Clarification for discussion of shared memory in `torch.from_numpy(ndarray)` and `torch.Tensor.numpy()` about pytorch-deep-learning HOT 6 OPEN

Just found the corrections page for the video here where this point has already been acknowledged: #98

But leaving this open for now as the book description (code lines 65-67 https://www.learnpytorch.io/00_pytorch_fundamentals/#pytorch-tensors-numpy) is still a little misleading.

Thanks for pointing that out.
Good idea.

from pytorch-deep-learning.

Hello.
Just wanted to comment this out, since it might also help a little bit to understand of the different DataTypes that they output when using torch.from_numpy() versus torch.Tensor()

Source:
PyTorch memory model: "torch.from_numpy()" vs "torch.Tensor()"

"""
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

answered Sep 18, 2018 by Viacheslav Kroilov

"""

from pytorch-deep-learning.

Just found the corrections page for the video here where this point has already been acknowledged: #98

But leaving this open for now as the book description (code lines 65-67 https://www.learnpytorch.io/00_pytorch_fundamentals/#pytorch-tensors-numpy) is still a little misleading.

from pytorch-deep-learning.