v3.2.6

UPDATE_HISTORY.md

@@ -419,3 +419,12 @@
   * `save`: Now saving images and labels for every batch.
   * `OnePixel`: Now supports targeted version.
   * `_get_target_label`: Now generates target label under evaluation mode and `torch.no_grad()`.
+
+
+
+
+
+
+### v3.2.6
+
+  * `_differential_evolution`: [bug fixed](https://github.com/Harry24k/adversarial-attacks-pytorch/issues/61).

torchattacks/__init__.py

@@ -25,4 +25,4 @@
 from .attacks.jitter import Jitter
 from .attacks.pixle import Pixle
 
-__version__ = '3.2.5'
+__version__ = '3.2.6'

torchattacks/attacks/_differential_evolution.py

@@ -11,12 +11,23 @@
 from __future__ import division, print_function, absolute_import
 import numpy as np
 from scipy.optimize import OptimizeResult, minimize
-from scipy.optimize.optimize import _status_message
+# from scipy.optimize.optimize import _status_message % for version < 1.8
+# from scipy.optimize.optimize_ import _status_message % for version >= 1.8
 from scipy._lib._util import check_random_state
 from six import string_types
 
 
 __all__ = ['differential_evolution']
+_status_message = {'success': 'Optimization terminated successfully.',
+                   'maxfev': 'Maximum number of function evaluations has '
+                              'been exceeded.',
+                   'maxiter': 'Maximum number of iterations has been '
+                              'exceeded.',
+                   'pr_loss': 'Desired error not necessarily achieved due '
+                              'to precision loss.',
+                   'nan': 'NaN result encountered.',
+                   'out_of_bounds': 'The result is outside of the provided '
+                                    'bounds.'}
 
 _MACHEPS = np.finfo(np.float64).eps