Remove batch predict. We'll add a new batch_predict function if needed.

fluid/adversarial/advbox/attacks/base.py

@@ -54,7 +54,7 @@ class Attack(object):
         """
         if adversary.original_label is None:
             adversary.original_label = np.argmax(
-                self.model.predict([(adversary.original, 0)]))
+                self.model.predict(adversary.original))
         if adversary.is_targeted_attack and adversary.target_label is None:
             if adversary.target is None:
                 raise ValueError(
@@ -62,7 +62,8 @@ class Attack(object):
                     'adversary.target_label or adversary.target must be set.')
             else:
                 adversary.target_label_label = np.argmax(
-                    self.model.predict([(adversary.target_label, 0)]))
+                    self.model.predict(
+                        self.model.scale_input(adversary.target)))
 
         logging.info('adversary:\noriginal_label: {}'
                      '\n          target_lable: {}'

fluid/adversarial/advbox/attacks/deepfool.py

@@ -33,7 +33,7 @@ class DeepFoolAttack(Attack):
 
         pre_label = adversary.original_label
         min_, max_ = self.model.bounds()
-        f = self.model.predict([(adversary.original, 0)])
+        f = self.model.predict(adversary.original)
         if adversary.is_targeted_attack:
             labels = [adversary.target_label]
         else:
@@ -44,7 +44,7 @@ class DeepFoolAttack(Attack):
             else:
                 labels = np.arange(class_count)
 
-        gradient = self.model.gradient([(adversary.original, pre_label)])
+        gradient = self.model.gradient(adversary.original, pre_label)
         x = adversary.original
         for iteration in xrange(iterations):
             w = np.inf
@@ -53,7 +53,7 @@ class DeepFoolAttack(Attack):
             for k in labels:
                 if k == pre_label:
                     continue
-                gradient_k = self.model.gradient([(x, k)])
+                gradient_k = self.model.gradient(x, k)
                 w_k = gradient_k - gradient
                 f_k = f[k] - f[pre_label]
                 w_k_norm = np.linalg.norm(w_k) + 1e-8
@@ -67,8 +67,8 @@ class DeepFoolAttack(Attack):
             x = x + (1 + overshoot) * r_i
             x = np.clip(x, min_, max_)
 
-            f = self.model.predict([(x, 0)])
-            gradient = self.model.gradient([(x, pre_label)])
+            f = self.model.predict(x)
+            gradient = self.model.gradient(x, pre_label)
             adv_label = np.argmax(f)
             logging.info('iteration = {}, f = {}, pre_label = {}'
                          ', adv_label={}'.format(iteration, f[pre_label],

fluid/adversarial/advbox/attacks/gradientsign.py

@@ -37,18 +37,18 @@ class GradientSignAttack(Attack):
         min_, max_ = self.model.bounds()
 
         if adversary.is_targeted_attack:
-            gradient = self.model.gradient([(adversary.original,
-                                             adversary.target_label)])
+            gradient = self.model.gradient(adversary.original,
+                                           adversary.target_label)
             gradient_sign = -np.sign(gradient) * (max_ - min_)
         else:
-            gradient = self.model.gradient([(adversary.original,
-                                             adversary.original_label)])
+            gradient = self.model.gradient(adversary.original,
+                                           adversary.original_label)
             gradient_sign = np.sign(gradient) * (max_ - min_)
 
         for epsilon in epsilons:
             adv_img = adversary.original + epsilon * gradient_sign
             adv_img = np.clip(adv_img, min_, max_)
-            adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
+            adv_label = np.argmax(self.model.predict(adv_img))
             logging.info('epsilon = {:.3f}, pre_label = {}, adv_label={}'.
                          format(epsilon, pre_label, adv_label))
             if adversary.try_accept_the_example(adv_img, adv_label):

fluid/adversarial/advbox/attacks/iterator_gradientsign.py

@@ -38,16 +38,16 @@ class IteratorGradientSignAttack(Attack):
             adv_img = adversary.original
             for _ in range(steps):
                 if adversary.is_targeted_attack:
-                    gradient = self.model.gradient([(adversary.original,
-                                                     adversary.target_label)])
+                    gradient = self.model.gradient(adversary.original,
+                                                   adversary.target_label)
                     gradient_sign = -np.sign(gradient) * (max_ - min_)
                 else:
-                    gradient = self.model.gradient([(adversary.original,
-                                                     adversary.original_label)])
+                    gradient = self.model.gradient(adversary.original,
+                                                   adversary.original_label)
                     gradient_sign = np.sign(gradient) * (max_ - min_)
                 adv_img = adv_img + gradient_sign * epsilon
                 adv_img = np.clip(adv_img, min_, max_)
-                adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
+                adv_label = np.argmax(self.model.predict(adv_img))
                 logging.info('epsilon = {:.3f}, pre_label = {}, adv_label={}'.
                              format(epsilon, pre_label, adv_label))
                 if adversary.try_accept_the_example(adv_img, adv_label):

