AugMixDataset的一些示例
第一个示例
# 2022.02.14-Changed for main script for wavemlp model# Huawei Technologies Co., Ltd. #!/usr/bin/env python""" ImageNet Training ScriptThis is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNettraining results with some of the latest networks and training techniques. It favours canonical PyTorchand standard Python style over trying to be able to 'do it all.' That said, it offers quite a few speedand 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 warningswarnings.filterwarnings('ignore')import argparseimport timeimport yamlimport osimport loggingfrom collections import OrderedDictfrom contextlib import suppressfrom datetime import datetimeimport mathimport torchimport torch.nn as nnimport torchvision.utilsfrom torch.nn.parallel import DistributedDataParallel as NativeDDPfrom timm.data import Dataset, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset from timm.models import create_model, resume_checkpoint, convert_splitbn_modelfrom timm.utils import *from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropyfrom timm.optim import create_optimizerfrom timm.scheduler import create_schedulerfrom timm.utils import ApexScaler, NativeScalerfrom data.myloader import create_loaderimport models.wavemlptry: from apex import amp from apex.parallel import DistributedDataParallel as ApexDDP from apex.parallel import convert_syncbn_model has_apex = Trueexcept ImportError: has_apex = Falsehas_native_amp = Falsetry: if getattr(torch.cuda.amp, 'autocast') is not None: has_native_amp = Trueexcept AttributeError: passtorch.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 belowconfig_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)parser.add_argument('-c', '--config', default='', type=str, metavar='FILE', help='YAML config file specifying default arguments')parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')# Dataset / Model parametersparser.add_argument('data', metavar='DIR', help='path to dataset')parser.add_argument('--model', default='resnet101', type=str, metavar='MODEL', help='Name of model to train (default: "countception"')parser.add_argument('--pretrained', action='store_true', default=False, help='Start with pretrained version of specified network (if avail)')parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH', help='Initialize model from this checkpoint (default: none)')parser.add_argument('--resume', default='', type=str, metavar='PATH', help='Resume full model and optimizer state from checkpoint (default: none)')parser.add_argument('--no-resume-opt', action='store_true', default=False, help='prevent resume of optimizer state when resuming model')parser.add_argument('--num-classes', type=int, default=1000, metavar='N', help='number of label classes (default: 1000)')parser.add_argument('--gp', default=None, type=str, metavar='POOL', help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')parser.add_argument('--img-size', type=int, default=None, metavar='N', help='Image patch size (default: None => model default)')parser.add_argument('--crop-pct', default=None, type=float, metavar='N', help='Input image center crop percent (for validation only)')parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN', help='Override mean pixel value of dataset')parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD', help='Override std deviation of of dataset')parser.add_argument('--interpolation', default='', type=str, metavar='NAME', help='Image resize interpolation type (overrides model)')parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N', help='input batch size for training (default: 32)')parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N', help='ratio of validation batch size to training batch size (default: 1)')# Optimizer parametersparser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER', help='Optimizer (default: "sgd"')parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON', help='Optimizer Epsilon (default: None, use opt default)')parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA', help='Optimizer Betas (default: None, use opt default)')parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='Optimizer momentum (default: 0.9)')parser.add_argument('--weight-decay', type=float, default=0.0001, help='weight decay (default: 0.0001)')parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM', help='Clip gradient norm (default: None, no clipping)')# Learning rate schedule parametersparser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER', help='LR scheduler (default: "step"')parser.add_argument('--lr', type=float, default=0.01, metavar='LR', help='learning rate (default: 