def train_on_poisoned_data(args, poisoned_data):
callable_ds = datasets.dataset_helper(args.dataset)
ds = callable_ds()
model = dnn_utils.model_helper(args.model_arch)()
model = dnn_utils.multi_gpu_wrap(model)
for x, y in zip(*poisoned_data):
ds.add_point_to_train(x, y)
model = mtp_utils.train_clean_model(ds, args, epochs=15)
# Compute metrics for said model
train_loader, val_loader = callable_ds().get_loaders(args.batch_size)
_, train_loss = dnn_utils.get_model_metrics(model, train_loader)
test_acc, _ = dnn_utils.get_model_metrics(model, val_loader)
(trn_sub_acc, _), (trn_nsub_acc, _) = dnn_utils.get_model_metrics(
model, train_loader, args.poison_class)
(tst_sub_acc, _), (tst_nsub_acc, _) = dnn_utils.get_model_metrics(
model, val_loader, args.poison_class)
# Print accuracies on target/non-target data
# On seen (train) and unseen (val) data
print("Total Acc: %.3f" % test_acc)
print('Train Target Acc : %.3f' % trn_sub_acc)
print('Train Collat Acc : %.3f' % trn_nsub_acc)
print('Test Target Acc : %.3f' % tst_sub_acc)
print('Test Collat Acc : %.3f' % tst_nsub_acc)
print()
def get_pop_accs(poison_model_path):
# Load target model theta_p, set to eval mode
theta_p = dnn_utils.model_helper("flat")(n_classes=2)
theta_p = theta_p.cuda()
theta_p.load_state_dict(ch.load(poison_model_path))
theta_p.eval()
# Report performance of poisoned model
train_loader, test_loader = datasets.dataset_helper("memory")(
path="./data/datasets/MNIST17/split_1.pt").get_loaders(512)
# Report accuracy on unseen population data
(tst_sub_acc, _), _ = dnn_utils.get_model_metrics(
model=theta_p,
loader=test_loader,
target_prop=0)
return tst_sub_acc
if args.dynamic_lr and (not args.use_optim_for_optimal):
raise ValueError("Dynamic LR only supported for optimizer currently")
if args.skip_bad and (args.start_opt_real or args.poison_data):
raise ValueError(
"Re-run and real/poison start data not supported yet.")
# Print all arguments
utils.flash_utils(args)
# Get number of classes
n_classes = datasets.dataset_helper(args.dataset)().n_classes
# Load target model theta_p, set to eval mode
theta_p = dnn_utils.model_helper(args.model_arch)(n_classes=n_classes)
theta_p = theta_p.cuda()
theta_p.load_state_dict(ch.load(args.poison_model_path))
theta_p.eval()
# Report performance of poisoned model
train_loader, test_loader = datasets.dataset_helper(
args.dataset)().get_loaders(512)
clean_acc, _ = dnn_utils.get_model_metrics(theta_p, test_loader)
print(utils.yellow_print("[Poisoned-model] Total Acc: %.4f" % clean_acc))
_, clean_total_loss = dnn_utils.get_model_metrics(theta_p, train_loader)
print(
utils.yellow_print("[Poisoned-model] Loss on train: %.4f" %
clean_total_loss))
# Report weight norm for poisoned model
poisoned_norm = dnn_utils.get_model_l2_norm(theta_p).item()
assert len(theta_values) > 0, 'Provide at least one theta value'
args.theta_values = theta_values
except Exception:
raise ValueError("Theta values not provided in correct format")
if args.verbose:
args.verbose_pretrain = True
args.verbose_oga = True
args.verbose_opt = True
args.verbose_precomp = True
# Print all arguments
utils.flash_utils(args)
# Load target model theta_p
theta_p = dnn_utils.model_helper(args.model_arch)()
theta_p = dnn_utils.multi_gpu_wrap(theta_p)
theta_p.load_state_dict(ch.load(args.poison_model_path))
theta_p.eval()
# Report performance of poisoned model
train_loader, test_loader = datasets.dataset_helper(
args.dataset)().get_loaders(args.batch_size)
clean_acc, _ = dnn_utils.get_model_metrics(theta_p, test_loader)
print(utils.yellow_print("[Poisoned-model] Total Acc: %.4f" % clean_acc))
_, clean_total_loss = dnn_utils.get_model_metrics(theta_p, train_loader)
print(
utils.yellow_print("[Poisoned-model] Loss on train: %.4f" %
clean_total_loss))
# Report weight norm for poisoned model
poisoned_norm = dnn_utils.get_model_l2_norm(theta_p).item()
args.verbose_precomp = True
# Print all arguments
utils.flash_utils(args)
# Get number of classes
n_classes = 2
# Report performance of poisoned model
train_loader, test_loader = datasets.dataset_helper("memory")(
path=args.path_1).get_loaders(512)
# Load target models theta_p, set to eval mode
thetas_p = []
for i, mp in enumerate(os.listdir(args.poison_model_dir)):
tp = dnn_utils.model_helper(args.poison_arch)(n_classes=n_classes)
tp = tp.cuda()
tp.load_state_dict(ch.load(os.path.join(args.poison_model_dir, mp)))
tp.eval()
thetas_p.append(tp)
# Report performance of poisoned model
clean_acc, _ = dnn_utils.get_model_metrics(tp, test_loader)
print(
utils.yellow_print("[Poisoned-model %d] Total Acc: %.4f" %
(i + 1, clean_acc)))
_, clean_total_loss = dnn_utils.get_model_metrics(tp, train_loader)
print(
utils.yellow_print("[Poisoned-model %d] Loss on train: %.4f" %
(i + 1, clean_total_loss)))
# Report weight norm for poisoned model