New algortihm for the perform that avoids one of the reshapes.

theano/tensor/elemwise.py

@@ -143,10 +143,6 @@ class DimShuffle(Op):
 
         # list of dimensions of the input to drop
         self.drop = []
-        # this maps i before dropping dimensions to j after dropping dimensions
-        # so self.shuffle can be set properly later on
-        i2j = {}
-        j = 0
         for i, b in enumerate(input_broadcastable):
             if i not in new_order:
                 # we want to drop this dimension because it's not a value in
@@ -158,14 +154,9 @@ class DimShuffle(Op):
                     raise ValueError(
                             "You cannot drop a non-broadcastable dimension.",
                             (input_broadcastable, new_order))
-            else:
-                i2j[i] = j
-                j += 1
 
-        # transposition of non-broadcastable dimensions
-        # This is how the dimensions will be permuted, without accounting for
-        # the extra 'x' broadcastable dimensions to insert.
-        self.shuffle = [i2j[x] for x in new_order if x != 'x']
+        # this is the list of the original dimensions that we keep
+        self.shuffle = [x for x in new_order if x != 'x']
 
         # list of dimensions of the output that are broadcastable and were not
         # in the original input
@@ -237,16 +228,12 @@ class DimShuffle(Op):
         res = input
         if type(res) != numpy.ndarray and type(res) != numpy.memmap:
             raise TypeError(res)
-        shape = list(res.shape)
-        for drop in reversed(self.drop):
-            shape.pop(drop)
-        res = res.reshape(shape)
 
         # transpose
-        res = res.transpose(self.shuffle)
+        res = res.transpose(self.shuffle+self.drop)
 
         # augment
-        shape = list(res.shape)
+        shape = list(res.shape[:len(self.shuffle)])
         for augm in self.augment:
             shape.insert(augm, 1)
         res = res.reshape(shape)
@@ -259,9 +246,6 @@ class DimShuffle(Op):
 
     def infer_shape(self, node, shapes):
         ishp, = shapes
-        ishp = list(ishp)
-        for drop in reversed(self.drop):
-            del ishp[drop]
         # transpose
         rval = [ishp[i] for i in self.shuffle]