Blame for gmnet/code/original_release/train_imagenet.py

import argparse
import logging
import os
import time
import glob
from collections import OrderedDict
from contextlib import suppress
from datetime import datetime
import shutil
import operator
import numpy as np

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

from timm import utils
from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
from timm.loss import JsdCrossEntropy, SoftTargetCrossEntropy, BinaryCrossEntropy, \
    LabelSmoothingCrossEntropy
from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint, \
    convert_splitbn_model, convert_sync_batchnorm, model_parameters, set_fast_norm
from timm.optim import create_optimizer_v2, optimizer_kwargs
from timm.scheduler import create_scheduler
# from timm.utils import ApexScaler, NativeScaler
from utils import ApexScalerAccum as ApexScaler
from utils import NativeScalerAccum as NativeScaler
from utils import auto_resume_helper, DistillationLoss
import gmnet
import pdb


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

try:
    from functorch.compile import memory_efficient_fusion
    has_functorch = True
except ImportError as e:
    has_functorch = False


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 parameters
group = parser.add_argument_group('Dataset parameters')
# Keep this argument outside of the dataset group because it is positional.
parser.add_argument('--data', metavar='DIR',
                    help='path to dataset')
group.add_argument('--dataset', '-d', metavar='NAME', default='',
                    help='dataset type (default: ImageFolder/ImageTar if empty)')
group.add_argument('--train-split', metavar='NAME', default='train',
                    help='dataset train split (default: train)')
group.add_argument('--val-split', metavar='NAME', default='validation',
                    help='dataset validation split (default: validation)')
group.add_argument('--dataset-download', action='store_true', default=False,
                    help='Allow download of dataset for torch/ and tfds/ datasets that support it.')
group.add_argument('--class-map', default='', type=str, metavar='FILENAME',
                    help='path to class to idx mapping file (default: "")')

# Model parameters
group = parser.add_argument_group('Model parameters')
group.add_argument('--model', default='resnet50', type=str, metavar='MODEL',
                    help='Name of model to train (default: "resnet50"')
group.add_argument('--pretrained', action='store_true', default=False,
                    help='Start with pretrained version of specified network (if avail)')
group.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
                    help='Initialize model from this checkpoint (default: none)')
group.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='Resume full model and optimizer state from checkpoint (default: none)')
group.add_argument('--no-resume-opt', action='store_true', default=False,
                    help='prevent resume of optimizer state when resuming model')
group.add_argument('--num-classes', type=int, default=None, metavar='N',
                    help='number of label classes (Model default if None)')
group.add_argument('--gp', default=None, type=str, metavar='POOL',
                    help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
group.add_argument('--img-size', type=int, default=None, metavar='N',
                    help='Image patch size (default: None => model default)')
group.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')
group.add_argument('--crop-pct', default=None, type=float,
                    metavar='N', help='Input image center crop percent (for validation only)')
group.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                    help='Override mean pixel value of dataset')
group.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
                    help='Override std deviation of dataset')
group.add_argument('--interpolation', default='', type=str, metavar='NAME',
                    help='Image resize interpolation type (overrides model)')
group.add_argument('-b', '--batch-size', type=int, default=128, metavar='N',
                    help='Input batch size for training (default: 128)')
group.add_argument('-vb', '--validation-batch-size', type=int, default=None, metavar='N',
                    help='Validation batch size override (default: None)')
group.add_argument('--channels-last', action='store_true', default=False,
                    help='Use channels_last memory layout')
scripting_group = group.add_mutually_exclusive_group()
scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true',
                    help='torch.jit.script the full model')
scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
                    help="Enable AOT Autograd support. (It's recommended to use this option with `--fuser nvfuser` together)")
group.add_argument('--fuser', default='', type=str,
                    help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
group.add_argument('--fast-norm', default=False, action='store_true',
                    help='enable experimental fast-norm')
group.add_argument('--grad-checkpointing', action='store_true', default=False,
                    help='Enable gradient checkpointing through model blocks/stages')

