/
trainer_sim_ssb.py
578 lines (498 loc) · 26.8 KB
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trainer_sim_ssb.py
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"""
SimMatch training + SSB training
"""
import logging
import time
import copy
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from dataset import TransformOpenMatch, cifar10_mean, cifar10_std, \
cifar100_std, cifar100_mean, normal_mean, \
normal_std, TransformFixMatch_Imagenet_2strong, TransformFixMatch_2strong
from tqdm import tqdm
from utils import AverageMeter, save_checkpoint, ova_ent, accuracy_open, accuracy, roc_id_ood, compute_roc
from utils import Logger
from utils import ova_loss, unlabeled_ova_neg_loss
import os
logger = logging.getLogger(__name__)
best_acc = 0
best_acc_val = 0
@torch.no_grad()
def update_bank(k, labels, index, mem_bank, labels_bank, ema_bank):
mem_bank[:, index] = F.normalize(ema_bank * mem_bank[:, index] + (1 - ema_bank) * k.t().detach())
labels_bank[index] = labels.detach()
class DistAlignQueueHook(object):
"""
Distribution Alignment Hook for conducting distribution alignment
"""
def __init__(self, num_classes, queue_length=128, p_target_type='uniform', p_target=None):
super().__init__()
self.num_classes = num_classes
self.queue_length = queue_length
# p_target
self.p_target_ptr, self.p_target = self.set_p_target(p_target_type, p_target)
print('distribution alignment p_target:', self.p_target.mean(dim=0))
# p_model
self.p_model = torch.zeros(self.queue_length, self.num_classes, dtype=torch.float)
self.p_model_ptr = torch.zeros(1, dtype=torch.long)
@torch.no_grad()
def dist_align(self, probs_x_ulb, probs_x_lb=None):
# update queue
self.update_p(probs_x_ulb, probs_x_lb)
# dist align
probs_x_ulb_aligned = probs_x_ulb * (self.p_target.mean(dim=0) + 1e-6) / (self.p_model.mean(dim=0) + 1e-6)
probs_x_ulb_aligned = probs_x_ulb_aligned / probs_x_ulb_aligned.sum(dim=-1, keepdim=True)
return probs_x_ulb_aligned
@torch.no_grad()
def update_p(self, probs_x_ulb, probs_x_lb):
# TODO: think better way?
# check device
if not self.p_target.is_cuda:
self.p_target = self.p_target.to(probs_x_ulb.device)
if self.p_target_ptr is not None:
self.p_target_ptr = self.p_target_ptr.to(probs_x_ulb.device)
if not self.p_model.is_cuda:
self.p_model = self.p_model.to(probs_x_ulb.device)
self.p_model_ptr = self.p_model_ptr.to(probs_x_ulb.device)
probs_x_ulb = probs_x_ulb.detach()
p_model_ptr = int(self.p_model_ptr)
self.p_model[p_model_ptr] = probs_x_ulb.mean(dim=0)
self.p_model_ptr[0] = (p_model_ptr + 1) % self.queue_length
if self.p_target_ptr is not None:
assert probs_x_lb is not None
p_target_ptr = int(self.p_target_ptr)
self.p_target[p_target_ptr] = probs_x_lb.mean(dim=0)
self.p_target_ptr[0] = (p_target_ptr + 1) % self.queue_length
def set_p_target(self, p_target_type='uniform', p_target=None):
assert p_target_type in ['uniform', 'gt', 'model']
# p_target
p_target_ptr = None
if p_target_type == 'uniform':
p_target = torch.ones(self.queue_length, self.num_classes, dtype=torch.float) / self.num_classes
elif p_target_type == 'model':
p_target = torch.zeros((self.queue_length, self.num_classes), dtype=torch.float)
