def validate(val_loader: DataLoader, model: ImageClassifier, esem,
source_classes: list, args: argparse.Namespace) -> float:
# switch to evaluate mode
model.eval()
esem.eval()
all_confidece = list()
all_consistency = list()
all_entropy = list()
all_indices = list()
all_labels = list()
with torch.no_grad():
for i, (images, labels) in enumerate(val_loader):
images = images.to(device)
labels = labels.to(device)
output, f = model(images)
values, indices = torch.max(F.softmax(output, -1), 1)
yt_1, yt_2, yt_3, yt_4, yt_5 = esem(f)
confidece = get_confidence(yt_1, yt_2, yt_3, yt_4, yt_5)
entropy = get_entropy(yt_1, yt_2, yt_3, yt_4, yt_5)
consistency = get_consistency(yt_1, yt_2, yt_3, yt_4, yt_5)
all_confidece.extend(confidece)
all_consistency.extend(consistency)
all_entropy.extend(entropy)
all_indices.extend(indices)
all_labels.extend(labels)
all_confidece = norm(torch.tensor(all_confidece))
all_consistency = norm(torch.tensor(all_consistency))
all_entropy = norm(torch.tensor(all_entropy))
all_score = (all_confidece + 1 - all_consistency + 1 - all_entropy) / 3
counters = AccuracyCounter(len(source_classes) + 1)
for (each_indice, each_label, score) in zip(all_indices, all_labels,
all_score):
if each_label in source_classes:
counters.add_total(each_label)
if score >= args.threshold and each_indice == each_label:
counters.add_correct(each_label)
else:
counters.add_total(-1)
if score < args.threshold:
counters.add_correct(-1)
print('---counters---')
print(counters.each_accuracy())
print(counters.mean_accuracy())
print(counters.h_score())
return counters.mean_accuracy()
def evaluate_source_common(val_loader: DataLoader, model: ImageClassifier,
esem, source_classes: list,
args: argparse.Namespace):
temperature = 1
# switch to evaluate mode
model.eval()
esem.eval()
common = []
target_private = []
all_confidece = list()
all_consistency = list()
all_entropy = list()
all_labels = list()
all_output = list()
source_weight = torch.zeros(len(source_classes)).to(device)
cnt = 0
with torch.no_grad():
for i, (images, labels) in enumerate(val_loader):
images = images.to(device)
# labels = labels.to(device)
output, f = model(images)
output = F.softmax(output, -1) / temperature
yt_1, yt_2, yt_3, yt_4, yt_5 = esem(f)
confidece = get_confidence(yt_1, yt_2, yt_3, yt_4, yt_5)
entropy = get_entropy(yt_1, yt_2, yt_3, yt_4, yt_5)
consistency = get_consistency(yt_1, yt_2, yt_3, yt_4, yt_5)
all_confidece.extend(confidece)
all_consistency.extend(consistency)
all_entropy.extend(entropy)
all_labels.extend(labels)
for each_output in output:
all_output.append(each_output)
# for (each_output, each_score, label) in zip(output, score, labels):
# if each_score >= args.threshold:
# source_weight += each_output
# cnt += 1
# if label in source_classes:
# common.append(each_score)
# else:
# target_private.append(each_score)
all_confidece = norm(torch.tensor(all_confidece))
all_consistency = norm(torch.tensor(all_consistency))
all_entropy = norm(torch.tensor(all_entropy))
all_score = (all_confidece + 1 - all_consistency + 1 - all_entropy) / 3
# args.threshold = torch.median(all_score)
# print('threshold = {}'.format(args.threshold))
for i in range(len(all_score)):
if all_score[i] >= args.threshold:
source_weight += all_output[i]
cnt += 1
if all_labels[i] in source_classes:
common.append(all_score[i])
else:
target_private.append(all_score[i])
hist, bin_edges = np.histogram(common, bins=10, range=(0, 1))
print(hist)
# print(bin_edges)
hist, bin_edges = np.histogram(target_private, bins=10, range=(0, 1))
print(hist)
# print(bin_edges)
source_weight = norm(source_weight / cnt)
print('---source_weight---')
print(source_weight)
return source_weight