# Optimizer parameters
group = parser.add_argument_group('Optimizer parameters')
group.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
                    help='Optimizer (default: "adamw"')
group.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
                    help='Optimizer Epsilon (default: None, use opt default)')
group.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
                    help='Optimizer Betas (default: None, use opt default)')
group.add_argument('--momentum', type=float, default=0.9, metavar='M',
                    help='Optimizer momentum (default: 0.9)')
group.add_argument('--weight-decay', type=float, default=0.05,
                    help='weight decay (default: 0.05)')
group.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
                    help='Clip gradient norm (default: None, no clipping)')
group.add_argument('--clip-mode', type=str, default='norm',
                    help='Gradient clipping mode. One of ("norm", "value", "agc")')
group.add_argument('--layer-decay', type=float, default=None,
                    help='layer-wise learning rate decay (default: None)')

# Learning rate schedule parameters
group = parser.add_argument_group('Learning rate schedule parameters')
group.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                    help='LR scheduler (default: "cosine"')
group.add_argument('--lr', type=float, default=0.05, metavar='LR',
                    help='learning rate (default: 0.05)')
group.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                    help='learning rate noise on/off epoch percentages')
group.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                    help='learning rate noise limit percent (default: 0.67)')
group.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                    help='learning rate noise std-dev (default: 1.0)')
group.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
                    help='learning rate cycle len multiplier (default: 1.0)')
group.add_argument('--lr-cycle-decay', type=float, default=0.5, metavar='MULT',
                    help='amount to decay each learning rate cycle (default: 0.5)')
group.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
                    help='learning rate cycle limit, cycles enabled if > 1')
group.add_argument('--lr-k-decay', type=float, default=1.0,
                    help='learning rate k-decay for cosine/poly (default: 1.0)')
group.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR',
                    help='warmup learning rate (default: 1e-6)')
group.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                    help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
group.add_argument('--epochs', type=int, default=300, metavar='N',
                    help='number of epochs to train (default: 300)')
parser.add_argument('--grad-accum-steps', default=1, type=int,
                    help='gradient accumulation steps')
group.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
                    help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
group.add_argument('--start-epoch', default=None, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
group.add_argument('--decay-milestones', default=[30, 60], type=int, nargs='+', metavar="MILESTONES",
                    help='list of decay epoch indices for multistep lr. must be increasing')
group.add_argument('--decay-epochs', type=float, default=100, metavar='N',
                    help='epoch interval to decay LR')
group.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
                    help='epochs to warmup LR, if scheduler supports')
group.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
                    help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
group.add_argument('--patience-epochs', type=int, default=10, metavar='N',
                    help='patience epochs for Plateau LR scheduler (default: 10')
group.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                    help='LR decay rate (default: 0.1)')

# Augmentation & regularization parameters
group = parser.add_argument_group('Augmentation and regularization parameters')
group.add_argument('--no-aug', action='store_true', default=False,
                    help='Disable all training augmentation, override other train aug args')
group.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
                    help='Random resize scale (default: 0.08 1.0)')
group.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
                    help='Random resize aspect ratio (default: 0.75 1.33)')
group.add_argument('--hflip', type=float, default=0.5,
                    help='Horizontal flip training aug probability')
group.add_argument('--vflip', type=float, default=0.,
                    help='Vertical flip training aug probability')
group.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                    help='Color jitter factor (default: 0.4)')
group.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME', ## rand-m1-mstd0.5-inc1
                    help='Use AutoAugment policy. "v0" or "original". (default: rand-m9-mstd0.5-inc1)'),
group.add_argument('--aug-repeats', type=float, default=0,
                    help='Number of augmentation repetitions (distributed training only) (default: 0)')
group.add_argument('--aug-splits', type=int, default=0,
                    help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
group.add_argument('--jsd-loss', action='store_true', default=False,
                    help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
group.add_argument('--bce-loss', action='store_true', default=False,
                    help='Enable BCE loss w/ Mixup/CutMix use.')
group.add_argument('--bce-target-thresh', type=float, default=None,
                    help='Threshold for binarizing softened BCE targets (default: None, disabled)')
group.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
                    help='Random erase prob (default: 0.25)')
group.add_argument('--remode', type=str, default='pixel',
                    help='Random erase mode (default: "pixel")')
group.add_argument('--recount', type=int, default=1,
                    help='Random erase count (default: 1)')
group.add_argument('--resplit', action='store_true', default=False,
                    help='Do not random erase first (clean) augmentation split')
group.add_argument('--mixup', type=float, default=0.8,
                    help='mixup alpha, mixup enabled if > 0. (default: 0.8)')
group.add_argument('--cutmix', type=float, default=1.0,
                    help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)')
group.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
                    help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
group.add_argument('--mixup-prob', type=float, default=1.0,
                    help='Probability of performing mixup or cutmix when either/both is enabled')
group.add_argument('--mixup-switch-prob', type=float, default=0.5,
                    help='Probability of switching to cutmix when both mixup and cutmix enabled')
group.add_argument('--mixup-mode', type=str, default='batch',
                    help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
group.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
                    help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
group.add_argument('--smoothing', type=float, default=0.1,
                    help='Label smoothing (default: 0.1)')
group.add_argument('--train-interpolation', type=str, default='random',
                    help='Training interpolation (random, bilinear, bicubic default: "random")')
group.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                    help='Dropout rate (default: 0.)')
group.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
                    help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
group.add_argument('--drop-path', type=float, default=None, metavar='PCT',
                    help='Drop path rate (default: None)')
group.add_argument('--drop-block', type=float, default=None, metavar='PCT',
                    help='Drop block rate (default: None)')
group.add_argument('--head-dropout', type=float, default=0.0, metavar='PCT',
                    help='dropout rate for classifier (default: 0.0)')

