if not os.path.exists(os.path.join('save', model_name, adv+'-'+str(eps)+'-'+str(int(n))+'-'+str(eps_iter))):
os.makedirs(os.path.join('save', model_name, adv+'-'+str(eps)+'-'+str(int(n))+'-'+str(eps_iter)))
with open(os.path.join('dataset', 'random1024', 'whole_data_and_whole_label.pkl'), 'rb') as fid:
whole_data, whole_label = pkl.load(fid)
if model_name == 'PointNet':
from pointnet.model import PointNetCls
model = PointNetCls(k=40, feature_transform=True, predict_logit=True)
checkpoint = 'pointnet/cls_model_201.pth'
else:
print('No such model architecture')
assert False
model = model.to(device)
model.load_state_dict(torch.load(checkpoint))
model.eval()
pytorch_utils.requires_grad_(model, False)
print("Model name\t%s" % model_name)
cnt = 0 # adv pointcloud successfully attacked
CNT = 0 # clean pointcloud correctly classified
for idx in tqdm(range(len(whole_data))):
x = whole_data[idx]
label = whole_label[idx]
with torch.no_grad():
num_workers=int(opt.workers))
print(len(dataset), len(test_dataset))
num_classes = len(dataset.classes)
print('classes', num_classes)
try:
os.makedirs(opt.outf)
except OSError:
pass
classifier = PointNetCls(k=num_classes,
feature_transform=opt.feature_transform)
if opt.gpu != -1:
classifier = torch.nn.DataParallel(classifier).to(device)
else:
classifier.to(device)
if opt.model != '':
classifier.load_state_dict(torch.load(opt.model))
optimizer = optim.Adam(classifier.parameters(),
lr=0.001,
betas=(0.9, 0.999))
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
num_batch = len(dataset) / opt.batchSize
for epoch in range(opt.nepoch):
scheduler.step()
for i, data in enumerate(dataloader, 0):
points, target = data