0.01)')parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct', help='learning rate noise on/off epoch percentages')parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT', help='learning rate noise limit percent (default: 0.67)')parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV', help='learning rate noise std-dev (default: 1.0)')parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT', help='learning rate cycle len multiplier (default: 1.0)')parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N', help='learning rate cycle limit')parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR', help='warmup learning rate (default: 0.0001)')parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR', help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')parser.add_argument('--epochs', type=int, default=200, metavar='N', help='number of epochs to train (default: 2)')parser.add_argument('--start-epoch', default=None, type=int, metavar='N', help='manual epoch number (useful on restarts)')parser.add_argument('--decay-epochs', type=float, default=30, metavar='N', help='epoch interval to decay LR')parser.add_argument('--warmup-epochs', type=int, default=3, metavar='N', help='epochs to warmup LR, if scheduler supports')parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N', help='epochs to cooldown LR at min_lr, after cyclic schedule ends')parser.add_argument('--patience-epochs', type=int, default=10, metavar='N', help='patience epochs for Plateau LR scheduler (default: 10')parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE', help='LR decay rate (default: 0.1)')# Augmentation & regularization parametersparser.add_argument('--no-aug', action='store_true', default=False, help='Disable all training augmentation, override other train aug args')parser.add_argument('--repeated-aug', action='store_true')parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT', help='Random resize scale (default: 0.08 1.0)')parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO', help='Random resize aspect ratio (default: 0.75 1.33)')parser.add_argument('--hflip', type=float, default=0.5, help='Horizontal flip training aug probability')parser.add_argument('--vflip', type=float, default=0., help='Vertical flip training aug probability')parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT', help='Color jitter factor (default: 0.4)')parser.add_argument('--aa', type=str, default=None, metavar='NAME', help='Use AutoAugment policy. "v0" or "original". (default: None)'),parser.add_argument('--aug-splits', type=int, default=0, help='Number of augmentation splits (default: 0, valid: 0 or >=2)')parser.add_argument('--jsd', action='store_true', default=False, help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')parser.add_argument('--reprob', type=float, default=0., metavar='PCT', help='Random erase prob (default: 0.)')parser.add_argument('--remode', type=str, default='const', help='Random erase mode (default: "const")')parser.add_argument('--recount', type=int, default=1, help='Random erase count (default: 1)')parser.add_argument('--resplit', action='store_true', default=False, help='Do not random erase first (clean) augmentation split')parser.add_argument('--mixup', type=float, default=0.0, help='mixup alpha, mixup enabled if > 0. (default: 0.)')parser.add_argument('--cutmix', type=float, default=0.0, help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None, help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')parser.add_argument('--mixup-prob', type=float, default=1.0, help='Probability of performing mixup or cutmix when either/both is enabled')parser.add_argument('--mixup-switch-prob', type=float, default=0.5, help='Probability of switching to cutmix when both mixup and cutmix enabled')parser.add_argument('--mixup-mode', type=str, default='batch', help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N', help='Turn off mixup after this epoch, disabled if 0 (default: 0)')parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)')parser.add_argument('--train-interpolation', type=str, default='random', help='Training interpolation (random, bilinear, bicubic default: "random")')parser.add_argument('--drop', type=float, default=0.0, metavar='PCT', help='Dropout rate (default: 0.)')parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT', help='Drop connect rate, DEPRECATED, use drop-path (default: None)')parser.add_argument('--drop-path', type=float, default=None, metavar='PCT', help='Drop path rate (default: None)')parser.add_argument('--drop-block', type=float, default=None, metavar='PCT', help='Drop block rate (default: None)')# Batch norm parameters (only works with gen_efficientnet