# Batch norm parameters (only works with gen_efficientnet based models currently)
group = parser.add_argument_group('Batch norm parameters', 'Only works with gen_efficientnet based models currently.')
group.add_argument('--bn-momentum', type=float, default=None,
                    help='BatchNorm momentum override (if not None)')
group.add_argument('--bn-eps', type=float, default=None,
                    help='BatchNorm epsilon override (if not None)')
group.add_argument('--sync-bn', action='store_true',
                    help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
group.add_argument('--dist-bn', type=str, default='reduce',
                    help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
group.add_argument('--split-bn', action='store_true',
                    help='Enable separate BN layers per augmentation split.')

# Model Exponential Moving Average
group = parser.add_argument_group('Model exponential moving average parameters')
group.add_argument('--model-ema', action='store_true', default=False,
                    help='Enable tracking moving average of model weights')
group.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.')
group.add_argument('--model-ema-decay', type=float, default=0.9998,
                    help='decay factor for model weights moving average (default: 0.9998)')

# Misc
group = parser.add_argument_group('Miscellaneous parameters')
group.add_argument('--seed', type=int, default=42, metavar='S',
                    help='random seed (default: 42)')
group.add_argument('--worker-seeding', type=str, default='all',
                    help='worker seed mode (default: all)')
group.add_argument('--log-interval', type=int, default=50, metavar='N',
                    help='how many batches to wait before logging training status')
group.add_argument('--recovery-interval', type=int, default=0, metavar='N',
                    help='how many batches to wait before writing recovery checkpoint')
group.add_argument('--checkpoint-hist', type=int, default=10, metavar='N',
                    help='number of checkpoints to keep (default: 10)')
group.add_argument('-j', '--workers', type=int, default=8, metavar='N',
                    help='how many training processes to use (default: 8)')
group.add_argument('--save-images', action='store_true', default=False,
                    help='save images of input bathes every log interval for debugging')
group.add_argument('--amp', action='store_true', default=False,
                    help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
group.add_argument('--apex-amp', action='store_true', default=False,
                    help='Use NVIDIA Apex AMP mixed precision')
group.add_argument('--native-amp', action='store_true', default=False,
                    help='Use Native Torch AMP mixed precision')
group.add_argument('--no-ddp-bb', action='store_true', default=False,
                    help='Force broadcast buffers for native DDP to off.')
group.add_argument('--pin-mem', action='store_true', default=False,
                    help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
group.add_argument('--no-prefetcher', action='store_true', default=False,
                    help='disable fast prefetcher')
group.add_argument('--output', default='/scratch/wang.huan/yf/starnet_output/', type=str, metavar='PATH',
                    help='path to output folder (default: none, current dir)')
group.add_argument('--experiment', default='', type=str, metavar='NAME',
                    help='name of train experiment, name of sub-folder for output')
group.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
                    help='Best metric (default: "top1"')
group.add_argument('--tta', type=int, default=0, metavar='N',
                    help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
group.add_argument("--local_rank", default=0, type=int)
group.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')
group.add_argument('--log-wandb', action='store_true', default=False,
                    help='log training and validation metrics to wandb')
group.add_argument('--auto-resume', action='store_true', default=False,
                    help='If use auto-resume to automatically resume, mainly for MS cluster.')
group.add_argument('--exp_tag', default='', type=str, metavar='NAME',
                    help='add tag for particular experiment')


