Small code refactor to remove duplicate code. This make sure the new cast is…

theano/tensor/opt.py

@@ -5710,23 +5710,24 @@ def local_opt_alloc(node):
         if node_inps.owner and isinstance(node_inps.owner.op, T.Alloc):
             input = node_inps.owner.inputs[0]
             shapes = node_inps.owner.inputs[1:]
-            if (node.op.axis is None or
-                    node.op.axis == tuple(range(input.ndim))):
-                try:
-                    val = get_scalar_constant_value(input,
-                                                    only_process_constants=True)
-                    assert val.size == 1
-                    # check which type of op
-                    size = T.mul(*shapes)
-                    if input.dtype == "float32":
-                        # shapes are ints and normally int64.
-                        # We don't want to have a float64 upcast here
-                        # if input is a float32.
-                        size = size.astype(input.dtype)
+            try:
+                val = get_scalar_constant_value(input,
+                                                only_process_constants=True)
+                assert val.size == 1
+                val = val.reshape(1)[0]
+                # check which type of op
+                size = T.mul(*shapes)
+                if input.dtype == "float32":
+                    # shapes are ints and normally int64.
+                    # We don't want to have a float64 upcast here
+                    # if input is a float32.
+                    size = size.astype(input.dtype)
+                if (node.op.axis is None or
+                        node.op.axis == tuple(range(input.ndim))):
                     if isinstance(node.op, T.Sum):
-                        val = val.reshape(1)[0] * size
+                        val = val * size
                     else:
-                        val = val.reshape(1)[0] ** size
+                        val = val ** size
                     # Sum can change the input dtype (upcast or bool
                     # -> float32) by default or by user request.
                     # We can ignore the acc_dtype, as there is only 1
@@ -5736,29 +5737,21 @@ def local_opt_alloc(node):
                     # dtype.
                     val = val.astype(node.outputs[0].dtype)
                     return [val]
-
-                except NotScalarConstantError:
-                    pass
-            else:
-                try:
-                    val = get_scalar_constant_value(input,
-                                                    only_process_constants=True)
-                    assert val.size == 1
-                    val = val.reshape(1)[0]
-                    to_prod = [shapes[i] for i in xrange(len(shapes))
-                               if i in node.op.axis]
-                    if to_prod:
-                        size = T.mul(*to_prod)
-                        if isinstance(node.op, T.Sum):
-                            val *= size
-                        else:
-                            val = val ** size
-                    val = val.astype(node.outputs[0].dtype)
-                    return [T.alloc(val,
-                                    *[shapes[i] for i in xrange(len(shapes))
-                                      if i not in node.op.axis])]
-                except NotScalarConstantError:
-                    pass
+                to_prod = [shapes[i] for i in xrange(len(shapes))
+                           if i in node.op.axis]
+                if to_prod:
+                    size = T.mul(*to_prod)
+                    if isinstance(node.op, T.Sum):
+                        val *= size
+                    else:
+                        val = val ** size
+                # See comments above.
+                val = val.astype(node.outputs[0].dtype)
+                return [T.alloc(val,
+                                *[shapes[i] for i in xrange(len(shapes))
+                                  if i not in node.op.axis])]
+            except NotScalarConstantError:
+                pass
 
 
 @register_specialize