In this example, I have a list of 1-d ndarray, with length 9, the list has 9 elements, and each one has shape=(2048,), so totally 9 * (2048,), I get these ndarray from mxnet so that each of the ndarray is <NDArray 2048 @cpu(0)> the array dtype=numpy.float32
If I use np.asarray to transform this list, it becomes the following result
shape=<class 'tuple'>: (9, 2048, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
Obviously, I want a 2-D array, with shape=(9, 2048), how to solve this problem?
ps: I discover this problem by saving a npy file and load it. I directly saved the list before converting it to a ndarray (so the np.save would transform the list to the ndarrary automatically) and after I loaded it, I found the shape has become something above, which is really abnormal
The answer below, np.vstack and np.array both works for the common list to ndarray problem but could not solve mine, so I doubt it is some special case of mxnet
You can use np.vstack. Here's an example:
import numpy as np
li = [np.zeros(2048) for _ in range(9)]
result = np.vstack(li)
print(result.shape)
This outputs (9, 2048) as desired.