based models currently)parser.add_argument('--bn-tf', action='store_true', default=False, help='Use Tensorflow BatchNorm defaults for models that support it (default: False)')parser.add_argument('--bn-momentum', type=float, default=None, help='BatchNorm momentum override (if not None)')parser.add_argument('--bn-eps', type=float, default=None, help='BatchNorm epsilon override (if not None)')parser.add_argument('--sync-bn', action='store_true', help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')parser.add_argument('--dist-bn', type=str, default='', help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')parser.add_argument('--split-bn', action='store_true', help='Enable separate BN layers per augmentation split.')# Model Exponential Moving Averageparser.add_argument('--model-ema', action='store_true', default=False, help='Enable tracking moving average of model weights')parser.add_argument('--model-ema-force-cpu', action='store_true', default=False, help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')parser.add_argument('--model-ema-decay', type=float, default=0.9998, help='decay factor for model weights moving average (default: 0.9998)')# Miscparser.add_argument('--seed', type=int, default=42, metavar='S', help='random seed (default: 42)')parser.add_argument('--log-interval', type=int, default=50, metavar='N', help='how many batches to wait before logging training status')parser.add_argument('--recovery-interval', type=int, default=0, metavar='N', help='how many batches to wait before writing recovery checkpoint')parser.add_argument('-j', '--workers', type=int, default=4, metavar='N', help='how many training processes to use (default: 1)')parser.add_argument('--num-gpu', type=int, default=1, help='Number of GPUS to use')parser.add_argument('--save-images', action='store_true', default=False, help='save images of input bathes every log interval for debugging')parser.add_argument('--amp', action='store_true', default=False, help='use NVIDIA Apex AMP or Native AMP for mixed precision training')parser.add_argument('--apex-amp', action='store_true', default=False, help='Use NVIDIA Apex AMP mixed precision')parser.add_argument('--native-amp', action='store_true', default=False, help='Use Native Torch AMP mixed precision')parser.add_argument('--channels-last', action='store_true', default=False, help='Use channels_last memory layout')parser.add_argument('--pin-mem', action='store_true', default=False, help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')parser.add_argument('--no-prefetcher', action='store_true', default=False, help='disable fast prefetcher')parser.add_argument('--output', default='', type=str, metavar='PATH', help='path to output folder (default: none, current dir)')parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC', help='Best metric (default: "top1"')parser.add_argument('--tta', type=int, default=0, metavar='N', help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')parser.add_argument("--local_rank", default=0, type=int)parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False, help='use the multi-epochs-loader to save time at the beginning of every epoch')parser.add_argument("--init_method", default='env://', type=str)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_textdef main(): 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 if args.distributed and args.num_gpu > 1: _logger.warning( 'Using more than one GPU per process in distributed mode is not allowed.Setting num_gpu to 1.') args.num_gpu = 1 args.device = 'cuda:0' args.world_size = 1 args.rank = 0 # global rank if args.distributed: args.num_gpu = 1 args.device = 'cuda:%d' % args.local_rank torch.cuda.set_device(args.local_rank) args.world_size = int(os.environ['WORLD_SIZE']) args.rank = int(os.environ['RANK']) torch.distributed.init_process_group(backend='nccl', init_method=args.init_method, rank=args.rank, world_size=args.world_size) args.world_size = torch.distributed.get_world_size() args.rank = torch.distributed.get_rank() assert args.rank >= 0 if args.distributed: _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 %d GPUs.' % args.num_gpu) torch.manual_seed(args.seed + args.rank) extra_dict = {} extra_dict['args'] = args model = create_model( args.model, pretrained=args.pretrained, num_classes=args.num_classes, drop_rate=args.drop, drop_connect_rate=args.drop_connect, drop_path_rate=args.drop_path, drop_block_rate=args.drop_block, global_pool=args.gp, bn_tf=args.bn_tf, bn_momentum=args.bn_momentum, bn_eps=args.bn_eps, checkpoint_path=args.initial_checkpoint, **extra_dict) ################### flops ################# if args.local_rank == 0: print(model) from thop import profile from thop.vision.basic_hooks import zero_ops if hasattr(model, 'default_cfg'): default_cfg = model.default_cfg input_size = [1] + list(default_cfg['input_size']) else: input_size = [1, 3, 224, 224] input = torch.randn(input_size)#.cuda() _, params = profile(model, inputs=(input, ), custom_ops={nn.BatchNorm2d: zero_ops}) print('params:',params) from torchprofile import profile_macs macs = profile_macs(model, input) print('model flops:', macs, 'input_size:', input_size) if args.local_rank == 0: _logger.info('Model %s created, param count: %d' %(args.model, sum([m.numel() for m in model.parameters()]))) data_config = resolve_data_config(vars(args), model=model, verbose=args.local_rank == 0) 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 if args.split_bn: assert num_aug_splits > 1 or args.resplit model = convert_splitbn_model(model, max(num_aug_splits, 2)) use_amp = None if args.amp: # for backwards compat, `--amp` arg tries apex before native amp if has_apex: args.apex_amp = True elif has_native_amp: args.native_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") if args.num_gpu > 1: if use_amp == 'apex': _logger.warning( 'Apex AMP does not work well with nn.DataParallel, disabling. Use DDP or Torch AMP.') use_amp = None model = nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda() assert not args.channels_last, "Channels last not supported with DP, use DDP." else: model.cuda() if args.channels_last: model = model.to(memory_format=torch.channels_last) optimizer = create_optimizer(args, model) 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) model_ema = None if args.model_ema: # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper model_ema = ModelEma( model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else '', resume=args.resume) if args.distributed: if args.sync_bn: assert not args.split_bn try: if has_apex and use_amp != 'native': # Apex SyncBN preferred unless native amp is activated model = convert_syncbn_model(model) else: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) if args.local_rank == 0: _logger.info( 'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using ' 'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.') except Exception as e: _logger.error('Failed to enable Synchronized BatchNorm. Install Apex or Torch >= 1.1') if has_apex and use_amp != 'native': # Apex DDP preferred unless native amp is activated if args.local_rank == 0: _logger.info("Using NVIDIA APEX DistributedDataParallel.") model = ApexDDP(model, delay_allreduce=True) else: if args.local_rank == 0: _logger.info("Using native Torch DistributedDataParallel.") #model = NativeDDP(model, device_ids=[args.local_rank]) # can use device str in Torch >= 1.1 model = NativeDDP(model, device_ids=[args.local_rank],find_unused_parameters=True) # NOTE: EMA model does not need to be wrapped by DDP 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)) if 'cifar100' in args.data: dataset_train = torchvision.datasets.CIFAR100(root=args.data, train=True, download=False, transform=None) elif 'cifar10' in args.data: dataset_train = torchvision.datasets.CIFAR10(root=args.data, train=True, download=False, transform=None) else: train_dir = os.path.join(args.data, 'train') if not os.path.exists(train_dir): _logger.error('Training folder does not exist at: {}'.format(train_dir)) exit(1) dataset_train = Dataset(train_dir) 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) if num_aug_splits > 1: dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits) 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_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, repeated_aug=args.repeated_aug ) if 'cifar100' in args.data: dataset_eval = torchvision.datasets.CIFAR100(root=args.data, train=False, download=False, transform=None) elif 'cifar10' in args.data: dataset_eval = torchvision.datasets.CIFAR10(root=args.data, train=False, download=False, transform=None) else: eval_dir = os.path.join(args.data, 'val') if not os.path.isdir(eval_dir): eval_dir = os.path.join(args.data, 'validation') if not os.path.isdir(eval_dir): _logger.error('Validation folder does not exist at: {}'.format(eval_dir)) exit(1) dataset_eval = Dataset(eval_dir) loader_eval = create_loader( dataset_eval, input_size=data_config['input_size'], batch_size=args.validation_batch_size_multiplier * args.batch_size, is_training=False, use_prefetcher=args.prefetcher, interpolation=data_config['interpolation'], mean=data_config['mean'], std=data_config['std'], num_workers=args.workers, distributed=args.distributed, crop_pct=data_config['crop_pct'], pin_memory=args.pin_mem, ) if args.jsd: assert