def singel_strength():
for i in range(9):
print('*' * 45)
print('Training {}_th model'.format(i + 1))
#print (i)
if i < 9:
eps = [0.9]
print('eps is {}'.format(eps))
print('*' * 45)
print('\n' * 2)
model = torch.load(
'/home/hankeji/Desktop/jsai/models/GTSRB_submodels_0.pkl').cpu(
)
print(id(model))
print(id(train_model))
for batch_idx, (data, target) in enumerate(train_loader):
data, target = Variable(data,
requires_grad=True), Variable(target)
optimizer0 = optim.Adam(model.parameters(),
lr=0.01,
weight_decay=1e-4)
output = model(data)
loss = cit(output, target)
optimizer0.zero_grad()
loss.backward()
optimizer0.step()
attack_model_index = random.sample([0, 1, 2, 4], 1)[0]
attack_model = model_list[attack_model_index].cpu()
optimizer1 = optim.Adam(attack_model.parameters(),
lr=0.01,
weight_decay=1e-4)
adv = symbolic_fgs(attack_model, cit, data, target, eps=eps)
data = Variable(adv.data, requires_grad=True)
output1 = model(data)
optimizer0.zero_grad()
loss = cit(output1, target)
loss.backward()
optimizer0.step()
torch.save(
model,
'/home/hankeji/Desktop/model_GTSRB/GTSRB_multi_model_du_test.pkl'
)
del (model)
gc.collect()
def mix_strength():
#model=model_list[0].cpu()
#a=b1(1, train_model)
#print (a)
eps0 = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
#eps0=[0.2, 0.6, 0.9]
#eps0=search_strength()
#tmp_n = np.random.randint(1, 9, 1)[0]
eps0 = random.sample(eps0, 3)
tmp = len(eps0)
print('\n' * 2)
print(tmp)
train_model = torch.load(
'/home/hankeji/Desktop/jsai/models/GTSRB_submodels_0.pkl').cpu()
for i in range(10):
eps = [eps0[i % 3]]
for batch_idx, (data, target) in enumerate(train_loader):
print(batch_idx)
data, target = Variable(data, requires_grad=True), Variable(target)
optimizer0 = optim.Adam(train_model.parameters(),
lr=0.01,
weight_decay=1e-4)
output = train_model(data)
loss = cit(output, target)
optimizer0.zero_grad()
loss.backward()
optimizer0.step()
attack_model_index = random.sample([0, 1, 2, 4], 1)[0]
attack_model = model_list[attack_model_index].cpu()
#optimizer1 = optim.Adam(attack_model.parameters(), lr=0.01, weight_decay=1e-4)
adv = symbolic_fgs(attack_model, cit, data, target, eps=eps)
data = Variable(adv.data, requires_grad=True)
output1 = train_model(data)
optimizer0.zero_grad()
loss = cit(output1, target)
loss.backward()
optimizer0.step()
torch.save(
train_model,
'/home/hankeji/Desktop/model_GTSRB/GTSRB_multi_model_random_strength.pkl'
)
def search_strength():
attack_index = random.sample([3], 1)[0]
attack_model = model_list[attack_index].cpu()
strength_list = []
strength_list = np.asarray(strength_list, np.float)
for i in range(9):
print('Searching scale of {}!'.format((i + 1) * 0.1))
eps = [(i + 1) * 0.1]
tmp_acc = []
tmp_acc = np.asarray(tmp_acc, np.float)
corr = 0
for j in range(9):
tmp_model = torch.load(
'/home/hankeji/Desktop/model_GTSRB/GTSRB_multi_model_' +
str((j + 1) * 0.1) + '.pkl').cpu()
for batch_idx, (data, target) in enumerate(test_loader):
data, target = Variable(data,
requires_grad=True), Variable(target)
if batch_idx > 0:
break
adv = symbolic_fgs(attack_model, cit, data, target, eps=eps)
data = Variable(adv.data, requires_grad=True)
output = tmp_model(data)
pred = output.data.max(1)[1]
corr += pred.eq(target.data).sum()
corr = corr / batch_size
tmp_acc = np.append(tmp_acc, corr)
tmp_acc = np.reshape(tmp_acc, (-1, 1))
tmp_acc = torch.from_numpy(tmp_acc)
tmp_acc_idx = tmp_acc.max(0)[1]
aa = float((tmp_acc_idx.numpy() + 1)) * 0.1
print(aa)
strength_list = np.append(strength_list,
float((tmp_acc_idx.numpy() + 1)) * 0.1)
final_strength_list = []
final_strength_list = np.asarray(final_strength_list, np.float)
for i in strength_list:
if i not in final_strength_list:
final_strength_list = np.append(final_strength_list, i)
print(final_strength_list)
return final_strength_list
def defense_eval():
acc = []
acc = np.asarray(acc, np.float)
a = 31
attack_model_index = random.sample([3], 1)[0]
attack_model = model_list[attack_model_index].cpu()
for i in range(9):
tmp_model = torch.load(
'/home/hankeji/Desktop/model_GTSRB/GTSRB_multi_model_' +
str((i + 1) * 0.1) + '.pkl').cpu()
