Python parse_dict_args Examples

Example #1

File: ISIC2017.py Project: FredericChai/Semi-supervised-training

def run(title, data_seed, **kwargs):
    print('run title: %s', title)
    ngpu = 1
    main.args = parse_dict_args(**kwargs)
    context = RunContext(__file__, kwargs['consistency'], kwargs['epochs'],
                         kwargs['labels'])
    main.main(context)

Example #2

def run(title, base_batch_size, base_labeled_batch_size, base_lr, n_labels,
        data_seed, **kwargs):
    LOG.info('run title: %s, data seed: %d', title, data_seed)

    ngpu = torch.cuda.device_count()
    assert ngpu > 0, "Expecting at least one GPU, found none."

    adapted_args = {
        'batch_size':
        base_batch_size * ngpu,
        'labeled_batch_size':
        base_labeled_batch_size * ngpu,
        'lr':
        base_lr * ngpu,
        'labels':
        'data-local/labels/cifar10/{}_balanced_labels/{:02d}.txt'.format(
            n_labels, data_seed),
        'data_seed':
        data_seed,
    }
    context = RunContext(__file__, "{}_{}".format(n_labels, data_seed))
    logfile = "{}/{}.log".format(context.result_dir, 'output')
    fh = logging.FileHandler(logfile)
    LOG.addHandler(fh)
    LOG.info('run title: %s, data seed: %d', title, data_seed)
    main.args = parse_dict_args(LOG, **adapted_args, **kwargs)
    main.main(context, LOG)

    LOG.info('Run finished, closing logfile.')
    LOG.removeHandler(fh)

Example #3

File: ISIC2017.py Project: FredericChai/Deep_learning_optimization

def run(title, base_batch_size, base_labeled_batch_size, base_lr, data_seed,
        **kwargs):
    LOG.info('run title: %s', title)
    ngpu = 1
    main.args = parse_dict_args(**kwargs)
    context = RunContext(__file__, args.consistency, args.epochs, args.labels)
    main.main(context)

Example #4

def parse_parameters(title, base_batch_size, base_labeled_batch_size, base_lr, n_labels, data_seed, **kwargs):
    ngpu = torch.cuda.device_count()
    adapted_args = {
            'batch_size': base_batch_size * ngpu,
            'labeled_batch_size': base_labeled_batch_size * ngpu,
            'lr': base_lr * ngpu,
            'labels': 'data-local/labels/cifar10/{}_balanced_labels/{:02d}.txt'.format(n_labels, data_seed),
    }
    # print(adapted_args)
    args = parse_dict_args(**adapted_args, **kwargs)
    return args

Example #5

File: cifar100_mt_shake_short_n10k.py Project: xiaopingzeng/fastswa-semi-sup

def run(title, base_batch_size, base_labeled_batch_size, base_lr, n_labels, data_seed, **kwargs):
    LOG.info('run title: %s', title)
    ngpu = torch.cuda.device_count()
    adapted_args = {
        'batch_size': base_batch_size * ngpu,
        'labeled_batch_size': base_labeled_batch_size * ngpu,
        'lr': base_lr * ngpu,
        'labels': 'data-local/labels/cifar100/{}_balanced_labels/{:02d}.txt'.format(n_labels, data_seed),
    }
    context = RunContext(__file__, "{}_{}".format(n_labels, data_seed))
    main.args = parse_dict_args(**adapted_args, **kwargs)
    main.main(context)

Example #6

def run(title, base_batch_size, base_labeled_batch_size, base_lr, n_labels, data_seed, **kwargs):
    LOG.info('run title: %s, data seed: %d', title, data_seed)

    ngpu = torch.cuda.device_count()
    assert ngpu > 0, "Expecting at least one GPU, found none."

    adapted_args = {
        'batch_size': base_batch_size * ngpu,
        'labeled_batch_size': base_labeled_batch_size * ngpu,
        'lr': base_lr * ngpu,
        'labels': 'data-local/labels/cifar10/{}_balanced_labels/{:02d}.txt'.format(n_labels, data_seed),
    }
    context = RunContext(__file__, "{}_{}".format(n_labels, data_seed))
    main_cnn_multi_label.args = parse_dict_args(**adapted_args, **kwargs)
    main_cnn_multi_label.main(context)

Example #7

File: cifar10_test.py Project: ys2899/mean-teacher

def run(title, base_batch_size, base_labeled_batch_size, base_lr, n_labels, data_seed, **kwargs):
    LOG.info('run title: %s, data seed: %d', title, data_seed)

    ngpu = torch.cuda.device_count()
    assert ngpu > 0, "Expecting at least one GPU, found none."

    adapted_args = {
        'batch_size': base_batch_size * ngpu,
        'labeled_batch_size': base_labeled_batch_size * ngpu,
        'lr': base_lr * ngpu,
        'labels': 'data-local/labels/cifar10/{}_balanced_labels/{:02d}.txt'.format(n_labels, data_seed),
    }
    context = RunContext(__file__, "{}_{}".format(n_labels, data_seed))
    main.args = parse_dict_args(**adapted_args, **kwargs)
    main.main(context)

Example #8

def run(title, base_batch_size, base_labeled_batch_size, base_lr, data_seed,
        **kwargs):
    LOG.info('run title: %s', title)
    ngpu = torch.cuda.device_count()
    adapted_args = {
        'batch_size':
        base_batch_size * ngpu,
        'labeled_batch_size':
        base_labeled_batch_size * ngpu,
        'lr':
        base_lr * ngpu,
        'labels':
        'data-local/labels/ilsvrc2012/128000_balanced_labels/{:02d}.txt'.
        format(data_seed),
    }
    context = RunContext(__file__, data_seed)
    main_cifar.args = parse_dict_args(**adapted_args, **kwargs)
    main_cifar.main(context)

Example #9

File: joannas_idea.py Project: EricaHD/SemiSupervisedLearning

def run(title, data_seed, **kwargs):
    LOG.info('run title: %s', title)
    context = RunContext('/scratch/jtb470/ssl_j/', __file__,
                         "{}".format(data_seed))
    main.args = parse_dict_args(**kwargs)
    main.main(context)