# Distillation parameters
group = parser.add_argument_group('Distillation Parameters')
group.add_argument('--teacher_model', default='regnety_160', type=str, metavar='MODEL',
                    help='Name of teacher model to train (default: "regnety_160"')
group.add_argument('--teacher-path', type=str,
                    default='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth')
group.add_argument('--distillation-type', default='none',
                    choices=['none', 'soft', 'hard'], type=str, help="")
group.add_argument('--distillation-alpha',
                    default=0.9, type=float, help="")
group.add_argument('--distillation-tau', default=4.0, type=float, help="")

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 main():
    utils.setup_default_logging()
    args, args_text = _parse_args()

    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
    #
    #print(args.distributed)
    if args.distributed:
        if 'LOCAL_RANK' in os.environ:
            args.local_rank = int(os.getenv('LOCAL_RANK'))
        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

    if args.rank == 0 and args.log_wandb:
        if has_wandb:
            wandb.init(project=args.experiment, config=args)
        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`")

    # 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")

    utils.random_seed(args.seed, args.rank)

    if args.fuser:
        utils.set_jit_fuser(args.fuser)
    if args.fast_norm:
        set_fast_norm()

        

    create_model_args = dict(
        model_name=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
    )

    model = create_model(**create_model_args)
    # import pdb
    # pdb.set_trace()

    load_npy_weights_directly(model, args.initial_checkpoint)

    import pdb
    # pdb.set_trace()

    if args.num_classes is None:
        # assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
        try:
            args.num_classes = model.num_classes  # FIXME handle model default vs config num_classes more elegantly
        except:
            args.num_classes = 1000

    if args.grad_checkpointing:
        model.set_grad_checkpointing(enable=True)

    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()])}')

    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:
        args.dist_bn = ''  # disable dist_bn when sync BN active
        assert not args.split_bn
        if has_apex and use_amp == 'apex':
            # Apex SyncBN used with Apex AMP
            # WARNING this won't currently work with models using BatchNormAct2d
            model = convert_syncbn_model(model)
        else:
            model = 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)
    if args.aot_autograd:
        assert has_functorch, "functorch is needed for --aot-autograd"
        model = memory_efficient_fusion(model)

    optimizer = create_optimizer_v2(model, **optimizer_kwargs(cfg=args))

    # 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.')


    # auto resume for MS clusters
    if args.auto_resume:
        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),
                "bs"+str(args.batch_size),
                "lr"+str(args.lr),
                "minlr" + str(args.min_lr),
                "wd"+str(args.weight_decay),
                "warmupepoch"+str(args.warmup_epochs),
                "smooth" + str(args.smoothing),
                "mixup" + str(args.mixup),
                "cutmix" + str(args.cutmix),
                "reprob" + str(args.reprob),
                "cj" + str(args.color_jitter),
                "aa" + str(args.aa),
                "distill"+str(args.distillation_type),
                str(data_config['input_size'][-1])
            ])
        if args.exp_tag:
            exp_name = exp_name + "-Tag_" + args.exp_tag
        output_dir = args.output if args.output else os.path.join('./OUTPUT/train', exp_name)
        auto_resume_file = auto_resume_helper(output_dir)
        if auto_resume_file:
            args.resume = auto_resume_file
            if args.local_rank == 0:
                _logger.info(f"Auto resume: Change resume file from: {args.resume} to: {auto_resume_file}")
        else:
            if args.local_rank == 0:
                _logger.info(f"Auto resume: No auto resume files found, ignored.")

