'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 args.model_name == 'PointNet2-SSG':
from pointnet2.models.pointnet2_ssg_cls import Pointnet2SSG
model = Pointnet2SSG(40, input_channels=0) # , use_xyz=True)
ckpt = torch.load(
'checkpoints_ssg/pointnet2_cls_best.pth.tar')['model_state']
elif args.model_name == 'PointNet2-MSG':
from pointnet2.models.pointnet2_msg_cls import Pointnet2MSG
model = Pointnet2MSG(40, input_channels=0) # , use_xyz=True)
ckpt = torch.load(
'checkpoints_msg/pointnet2_cls_best.pth.tar')['model_state']
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
def test_xyz():
model = Pointnet2MSG(3, input_channels=3)
pytest.helpers.cls_test_xyz(model, model_fn_decorator(nn.CrossEntropyLoss()))