!103 Avoid error of graph topological order

Merge pull request !103 from pkuliuliu/master

example/mnist_demo/mnist_defense_nad.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """defense example using nad"""
+import os
 import sys
 
 import numpy as np
@@ -19,41 +20,43 @@ from mindspore import Tensor
 from mindspore import context
 from mindspore import nn
 from mindspore.nn import SoftmaxCrossEntropyWithLogits
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train import Model
+from mindspore.train.callback import LossMonitor
 
-from lenet5_net import LeNet5
 from mindarmour.attacks import FastGradientSignMethod
 from mindarmour.defenses import NaturalAdversarialDefense
 from mindarmour.utils.logger import LogUtil
 
+from lenet5_net import LeNet5
+
 sys.path.append("..")
 from data_processing import generate_mnist_dataset
 
 
 LOGGER = LogUtil.get_instance()
+LOGGER.set_level("INFO")
 TAG = 'Nad_Example'
 
 
 def test_nad_method():
     """
-    NAD-Defense test for CPU device.
+    NAD-Defense test.
     """
-    # 1. load trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
+    mnist_path = "./MNIST_unzip/"
+    batch_size = 32
+    # 1. train original model
+    ds_train = generate_mnist_dataset(os.path.join(mnist_path, "train"),
+                                      batch_size=batch_size, repeat_size=1)
     net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)
-
-    nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt,
-                                    bounds=(0.0, 1.0), eps=0.3)
+    model = Model(net, loss, opt, metrics=None)
+    model.train(10, ds_train, callbacks=[LossMonitor()],
+                dataset_sink_mode=False)
 
     # 2. get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds_test = generate_mnist_dataset(data_list, batch_size=batch_size)
+    ds_test = generate_mnist_dataset(os.path.join(mnist_path, "test"),
+                                     batch_size=batch_size, repeat_size=1)
     inputs = []
     labels = []
     for data in ds_test.create_tuple_iterator():
@@ -73,16 +76,15 @@ def test_nad_method():
         label_pred = np.argmax(logits, axis=1)
         acc_list.append(np.mean(batch_labels == label_pred))
 
-    LOGGER.debug(TAG, 'accuracy of TEST data on original model is : %s',
-                 np.mean(acc_list))
+    LOGGER.info(TAG, 'accuracy of TEST data on original model is : %s',
+                np.mean(acc_list))
 
     # 4. get adv of test data
     attack = FastGradientSignMethod(net, eps=0.3, loss_fn=loss)
     adv_data = attack.batch_generate(inputs, labels)
-    LOGGER.debug(TAG, 'adv_data.shape is : %s', adv_data.shape)
+    LOGGER.info(TAG, 'adv_data.shape is : %s', adv_data.shape)
 
     # 5. get accuracy of adv data on original model
-    net.set_train(False)
     acc_list = []
     batchs = adv_data.shape[0] // batch_size
     for i in range(batchs):
@@ -92,11 +94,13 @@ def test_nad_method():
         label_pred = np.argmax(logits, axis=1)
         acc_list.append(np.mean(batch_labels == label_pred))
 
-    LOGGER.debug(TAG, 'accuracy of adv data on original model is : %s',
-                 np.mean(acc_list))
+    LOGGER.info(TAG, 'accuracy of adv data on original model is : %s',
+                np.mean(acc_list))
 
     # 6. defense
     net.set_train()
+    nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt,
+                                    bounds=(0.0, 1.0), eps=0.3)
     nad.batch_defense(inputs, labels, batch_size=32, epochs=10)
 
     # 7. get accuracy of test data on defensed model
@@ -110,8 +114,8 @@ def test_nad_method():
         label_pred = np.argmax(logits, axis=1)
         acc_list.append(np.mean(batch_labels == label_pred))
 
-    LOGGER.debug(TAG, 'accuracy of TEST data on defensed model is : %s',
-                 np.mean(acc_list))
+    LOGGER.info(TAG, 'accuracy of TEST data on defensed model is : %s',
+                np.mean(acc_list))
 
     # 8. get accuracy of adv data on defensed model
     acc_list = []
@@ -123,11 +127,11 @@ def test_nad_method():
         label_pred = np.argmax(logits, axis=1)
         acc_list.append(np.mean(batch_labels == label_pred))
 
-    LOGGER.debug(TAG, 'accuracy of adv data on defensed model is : %s',
-                 np.mean(acc_list))
+    LOGGER.info(TAG, 'accuracy of adv data on defensed model is : %s',
+                np.mean(acc_list))
 
 
 if __name__ == '__main__':
     # device_target can be "CPU", "GPU" or "Ascend"
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     test_nad_method()

mindarmour/defenses/adversarial_defense.py

@@ -136,6 +136,7 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
         self._replace_ratio = check_param_in_range('replace_ratio',
                                                    replace_ratio,
                                                    0, 1)
+        self._graph_initialized = False
 
     def defense(self, inputs, labels):
         """
@@ -150,6 +151,9 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
         """
         inputs, labels = check_pair_numpy_param('inputs', inputs, 'labels',
                                                 labels)
+        if not self._graph_initialized:
+            self._train_net(Tensor(inputs), Tensor(labels))
+            self._graph_initialized = True
 
         x_len = inputs.shape[0]
         n_adv = int(np.ceil(self._replace_ratio*x_len))