num_aug_splits > 1 # JSD only valid with aug splits set train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda() elif mixup_active: # smoothing is handled with mixup target transform train_loss_fn = SoftTargetCrossEntropy().cuda() elif args.smoothing: train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda() else: train_loss_fn = nn.CrossEntropyLoss().cuda() validate_loss_fn = nn.CrossEntropyLoss().cuda() eval_metric = args.eval_metric best_metric = None best_epoch = None saver = None output_dir = '' if args.local_rank == 0: output_base = args.output if args.output else './output' exp_name = '-'.join([ datetime.now().strftime("%Y%m%d-%H%M%S"), args.model, str(data_config['input_size'][-1]) ]) output_dir = get_outdir(output_base, 'train', exp_name) decreasing = True if eval_metric == 'loss' else False saver = CheckpointSaver( model=model, optimizer=optimizer, args=args, model_ema=model_ema, amp_scaler=loss_scaler, checkpoint_dir=output_dir, recovery_dir=output_dir, decreasing=decreasing,max_history=args.max_history) ###max_history 最多保存的checkpoint数 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: loader_train.sampler.set_epoch(epoch) train_metrics = train_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) 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.ema, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, log_suffix=' (EMA)') eval_metrics = ema_eval_metrics if lr_scheduler is not None: # step LR for next epoch lr_scheduler.step(epoch + 1, eval_metrics[eval_metric]) update_summary( epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'), write_header=best_metric is None) if saver is not None: # save proper checkpoint with eval metric save_metric = eval_metrics[eval_metric] best_metric, best_epoch = saver.save_checkpoint(epoch, metric=save_metric) except KeyboardInterrupt: pass if best_metric is not None: _logger.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch)) def train_epoch( epoch, model, loader, optimizer, loss_fn, args, lr_scheduler=None, saver=None, output_dir='', amp_autocast=suppress, loss_scaler=None, model_ema=None, mixup_fn=None): 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() top1_m = AverageMeter() top5_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) acc1, acc5 = accuracy(output, target.argmax(dim=-1), topk=(1, 5)) if args.distributed: acc1 = reduce_tensor(acc1, args.world_size) acc5 = reduce_tensor(acc5, args.world_size) 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, parameters=model.parameters(), create_graph=second_order) else: loss.backward(create_graph=second_order) if args.clip_grad is not None: torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad) optimizer.step() torch.cuda.synchronize() top1_m.update(acc1.item(), output.size(0)) top5_m.update(acc5.item(), output.size(0)) if model_ema is not None: model_ema.update(model) num_updates += 1 batch_time_m.update(time.time() - end) if last_batch or batch_idx % args.log_interval == 0: lrl = [param_group['lr'] for param_group in optimizer.param_groups] lr = sum(lrl) / len(lrl) if args.distributed: reduced_loss = reduce_tensor(loss.data, args.world_size) losses_m.update(reduced_loss.item(), input.size(0)) if args.local_rank == 0: _logger.info( 'Train: {} [{:>4d}/{} ({:>3.0f}%)] ' 'Loss: {loss.val:>9.6f} ({loss.avg:>6.4f}) ' 'Time: {batch_time.val:.3f}s, {rate:>7.2f}/s ' '({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s) ' 'LR: {lr:.3e} ' 'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format( epoch, batch_idx, len(loader), 100. * batch_idx / last_idx, loss=losses_m, batch_time=batch_time_m, rate=input.size(0) * args.world_size / batch_time_m.val, rate_avg=input.size(0) * args.world_size / batch_time_m.avg, lr=lr, data_time=data_time_m)) _logger.info( 'Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f}) ' 'Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(top1=top1_m, top5=top5_m)) if args.save_images and output_dir: torchvision.utils.save_image( input, os.path.join(output_dir, 'train-batch-%d.jpg' % batch_idx), padding=0, normalize=True) if saver is not None and args.recovery_interval and ( last_batch or (batch_idx + 1) % args.recovery_interval == 0): saver.save_recovery(epoch, batch_idx=batch_idx) if lr_scheduler is not None: lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg) end = time.time() # end for if hasattr(optimizer, 'sync_lookahead'): optimizer.sync_lookahead() return OrderedDict([('loss', losses_m.avg)])def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=''): batch_time_m = AverageMeter() losses_m = AverageMeter() top1_m = AverageMeter() top5_m = AverageMeter() model.eval() end = time.time() last_idx = len(loader) - 