[FIXED] numpy: how to keep datatype of half precision array scalar input when adding a number

Issue

I have a half precision input that may be an array scalar x = np.float32(3). I'd like to add one to it: y = x + 1. However y will be float64 instead of float32.

I can only think of two workarounds:

• convert the input to 1d array: x = np.float32([3]) so that y = x + 1 is float32
• convert 1 into lower precision: y = np.float32(3) + np.float16(1) is float32

However, I have a lot of functions, so the above fixes require me to add if-else statements to each function... Are there any better ways? Thanks!

Solution

You can use numpy.add(..., dtype=np.float64) like below:

>>> res = np.add(np.array([3], dtype=np.float32), np.float16(1), dtype=np.float64)
>>> res
array([4.])

>>> res.dtype
dtype('float64')

Answered By - I'mahdi