improving the code

theano/tensor/sort.py

@@ -62,8 +62,7 @@ class SortOp(theano.Op):
             self, 0, axis,
             "Currently, we only implement the gradient on sort for vector"
             " matrix (and axis is None or 0) and tensor3")
-        if isinstance(axis, theano.Constant):
-            if isinstance(axis, theano.Constant) and axis.data is not None:
+        if (isinstance(axis, theano.Constant) or (isinstance(axis, theano.tensor.TensorVariable) and axis.ndim==0)) and axis.data is not None:
             indices = self.__get_argsort_indices(a, axis)
             inp_grad = output_grads[0][tuple(indices)]
         elif (axis is None or
@@ -76,6 +75,13 @@ class SortOp(theano.Op):
             " sort(x, axis+eps) is undefined")
         return [inp_grad, axis_grad]
 
+    def __get_expanded_dim(self, a, axis, i):
+        index_shape = [1] * a.ndim
+        index_shape[i] = a.shape[i]
+        # it's a way to emulate numpy.ogrid[0: a.shape[0], 0: a.shape[1], 0: a.shape[2]]
+        index_val = theano.tensor.arange(a.shape[i]).reshape(index_shape)
+        return index_val
+
     def __get_argsort_indices(self, a, axis):
         """Calculates indices which can be used to reverse
         sorting operation of "a" tensor along "axis"
@@ -94,11 +100,9 @@ class SortOp(theano.Op):
                 (isinstance(axis, theano.Constant) and axis.data is None)):
             return rev_idx
         indices = []
-        if axis.data >= 0:
-            axis_data = axis.data
-        else:
-            axis_data = a.ndim + axis.data
+        axis_data = theano.tensor.switch(theano.tensor.ge(axis.data, 0), axis.data, a.ndim + axis.data)
         for i in range(a.ndim):
+<<<<<<< HEAD
             if i == axis_data:
                 indices.append(rev_idx)
             else:
@@ -106,6 +110,10 @@ class SortOp(theano.Op):
                 index_shape[i] = a.shape[i]
                 # it's a way to emulate numpy.ogrid[0: a.shape[0], 0: a.shape[1], 0: a.shape[2]]
                 indices.append(theano.tensor.arange(a.shape[i]).reshape(index_shape))
+=======
+            index_val = theano.tensor.switch(theano.tensor.eq(i, axis_data), rev_idx, self.__get_expanded_dim(a, axis, i))
+            indices.append(index_val)
+>>>>>>> b4e4ae5... improving the code
         return indices
     """
     def R_op(self, inputs, eval_points):