fluid/adversarial/advbox/models/base.py

@@ -43,26 +43,31 @@ class Model(object):
         return self._channel_axis
 
     def _process_input(self, input_):
-        res = input_
+        res = None
         sub, div = self._preprocess
         if np.any(sub != 0):
             res = input_ - sub
         assert np.any(div != 0)
         if np.any(div != 1):
+            if res is None:  # "res = input_ - sub" is not executed!
+                res = input_ / div
+            else:
                 res /= div
+        if res is None:  # "res = (input_ - sub)/ div" is not executed!
+            return input_
         return res
 
     @abstractmethod
-    def predict(self, image_batch):
+    def predict(self, data):
         """
-        Calculate the prediction of the image batch.
+        Calculate the prediction of the data.
 
         Args:
-            image_batch(numpy.ndarray): image batch of shape (batch_size,
+            data(numpy.ndarray): input data with shape (size,
             height, width, channels).
 
         Return:
-            numpy.ndarray: predictions of the images with shape (batch_size,
+            numpy.ndarray: predictions of the data with shape (batch_size,
                 num_of_classes).
         """
         raise NotImplementedError
@@ -78,12 +83,14 @@ class Model(object):
         raise NotImplementedError
 
     @abstractmethod
-    def gradient(self, image_batch):
+    def gradient(self, data, label):
         """
         Calculate the gradient of the cross-entropy loss w.r.t the image.
 
         Args:
-            image_batch(list): The image and label tuple list.
+            data(numpy.ndarray): input data with shape (size, height, width,
+            channels).
+            label(int): Label used to calculate the gradient.
 
         Return:
             numpy.ndarray: gradient of the cross-entropy loss w.r.t the image

fluid/adversarial/advbox/models/paddle.py

@@ -3,6 +3,7 @@ Paddle model
 """
 from __future__ import absolute_import
 
+import numpy as np
 import paddle.v2.fluid as fluid
 
 from .base import Model
@@ -54,24 +55,28 @@ class PaddleModel(Model):
         self._gradient = filter(lambda p: p[0].name == self._input_name,
                                 param_grads)[0][1]
 
-    def predict(self, image_batch):
+    def predict(self, data):
         """
-            Predict the label of the image_batch.
+        Calculate the prediction of the data.
 
         Args:
-                image_batch(list): The image and label tuple list.
+            data(numpy.ndarray): input data with shape (size,
+            height, width, channels).
+
         Return:
-                numpy.ndarray: predictions of the images with shape (batch_size,
+            numpy.ndarray: predictions of the data with shape (batch_size,
                 num_of_classes).
         """
+        scaled_data = self._process_input(data)
         feeder = fluid.DataFeeder(
             feed_list=[self._input_name, self._logits_name],
             place=self._place,
             program=self._program)
         predict_var = self._program.block(0).var(self._predict_name)
         predict = self._exe.run(self._program,
-                                feed=feeder.feed(image_batch),
+                                feed=feeder.feed([(scaled_data, 0)]),
                                 fetch_list=[predict_var])
+        predict = np.squeeze(predict, axis=0)
         return predict
 
     def num_classes(self):
@@ -85,21 +90,27 @@ class PaddleModel(Model):
         assert len(predict_var.shape) == 2
         return predict_var.shape[1]
 
-    def gradient(self, image_batch):
+    def gradient(self, data, label):
         """
-        Calculate the gradient of the loss w.r.t the input.
+        Calculate the gradient of the cross-entropy loss w.r.t the image.
 
         Args:
-            image_batch(list): The image and label tuple list.
+            data(numpy.ndarray): input data with shape (size, height, width,
+            channels).
+            label(int): Label used to calculate the gradient.
+
         Return:
-            list: The list of the gradient of the image.
+            numpy.ndarray: gradient of the cross-entropy loss w.r.t the image
+                with the shape (height, width, channel).
         """
+        scaled_data = self._process_input(data)
+
         feeder = fluid.DataFeeder(
             feed_list=[self._input_name, self._logits_name],
             place=self._place,
             program=self._program)
 
         grad, = self._exe.run(self._program,
-                              feed=feeder.feed(image_batch),
+                              feed=feeder.feed([(scaled_data, label)]),
                               fetch_list=[self._gradient])
-        return grad.reshape(image_batch[0][0].shape)
+        return grad.reshape(data.shape)