def __init__(self):
self.args = cli.parse_commandline_args()
self.context = RunContext(logging)
self.training_log = self.context.create_train_log("training")
self.results_all_log = self.context.create_results_all_log(
"results_all")
useCuda = torch.cuda.is_available()
self.device = torch.device("cuda" if useCuda else "cpu")
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def get_current_consistency_weight(epoch):
# Consistency ramp-up from https://arxiv.org/abs/1610.02242
return args.consistency * ramps.sigmoid_rampup(epoch,
args.consistency_rampup)
def accuracy(output, target, topk=(1, )):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
labeled_minibatch_size = max(target.ne(NO_LABEL).sum(), 1e-8)
_, 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 / labeled_minibatch_size.float()))
return res
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
args = cli.parse_commandline_args()
main(RunContext(__file__, 0))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def get_current_consistency_weight(epoch):
# Consistency ramp-up from https://arxiv.org/abs/1610.02242
return args.consistency * ramps.sigmoid_rampup(epoch, args.consistency_rampup)
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
labeled_minibatch_size = max(target.ne(NO_LABEL).sum(), 1e-8)
_, 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 / labeled_minibatch_size))
return res
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
args = cli.parse_commandline_args()
main(RunContext(__file__, 0))
