MAINT: Speed up normalize_axis_tuple by about 30%

This is used in `np.moveaxis`, which is relatively slow.
Timings change as follows:
```
import numpy as np
from numpy.core.numeric import normalize_axis_tuple
%timeit normalize_axis_tuple([0, 1, 2], 4, 'parrot')
a = np.zeros((10,20,30))
%timeit np.moveaxis(a, [0,1,2], [2,1,0])
100000 loops, best of 3: 5.56 µs per loop -> 4.17 µs
```

1 files changed, 8 insertions, 5 deletions

diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index b49a7f551..a9fa3efe0 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -1509,11 +1509,14 @@ def normalize_axis_tuple(axis, ndim, argname=None, allow_duplicate=False):
     --------
     normalize_axis_index : normalizing a single scalar axis
     """
-    try:
-        axis = [operator.index(axis)]
-    except TypeError:
-        axis = tuple(axis)
-    axis = tuple(normalize_axis_index(ax, ndim, argname) for ax in axis)
+    # Speed-up most common cases.
+    if not isinstance(axis, (list, tuple)):
+        try:
+            axis = [operator.index(axis)]
+        except TypeError:
+            pass
+    # Going via an iterator directly is slower than via list comprehension.
+    axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis])
     if not allow_duplicate and len(set(axis)) != len(axis):
         if argname:
             raise ValueError('repeated axis in `{}` argument'.format(argname))