def val_process_unknown(classes_to_process, feature_dic, evm_model, args_evm,
gpu, Q, done_event):
with torch.no_grad():
top1_Meter = Average_Meter()
Pr_iterator = EVM_Inference(classes_to_process, feature_dic, args_evm,
gpu, evm_model)
for k, pr in enumerate(Pr_iterator):
r = pr[1][1].cuda(gpu)
m, m_i = torch.max(r, dim=1)
u = (1 - m).view(-1, 1)
q = torch.cat((u, r), 1)
_, y = torch.max(1, dim=1)
acc = torch.sum(y == 0) / q.shape[0]
top1_Meter.update(acc[0].item(), r.size(0))
Q.put((gpu, top1_Meter.avg, top1_Meter.count))
done_event.wait()
del r, m, m_i, u, q, y
del acc, top1_Meter, Pr_iterator
def val_process(classes_to_process, feature_dic, evm_model, args_evm, gpu, Q,
done_event):
with torch.no_grad():
top1_Meter = Average_Meter()
Pr_iterator = EVM_Inference(classes_to_process, feature_dic, args_evm,
gpu, evm_model)
for k, pr in enumerate(Pr_iterator):
r = pr[1][1].cuda(gpu)
m, m_i = torch.max(r, dim=1)
u = (1 - m).view(-1, 1)
q = torch.cat((u, r), 1)
norm = torch.norm(q, p=1, dim=1)
p = q / norm[:, None]
L = ((pr[1][0]) * torch.ones(r.shape[0])).long().cuda(gpu)
acc = accuracy(p, L, topk=(1, ))
top1_Meter.update(acc[0].item(), r.size(0))
Q.put((gpu, top1_Meter.avg, top1_Meter.count))
done_event.wait()
del r, m, m_i, u, q, norm, p
del L, acc, top1_Meter, Pr_iterator
del x_supervised, x_moco, Logit
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
# FV_supervised = FV_supervised.cpu()
# FV_moco_places = FV_moco_places.cpu()
FV = torch.cat((FV_supervised, FV_moco_places), 1)
Logit = linear_model(FV)
softmax = SoftMax(Logit)
# FV = FV.double()
if args_owl.ood == "evm":
feature_dict = OrderedDict()
feature_dict[0] = FV.detach().clone().double()
Pr_iterator = EVM_Inference([0], feature_dict, args_evm, 0, evm_model)
for j, pr in enumerate(Pr_iterator):
prob = pr[1][1] # .cuda()
assert j == 0
del Pr_iterator, pr
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
P_max_all, _ = torch.max(prob, axis=1)
predicted_known = P_max_all >= threshold_evm
elif args_owl.ood == "energy":
negatvie_energy = torch.logsumexp(Logit, dim=1)
predicted_known = negatvie_energy >= threshold_energy
elif args_owl.ood == "softmax":
sm, _ = torch.max(softmax, axis=1)
predicted_known = sm >= threshold_softmax
sm, _ = torch.max(softmax, axis=1)
predicted_known = sm >= threshold_softmax
predicted_unknown = ~predicted_known
n = 1 + number_of_known_classes + number_of_discovered_classes
probability_tensor = torch.zeros(batch_size, n, dtype=torch.double)
probability_tensor[predicted_known, 1 : (1 + number_of_known_classes)] = softmax[predicted_known, :].cpu()
if torch.sum(predicted_unknown) > 0:
if number_of_discovered_classes > 0:
FV_predicted_unknown = FV[predicted_unknown, :]
feature_dict_predicted_unknown = OrderedDict()
feature_dict_predicted_unknown[0] = FV_predicted_unknown.double()
Pr_iterator_predicted_unknown = EVM_Inference(
[0], feature_dict_predicted_unknown, args_evm_incremental, 0, evm_model_incremental
)
for j_predicted_unknown, pr_predicted_unknown in enumerate(Pr_iterator_predicted_unknown):
prob_predicted_unknown = pr_predicted_unknown[1][1] # .cuda()
assert j_predicted_unknown == 0
del Pr_iterator_predicted_unknown, pr_predicted_unknown
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
P_max_predicted_unknown, _ = torch.max(prob_predicted_unknown, axis=1)
pu_predicted_unknown = 1 - P_max_predicted_unknown
probability_tensor[predicted_unknown, 0] = pu_predicted_unknown
probability_tensor[predicted_unknown, 1 + number_of_known_classes :] = prob_predicted_unknown
else:
probability_tensor[predicted_unknown, 0] = 1.0
x_moco = x_moco.cuda()
FV_supervised, Logit = cnn_model_supervised(x_supervised)
FV_moco_places, Logit = cnn_model_moco_places(x_moco)
del x_supervised, x_moco, Logit
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
FV_supervised = FV_supervised.cpu()
FV_moco_places = FV_moco_places.cpu()
FV = torch.cat((FV_supervised, FV_moco_places), 1)
FV_track[(i * 100):((i + 1) * 100), :] = FV
feature_dict = OrderedDict()
feature_dict[0] = FV.double()
Pr_iterator = EVM_Inference([0], feature_dict, args_evm, 0, evm_model)
for j, pr in enumerate(Pr_iterator):
prob = pr[1][1] # .cuda()
assert j == 0
del Pr_iterator, pr
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
n = 1 + number_of_known_classes + number_of_discovered_classes
probability_tensor = torch.zeros(prob.shape[0], n)
probability_tensor[:, 1:] = prob
P_max_all, _ = torch.max(prob, axis=1)
pu = 1 - P_max_all
probability_tensor[:, 0] = pu
norm = torch.norm(probability_tensor, p=1, dim=1)
normalized_tensor = probability_tensor / norm[:, None]
