How to change the dtype of a ndarray to custom one in numpy?

I made a dtype that is:

mytype = np.dtype([('a',np.uint8), ('b',np.uint8), ('c',np.uint8)])

so the array using this dtype:

test1 = np.zeros(3, dtype=mytype)

test1 is:

array([(0, 0, 0), (0, 0, 0), (0, 0, 0)],
      dtype=[('a', '|u1'), ('b', '|u1'), ('c', '|u1')])

Now I have test2:

test2 = np.array([[1,2,3], [4,5,6], [7,8,9]])

When I use test2.astype(mytype), the result is not what I want to be:

array([[(1, 1, 1), (2, 2, 2), (3, 3, 3)],
       [(4, 4, 4), (5, 5, 5), (6, 6, 6)],
       [(7, 7, 7), (8, 8, 8), (9, 9, 9)]],
      dtype=[('a', '|u1'), ('b', '|u1'), ('c', '|u1')])

I want the result to be:

array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
      dtype=[('a', '|u1'), ('b', '|u1'), ('c', '|u1')])

Is there any way? Thanks.

You can use the fromarrays method of numpy.core.records (see documentation):

np.rec.fromarrays(test2.T, mytype)
Out[13]: 
rec.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], 
      dtype=[('a', '|u1'), ('b', '|u1'), ('c', '|u1')])

The array has to be transposd first because the functions regards the rows of the array as the columns of the structured array in the output. See also this question: Converting a 2D numpy array to a structured array