1 with torch.no_grad(): for batch_idx, (input, target) in enumerate(loader): last_batch = batch_idx == last_idx if not args.prefetcher: input = input.cuda() target = target.cuda() if args.channels_last: input = input.contiguous(memory_format=torch.channels_last) with amp_autocast(): output = model(input) if isinstance(output, (tuple, list)): output = output[0] # 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 metricsif __name__ == '__main__': main()第二个示例
def main(): setup_default_logging() args, args_text = _parse_args() if 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`") args.prefetcher = not args.no_prefetcher args.distributed = False if 'WORLD_SIZE' in os.environ: args.distributed = int(os.environ['WORLD_SIZE']) > 1 args.world_size = int(os.environ['WORLD_SIZE']) args.rank = 0 # global rank if args.distributed: args.device = 'npu:%d' % args.local_rank torch.npu.set_device(args.local_rank) torch.distributed.init_process_group(backend='hccl', world_size=args.world_size, rank=args.local_rank) 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: args.device = f'npu:{args.local_rank}' torch.npu.set_device(args.device) _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) model = create_model( args.model, pretrained=args.pretrained, num_classes=args.num_classes, drop_rate=args.drop, drop_connect_rate=args.drop_connect, # DEPRECATED, use drop_path drop_path_rate=args.drop_path, drop_block_rate=args.drop_block, global_pool=args.gp, bn_tf=args.bn_tf, bn_momentum=args.bn_momentum, bn_eps=args.bn_eps, scriptable=args.torchscript, checkpoint_path=args.initial_checkpoint) 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.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.npu() if args.channels_last: model = model.to(memory_format=torch.channels_last) # setup synchronized BatchNorm for distributed training if args.distributed and args.sync_bn: assert not args.split_bn if has_apex and use_amp != 'native': # Apex SyncBN preferred unless native amp is activated model = convert_syncbn_model(model) else: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) if args.local_rank == 0: _logger.info( 'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using ' 'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.') if args.torchscript: assert not use_amp == 'apex', 'Cannot use APEX AMP with torchscripted model' assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model' model = torch.jit.script(model) optimizer = create_optimizer_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='O2', loss_scale=64, combine_grad=True) 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.npu.amp.autocast loss_scaler = NativeScaler() if args.local_rank == 0: _logger.info('Using native Torch AMP. Training in mixed precision.') else: if args.local_rank == 0: _logger.info('AMP not enabled. Training in float32.') # optionally resume from a checkpoint resume_epoch = None if args.resume: resume_epoch = resume_checkpoint( model, args.resume, optimizer=None if args.no_resume_opt else optimizer, loss_scaler=None if args.no_resume_opt else loss_scaler, log_info=args.local_rank == 0) # setup exponential moving average of model weights, SWA could be used here too model_ema = None if args.model_ema: # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper model_ema = ModelEmaV2( model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else None) if args.resume: load_checkpoint(model_ema.module, args.resume, use_ema=True) # setup distributed training if args.distributed: # if has_apex and use_amp != 'native': # # Apex DDP preferred unless native amp is activated # if args.local_rank == 0: # _logger.info("Using NVIDIA APEX DistributedDataParallel.") # model = ApexDDP(model, delay_allreduce=True) # else: # if args.local_rank == 0: # _logger.info("Using native Torch DistributedDataParallel.") # model = NativeDDP(model, device_ids=[args.local_rank]) # can use device str in Torch >= 1.1 # NOTE: EMA model does not need to be wrapped by DDP if args.local_rank == 0: _logger.info("Using native Torch DistributedDataParallel.") model = NativeDDP(model, device_ids=[args.local_rank], broadcast_buffers=False) # can use device str in Torch >= 1.1 # 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_dir, split=args.train_split, is_training=True, 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, 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) # 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_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 ) loader_eval = create_loader( dataset_eval, input_size=data_config['input_size'], batch_size=args.validation_batch_size_multiplier * args.batch_size, is_training=False, use_prefetcher=args.prefetcher, interpolation=data_config['interpolation'], mean=data_config['mean'], std=data_config['std'], num_workers=args.workers, distributed=args.distributed, crop_pct=data_config['crop_pct'], pin_memory=args.pin_mem, ) # setup loss function if args.jsd: assert num_aug_splits > 1 # JSD only valid with aug splits set train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).npu() elif mixup_active: # smoothing is handled with mixup target transform train_loss_fn = SoftTargetCrossEntropy().npu() elif args.smoothing: train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).npu() else: train_loss_fn = nn.CrossEntropyLoss().npu() validate_loss_fn = nn.CrossEntropyLoss().npu() # 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.local_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) 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) if args.distributed and args.dist_bn in ('broadcast', 'reduce'): if args.local_rank == 0: _logger.info("Distributing BatchNorm running means and vars") distribute_bn(model, args.world_size, args.dist_bn == 'reduce') eval_metrics = validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast) if model_ema is not None and not args.model_ema_force_cpu: if args.distributed and args.dist_bn in ('broadcast', 'reduce'): distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce') ema_eval_metrics = validate( model_ema.module, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, log_suffix=' (EMA)') eval_metrics = ema_eval_metrics if lr_scheduler is not None: # step LR for next epoch lr_scheduler.step(epoch + 1, eval_metrics[eval_metric]) 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 saver is not None: # save proper checkpoint with eval metric save_metric = eval_metrics[eval_metric] best_metric, best_epoch = saver.save_checkpoint(epoch, metric=save_metric) 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(第三个示例
def main(): 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 = 'npu:0' args.world_size = 1 args.rank = 0 # global rank if args.distributed: args.device = 'npu:%d' % args.local_rank torch.npu.set_device(args.local_rank) torch.distributed.init_process_group(backend='hccl', init_method='env://') args.world_size = torch.distributed.get_world_size() args.rank = torch.distributed.get_rank() _logger.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'% (args.rank, args.world_size)) else: _logger.info('Training with a single process on 1 GPUs.') assert args.rank >= 0 # resolve AMP arguments based on PyTorch / Apex availability use_amp = None if args.amp: # `--amp` chooses native amp before apex (APEX ver not actively maintained) if has_native_amp: args.native_amp = True elif has_apex: args.apex_amp = True if args.apex_amp and has_apex: use_amp = 'apex' elif args.native_amp and has_native_amp: use_amp = 'native' elif args.apex_amp or args.native_amp: _logger.warning("Neither APEX or native Torch AMP is available, using float32. " "Install NVIDA apex or upgrade to PyTorch 1.6") torch.manual_seed(args.seed + args.rank) if args.pretrained_load: model = create_model( args.model, pretrained=args.pretrained, num_classes=args.num_classes, drop_rate=args.drop, drop_connect_rate=args.drop_connect, # DEPRECATED, use drop_path drop_path_rate=args.drop_path, drop_block_rate=args.drop_block, global_pool=args.gp, bn_tf=args.bn_tf, bn_momentum=args.bn_momentum, bn_eps=args.bn_eps, scriptable=args.torchscript, checkpoint_path=args.initial_checkpoint) checkpoint = torch.load(args.resume_pth, map_location='cpu') model.load_state_dict(checkpoint) else: model = create_model( args.model, pretrained=args.pretrained, num_classes=args.num_classes, drop_rate=args.drop, drop_connect_rate=args.drop_connect, # DEPRECATED, use drop_path drop_path_rate=args.drop_path, drop_block_rate=args.drop_block, global_pool=args.gp, bn_tf=args.bn_tf, bn_momentum=args.bn_momentum, bn_eps=args.bn_eps, scriptable=args.torchscript, checkpoint_path=args.initial_checkpoint) 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.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.npu() if args.channels_last: model = model.to(memory_format=torch.channels_last) # setup synchronized BatchNorm for distributed training if args.distributed and args.sync_bn: assert not args.split_bn if has_apex and use_amp != 'native': # Apex SyncBN preferred unless native amp is activated model = convert_syncbn_model(model) else: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) if args.local_rank == 0: _logger.info( 'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using ' 'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.') if args.torchscript: assert not use_amp == 'apex', 'Cannot use APEX AMP with torchscripted model' assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model' model = torch.jit.script(model) optimizer = create_optimizer_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.npu.