p_target_ptr = torch.zeros(1, dtype=torch.long)
else:
assert p_target is not None
if isinstance(p_target, np.ndarray):
p_target = torch.from_numpy(p_target)
p_target = p_target.unsqueeze(0).repeat((self.queue_length, 1))
return p_target_ptr, p_target
def train(args, labeled_trainloader, unlabeled_dataset, test_loader, val_loader,
ood_loaders, model, optimizer, ema_model, scheduler):
global best_acc
global best_acc_val
test_accs = []
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
losses_x = AverageMeter()
losses_o = AverageMeter()
losses_o_u = AverageMeter()
losses_oem = AverageMeter()
losses_socr = AverageMeter()
losses_fix = AverageMeter()
mask_probs = AverageMeter()
used_c = AverageMeter()
total_c = AverageMeter()
used_ood = AverageMeter()
mask_neg_percent_ova = AverageMeter()
used_neg_prec_ova = AverageMeter()
end = time.time()
default_out = "Epoch: {epoch}/{epochs:4}. " \
"LR: {lr:.6f}. " \
"Lab: {loss_x:.4f}. " \
"Open: {loss_o:.4f}"
output_args = vars(args)
default_out += " OEM {loss_oem:.4f}"
default_out += " SOCR {loss_socr:.4f}"
default_out += " Fix {loss_fix:.4f}"
model.train()
unlabeled_dataset_all = copy.deepcopy(unlabeled_dataset)
if args.dataset == 'cifar10':
mean = cifar10_mean
std = cifar10_std
func_trans = TransformFixMatch_2strong
elif args.dataset == 'cifar100' or args.dataset == 'cross':
mean = cifar100_mean
std = cifar100_std
func_trans = TransformFixMatch_2strong
elif 'imagenet' in args.dataset:
mean = normal_mean
std = normal_std
func_trans = TransformFixMatch_Imagenet_2strong
unlabeled_dataset_all.transform = func_trans(mean=mean, std=std)
labeled_trainloader.dataset.transform = func_trans(mean=mean, std=std)
train_sampler = RandomSampler if args.local_rank == -1 else DistributedSampler
if args.local_rank in [-1, 0]:
logger_custom = Logger(os.path.join(args.out, 'log.txt'), title='cifar')
logger_custom.set_names(['train_loss', 'train_loss_x', 'train_loss_o', 'train_loss_o_u', 'train_loss_oem',
'train_loss_socr', 'train_loss_fix',
'total_acc', 'Mask', 'Used_acc', 'Used OOD', 'mask_neg_percent_ova', 'Used_neg_prec_ova',
'Test Acc.', 'Test Loss', 'test_overall',
'test_unk', 'test_roc', 'test_roc_softm', 'val_acc',
'Test ROC C10', 'Test ROC C100', 'Test ROC SVHN', 'Test ROC lsun', 'Test ROC imagenet',
'test_unlabeled_acc', 'test_unlabeled_roc'])
ema_bank = 0.7
lambda_in = 1.0
lambda_u = 1.0
T = 0.1
# p_cutoff = 0.95
proj_size = 128
K = len(labeled_trainloader.dataset)
smoothing_alpha = 0.9
da_len = 32
dist_align = DistAlignQueueHook(num_classes=args.num_classes, queue_length=da_len, p_target_type='uniform')
mem_bank = torch.randn(proj_size, K).to(args.device)
mem_bank = F.normalize(mem_bank, dim=0)
labels_bank = torch.zeros(K, dtype=torch.long).to(args.device)
for epoch in range(args.start_epoch, args.epochs):
print('\nEpoch: [%d | %d]' % (epoch + 1, args.epochs))
for g in optimizer.param_groups:
print(f"lr={g['lr']}")
output_args["epoch"] = epoch
unlabeled_trainloader = DataLoader(unlabeled_dataset,
sampler = train_sampler(unlabeled_dataset),
batch_size = args.batch_size * args.mu,
num_workers = args.num_workers,