    # 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 = utils.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))

    # create the train and eval datasets
    dataset_train = create_dataset(
        args.dataset, root=args.data, 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, split=args.val_split, is_training=False,
        class_map=args.class_map,
        download=args.dataset_download,
        batch_size=args.batch_size)

    total_batch_size = args.batch_size * args.grad_accum_steps * args.world_size
    num_training_steps_per_epoch = len(dataset_train) // total_batch_size
    if args.local_rank == 0:
        _logger.info('Total batch size: {}'.format(total_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)

    # 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_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,
        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,
        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_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()
    train_loss_fn = train_loss_fn.cuda()
    validate_loss_fn = nn.CrossEntropyLoss().cuda()


    ##### set up the distillation related ###
    teacher_model = None
    if args.distillation_type != 'none':
        teach_pretrained = False if args.teacher_path else True
        if args.local_rank == 0:
            _logger.info(f'Using distillation, create teacher model: {args.teacher_model}')
            if teach_pretrained:
                _logger.info('Teacher model load pre-trained checkpoint')
            else:
                _logger.info(f'Teacher model load checkpoint: {args.teacher_path}')
        teacher_model = create_model(
            args.teacher_model,
            pretrained=teach_pretrained,
            num_classes=args.num_classes,
            global_pool='avg',
        )
        if args.teacher_path:
            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.cuda()
        teacher_model.eval()

    # wrap the criterion in custom DistillationLoss, no matter if use Distillation.
    # which just dispatches to the original criterion if args.distillation_type is 'none'
    train_loss_fn = DistillationLoss(
        train_loss_fn, teacher_model, args.distillation_type,
        args.distillation_alpha, args.distillation_tau, args.num_classes
    )

    # setup checkpoint saver and eval metric tracking
    eval_metric = args.eval_metric
    best_metric = None
    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),
                "bs" + str(args.batch_size),
                "lr" + str(args.lr),
                "minlr" + str(args.min_lr),
                "wd" + str(args.weight_decay),
                "warmupepoch" + str(args.warmup_epochs),
                "smooth" + str(args.smoothing),
                "mixup" + str(args.mixup),
                "cutmix" + str(args.cutmix),
                "reprob" + str(args.reprob),
                "cj" + str(args.color_jitter),
                "aa" + str(args.aa),
                "distill" + str(args.distillation_type),
                str(data_config['input_size'][-1])
            ])
        if args.exp_tag:
            exp_name = exp_name + "-Tag_" + args.exp_tag
        output_dir = utils.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)

    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,
                grad_accum_steps=args.grad_accum_steps, num_training_steps_per_epoch=num_training_steps_per_epoch
                )

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

            if output_dir is not None:
                utils.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 saver is not None:
                # save proper checkpoint with eval metric
                save_metric = eval_metrics[eval_metric]
                best_metric, best_epoch = saver.my_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))


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,
        grad_accum_steps=1, num_training_steps_per_epoch=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 = utils.AverageMeter()
    data_time_m = utils.AverageMeter()
    losses_m = utils.AverageMeter()

    model.train()
    optimizer.zero_grad()

    end = time.time()
    last_idx = len(loader) - 1
    num_updates = epoch * len(loader)
    for batch_idx, (input, target) in enumerate(loader):
        step = batch_idx // grad_accum_steps
        if step >= num_training_steps_per_epoch:
            continue
        # last_batch = batch_idx == last_idx
        last_batch = ((batch_idx + 1) // grad_accum_steps) == num_training_steps_per_epoch
        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():
            import pdb
            # pdb.set_trace()
            output = model(input)
            loss = loss_fn(input, output, target)

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


        update_grad = (batch_idx + 1) % grad_accum_steps == 0
        loss_update = loss / grad_accum_steps
        if loss_scaler is not None:
            loss_scaler(
                loss_update, 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, update_grad=update_grad)
        else:
            loss_update.backward(create_graph=second_order)
            if update_grad:
                if args.clip_grad is not None:
                    utils.dispatch_clip_grad(
                        model_parameters(model, exclude_head='agc' in args.clip_mode),
                        value=args.clip_grad, mode=args.clip_mode)
                optimizer.step()

        if update_grad:
            optimizer.zero_grad()
            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 = utils.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()

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


def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=''):
    batch_time_m = utils.AverageMeter()
    losses_m = utils.AverageMeter()
    top1_m = utils.AverageMeter()
    top5_m = utils.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 = utils.accuracy(output, target, topk=(1, 5))

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


def load_npy_weights_directly(model: nn.Module, numpy_filepath: str):
    """
    加载 NumPy 格式的权重，并将其加载到 PyTorch 模型实例中。

    参数:
        model (nn.Module): 已经实例化好的 PyTorch 模型，权重将直接加载到此实例中。
        numpy_filepath (str): NumPy 权重文件的路径 (.npy 或 .npz)。

