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': 'third_party/data-local/labels/cifar10/{}_balanced_labels/{:02d}.txt'.format(n_labels, data_seed),
}
context = RunContext(__file__, "{}_{}".format(n_labels, data_seed))
dual_student.args = parse_dict_args(**adapted_args, **kwargs)
dual_student.main(context)
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': 'third_party/data-local/labels/usps/{}_balanced_labels/{:02d}.txt'.format(n_labels, data_seed),
}
context = RunContext(__file__, "{}_{}".format(n_labels, data_seed))
fh = logging.FileHandler('{0}/log.txt'.format(context.result_dir))
fh.setLevel(logging.INFO)
LOG.addHandler(fh)
dual_student.args = parse_dict_args(**adapted_args, **kwargs)
dual_student.main(context)
