import argparse import os import shutil import time import random import numpy as np import torch from torch.autograd import Variable import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.distributed as dist import torch.optim import torch.utils.data import torch.utils.data.distributed import torchvision.transforms as transforms import torchvision.datasets as datasets try: from apex.parallel import DistributedDataParallel as DDP from apex.fp16_utils import * from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.") import image_classification.resnet as models import image_classification.logger as log from image_classification.smoothing import LabelSmoothing from image_classification.mixup import NLLMultiLabelSmooth, MixUpWrapper from image_classification.dataloaders import * from image_classification.training import * from image_classification.utils import * def add_parser_arguments(parser): model_names = models.resnet_versions.keys() model_configs = models.resnet_configs.keys() parser.add_argument('data', metavar='DIR', help='path to dataset') parser.add_argument('--data-backend', metavar='BACKEND', default='pytorch', choices=DATA_BACKEND_CHOICES) parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet50', choices=model_names, help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet50)') parser.add_argument('--model-config', '-c', metavar='CONF', default='classic', choices=model_configs, help='model configs: ' + ' | '.join(model_configs) + '(default: classic)') parser.add_argument('-j', '--workers', default=5, type=int, metavar='N', help='number of data loading workers (default: 5)') parser.add_argument('--epochs', default=90, type=int, metavar='N', help='number of total epochs to run') parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)') parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 256) per gpu') parser.add_argument('--optimizer-batch-size', default=-1, type=int, metavar='N', help='size of a total batch size, for simulating bigger batches') parser.add_argument('--lr', '--learning-rate', default=0.1, type=float, metavar='LR', help='initial learning rate') parser.add_argument('--lr-schedule', default='step', type=str, metavar='SCHEDULE', choices=['step','linear','cosine']) parser.add_argument('--warmup', default=0, type=int, metavar='E', help='number of warmup epochs') parser.add_argument('--label-smoothing', default=0.0, type=float, metavar='S', help='label smoothing') parser.add_argument('--mixup', default=0.0, type=float, metavar='ALPHA', help='mixup alpha') parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum') parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)') parser.add_argument('--bn-weight-decay', action='store_true', help='use weight_decay on batch normalization learnable parameters, default: false)') parser.add_argument('--nesterov', action='store_true', help='use nesterov momentum, default: false)') parser.add_argument('--print-freq', '-p', default=10, type=int, metavar='N', help='print frequency (default: 10)') parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') parser.add_argument('--pretrained-weights', default='', type=str, metavar='PATH', help='load weights from here') parser.add_argument('--fp16', action='store_true', help='Run model fp16 mode.') parser.add_argument('--static-loss-scale', type=float, default=1, help='Static loss scale, positive power of 2 values can improve fp16 convergence.') parser.add_argument('--dynamic-loss-scale', action='store_true', help='Use dynamic loss scaling. If supplied, this argument supersedes ' + '--static-loss-scale.') parser.add_argument('--prof', type=int, default=-1, help='Run only N iterations') parser.add_argument('--amp', action='store_true', help='Run model AMP (automatic mixed precision) mode.') parser.add_argument("--local_rank", default=0, type=int) parser.add_argument('--seed', default=None, type=int, help='random seed used for np and pytorch') parser.add_argument('--gather-checkpoints', action='store_true', help='Gather checkpoints throughout the training') parser.add_argument('--raport-file', default='experiment_raport.json', type=str, help='file in which to store JSON experiment raport') parser.add_argument('--final-weights', default='model.pth.tar', type=str, help='file in which to store final model weights') parser.add_argument('--evaluate', action='store_true', help='evaluate checkpoint/model') parser.add_argument('--training-only', action='store_true', help='do not evaluate') parser.add_argument('--no-checkpoints', action='store_false', dest='save_checkpoints') parser.add_argument('--workspace', type=str, default='./') def main(args): exp_start_time = time.time() global best_prec1 best_prec1 = 0 args.distributed = False if 'WORLD_SIZE' in os.environ: args.distributed = int(os.environ['WORLD_SIZE']) > 1 args.gpu = 0 args.world_size = 1 if args.distributed: args.gpu = args.local_rank % torch.cuda.device_count() torch.cuda.set_device(args.gpu) dist.init_process_group(backend='nccl', init_method='env://') args.world_size = torch.distributed.get_world_size() if args.amp