looping over dimensions

theano/tensor/opt.py

@@ -4184,16 +4184,16 @@ def local_useless_reshape(node):
 
     dimshuffle_new_order = []
     new_output_shape = []
-    i = 0  # index over the output of the new reshape
-    import ipdb; ipdb.set_trace()
-    for dim in extract_constant(output_shape, only_process_constants=True):
+    index = 0  # index over the output of the new reshape
+    for i in xrange(output.ndim):
+        dim = extract_constant(output_shape[i], only_process_constants=False)
         if dim == 1:
             dimshuffle_new_order.append('x')
         else:
-            dimshuffle_new_order.append(i)
+            dimshuffle_new_order.append(index)
             new_output_shape.append(dim)
-            i = i + 1
-    if i != output.ndim:
+            index = index + 1
+    if index != output.ndim:
         inner = op.__class__(len(new_output_shape))(input, new_output_shape)
         return [DimShuffle(inner.type.broadcastable, dimshuffle_new_order)(inner)]
 

theano/tensor/tests/test_opt.py

@@ -6194,17 +6194,16 @@ class Test_local_useless_reshape(unittest.TestCase):
                                    "TensorConstant{[1 5 1 6 1 1]})]"))
 
         reshape_lift.optimize(g)
-        import ipdb; ipdb.set_trace()
         self.assertTrue(str(g) == "[DimShuffle{x,0}"
-                                  "(Reshape{2}(<TensorType(float64, vector)>, "
-                                  "TensorConstant{4})), "
+                                  "(<TensorType(float64, vector)>), "
                                   "DimShuffle{x,0,x,1,x,x}"
-                                  "Reshape{6}(<TensorType(float64, matrix)>, "
-                                  "TensorConstant{[5 6]})]")
+                                  "(Reshape{2}(<TensorType(float64, matrix)>, "
+                                  "TensorConstant{[5 6]}))]")
 
         # Check stacktrace was copied over correctly after opt was applied
         self.assertTrue(hasattr(g.outputs[0].tag, 'trace'))
 
+
 def test_local_reshape_lift():
     x = tensor.tensor4()
     out = T.exp(x).reshape([x.size])