drop_last = True)
unlabeled_trainloader_all = DataLoader(unlabeled_dataset_all,
sampler=train_sampler(unlabeled_dataset_all),
batch_size=args.batch_size * args.mu,
num_workers=args.num_workers,
drop_last=True)
labeled_iter = iter(labeled_trainloader)
unlabeled_iter = iter(unlabeled_trainloader)
unlabeled_all_iter = iter(unlabeled_trainloader_all)
for batch_idx in range(args.eval_step):
try:
(inputs_x_w, inputs_x_s, inputs_x_s2, inputs_x), targets_x, ind_x = labeled_iter.next()
except:
labeled_iter = iter(labeled_trainloader)
(inputs_x_w, inputs_x_s, inputs_x_s2, inputs_x), targets_x, ind_x = labeled_iter.next()
try:
(inputs_u_w, inputs_u_s, _), targets_u_gt, _ = unlabeled_iter.next()
except:
unlabeled_iter = iter(unlabeled_trainloader)
(inputs_u_w, inputs_u_s, _), targets_u_gt, _ = unlabeled_iter.next()
try:
(inputs_all_w, inputs_all_s, inputs_all_s2, inputs_all), targets_all_u, ind_u = unlabeled_all_iter.next()
targets_all_u[targets_all_u >= args.num_classes] = args.num_classes
except:
unlabeled_all_iter = iter(unlabeled_trainloader_all)
(inputs_all_w, inputs_all_s, inputs_all_s2, inputs_all), targets_all_u, ind_u = unlabeled_all_iter.next()
targets_all_u[targets_all_u >= args.num_classes] = args.num_classes
data_time.update(time.time() - end)
b_size = inputs_x.shape[0] # 64
num_ulb = inputs_u_w.shape[0]
bank = mem_bank.clone().detach()
inputs = torch.cat([inputs_x_w, inputs_x, inputs_x_s, inputs_x_s2,
inputs_all_w, inputs_all, inputs_all_s, inputs_all_s2], 0).to(args.device)
targets_x = targets_x.to(args.device)
ind_x = ind_x.to(args.device)
outputs = model(inputs) # [384, 55], [384, 110]
logits, logits_open, feats = outputs['logits'], outputs['logits_open'], outputs['feat']
ema_feats_x_lb = feats[:b_size]
logits_open_u1, logits_open_u2, logits_open_s1, logits_open_s2 = logits_open[4*b_size:].chunk(4)
Lx = F.cross_entropy(logits[:2*b_size], targets_x.repeat(2), reduction='mean')
Lo = ova_loss(args, logits_open[:2*b_size], logits_open[2*b_size:4*b_size], targets_x.repeat(2), targets_x.repeat(2))
# unlabeled OVA loss starts
if epoch >= args.start_fix and args.lambda_ova_u != 0:
with torch.no_grad():
logits_open_w = logits_open_u1.view(logits_open_u1.size(0), 2, -1)
logits_open_w = F.softmax(logits_open_w, 1)
know_score_w = logits_open_w[:, 1, :] # [bs, num_class]
neg_mask = (know_score_w <= args.ova_unlabeled_threshold).float() # [bs, num_class]
mask_neg_percent_ova.update(neg_mask.mean(dim=1).mean().item())
tmp = torch.zeros((neg_mask.size(0), neg_mask.size(1) + 1)) # [bs, num_class]
tmp.scatter_(1, targets_all_u.view(-1, 1), 1)
gt_mask = (1 - tmp).float()
gt_mask = gt_mask[:, :-1]
if neg_mask.cpu().view(-1).sum() != 0:
prec = ((neg_mask.cpu() == gt_mask) * neg_mask.cpu()).view(-1).sum() / neg_mask.cpu().view(
-1).sum()
used_neg_prec_ova.update(prec.item())
Lo_u = unlabeled_ova_neg_loss(args, logits_open_u1, logits_open_u2, logits_open_s1, logits_open_s2, neg_mask)
else:
Lo_u = torch.zeros(1).to(args.device).mean()
# unlabeled OVA loss ends
# Open-set entropy minimization
L_oem = ova_ent(logits_open_u1) / 2.
L_oem += ova_ent(logits_open_u2) / 2.
