finish batching for newtonfool

Author: minhitbk

art/attacks/newtonfool.py

@@ -69,6 +69,7 @@ def generate(self, x, **kwargs):
 
                 # Compute the gradients and norm
                 grads = self.classifier.class_gradient(batch, label=l, logits=False)
+                grads = np.squeeze(grads, axis=1)
                 norm_grad = np.linalg.norm(np.reshape(grads, (batch.shape[0], -1)), axis=1)
 
                 # Theta

art/attacks/newtonfool_unittest.py

@@ -17,7 +17,7 @@
 
 logger = logging.getLogger('testLogger')
 
-BATCH_SIZE, NB_TRAIN, NB_TEST = 100, 1000, 10
+BATCH_SIZE, NB_TRAIN, NB_TEST = 100, 1000, 10000
 
 
 class Model(nn.Module):
@@ -84,8 +84,30 @@ def test_tfclassifier(self):
         tfc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2)
 
         # Attack
-        nf = NewtonFool(tfc, max_iter=5)
-        x_test_adv = nf.generate(x_test)
+        import time
+        nf = NewtonFool(tfc, max_iter=50)
+
+        print("Test Tensorflow....")
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=1)
+        endtime = time.clock()
+        print(1, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=10)
+        endtime = time.clock()
+        print(10, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=100)
+        endtime = time.clock()
+        print(100, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=1000)
+        endtime = time.clock()
+        print(1000, endtime - starttime)
+
         self.assertFalse((x_test == x_test_adv).all())
 
         y_pred = tfc.predict(x_test)
@@ -118,8 +140,30 @@ def test_krclassifier(self):
         krc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2)
 
         # Attack
-        nf = NewtonFool(krc, max_iter=5)
-        x_test_adv = nf.generate(x_test)
+        import time
+        nf = NewtonFool(krc, max_iter=50)
+
+        print("Test Keras....")
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=1)
+        endtime = time.clock()
+        print(1, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=10)
+        endtime = time.clock()
+        print(10, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=100)
+        endtime = time.clock()
+        print(100, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=1000)
+        endtime = time.clock()
+        print(1000, endtime - starttime)
+
         self.assertFalse((x_test == x_test_adv).all())
 
         y_pred = krc.predict(x_test)
@@ -151,8 +195,30 @@ def test_ptclassifier(self):
         ptc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=1)
 
         # Attack
-        nf = NewtonFool(ptc, max_iter=5)
-        x_test_adv = nf.generate(x_test)
+        import time
+        nf = NewtonFool(ptc, max_iter=50)
+
+        print("Test Pytorch....")
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=1)
+        endtime = time.clock()
+        print(1, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=10)
+        endtime = time.clock()
+        print(10, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=100)
+        endtime = time.clock()
+        print(100, endtime - starttime)
+
+        starttime = time.clock()
+        x_test_adv = nf.generate(x_test, batch_size=1000)
+        endtime = time.clock()
+        print(1000, endtime - starttime)
+
         self.assertFalse((x_test == x_test_adv).all())
 
         y_pred = ptc.predict(x_test)