gossip_sgd.py [518:550]:
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    lr = None
    if args.warmup and epoch < 5:  # warmup to scaled lr
        if target_lr <= args.lr:
            lr = target_lr
        else:
            assert itr is not None and itr_per_epoch is not None
            count = epoch * itr_per_epoch + itr + 1
            incr = (target_lr - args.lr) * (count / (5 * itr_per_epoch))
            lr = args.lr + incr
    else:
        lr = target_lr
        for e in args.lr_schedule:
            if epoch >= e:
                lr *= args.lr_schedule[e]

    if lr is not None:
        log.debug('Updating learning rate to {}'.format(lr))
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr


def make_dataloader(args, train=True):
    """ Returns train/val distributed dataloaders (cf. ImageNet in 1hr) """

    data_dir = args.dataset_dir
    train_dir = os.path.join(data_dir, 'train')
    val_dir = os.path.join(data_dir, 'val')

    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    if train:
        log.debug('fpaths train {}'.format(train_dir))
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gossip_sgd_adpsgd.py [541:573]:
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    lr = None
    if args.warmup and epoch < 5:  # warmup to scaled lr
        if target_lr <= args.lr:
            lr = target_lr
        else:
            assert itr is not None and itr_per_epoch is not None
            count = epoch * itr_per_epoch + itr + 1
            incr = (target_lr - args.lr) * (count / (5 * itr_per_epoch))
            lr = args.lr + incr
    else:
        lr = target_lr
        for e in args.lr_schedule:
            if epoch >= e:
                lr *= args.lr_schedule[e]

    if lr is not None:
        log.debug('Updating learning rate to {}'.format(lr))
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr


def make_dataloader(args, train=True):
    """ Returns train/val distributed dataloaders (cf. ImageNet in 1hr) """

    data_dir = args.dataset_dir
    train_dir = os.path.join(data_dir, 'train')
    val_dir = os.path.join(data_dir, 'val')

    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    if train:
        log.debug('fpaths train {}'.format(train_dir))
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