Completed the creation of the new returned Subtensor op

theano/tensor/opt_uncanonicalize.py

@@ -189,6 +189,8 @@ def local_dimshuffle_subtensor(node):
         if 'x' in node.op.new_order :
             return False
         new_order = node.op.new_order
+        # new order could be empty
+        if len(new_order) > 1:
             past_dim = new_order[0]
             for dim in new_order[1:]:
                 if not dim > past_dim:
@@ -209,14 +211,31 @@ def local_dimshuffle_subtensor(node):
                 return False
 
             # create a new idx_list for a new Subtensor object
-            # idx_list could be longer than the len(missing_dims), that would happen with
-            # x[0, :, :].dimshuffle(1,0)
+            # have to loop on idx_list and inputs
+            # inputs has the length of sum of non None elements of idx_list (check in slice!)
+            # len(missing_dims) can be < len(idx_list), this happens if
+            # tensor was indexed such as x[scalar, :, :], check that as well
             new_idx_list = list(input_.owner.op.idx_list)
-            offset = len(new_idx_list) - len(missing_dims)
+            new_inputs = [input_.owner.inputs[0]]
             zero = T.constant(0)
-            for dim in missing_dims:
-                new_idx_list[dim + offset] = slice(zero, None, None)
-
-            input_.owner.op.idx_list = tuple(new_idx_list)
-            return [input_]
+            slice_attr_list = ['start','stop','step']
+            j = 0
+            import ipdb; ipdb.set_trace()
+            for idx in input_.owner.op.idx_list:
+                if isinstance(idx, slice):
+                    past_j = j
+                    for slice_attr in slice_attr_list:
+                        if getattr(idx, slice_attr) is not None:
+                            new_inputs += [input_.owner.inputs[1+j]]
+                            j += 1
+                    if past_j == j:
+                        # here is a slice(None, None, None), that's where
+                        # we want to index with 0.
+                        new_idx_list[j] = zero
+                        new_inputs += [zero]
+                else:
+                    new_inputs += [input_.owner.inputs[1+j]]
+                    j += 1
+            import ipdb; ipdb.set_trace()
+            return [Subtensor(new_idx_list)(*new_inputs)]
     return False

theano/tensor/tests/test_opt_uncanonicalize.py

@@ -178,10 +178,9 @@ def test_local_dimshuffle_subtensor():
     x = tensor.patternbroadcast(x, (False, True, False, False))
     i = tensor.iscalar('i')
 
-    out = x[i, :, 10:30, :-1].dimshuffle(1,2)
+    out = x[i, :, 10:30, ::-1].dimshuffle(1,2)
 
     g = FunctionGraph([x,i], [out])
-    import ipdb; ipdb.set_trace()
     dimshuffle_subtensor(g)
 
     topo = g.toposort()