    返回:
        bool: 如果加载成功返回 True，否则返回 False。
    """
    print(f"--- 开始从 NumPy 文件加载权重到模型实例: {numpy_filepath} ---")
    
    # --- 1. 加载 NumPy 权重 ---
    weights_dict = {}
    if numpy_filepath.endswith('.npz'):
        numpy_weights = np.load(numpy_filepath)
        weights_dict = {key: torch.from_numpy(numpy_weights[key]) for key in numpy_weights}
    elif numpy_filepath.endswith('.npy'):
        try:
            raw_data = np.load(numpy_filepath, allow_pickle=True)
            if raw_data.ndim == 0 and isinstance(raw_data.item(), dict):
                weights_dict = {k: torch.from_numpy(v) for k, v in raw_data.item().items()}
            else:
                raise ValueError("单个 .npy 文件必须包含一个字典。")
        except Exception as e:
            print(f"错误: 无法解析单个 .npy 文件为字典。{e}")
            return False
    else:
        print("错误: 不支持的文件格式。请使用 .npy 或 .npz。")
        return False

    # --- 2. 映射和构建新的 state_dict ---
    pytorch_state_dict = model.state_dict()
    new_state_dict = {}
    loaded_params_count = 0
    
    for np_key, np_tensor in weights_dict.items():
        # --- 键名对齐逻辑 ---
        pt_key = np_key
        
        # 1. 修复 stages.x.y.f1/g.conv.* -> stages.x.y.f1/g.layer.* 的差异
        if '.f1.conv.' in pt_key:
            pt_key = pt_key.replace('.f1.conv.', '.f1.layer.')
        if '.g.conv.' in pt_key:
            pt_key = pt_key.replace('.g.conv.', '.g.layer.')
        
        # 2. 修复 stages.x.y.f1/g.bn.* 键（在 PyTorch state_dict 中不存在）
        # 如果模型定义中 f1/g 没有 BN 层，则必须跳过这些键。
        if '.f1.bn.' in np_key or '.g.bn.' in np_key:
            print(f"  [跳过] 键 '{np_key}' (可能因 f1/g 没有 BN 层而多余)。")
            continue
        
        # 3. 修复 stages.x.y.gamma 键（如果有）
        # 暂时不需要额外操作。

        # --- 形状处理和赋值 ---
        mapped_tensor = np_tensor.clone().float() # 确保是浮点数且独立

        
        if pt_key in pytorch_state_dict:
            target_shape = pytorch_state_dict[pt_key].shape
            
            # --- ！！！关键形状检查和转置逻辑 ！！！ ---
            if mapped_tensor.shape != target_shape:
                
                # [新增逻辑] 检查是否为 Pointwise Conv (4D) 到 Linear (2D) 的转换
                if mapped_tensor.dim() == 4 and mapped_tensor.size()[-2:] == torch.Size([1, 1]):
                    # 尝试 Squeeze 移除最后的 1x1 维度
                    temp_tensor = mapped_tensor.squeeze()
                    if temp_tensor.shape == target_shape:
                        print(f"  [Squeeze] 键 '{pt_key}' 形状不匹配 ({mapped_tensor.size()} vs {target_shape})，已执行 Squeeze (4D -> 2D)。")
                        mapped_tensor = temp_tensor
                        
                # 尝试对 Linear Layer/FC Head 进行转置 (最常见的需求)
                if mapped_tensor.dim() == 2 and mapped_tensor.T.shape == target_shape:
                    print(f"  [转置] 键 '{pt_key}' 形状不匹配 ({mapped_tensor.size()} vs {target_shape})，已尝试转置。")
                    mapped_tensor = mapped_tensor.T
                
                # 尝试对 Conv Layer 进行 permute (如果 NumPy 格式是 [H, W, in_C, out_C])
                # 只有当 NumPy 存储的 Conv 格式不是 PyTorch 默认的 [out_C, in_C, H, W] 时才需要
                elif mapped_tensor.dim() == 4 and mapped_tensor.permute(3, 2, 0, 1).shape == target_shape:
                    print(f"  [Permute] 键 '{pt_key}' 形状不匹配 ({mapped_tensor.size()} vs {target_shape})，已尝试 permute (3, 2, 0, 1)。")
                    mapped_tensor = mapped_tensor.permute(3, 2, 0, 1)