# Soft consistenty regularization
logits_open_u1_ = logits_open_u1.view(logits_open_u1.size(0), 2, -1)
logits_open_u2_ = logits_open_u2.view(logits_open_u2.size(0), 2, -1)
logits_open_u1_ = F.softmax(logits_open_u1_, 1)
logits_open_u2_ = F.softmax(logits_open_u2_, 1)
L_socr = torch.mean(torch.sum(torch.sum(torch.abs(
logits_open_u1_ - logits_open_u2_)**2, 1), 1))
if epoch >= args.start_fix:
inputs_ws = torch.cat([inputs_u_w, inputs_u_s], 0).to(args.device)
outputs = model(inputs_ws) # [256, 55], [256, 110]
logits, logits_open_fix, feats = outputs['logits'], outputs['logits_open'], outputs['feat']
logits_u_w, logits_u_s = logits.chunk(2)
ema_feats_x_ulb_w, feats_x_ulb_s = feats.chunk(2)
with torch.no_grad():
ema_probs_x_ulb_w = F.softmax(logits_u_w, dim=-1)
ema_probs_x_ulb_w = dist_align.dist_align(probs_x_ulb=ema_probs_x_ulb_w.detach())
with torch.no_grad():
teacher_logits = ema_feats_x_ulb_w @ bank
teacher_prob_orig = F.softmax(teacher_logits / T, dim=1)
factor = ema_probs_x_ulb_w.gather(1, labels_bank.expand([num_ulb, -1]))
teacher_prob = teacher_prob_orig * factor
teacher_prob /= torch.sum(teacher_prob, dim=1, keepdim=True)
if smoothing_alpha < 1:
bs = teacher_prob_orig.size(0)
aggregated_prob = torch.zeros([bs, args.num_classes], device=teacher_prob_orig.device)
aggregated_prob = aggregated_prob.scatter_add(1, labels_bank.expand([bs, -1]),
teacher_prob_orig)
probs_x_ulb_w = ema_probs_x_ulb_w * smoothing_alpha + aggregated_prob * (
1 - smoothing_alpha)
else:
probs_x_ulb_w = ema_probs_x_ulb_w
student_logits = feats_x_ulb_s @ bank
student_prob = F.softmax(student_logits / T, dim=1)
in_loss = torch.sum(-teacher_prob.detach() * torch.log(student_prob), dim=1).mean()
if epoch == 0:
in_loss *= 0.0
probs_x_ulb_w = ema_probs_x_ulb_w
max_probs, targets_u = torch.max(probs_x_ulb_w.detach(), dim=-1)
mask = max_probs.ge(args.threshold).float()
mask_probs.update(mask.mean().item())
L_fix = (F.cross_entropy(logits_u_s, targets_u, reduction='none') * mask).mean()
total_acc = targets_u.cpu().eq(targets_u_gt).float().view(-1)
if mask.sum() != 0:
used_c.update(total_acc[mask != 0].mean(0).item(), mask.sum())
tmp = (targets_u_gt[mask != 0] == args.num_classes).float()
used_ood.update(tmp.mean().item())
total_c.update(total_acc.mean(0).item())
else:
L_fix = torch.zeros(1).to(args.device).mean()
in_loss = torch.zeros(1).to(args.device).mean()
loss = args.lambda_x * Lx + args.lambda_ova * Lo + args.lambda_oem * L_oem \
+ args.lambda_socr * L_socr + lambda_u * L_fix + args.lambda_ova_u * Lo_u + lambda_in * in_loss
update_bank(ema_feats_x_lb, targets_x, ind_x, mem_bank, labels_bank, ema_bank)
loss.backward()
losses.update(loss.item())
losses_x.update(Lx.item())
losses_o.update(Lo.item())
losses_o_u.update(Lo_u.item())
losses_oem.update(L_oem.item())
losses_socr.update(L_socr.item())
losses_fix.update(L_fix.item())
output_args["batch"] = batch_idx
output_args["loss_x"] = losses_x.avg
output_args["loss_o"] = losses_o.avg
output_args["loss_oem"] = losses_oem.avg
output_args["loss_socr"] = losses_socr.avg
output_args["loss_fix"] = losses_fix.avg
output_args["lr"] = [group["lr"] for group in optimizer.param_groups][0]
optimizer.step()
if (args.opt != 'adam') and (not args.no_scheduler):
scheduler.step()
if args.use_ema:
ema_model.update(model)
model.zero_grad()
batch_time.update(time.time() - end)
end = time.time()
if args.use_ema:
test_model = ema_model.ema
else:
test_model = model
if args.local_rank in [-1, 0]:
test_unlabeled_data = copy.deepcopy(unlabeled_dataset_all)
test_unlabeled_data.transform = test_loader.dataset.transform