and args.fp16: print("Please use only one of the --fp16/--amp flags") exit(1) if args.seed is not None: print("Using seed = {}".format(args.seed)) torch.manual_seed(args.seed + args.local_rank) torch.cuda.manual_seed(args.seed + args.local_rank) np.random.seed(seed=args.seed + args.local_rank) random.seed(args.seed + args.local_rank) def _worker_init_fn(id): np.random.seed(seed=args.seed + args.local_rank + id) random.seed(args.seed + args.local_rank + id) else: def _worker_init_fn(id): pass if args.fp16: assert torch.backends.cudnn.enabled, "fp16 mode requires cudnn backend to be enabled." if args.static_loss_scale != 1.0: if not args.fp16: print("Warning: if --fp16 is not used, static_loss_scale will be ignored.") if args.optimizer_batch_size < 0: batch_size_multiplier = 1 else: tbs = args.world_size * args.batch_size if args.optimizer_batch_size % tbs != 0: print("Warning: simulated batch size {} is not divisible by actual batch size {}".format(args.optimizer_batch_size, tbs)) batch_size_multiplier = int(args.optimizer_batch_size/ tbs) print("BSM: {}".format(batch_size_multiplier)) pretrained_weights = None if args.pretrained_weights: if os.path.isfile(args.pretrained_weights): print("=> loading pretrained weights from '{}'".format(args.pretrained_weights)) pretrained_weights = torch.load(args.pretrained_weights) else: print("=> no pretrained weights found at '{}'".format(args.resume)) # optionally resume from a checkpoint if args.resume: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume, map_location = lambda storage, loc: storage.cuda(args.gpu)) args.start_epoch = checkpoint['epoch'] best_prec1 = checkpoint['best_prec1'] model_state = checkpoint['state_dict'] optimizer_state = checkpoint['optimizer'] print("=> loaded checkpoint '{}' (epoch {})" .format(args.resume, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(args.resume)) model_state = None optimizer_state = None else: model_state = None optimizer_state = None loss = nn.CrossEntropyLoss if args.mixup > 0.0: loss = lambda: NLLMultiLabelSmooth(args.label_smoothing) elif args.label_smoothing > 0.0: loss = lambda: LabelSmoothing(args.label_smoothing) model_and_loss = ModelAndLoss( (args.arch, args.model_config), loss, pretrained_weights=pretrained_weights, cuda = True, fp16 = args.fp16) # Create data loaders and optimizers as needed if args.data_backend == 'pytorch': get_train_loader = get_pytorch_train_loader get_val_loader = get_pytorch_val_loader elif args.data_backend == 'dali-gpu': get_train_loader = get_dali_train_loader(dali_cpu=False) get_val_loader = get_dali_val_loader() elif args.data_backend == 'dali-cpu': get_train_loader = get_dali_train_loader(dali_cpu=True) get_val_loader = get_dali_val_loader() train_loader, train_loader_len = get_train_loader(args.data, args.batch_size, 1000, args.mixup > 0.0, workers=args.workers, fp16=args.fp16) if args.mixup != 0.0: train_loader = MixUpWrapper(args.mixup, 1000, train_loader) val_loader, val_loader_len = get_val_loader(args.data, args.batch_size, 1000, False, workers=args.workers, fp16=args.fp16) if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: logger = log.Logger( args.print_freq, [ log.JsonBackend(os.path.join(args.workspace, args.raport_file), log_level=1), log.StdOut1LBackend(train_loader_len, val_loader_len, args.epochs, log_level=0), ]) for k, v in args.__dict__.items(): logger.log_run_tag(k, v) else: logger = None optimizer = get_optimizer(list(model_and_loss.model.named_parameters()), args.fp16, args.lr, args.momentum, args.weight_decay, nesterov = args.nesterov, bn_weight_decay = args.bn_weight_decay, state=optimizer_state, static_loss_scale = args.static_loss_scale, dynamic_loss_scale = args.dynamic_loss_scale) if args.lr_schedule == 'step': lr_policy = lr_step_policy(args.lr, [30,60,80], 0.1, args.warmup, logger=logger) elif args.lr_schedule == 'cosine': lr_policy = lr_cosine_policy(args.lr, args.warmup, args.epochs, logger=logger) elif args.lr_schedule == 'linear': lr_policy = lr_linear_policy(args.lr, args.warmup, args.epochs, logger=logger) if args.amp: model_and_loss, optimizer = amp.initialize( model_and_loss, optimizer, opt_level="O2", loss_scale="dynamic" if args.dynamic_loss_scale else args.static_loss_scale) if args.distributed: model_and_loss.distributed() model_and_loss.load_model_state(model_state) train_loop( model_and_loss, optimizer, lr_policy, train_loader, val_loader, args.epochs, args.fp16, logger, should_backup_checkpoint(args), use_amp=args.amp, batch_size_multiplier = batch_size_multiplier, start_epoch = args.start_epoch, best_prec1 = best_prec1, prof=args.prof, skip_training = args.evaluate, skip_validation = args.training_only, save_checkpoints=args.save_checkpoints and not args.evaluate, checkpoint_dir=args.workspace) exp_duration = time.time() - exp_start_time if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: logger.end() print("Experiment ended") if __name__ == '__main__': parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') add_parser_arguments(parser) args = parser.parse_args() cudnn.benchmark = True main(args)