            # 最终检查形状是否匹配
            if mapped_tensor.shape == target_shape:
                new_state_dict[pt_key] = mapped_tensor
                loaded_params_count += 1
            else:
                print(f"警告: 形状依然不匹配。PyTorch: {target_shape}, NumPy: {mapped_tensor.size()}. 键: {pt_key}")
        else:
            # 这里的警告会捕获那些 NumPy 中有，但 PyTorch 模型中没有的键
            print(f"警告: NumPy 键 '{np_key}' (映射后为 '{pt_key}') 未在 PyTorch 模型中找到对应项，已跳过。")


    # --- 3. 直接加载到模型中 ---
    if loaded_params_count > 0:
        # 使用 strict=False 忽略那些 PyTorch 模型中存在但 NumPy 文件中缺失的键 
        model.load_state_dict(new_state_dict, strict=False)
        
        print(f"\n✅ 加载成功！已加载 {loaded_params_count} 个参数到模型实例。")
        return True
    else:
        print("\n❌ 加载失败：未能加载任何参数。请检查您的 NumPy 文件键名是否正确。")
        return False


class CheckpointSaver:
    def __init__(
            self,
            model,
            optimizer,
            args=None,
            model_ema=None,
            amp_scaler=None,
            checkpoint_prefix='checkpoint',
            recovery_prefix='recovery',
            checkpoint_dir='',
            recovery_dir='',
            decreasing=False,
            max_history=10,
            unwrap_fn=utils.model.unwrap_model):

        # objects to save state_dicts of
        self.model = model
        self.optimizer = optimizer
        self.args = args
        self.model_ema = model_ema
        self.amp_scaler = amp_scaler

        # state
        self.checkpoint_files = []  # (filename, metric) tuples in order of decreasing betterness
        self.best_epoch = None
        self.best_metric = None
        self.curr_recovery_file = ''
        self.last_recovery_file = ''

        # config
        self.checkpoint_dir = checkpoint_dir
        self.recovery_dir = recovery_dir
        self.save_prefix = checkpoint_prefix
        self.recovery_prefix = recovery_prefix
        self.extension = '.pth.tar'
        self.decreasing = decreasing  # a lower metric is better if True
        self.cmp = operator.lt if decreasing else operator.gt  # True if lhs better than rhs
        self.max_history = max_history
        self.unwrap_fn = unwrap_fn
        assert self.max_history >= 1

    def my_save_checkpoint(self, epoch, metric=None):
        assert epoch >= 0
        tmp_save_path = os.path.join(self.checkpoint_dir, 'tmp' + self.extension)
        last_save_path = os.path.join(self.checkpoint_dir, 'last' + self.extension)
        self._save(tmp_save_path, epoch, metric)
        # if os.path.exists(last_save_path):
        #     os.unlink(last_save_path)  # required for Windows support.
        os.rename(tmp_save_path, last_save_path)
        worst_file = self.checkpoint_files[-1] if self.checkpoint_files else None
        if (len(self.checkpoint_files) < self.max_history
                or metric is None or self.cmp(metric, worst_file[1])):
            if len(self.checkpoint_files) >= self.max_history:
                self._cleanup_checkpoints(1)
            filename = '-'.join([self.save_prefix, str(epoch)]) + self.extension
            save_path = os.path.join(self.checkpoint_dir, filename)
            shutil.copy2(last_save_path, save_path)
            self.checkpoint_files.append((save_path, metric))
            self.checkpoint_files = sorted(
                self.checkpoint_files, key=lambda x: x[1],
                reverse=not self.decreasing)  # sort in descending order if a lower metric is not better

            checkpoints_str = "Current checkpoints:\n"
            for c in self.checkpoint_files:
                checkpoints_str += ' {}\n'.format(c)
            _logger.info(checkpoints_str)

            if metric is not None and (self.best_metric is None or self.cmp(metric, self.best_metric)):
                self.best_epoch = epoch
                self.best_metric = metric
                best_save_path = os.path.join(self.checkpoint_dir, 'model_best' + self.extension)
                # if os.path.exists(best_save_path):
                #     os.unlink(best_save_path)
                shutil.copy2(last_save_path, best_save_path)