test_unlabeled_data.return_idx = False
test_unlabeled_loader = DataLoader(test_unlabeled_data,
shuffle=False, batch_size=args.batch_size,
num_workers=args.num_workers, drop_last=False)
_, test_unlabeled_acc_close, _, _, test_unlabeled_roc, _, _, _, _ = test(args, test_unlabeled_loader,
test_model, epoch)
val_acc = test(args, val_loader, test_model, epoch, val=True)
test_loss, test_acc_close, test_overall, \
test_unk, test_roc, test_roc_softm, test_id, f1_mi, f1_ma \
= test(args, test_loader, test_model, epoch)
ood_dataset_roc = {'cifar10': 0, 'cifar100': 0, 'svhn': 0, 'lsun': 0, 'imagenet': 0}
for ood in ood_loaders.keys():
roc_ood = test_ood(args, test_id, ood_loaders[ood], test_model)
logger.info("ROC vs {ood}: {roc}".format(ood=ood, roc=roc_ood))
ood_dataset_roc[ood] = roc_ood
logger_custom.append(
[losses.avg, losses_x.avg, losses_o.avg, losses_o_u.avg, losses_oem.avg, losses_socr.avg, losses_fix.avg,
total_c.avg, mask_probs.avg, used_c.avg, used_ood.avg, mask_neg_percent_ova.avg, used_neg_prec_ova.avg,
test_acc_close, test_loss, test_overall, test_unk, test_roc, test_roc_softm, val_acc,
ood_dataset_roc['cifar10'], ood_dataset_roc['cifar100'], ood_dataset_roc['svhn'],
ood_dataset_roc['lsun'], ood_dataset_roc['imagenet'],
test_unlabeled_acc_close, test_unlabeled_roc])
logger_custom.set_names(['train_loss', 'train_loss_x', 'train_loss_o', 'train_loss_o_u', 'train_loss_oem',
'train_loss_socr', 'train_loss_fix',
'total_acc', 'Mask', 'Used_acc', 'Used OOD', 'mask_neg_percent_ova',
'Used_neg_prec_ova',
'Test Acc.', 'Test Loss', 'test_overall',
'test_unk', 'test_roc', 'test_roc_softm', 'val_acc',
'Test ROC C10', 'Test ROC C100', 'Test ROC SVHN', 'Test ROC lsun',
'Test ROC imagenet',
'test_unlabeled_acc', 'test_unlabeled_roc'])
is_best = val_acc > best_acc_val
best_acc_val = max(val_acc, best_acc_val)
if is_best:
overall_valid = test_overall
close_valid = test_acc_close
unk_valid = test_unk
roc_valid = test_roc
roc_softm_valid = test_roc_softm
model_to_save = model.module if hasattr(model, "module") else model
if args.use_ema:
ema_to_save = ema_model.ema.module if hasattr(
ema_model.ema, "module") else ema_model.ema
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model_to_save.state_dict(),
'ema_state_dict': ema_to_save.state_dict() if args.use_ema else None,
'acc close': test_acc_close,
'acc overall': test_overall,
'unk': test_unk,
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}, is_best, args.out)
test_accs.append(test_acc_close)
logger.info('Best val closed acc: {:.3f}'.format(best_acc_val))
logger.info('Valid closed acc: {:.3f}'.format(close_valid))
logger.info('Valid overall acc: {:.3f}'.format(overall_valid))
logger.info('Valid unk acc: {:.3f}'.format(unk_valid))
logger.info('Valid roc: {:.3f}'.format(roc_valid))
logger.info('Valid roc soft: {:.3f}'.format(roc_softm_valid))
logger.info('Mean top-1 acc: {:.3f}\n'.format(
np.mean(test_accs[-20:])))
if args.local_rank in [-1, 0]:
logger_custom.close()
def test(args, test_loader, model, epoch, val=False):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
acc = AverageMeter()
f1_mi = AverageMeter()
f1_ma = AverageMeter()
unk = AverageMeter()
top5 = AverageMeter()
end = time.time()
if not args.no_progress:
test_loader = tqdm(test_loader,
disable=args.local_rank not in [-1, 0])
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
data_time.update(time.time() - end)
model.eval()
inputs = inputs.to(args.device)
targets = targets.to(args.device)
out_dict = model(inputs) # [bs, num_class], [bs, 2 * num_class]