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 npu(), DP wrapper, and AMP but before SyncBN and DDP wrapper model_ema = ModelEmaV2( model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else None) if args.resume: load_checkpoint(model_ema.module, args.resume, use_ema=True) # setup distributed training if args.distributed: if has_apex and use_amp != 'native': # Apex DDP preferred unless native amp is activated if args.local_rank == 0: _logger.info("Using NVIDIA APEX DistributedDataParallel.") model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], broadcast_buffers=False) else: if args.local_rank == 0: _logger.info("Using native Torch DistributedDataParallel.") model = NativeDDP(model, device_ids=[args.local_rank]) # can use device str in Torch >= 1.1 # NOTE: EMA model does not need to be wrapped by DDP # setup learning rate schedule and starting epoch lr_scheduler, num_epochs = create_scheduler(args, optimizer) start_epoch = 0 if args.start_epoch is not None: # a specified start_epoch will always override the resume epoch start_epoch = args.start_epoch elif resume_epoch is not None: start_epoch = resume_epoch if lr_scheduler is not None and start_epoch > 0: lr_scheduler.step(start_epoch) if args.local_rank == 0: _logger.info('Scheduled epochs: {}'.format(num_epochs)) # create the train and eval datasets dataset_train = create_dataset( args.dataset, root=args.data_path, split=args.train_split, is_training=True, batch_size=args.batch_size, repeats=args.epoch_repeats) dataset_eval = create_dataset( args.dataset, root=args.data_path, split=args.val_split, is_training=False, batch_size=args.batch_size) # setup mixup / cutmix collate_fn = None mixup_fn = None mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None if mixup_active: mixup_args = dict( mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax, prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode, label_smoothing=args.smoothing, num_classes=args.num_classes) 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_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 ) loader_eval = create_loader( dataset_eval, input_size=data_config['input_size'], batch_size=args.validation_batch_size_multiplier * args.batch_size, is_training=False, use_prefetcher=args.prefetcher, interpolation=data_config['interpolation'], mean=data_config['mean'], std=data_config['std'], num_workers=args.workers, distributed=args.distributed, crop_pct=data_config['crop_pct'], pin_memory=args.pin_mem, ) # setup loss function if args.jsd: assert num_aug_splits > 1 # JSD only valid with aug splits set train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).npu() elif mixup_active: # smoothing is handled with mixup target transform train_loss_fn = SoftTargetCrossEntropy().npu() elif args.smoothing: train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).npu() else: train_loss_fn = nn.CrossEntropyLoss().npu() validate_loss_fn = nn.CrossEntropyLoss().npu() # setup checkpoint saver and eval metric tracking eval_metric = args.eval_metric best_metric = None best_epoch = None saver = None output_dir = '' if args.local_rank == 0: 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) 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) if args.distributed and args.dist_bn in ('broadcast', 'reduce'): if args.local_rank == 0: _logger.info("Distributing BatchNorm running means and vars") distribute_bn(model, args.world_size, args.dist_bn == 'reduce') eval_metrics = validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast) if model_ema is not None and not args.model_ema_force_cpu: if args.distributed and args.dist_bn in ('broadcast', 'reduce'): distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce') ema_eval_metrics = validate( model_ema.module, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, log_suffix=' (EMA)') eval_metrics = ema_eval_metrics if lr_scheduler is not None: # step LR for next epoch lr_scheduler.step(epoch + 1, eval_metrics[eval_metric]) update_summary( epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'), write_header=best_metric is None) if saver is not None: # save proper checkpoint with eval metric save_metric = eval_metrics[eval_metric] best_metric, best_epoch = saver.save_checkpoint(epoch, metric=save_metric) 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(剩下的详见：
https://programtalk.com/python-more-examples/timm.data.AugMixDataset/