        return (None, None) if self.best_metric is None else (self.best_metric, self.best_epoch)

    def save_checkpoint(self, epoch, metric=None):
        assert epoch >= 0
        tmp_save_path = os.path.join(self.checkpoint_dir, 'tmp' + self.extension)
        last_save_path = os.path.join(self.checkpoint_dir, 'last' + self.extension)
        self._save(tmp_save_path, epoch, metric)
        if os.path.exists(last_save_path):
            os.unlink(last_save_path)  # required for Windows support.
        os.rename(tmp_save_path, last_save_path)
        worst_file = self.checkpoint_files[-1] if self.checkpoint_files else None
        if (len(self.checkpoint_files) < self.max_history
                or metric is None or self.cmp(metric, worst_file[1])):
            if len(self.checkpoint_files) >= self.max_history:
                self._cleanup_checkpoints(1)
            filename = '-'.join([self.save_prefix, str(epoch)]) + self.extension
            save_path = os.path.join(self.checkpoint_dir, filename)
            os.link(last_save_path, save_path)
            self.checkpoint_files.append((save_path, metric))
            self.checkpoint_files = sorted(
                self.checkpoint_files, key=lambda x: x[1],
                reverse=not self.decreasing)  # sort in descending order if a lower metric is not better

            checkpoints_str = "Current checkpoints:\n"
            for c in self.checkpoint_files:
                checkpoints_str += ' {}\n'.format(c)
            _logger.info(checkpoints_str)

            if metric is not None and (self.best_metric is None or self.cmp(metric, self.best_metric)):
                self.best_epoch = epoch
                self.best_metric = metric
                best_save_path = os.path.join(self.checkpoint_dir, 'model_best' + self.extension)
                if os.path.exists(best_save_path):
                    os.unlink(best_save_path)
                os.link(last_save_path, best_save_path)

        return (None, None) if self.best_metric is None else (self.best_metric, self.best_epoch)

    def _save(self, save_path, epoch, metric=None):
        save_state = {
            'epoch': epoch,
            'arch': type(self.model).__name__.lower(),
            'state_dict': utils.model.get_state_dict(self.model, self.unwrap_fn),
            'optimizer': self.optimizer.state_dict(),
            'version': 2,  # version < 2 increments epoch before save
        }
        if self.args is not None:
            save_state['arch'] = self.args.model
            save_state['args'] = self.args
        if self.amp_scaler is not None:
            save_state[self.amp_scaler.state_dict_key] = self.amp_scaler.state_dict()
        if self.model_ema is not None:
            save_state['state_dict_ema'] = utils.model.get_state_dict(self.model_ema, self.unwrap_fn)
        if metric is not None:
            save_state['metric'] = metric
        torch.save(save_state, save_path)

    def _cleanup_checkpoints(self, trim=0):
        trim = min(len(self.checkpoint_files), trim)
        delete_index = self.max_history - trim
        if delete_index < 0 or len(self.checkpoint_files) <= delete_index:
            return
        to_delete = self.checkpoint_files[delete_index:]
        for d in to_delete:
            try:
                _logger.debug("Cleaning checkpoint: {}".format(d))
                os.remove(d[0])
            except Exception as e:
                _logger.error("Exception '{}' while deleting checkpoint".format(e))
        self.checkpoint_files = self.checkpoint_files[:delete_index]

    def save_recovery(self, epoch, batch_idx=0):
        assert epoch >= 0
        filename = '-'.join([self.recovery_prefix, str(epoch), str(batch_idx)]) + self.extension
        save_path = os.path.join(self.recovery_dir, filename)
        self._save(save_path, epoch)
        if os.path.exists(self.last_recovery_file):
            try:
                _logger.debug("Cleaning recovery: {}".format(self.last_recovery_file))
                os.remove(self.last_recovery_file)
            except Exception as e:
                _logger.error("Exception '{}' while removing {}".format(e, self.last_recovery_file))
        self.last_recovery_file = self.curr_recovery_file
        self.curr_recovery_file = save_path

    def find_recovery(self):
        recovery_path = os.path.join(self.recovery_dir, self.recovery_prefix)
        files = glob.glob(recovery_path + '*' + self.extension)
        files = sorted(files)
        return files[0] if len(files) else ''



if __name__ == '__main__':
    main()