Correct parameter setting in defenses

Author: Irina Nicolae

src/defences/feature_squeezing.py

@@ -1,7 +1,7 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import numpy as np
-from tensorflow import rint
+import tensorflow as tf
 
 from src.defences.preprocessor import Preprocessor
 
@@ -21,7 +21,7 @@ def __init__(self, bit_depth=8):
         self.is_fitted = True
         self.set_params(bit_depth=bit_depth)
 
-    def __call__(self, x_val, bit_depth=8):
+    def __call__(self, x_val, bit_depth=None):
         """
         Apply feature squeezing to sample x_val.
 
@@ -30,16 +30,17 @@ def __call__(self, x_val, bit_depth=8):
         :return: Squeezed sample
         :rtype: np.ndarray
         """
-        self.set_params(bit_depth=bit_depth)
+        if bit_depth is not None:
+            self.set_params(bit_depth=bit_depth)
 
-        max_value = int(2 ** bit_depth - 1)
+        max_value = np.rint(2 ** self.bit_depth - 1)
         return np.rint(x_val * max_value) / max_value
 
     def fit(self, x_val, y_val=None, **kwargs):
         """No parameters to learn for this method; do nothing."""
         pass
 
-    def _tf_predict(self, x, bit_depth=8):
+    def _tf_predict(self, x, bit_depth=None):
         """
         Apply feature squeezing on tf.Tensor.
 
@@ -48,10 +49,11 @@ def _tf_predict(self, x, bit_depth=8):
         :return: Squeezed sample
         :rtype: tf.Tensor
         """
-        self.set_params(bit_depth=bit_depth)
+        if bit_depth is not None:
+            self.set_params(bit_depth=bit_depth)
 
-        max_value = int(2 ** bit_depth - 1)
-        x = rint(x * max_value) / max_value
+        max_value = int(2 ** self.bit_depth - 1)
+        x = tf.rint(x * max_value) / max_value
         return x
 
     def set_params(self, **kwargs):

src/defences/spatial_smoothing.py

@@ -20,7 +20,7 @@ def __init__(self, window_size=3):
         self.is_fitted = True
         self.set_params(window_size=window_size)
 
-    def __call__(self, x_val, window_size=3):
+    def __call__(self, x_val, window_size=None):
         """
         Apply local spatial smoothing to sample x_val.
         :param x_val: (np.ndarray) Sample to smooth. `x_val` is supposed to
@@ -29,8 +29,10 @@ def __call__(self, x_val, window_size=3):
         :return: Smoothed sample
         :rtype: np.ndarray
         """
-        self.set_params(window_size=window_size)
-        size = (1, window_size, window_size, 1)
+        if window_size is not None:
+            self.set_params(window_size=window_size)
+
+        size = (1, self.window_size, self.window_size, 1)
         result = ndimage.filters.median_filter(x_val, size=size, mode="reflect")
 
         return result
@@ -55,9 +57,3 @@ def set_params(self, **kwargs):
             raise ValueError("Sliding window size must be a positive integer")
 
         return True
-
-
-
-
-
-

src/defences/spatial_smoothing_unittest.py

@@ -25,14 +25,14 @@ def test_fix(self):
         # Start to test
         preprocess = SpatialSmoothing()
         smoothed_x = preprocess(x, 3)
-        self.assertTrue((smoothed_x==np.array(
+        self.assertTrue((smoothed_x == np.array(
             [[[[2], [3], [3]], [[4], [5], [6]], [[5], [6], [6]]]])).all())
 
         smoothed_x = preprocess(x, 1)
-        self.assertTrue((smoothed_x==x).all())
+        self.assertTrue((smoothed_x == x).all())
 
         smoothed_x = preprocess(x, 2)
-        self.assertTrue((smoothed_x==np.array(
+        self.assertTrue((smoothed_x == np.array(
             [[[[1], [2], [3]], [[7], [7], [8]], [[7], [7], [8]]]])).all())