You have to:

• stack list of np.array together (Enhanced ones)
• convert it to PyTorch tensors via torch.from_numpy function

For example:

import numpy as np

some_data = [np.random.randn(3, 12, 12) for _ in range(5)]

stacked = np.stack(some_data)
tensor = torch.from_numpy(stacked)

Please note that each np.array in the list has to be of the same shape

For different shapes one could do that:

import numpy as np
import torch

some_data = [np.random.randn(3, 12, 12) for _ in range(5)] + [
    np.random.randn(6, 12, 12)
]

stacked = np.concatenate(some_data).reshape(-1, 3, 12, 12)
tensor = torch.from_numpy(stacked)

print(tensor.shape)

In your case:

r = torch.from_numpy(np.concatenate(z).reshape(-1, 3, 12, 12))

from_numpy() automatically inherits input array dtype. On the other hand, torch.Tensor is an alias for torch.FloatTensor.

Therefore, if you pass int64 array to torch.Tensor, output tensor is float tensor and they wouldn't share the storage. torch.from_numpy gives you torch.LongTensor as expected.

a = np.arange(10)
ft = torch.Tensor(a)  # same as torch.FloatTensor
it = torch.from_numpy(a)

a.dtype  # == dtype('int64')
ft.dtype  # == torch.float32
it.dtype  # == torch.int64