########################### #add inception model #with bench data v1 #20191106:add synthetic data v2 #used for large-scale test, change the iter of each epoch based on the total dataset ########################### import argparse import os import random import shutil import time import warnings import torch 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.multiprocessing as mp import torch.utils.data import torch.utils.data.distributed import torchvision.transforms as transforms import torchvision.datasets as datasets import torchvision.models as models model_names = sorted(name for name in models.__dict__ if name.islower() and not name.startswith("__") and callable(models.__dict__[name])) parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') parser.add_argument('data', metavar='DIR', help='path to dataset') parser.add_argument('--synthetic', action='store_true', help='use snthetic data') parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=model_names, help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet18)') parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', help='number of data loading workers (default: 4)') 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), this is the total ' 'batch size of all GPUs on the current node when ' 'using Data Parallel or Distributed Data Parallel') parser.add_argument('--lr', '--learning-rate', default=0.1, type=float, metavar='LR', help='initial learning rate', dest='lr') parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum') parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)', dest='weight_decay') parser.add_argument('-p', '--print-freq', 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('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set') parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model') parser.add_argument('--world-size', default=-1, type=int, help='number of nodes for distributed training') parser.add_argument('--rank', default=-1, type=int, help='node rank for distributed training') parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str, help='url used to set up distributed training') parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend') parser.add_argument('--seed', default=None, type=int, help='seed for initializing training. ') parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.') parser.add_argument('--multiprocessing-distributed', action='store_true', help='Use multi-processing distributed training to launch ' 'N processes per node, which has N GPUs. This is the ' 'fastest way to use PyTorch for either single node or ' 'multi node data parallel training') best_acc1 = 0 def main(): args = parser.parse_args() if args.seed is not None: random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True warnings.warn('You have chosen to seed training. ' 'This will turn on the CUDNN deterministic setting, ' 'which can slow down your training considerably! ' 'You may see unexpected behavior when restarting ' 'from checkpoints.') if args.gpu is not None: warnings.warn('You have chosen a specific GPU. This will completely ' 'disable data parallelism.') if args.dist_url == "env://" and args.world_size == -1: args.world_size = int(os.environ["WORLD_SIZE"]) args.distributed = args.world_size > 1 or args.multiprocessing_distributed ngpus_per_node = torch.cuda.device_count() if args.multiprocessing_distributed: # Since we have ngpus_per_node processes per node, the total world_size # needs to be adjusted accordingly args.world_size = ngpus_per_node * args.world_size # Use torch.multiprocessing.spawn to launch distributed processes: the # main_worker process function mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args)) else: # Simply call main_worker function main_worker(args.gpu, ngpus_per_node, args) def main_worker(gpu, ngpus_per_node, args): global best_acc1 args.gpu = gpu if args.gpu is not None: print("Use GPU: {} for training".format(args.gpu)) if args.distributed: if args.dist_url == "env://" and args.rank == -1: args.rank = int(os.environ["RANK"]) if args.multiprocessing_distributed: # For multiprocessing distributed training, rank needs to be the # global rank among all the processes args.rank = args.rank * ngpus_per_node + gpu dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) # create model if args.pretrained: print("=> using pre-trained model '{}'".format(args.arch)) model = models.__dict__[args.arch](pretrained=True) # else: # print("=> creating model '{}'".format(args.arch)) # model = models.