fix bugs and modify func param name

adversarial/advbox/attacks/base.py

@@ -18,22 +18,22 @@ class Attack(object):
     def __init__(self, model):
         self.model = model
 
-    def __call__(self, image_batch):
+    def __call__(self, image_label):
         """
         Generate the adversarial sample.
 
         Args:
-        image_batch(list): The image and label tuple list.
+        image_label(list): The image and label tuple list with one element.
         """
-        adv_img = self._apply(image_batch)
+        adv_img = self._apply(image_label)
         return adv_img
 
     @abstractmethod
-    def _apply(self, image_batch):
+    def _apply(self, image_label):
         """
         Search an adversarial example.
 
         Args:
-        image_batch(list): The image and label tuple list.
+        image_batch(list): The image and label tuple list with one element.
         """
         raise NotImplementedError

adversarial/advbox/attacks/gradientsign.py

@@ -15,18 +15,19 @@ class GradientSignAttack(Attack):
     Paper link: https://arxiv.org/abs/1412.6572
     """
 
-    def _apply(self, image_batch, epsilons=1000):
-        pre_label = np.argmax(self.model.predict(image_batch))
+    def _apply(self, image_label, epsilons=1000):
+        assert len(image_label) == 1
+        pre_label = np.argmax(self.model.predict(image_label))
 
         min_, max_ = self.model.bounds()
-        gradient = self.model.gradient(image_batch)
+        gradient = self.model.gradient(image_label)
         gradient_sign = np.sign(gradient) * (max_ - min_)
 
         if not isinstance(epsilons, Iterable):
             epsilons = np.linspace(0, 1, num=epsilons + 1)
 
         for epsilon in epsilons:
-            adv_img = image_batch[0][0].reshape(
+            adv_img = image_label[0][0].reshape(
                 gradient_sign.shape) + epsilon * gradient_sign
             adv_img = np.clip(adv_img, min_, max_)
             adv_label = np.argmax(self.model.predict([(adv_img, 0)]))

adversarial/advbox/models/base.py

@@ -81,8 +81,7 @@ class Model(object):
         Calculate the gradient of the cross-entropy loss w.r.t the image.
 
         Args:
-            image(numpy.ndarray): image with shape (height, width, channel)
-            label(int): image label used to cal gradient.
+            image_batch(list): The image and label tuple list.
 
         Return:
             numpy.ndarray: gradient of the cross-entropy loss w.r.t the image with

adversarial/advbox/models/paddle.py

@@ -49,7 +49,7 @@ class PaddleModel(Model):
         loss = self._program.block(0).var(self._cost_name)
         param_grads = fluid.backward.append_backward(
             loss, parameter_list=[self._input_name])
-        self._gradient = param_grads[0][1]
+        self._gradient = dict(param_grads)[self._input_name]
 
     def predict(self, image_batch):
         """

adversarial/fluid_mnist.py

@@ -15,7 +15,6 @@ def mnist_cnn_model(img):
     Returns:
         Variable: the label prediction
     """
-    #conv1 = fluid.nets.conv2d()
     conv_pool_1 = fluid.nets.simple_img_conv_pool(
         input=img,
         num_filters=20,
@@ -73,19 +72,15 @@ def main():
             pass_acc = accuracy.eval(exe)
             print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc="
                   + str(pass_acc))
-            # print loss, acc
             if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD:
-                # if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
                 break
 
-#                exit(0)
-
         pass_acc = accuracy.eval(exe)
         print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
     fluid.io.save_params(
         exe, dirname='./mnist', main_program=fluid.default_main_program())
     print('train mnist done')
-    exit(1)
+
 
 if __name__ == '__main__':
     main()