imagenet.py

from __future__ import print_function, division
import argparse, time, logging, os
import random, shutil, warnings

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets

from misc import RandomLighting
from misc import LRScheduler, LRSequential
from misc import SGD 
from misc import CrossEntropyLoss_LS
from tensorboardX import SummaryWriter

model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))

def parse_args():
    parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
    parser.add_argument('--data', metavar='DIR', default='/media/shared-corpus/imagenet/',
                        help='path to dataset')
    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=120, 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=128, 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=0.0001, type=float,
                        metavar='W', help='weight decay (default: 1e-4)',
                        dest='weight_decay')
    parser.add_argument('-p', '--print-freq', default=100, 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', default=False,
                        help='evaluate model on validation set')
    parser.add_argument('--pretrained', dest='pretrained', action='store_true', default=False,
                        help='use pre-trained model')
    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('--lr-mode', type=str, default='step',
                        help='learning rate scheduler mode. options are step, poly and cosine.')
    parser.add_argument('--lr-decay', type=float, default=0.1,
                        help='decay rate of learning rate. default is 0.1.')
    parser.add_argument('--lr-decay-period', type=int, default=0,
                        help='interval for periodic learning rate decays. default is 0 to disable.')
    parser.add_argument('--lr-decay-epoch', type=str, default='40,60',
                        help='epochs at which learning rate decays. default is 40,60.')
    parser.add_argument('--warmup-lr', type=float, default=0.0,
                        help='starting warmup learning rate. default is 0.0.')
    parser.add_argument('--warmup-epochs', type=int, default=0,
                        help='number of warmup epochs.')
    parser.add_argument('--no-wd', action='store_true',
                        help='whether to remove weight decay on bias, and beta/gamma for batchnorm layers.')
    parser.add_argument('--label-smoothing', action='store_true',
                        help='use label smoothing or not in training. default is false.')
    parser.add_argument('--last-gamma', action='store_true',
                        help='whether to init gamma of the last BN layer in each bottleneck to 0.')
    parser.add_argument('--visual', dest='visual', action='store_true', default=False,
                        help='whether to visualize traning using tensorboardX')


    args = parser.parse_args()
    return args


args = parse_args()

# set up train folder
current_time = time.strftime('%m%d-%H-%M-%S-', time.localtime(time.time()))
current = 'checkpoints/' + current_time + args.arch  + '/'
os.mkdir(current)

filehandler = logging.FileHandler(os.path.join(current, 'log.txt'))
streamhandler = logging.StreamHandler()
logger = logging.getLogger('')
logger.setLevel(logging.INFO)
logger.addHandler(filehandler)
logger.addHandler(streamhandler)
logger.info(args)

writer = SummaryWriter(current)

best_acc1 = 0

def main():
    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.')

    ngpus_per_node = torch.cuda.device_count()
    main_worker(ngpus_per_node, args)

def main_worker(ngpus_per_node, args):
    global best_acc1

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    # 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))
        if 'resnet' in args.arch:
            model = models.__dict__[args.arch](zero_init_residual=args.last_gamma)
        else :
            model = models.__dict__[args.arch]()
    if 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()
    logger.info(model)


    # define loss function (criterion) and optimizer
    if args.label_smoothing is False:
        criterion = nn.CrossEntropyLoss().cuda(args.gpu)
    else :
        criterion = CrossEntropyLoss_LS().cuda(args.gpu)


    optimizer = SGD(model.parameters(), args.lr,
                    momentum=args.momentum,
                    nesterov=True,
                    weight_decay=args.weight_decay,
                    no_wd=args.no_wd)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            logger.info("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            strs = ("=> loaded checkpoint '{}' (epoch {}, best_acc {})"
                    .format(args.resume, checkpoint['epoch'], best_acc1))
            logger.info(strs)
        else:
            logger.info("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # define lrschedular
    num_training_samples = 1281167
    lr_decay = args.lr_decay
    lr_decay_period = args.lr_decay_period
    if args.lr_decay_period > 0:
        lr_decay_epoch = list(range(lr_decay_period, args.epochs, lr_decay_period))
    else:
        lr_decay_epoch = [int(i) for i in args.lr_decay_epoch.split(',')]
    lr_decay_epoch = [e - args.warmup_epochs for e in lr_decay_epoch]
    num_batches = num_training_samples // args.batch_size
    lr_scheduler = LRSequential([
        LRScheduler('linear', base_lr=0.0, target_lr=args.lr,
                    nepochs=args.warmup_epochs, iters_per_epoch=num_batches),
        LRScheduler(args.lr_mode, base_lr=args.lr, target_lr=0,
                    nepochs=args.epochs - args.warmup_epochs,
                    iters_per_epoch=num_batches,
                    step_epoch=lr_decay_epoch,
                    step_factor=lr_decay, power=2)
    ],
        offset=args.start_epoch * num_batches)