__dict__[args.arch]() else: if (args.arch== "inception_v3"): print("=> creating model '{}'".format(args.arch)) model = models.__dict__[args.arch](aux_logits=False) else: print("=> creating model '{}'".format(args.arch)) model = models.__dict__[args.arch]() if args.distributed: # For multiprocessing distributed, DistributedDataParallel constructor # should always set the single device scope, otherwise, # DistributedDataParallel will use all available devices. if args.gpu is not None: torch.cuda.set_device(args.gpu) model.cuda(args.gpu) # When using a single GPU per process and per # DistributedDataParallel, we need to divide the batch size # ourselves based on the total number of GPUs we have args.batch_size = int(args.batch_size / ngpus_per_node) args.workers = int(args.workers / ngpus_per_node) model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) else: model.cuda() # DistributedDataParallel will divide and allocate batch_size to all # available GPUs if device_ids are not set model = torch.nn.parallel.DistributedDataParallel(model) elif args.gpu is not None: torch.cuda.set_device(args.gpu) model = model.cuda(args.gpu) else: # DataParallel will divide and allocate batch_size to all available GPUs if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): model.features = torch.nn.DataParallel(model.features) model.cuda() else: model = torch.nn.DataParallel(model).cuda() # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss().cuda(args.gpu) optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) # 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) args.start_epoch = checkpoint['epoch'] best_acc1 = checkpoint['best_acc1'] if args.gpu is not None: # best_acc1 may be from a checkpoint from a different GPU best_acc1 = best_acc1.to(args.gpu) model.load_state_dict(checkpoint['state_dict']) optimizer.load_state_dict(checkpoint['optimizer']) print("=> loaded checkpoint '{}' (epoch {})" .format(args.resume, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True # Data loading code traindir = os.path.join(args.data, 'train') valdir = os.path.join(args.data, 'val') normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) #######add inceptionby aiss 0710 if args.synthetic: print("use syn date!") train_dataset = datasets.ImageFolder( traindir, transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])) if not args.synthetic else \ datasets.FakeData(1281280, num_classes=1000, transform=transforms.ToTensor()) if args.distributed: train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) else: train_sampler = None train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=False, sampler=train_sampler) #global len_train_loader #len_train_loader=len(train_loader) #print("iters per epoch: ",len(train_loader)) val_loader = torch.utils.data.DataLoader( datasets.ImageFolder(valdir, transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize, ])) if not args.synthetic else datasets.FakeData(20000, num_classes=1000, transform=transforms.ToTensor()), batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=False) # if args.evaluate: # validate(val_loader, model, criterion, args) # return #####add by aiss global is_stop is_stop = 0 # HC debug print("Begin training ..."); for epoch in range(args.start_epoch, args.epochs): if args.distributed: train_sampler.set_epoch(epoch) adjust_learning_rate(optimizer, epoch, args) # train for one epoch train(train_loader, model, criterion, optimizer, epoch, args) print("is_stop is ", is_stop) if is_stop == 1: break #acc1 = validate(val_loader, model, criterion, args) # #remember best acc@1 and save checkpoint #is_best = acc1 > best_acc1 #best_acc1 = max(acc1, best_acc1) #if not args.multiprocessing_distributed or (args.multiprocessing_distributed # and args.rank % ngpus_per_node == 0): # save_checkpoint({ # 'epoch': epoch + 1, # 'arch': args.arch, # 'state_dict': model.state_dict(), # 'best_acc1': best_acc1, # 'optimizer' : optimizer.state_dict(), # }, is_best) def train(train_loader, model, criterion, optimizer, epoch, args): batch_time = AverageMeter('Time', ':6.3f') ######add by aiss 0708 # images_per_sec=AverageMeter('Perf', ':6.4f') data_time = AverageMeter('Data', ':6.3f') losses = AverageMeter('Loss', ':.4e') top1 = AverageMeter('Acc@1', ':6.2f') top5 = AverageMeter('Acc@5', ':6.2f') progress = ProgressMeter( len(train_loader), [batch_time, data_time, losses, top1, top5], prefix="Epoch: [{}]".format(epoch)) # switch to train mode model.train() end = time.time() ##########add by