I believe you also have to use .detach(). I had to convert my Tensor to a numpy array on Colab which uses CUDA and GPU. I did it like the following:

Copy# this is just my embedding matrix which is a Torch tensor object
embedding = learn.model.u_weight

embedding_list = list(range(0, 64382))

input = torch.cuda.LongTensor(embedding_list)
tensor_array = embedding(input)
# the output of the line below is a numpy array
tensor_array.cpu().detach().numpy()

The data precision is the same, it's just that the format used by PyTorch to print the values is different, it will round the floats down:

>>> test_torch = torch.from_numpy(test)
>>> test_torch
tensor([0.0117, 0.0176, 0.0293], dtype=torch.float64)

You can check that it matches your original input by converting to a list with tolist:

>>> test_torch.tolist()
[0.01171875, 0.01757812, 0.02929688]

These are general operations in pytorch and available in the documentation. PyTorch allows easy interfacing with numpy. There is a method called from_numpy and the documentation is available here

import numpy as np 
import torch 
array = np.arange(1, 11)
tensor = torch.from_numpy(array)

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))