp_autocast():
            if args.contrastive:
                _, feature = model(input, return_feature=True)
                f1, f2 = torch.split(feature, [bsz, bsz], dim=0)
                feature = torch.cat([f1.unsqueeze(1), f2.unsqueeze(1)], dim=1)
                loss = loss_fn(feature, target)
            else:
                output = model(input)
                loss = loss_fn(output, target)
        
        if not args.distributed:
            losses_m.update(loss.item(), input.size(0))
        else:
            reduced_loss = reduce_tensor(loss.data, args.world_size)
            losses_m.update(reduced_loss.item(), input.size(0))

        loss.backward(create_graph=second_order)

        torch.cuda.synchronize()
        batch_time_m.update(time.time() - end)

        if args.local_rank == 0 and (last_batch or batch_idx % args.log_interval == 0):
            log_name = 'Gradient' + 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})'.format(
                    log_name, batch_idx, last_idx, batch_time=batch_time_m, loss=losses_m))
    
        end = time.time()
    
    return 


def train_one_epoch(
        epoch, model, loader, optimizer, loss_fn, args,
        lr_scheduler=None, saver=None, output_dir=None, amp_autocast=suppress,
        loss_scaler=None, model_ema=None, mixup_fn=None, mask=None, tuning_mode=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)
        if args.channels_last:
            input = input.contiguous(memory_format=torch.channels_last)

        with amp_autocast():
            output = model(input)
            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,
                mask=mask, model=model, tuning_mode = tuning_mode)
        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:#.4g} ({loss.avg:#.3g})  '
                    '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()

    lrl = [param_group['lr'] for param_group in optimizer.param_groups]
    lr = sum(lrl) / len(lrl)
    if args.log_wandb and has_wandb:
        wandb.log({"lr": lr,
                   "epoch": epoch,})

    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]

            # 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()