aiss############## len_train_loader=len(train_loader) print("iters per epoch: ",len(train_loader)) for i, (images, target) in enumerate(train_loader): # measure data loading time data_time.update(time.time() - end) #add by aiss if i == 10: time_start=time.time() torch.cuda.synchronize() gpu_start_time=time.time() if args.gpu is not None: images = images.cuda(args.gpu, non_blocking=True) target = target.cuda(args.gpu, non_blocking=True) # compute output output = model(images) loss = criterion(output, target) # measure accuracy and record loss acc1, acc5 = accuracy(output, target, topk=(1, 5)) losses.update(loss.item(), images.size(0)) top1.update(acc1[0], images.size(0)) top5.update(acc5[0], images.size(0)) # compute gradient and do SGD step optimizer.zero_grad() loss.backward() optimizer.step() # measure elapsed time batch_time.update(time.time() - end) end = time.time() ####add by aiss #print batch_time.val, batch_time.count, batch_time.sum #images_per_sec= args.batch_size / batch_time.val if i % args.print_freq == 0: progress.display(i) # print("Performance: {} ", images_per_sec) #print("Performance: {} samples/sec".format(images_per_sec)) # print("step time cost: {} sec".format(batch_time.val)) #if i == len_train_loader-10: if i == 1010: is_stop=1 time_end=end time_print_step=time_end-time_start #####wait for gpu op finished######## torch.cuda.synchronize() gpu_end_time=time.time() gpu_time_print_step=gpu_end_time-gpu_start_time # HC debug print("batch_size is ", args.batch_size) print("world_size is ", args.world_size) ########aiss debug#### print("time_start:%s time_end:%s gap:%s" %(time_start,time_end,time_print_step)) print("gpu_start_time:%s gpu_end_time:%s gap:%s" %(gpu_start_time,gpu_end_time,gpu_time_print_step)) if args.world_size == -1: images_total_per_sec=args.batch_size * 1000 / time_print_step else: images_total_per_sec=args.batch_size * 1000 * args.world_size / time_print_step print("Total Performance: {} samples/sec".format(images_total_per_sec)) break def validate(val_loader, model, criterion, args): batch_time = AverageMeter('Time', ':6.3f') losses = AverageMeter('Loss', ':.4e') top1 = AverageMeter('Acc@1', ':6.2f') top5 = AverageMeter('Acc@5', ':6.2f') progress = ProgressMeter( len(val_loader), [batch_time, losses, top1, top5], prefix='Test: ') # switch to evaluate mode model.eval() with torch.no_grad(): end = time.time() for i, (images, target) in enumerate(val_loader): if args.gpu is not None: images = images.cuda(args.gpu, non_blocking=True) target = target.cuda(args.gpu, non_blocking=True) # compute output output = model(images) loss = criterion(output, target) # measure accuracy and record loss acc1, acc5 = accuracy(output, target, topk=(1, 5)) losses.update(loss.item(), images.size(0)) top1.update(acc1[0], images.size(0)) top5.update(acc5[0], images.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: progress.display(i) # TODO: this should also be done with the ProgressMeter print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}' .format(top1=top1, top5=top5)) return top1.avg def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): torch.save(state, filename) if is_best: shutil.copyfile(filename, 'model_best.pth.tar') class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self, name, fmt=':f'): self.name = name self.fmt = fmt self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def __str__(self): fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' return fmtstr.format(**self.__dict__) class ProgressMeter(object): def __init__(self, num_batches, meters, prefix=""): self.batch_fmtstr = self._get_batch_fmtstr(num_batches) self.meters = meters self.prefix = prefix def display(self, batch): entries = [self.prefix + self.batch_fmtstr.format(batch)] entries += [str(meter) for meter in self.meters] print('\t'.join(entries)) def _get_batch_fmtstr(self, num_batches): num_digits = len(str(num_batches // 1)) fmt = '{:' + str(num_digits) + 'd}' return '[' + fmt + '/' + fmt.format(num_batches) + ']' def adjust_learning_rate(optimizer, epoch, args): """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" lr = args.lr * (0.1 ** (epoch // 30)) for param_group in optimizer.param_groups: param_group['lr'] = lr def accuracy(output, target, topk=(1,)): """Computes the accuracy over the k top predictions for the specified values of k""" with torch.no_grad(): maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True) res.append(correct_k.mul_(100.0 / batch_size)) return res if __name__ == '__main__': main()