#acc=np.append(acc, i)
for i in range(a):
if i == 0:
eps = [0]
else:
eps = [(i) * 0.03]
acc = np.append(acc, eps)
corr = 0
for batch_idx, (data, target) in enumerate(test_loader):
if batch_idx > 0:
break
print(batch_idx)
data, target = Variable(data,
requires_grad=True), Variable(target)
data = symbolic_fgs(attack_model, cit, data, target, eps=eps)
data = Variable(data.data, requires_grad=True)
output = tmp_model(data)
pred = output.data.max(1)[1]
corr += pred.eq(target.data).cpu().sum()
#print (batch_idx+1)
corr = corr / ((batch_idx) * batch_size)
acc = np.append(acc, corr)
acc = np.reshape(acc, (9, a, 2))
#print acc
np.save(
'/home/hankeji/Desktop/jsai/data/GTSRB_model_acc_single_modle_clean.npy',
acc) #_single model _mix_strength [0.1,0.6,0.8]
return acc
def ori_attack():
model = torch.load(
'/home/hankeji/Desktop/jsai/models/GTSRB_submodels_0.pkl').cpu()
corr = 0
#corr_du=0
#corr_ori=0
acc = []
acc = np.asarray(acc, np.float)
attack_index = random.sample([3], 1)[0]
attack_model = model_list[attack_index].cpu()
a = 31
for i in range(a):
if i == 0:
eps = [0]
else:
eps = [(i) * 0.03]
print('*' * 45)
print('eps is {}'.format(eps[0]))
print('*' * 45)
print('\n' * 2)
for batch_idx, (data, target) in enumerate(test_loader):
if batch_idx > 0:
break
data, target = Variable(data, requires_grad=True), Variable(target)
adv = symbolic_fgs(attack_model, cit, data, target, eps=eps)
data = Variable(adv.data, requires_grad=True)
output = model(data)
pred = output.data.max(1)[1]
corr += pred.eq(target.data).sum()
corr = corr / (batch_idx * batch_size)
acc = np.append(acc, eps)
acc = np.append(acc, corr)
acc = np.reshape(acc, (-1, 2))
np.save('/home/hankeji/Desktop/jsai/data/GTSRB_ori.npy', acc)
return acc
def vs():
attack_index = random.sample([3], 1)[0]
attack_model = model_list[attack_index].cpu()
mm = torch.load(
'/home/hankeji/Desktop/model_GTSRB/GTSRB_multi_model_all_strength.pkl')
ms = torch.load(
'/home/hankeji/Desktop/model_GTSRB/GTSRB_multi_model_0.3.pkl')
m_all = torch.load(
'/home/hankeji/Desktop/model_GTSRB/GTSRB_multi_model_all_strength.pkl')
m_random = torch.load(
'/home/hankeji/Desktop/model_GTSRB/GTSRB_multi_model_random_strength.pkl'
)
m_sm = torch.load(
'/home/hankeji/Desktop/model_GTSRB/GTSRB_single_model_multi_strength.pkl'
)
m_ss = torch.load(
'/home/hankeji/Desktop/model_GTSRB/GTSRB_single_model_0.3.pkl')
acc = []
acc = np.asarray(acc, np.float)
a = 31
for i in range(a):
acc_mm = 0
acc_ms = 0
acc_m_all = 0
acc_m_random = 0
acc_sm = 0
acc_ss = 0
if i == 0:
eps = [0]
else:
eps = [(i + 1) * 0.03]
for batch_idx, (data, target) in enumerate(test_loader):
#print (data[0])
if batch_idx > 0:
break
print('Here coming {}_th batch'.format(batch_idx + 1))
data, target = Variable(data, requires_grad=True), Variable(target)
adv = symbolic_fgs(attack_model, cit, data, target, eps)
data = Variable(adv.data, requires_grad=True)
output1 = mm(data)
pred1 = output1.data.max(1)[1]
acc_mm += pred1.eq(target.data).sum()
output2 = ms(data)
pred2 = output2.data.max(1)[1]
acc_ms += pred2.eq(target.data).sum()
output3 = m_all(data)
pred3 = output3.data.max(1)[1]
acc_m_all += pred3.eq(target.data).sum()
output4 = m_random(data)
pred4 = output4.data.max(1)[1]
acc_m_random += pred4.eq(target.data).sum()
output5 = m_sm(data)
pred5 = output5.data.max(1)[1]
acc_sm += pred5.eq(target.data).sum()
output6 = m_ss(data)
pred6 = output6.data.max(1)[1]
acc_ss += pred6.eq(target.data).sum()
acc_mm = acc_mm / batch_size #len(test_loader.dataset)
acc_ms = acc_ms / batch_size # len(test_loader.dataset)
acc_m_all = acc_m_all / batch_size #len(test_loader.dataset)
acc_m_random = acc_m_random / batch_size #len(test_loader.dataset)
acc_sm = acc_sm / batch_size #len(test_loader.dataset)
acc_ss = acc_ss / batch_size #len(test_loader.dataset)
acc = np.append(acc, eps)
acc = np.append(acc, acc_mm)
acc = np.append(acc, acc_ms)
acc = np.append(acc, acc_m_all)
acc = np.append(acc, acc_m_random)
acc = np.append(acc, acc_sm)
acc = np.append(acc, acc_ss)
acc = np.reshape(acc, (a, -1))
np.save('/home/hankeji/Desktop/jsai/data/GTSRB_mmVSms_7.npy', acc)
return acc