outputs, outputs_open = out_dict['logits'], out_dict['logits_open'] # [bs, num_class], [bs, 2 * num_class]
outputs = F.softmax(outputs, 1) # [bs, num_class]
out_open = F.softmax(outputs_open.view(outputs_open.size(0), 2, -1), 1) # [bs, 2, num_class]
tmp_range = torch.arange(0, out_open.size(0)).long().cuda()
pred_close = outputs.data.max(1)[1] # [bs,]
unk_score = out_open[tmp_range, 0, pred_close] # [bs,]
known_score = outputs.max(1)[0] # [bs,]
targets_unk = targets >= int(outputs.size(1))
targets[targets_unk] = int(outputs.size(1))
known_targets = targets < int(outputs.size(1))#[0]
known_pred = outputs[known_targets]
known_targets = targets[known_targets]
if len(known_pred) > 0:
prec1, prec5 = accuracy(known_pred, known_targets, topk=(1, 5))
top1.update(prec1.item(), known_pred.shape[0])
top5.update(prec5.item(), known_pred.shape[0])
ind_unk = unk_score > 0.5
pred_close[ind_unk] = int(outputs.size(1))
acc_all, unk_acc, size_unk = accuracy_open(pred_close,
targets,
num_classes=int(outputs.size(1)))
acc.update(acc_all.item(), inputs.shape[0])
unk.update(unk_acc, size_unk)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx == 0:
unk_all = unk_score
known_all = known_score
label_all = targets
else:
unk_all = torch.cat([unk_all, unk_score], 0)
known_all = torch.cat([known_all, known_score], 0)
label_all = torch.cat([label_all, targets], 0)
if not args.no_progress:
test_loader.set_description("Test Iter: {batch:4}/{iter:4}. "
"Data: {data:.3f}s."
"Batch: {bt:.3f}s. "
"Loss: {loss:.4f}. "
"Closed t1: {top1:.3f} "
"t5: {top5:.3f} "
"acc: {acc:.3f}. "
"unk: {unk:.3f}. ".format(
batch=batch_idx + 1,
iter=len(test_loader),
data=data_time.avg,
bt=batch_time.avg,
loss=losses.avg,
top1=top1.avg,
top5=top5.avg,
acc=acc.avg,
unk=unk.avg,
))
if not args.no_progress:
test_loader.close()
# ROC calculation
unk_all = unk_all.data.cpu().numpy()
known_all = known_all.data.cpu().numpy()
label_all = label_all.data.cpu().numpy()
if not val:
roc = compute_roc(unk_all, label_all,
num_known=int(outputs.size(1)))
roc_soft = compute_roc(-known_all, label_all,
num_known=int(outputs.size(1)))
ind_known = np.where(label_all < int(outputs.size(1)))[0]
id_score = unk_all[ind_known]
logger.info("Closed acc: {:.4f}".format(top1.avg))
logger.info("Overall acc: {:.4f}".format(acc.avg))
logger.info("Unk acc: {:.4f}".format(unk.avg))
logger.info("ROC: {:.4f}".format(roc))
logger.info("ROC Softmax: {:.4f}".format(roc_soft))
return losses.avg, top1.avg, acc.avg, \
unk.avg, roc, roc_soft, id_score, f1_mi.avg, f1_ma.avg
else:
logger.info("Closed acc: {:.3f}".format(top1.avg))
return top1.avg
def test_ood(args, test_id, test_loader, model):
batch_time = AverageMeter()
data_time = AverageMeter()
end = time.time()
if not args.no_progress:
test_loader = tqdm(test_loader,
disable=args.local_rank not in [-1, 0])
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
data_time.update(time.time() - end)
model.eval()
inputs = inputs.to(args.device)
out_dict = model(inputs)
outputs, outputs_open = out_dict['logits'], out_dict['logits_open']
out_open = F.softmax(outputs_open.view(outputs_open.size(0), 2, -1), 1)
tmp_range = torch.arange(0, out_open.size(0)).long().cuda()
pred_close = outputs.data.max(1)[1]
unk_score = out_open[tmp_range, 0, pred_close]
batch_time.update(time.time() - end)
end = time.time()
if batch_idx == 0:
unk_all = unk_score
else:
unk_all = torch.cat([unk_all, unk_score], 0)
if not args.no_progress:
test_loader.close()
# ROC calculation
unk_all = unk_all.data.cpu().numpy()
roc = roc_id_ood(test_id, unk_all)
return roc