Rename labels in classifier

Author: Irina Nicolae

art/classifiers/classifier.py

@@ -113,14 +113,14 @@ def class_gradient(self, x, logits=False):
         raise NotImplementedError
 
     @abc.abstractmethod
-    def loss_gradient(self, x, labels):
+    def loss_gradient(self, x, y):
         """
         Compute the gradient of the loss function w.r.t. `x`.
 
         :param x: Sample input with shape as expected by the model.
         :type x: `np.ndarray`
-        :param labels: Correct labels, one-vs-rest encoding.
-        :type labels: `np.ndarray`
+        :param y: Correct labels, one-vs-rest encoding.
+        :type y: `np.ndarray`
         :return: Array of gradients of the same shape as `x`.
         :rtype: `np.ndarray`
         """

art/classifiers/keras.py

@@ -69,18 +69,18 @@ def __init__(self, clip_values, model, use_logits=False, channel_index=3, defenc
         self._class_grads = k.function([self._input], class_grads)
         self._preds = k.function([self._input], [preds])
 
-    def loss_gradient(self, x, labels):
+    def loss_gradient(self, x, y):
         """
         Compute the gradient of the loss function w.r.t. `x`.
 
         :param x: Sample input with shape as expected by the model.
         :type x: `np.ndarray`
-        :param labels: Correct labels, one-vs-rest encoding.
-        :type labels: `np.ndarray`
+        :param y: Correct labels, one-vs-rest encoding.
+        :type y: `np.ndarray`
         :return: Array of gradients of the same shape as `x`.
         :rtype: `np.ndarray`
         """
-        return self._loss_grads([x, np.argmax(labels, axis=1)])[0]
+        return self._loss_grads([x, np.argmax(y, axis=1)])[0]
 
     def class_gradient(self, x, logits=False):
         """

art/classifiers/pytorch.py

@@ -170,14 +170,14 @@ def class_gradient(self, x, logits=False):
 
         return grds
 
-    def loss_gradient(self, x, labels):
+    def loss_gradient(self, x, y):
         """
         Compute the gradient of the loss function w.r.t. `x`.
 
         :param x: Sample input with shape as expected by the model.
         :type x: `np.ndarray`
-        :param labels: Correct labels, one-vs-rest encoding.
-        :type labels: `np.ndarray`
+        :param y: Correct labels, one-vs-rest encoding.
+        :type y: `np.ndarray`
         :return: Array of gradients of the same shape as `x`.
         :rtype: `np.ndarray`
         """
@@ -187,7 +187,7 @@ def loss_gradient(self, x, labels):
         inputs_t.requires_grad = True
 
         # Convert the labels to Tensors
-        labels_t = torch.from_numpy(labels)
+        labels_t = torch.from_numpy(y)
 
         # Compute the gradient and return
         (_, m_output) = self._model(inputs_t)

art/classifiers/tensorflow.py

@@ -161,14 +161,14 @@ def class_gradient(self, x, logits=False):
 
         return grds
 
-    def loss_gradient(self, x, labels):
+    def loss_gradient(self, x, y):
         """
         Compute the gradient of the loss function w.r.t. `x`.
 
         :param x: Sample input with shape as expected by the model.
         :type x: `np.ndarray`
-        :param labels: Correct labels, one-vs-rest encoding.
-        :type labels: `np.ndarray`
+        :param y: Correct labels, one-vs-rest encoding.
+        :type y: `np.ndarray`
         :return: Array of gradients of the same shape as `x`.
         :rtype: `np.ndarray`
         """
@@ -177,6 +177,6 @@ def loss_gradient(self, x, labels):
             raise ValueError("Need the loss function to compute the loss gradient.")
 
         # Compute the gradient and return
-        grds = self._sess.run(self._loss_grads, feed_dict={self._input_ph: x, self._output_ph: labels})
+        grds = self._sess.run(self._loss_grads, feed_dict={self._input_ph: x, self._output_ph: y})
 
         return grds