    # Data loading 
    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])
    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ColorJitter(brightness=0.4, contrast=0.4,
                                   saturation=0.4),
            transforms.ToTensor(),
            RandomLighting(0.1),
            normalize
        ]))
    train_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=True,
        num_workers=args.workers, pin_memory=True, sampler=train_sampler,
        drop_last=True)

    val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)
    
    # evaluate
    if args.evaluate:
        validate(val_loader, model, criterion)
        return

    for epoch in range(args.start_epoch, args.epochs):

        # train for one epoch
        train(train_loader, model, criterion, optimizer, lr_scheduler, epoch)

        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, epoch)

        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)
        logger.info('epoch : %d, best acc : %f' % (epoch, best_acc1))
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_acc1': best_acc1,
            'optimizer': optimizer.state_dict(),
        }, is_best, current=current)


def train(train_loader, model, criterion, optimizer, lr_scheduler, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to train mode
    model.train()
    end = time.time()
    for i, (input, target) in enumerate(train_loader):

        # measure data loading time
        data_time.update(time.time() - end)

        if args.gpu is not None:
            input = input.cuda(args.gpu, non_blocking=True)
        target = target.cuda(args.gpu, non_blocking=True)
        # compute output
        output = model(input)
        loss = criterion(output, target)
        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), input.size(0))
        top1.update(acc1[0], input.size(0))
        top5.update(acc5[0], input.size(0))

        # compute gradient, do SGD step, and adjust learning rate
        lr = lr_scheduler.step()
        adjust_learning_rate(optimizer, lr)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()
        if i % args.print_freq == 0:
            str = 'Epoch: [{0}][{1}/{2}]\t'
            str += 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
            str += 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
            str += 'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
            str += 'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
            str += 'Acc@5 {top5.val:.3f} ({top5.avg:.3f})\t'
            str += 'lr = {lr:.7f}'
            str = str.format(
                epoch, i, len(train_loader), batch_time=batch_time,
                data_time=data_time, loss=losses, top1=top1, top5=top5, lr=lr)
            logger.info(str)

    # add weight and grad histogram 
    if args.visual:
        for name, value in model.named_parameters():  # weight/grad dist
            writer.add_histogram('train.' + name, value.clone().cpu().data.numpy(), epoch, 'auto')
            writer.add_histogram('train.' + name + '-grad', value.grad.clone().cpu().data.numpy(), epoch, 'auto')


def validate(val_loader, model, criterion, epoch=0):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()
    with torch.no_grad():
        end = time.time()

        for i, (input, target) in enumerate(val_loader):

            if args.gpu is not None:
                input = input.cuda(args.gpu, non_blocking=True)
            target = target.cuda(args.gpu, non_blocking=True)
            # compute output
            output = model(input)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), input.size(0))
            top1.update(acc1[0], input.size(0))
            top5.update(acc5[0], input.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()
            if i % args.print_freq == 0:
                str = 'Test: [{0}/{1}]\t'
                str += 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                str += 'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                str += 'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                str += 'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'

                str = str.format(
                    i, len(val_loader), batch_time=batch_time, loss=losses,
                    top1=top1, top5=top5)
                logger.info(str)

        logger.info(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
                    .format(top1=top1, top5=top5))

        writer.add_scalar('acc1', float(top1.avg), epoch)
        writer.add_scalar('loss', float(losses.avg), epoch)

        current_time = time.strftime('%y-%m-%d-%H:%M:%S', time.localtime(time.time()))
        logger.info("current time : " + current_time)

    return top1.avg


def save_checkpoint(state, is_best, current, filename='checkpoint.pth.tar'):
    checkpoint_filename = current + filename
    torch.save(state, checkpoint_filename)
    if is_best:
        shutil.copyfile(checkpoint_filename, current + 'model_best.pth.tar')
    

class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        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 adjust_learning_rate(optimizer, lr):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    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].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


if __name__ == '__main__':
    main()