def test(epoch, patch, patch_shape):
netClassifier.eval()
cor = 0
total = 0
smoothed_classifier = Smooth(netClassifier, 10, opt.sigma)
for batch_idx, (data, labels) in enumerate(test_loader):
if labels.item() == target:
continue
if torch.cuda.is_available:
data = data.cuda()
labels = labels.cuda()
data, labels = Variable(data), Variable(labels)
data = data[:, [2, 1, 0], :, :] # rgb to bgr
prediction = netClassifier(data)
total += 1
# transform path
data_shape = data.data.cpu().numpy().shape
if patch_type == 'circle':
patch, mask, patch_shape = circle_transform(
patch, data_shape, patch_shape, image_size)
elif patch_type == 'square':
patch, mask = square_transform(patch, data_shape, patch_shape,
image_size)
patch, mask = torch.FloatTensor(patch), torch.FloatTensor(mask)
if torch.cuda.is_available:
patch, mask = patch.cuda(), mask.cuda()
patch, mask = Variable(patch), Variable(mask)
adv_x = torch.mul((1 - mask), data) + torch.mul(mask, patch)
adv_x = torch.clamp(adv_x, min_out, max_out)
#adv_label = netClassifier(adv_x).data.max(1)[1][0]
ori_label = labels.data[0]
if epoch == opt.epochs:
prediction = smoothed_classifier.predict(adv_x, opt.N, opt.alpha,
opt.batch)
cor += int(prediction == int(labels))
# log the prediction and whether it was correct
#print("{}\t{}\t{}\t{}\t{}".format(labels, prediction, cor, time_elapsed), file=f, flush=True)
masked_patch = torch.mul(mask, patch)
patch = masked_patch.data.cpu().numpy()
new_patch = np.zeros(patch_shape)
for i in range(new_patch.shape[0]):
for j in range(new_patch.shape[1]):
new_patch[i][j] = submatrix(patch[i][j])
patch = new_patch
if epoch == opt.epochs:
print("final accuracy is ", cor / total)
else:
print("continue to run")
def test(epoch, patch, patch_shape):
netClassifier.eval()
cor = 0
total = 0
smoothed_classifier = Smooth(netClassifier, 16, opt.sigma)
for batch_idx, (data, labels) in enumerate(test_loader):
if labels.item() == target:
continue
if torch.cuda.is_available:
data = data.cuda()
labels = labels.cuda()
data, labels = Variable(data), Variable(labels)
prediction = netClassifier(data)
# only computer adversarial examples on examples that are originally classified correctly
# if prediction.data.max(1)[1][0] != labels.data[0]:
# continue
total += 1
# transform path
data_shape = data.data.cpu().numpy().shape
if patch_type == 'circle':
patch, mask, patch_shape = circle_transform(
patch, data_shape, patch_shape, image_size)
elif patch_type == 'square':
patch, mask = square_transform(patch, data_shape, patch_shape,
image_size)
patch, mask = torch.FloatTensor(patch), torch.FloatTensor(mask)
if torch.cuda.is_available:
patch, mask = patch.cuda(), mask.cuda()
patch, mask = Variable(patch), Variable(mask)
adv_x = torch.mul((1 - mask), data) + torch.mul(mask, patch)
adv_x = torch.clamp(adv_x, min_out, max_out)
ori_label = labels.data[0]
if epoch == opt.epochs:
prediction = smoothed_classifier.predict(adv_x, opt.N, opt.alpha,
opt.batch)
cor += int(prediction == int(labels))
#print(total)
if total % 100 == 0:
print(cor / total * 100)
masked_patch = torch.mul(mask, patch)
patch = masked_patch.data.cpu().numpy()
new_patch = np.zeros(patch_shape)
for i in range(new_patch.shape[0]):
for j in range(new_patch.shape[1]):
new_patch[i][j] = submatrix(patch[i][j])
patch = new_patch
print("The final accuracy is ", cor / total * 100)
# x = x + delta
# make the prediction
count = 0
correct = 0
for m in range(5):
change = pgd_glass(base_classifier,
x,
labels,
glass,
epsilon=1,
alpha=0.07843,
num_iter=i,
randomize=True)
x = x + change
prediction = smoothed_classifier.predict(
x, args.N, args.alpha, args.batch)
#print("label is ", label, "prediction is ", prediction)
after_time = time()
correct += int(prediction == int(label))
time_elapsed = str(
datetime.timedelta(seconds=(after_time - before_time)))
if correct == 5:
cor = cor + 1
# log the prediction and whether it was correct
print("{}\t{}\t{}\t{}\t{}".format(i, label, prediction, correct,
time_elapsed),
file=f,
flush=True)
# if correct==1 :
# cor +=1
