torch.tensor - v1.5.0

PyTorch documentation
https://pytorch.org/docs/stable/

torch.tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False) -> Tensor

Constructs a tensor with data.
用 data 构造一个张量。

torch.tensor() always copies data. If you have a Tensor data and want to avoid a copy, use torch.Tensor.requires_grad_() or torch.Tensor.detach(). If you have a NumPy ndarray and want to avoid a copy, use torch.as_tensor().
torch.tensor() 总是复制数据。如果您有一个 Tensor data 并且想要避免复制，请使用torch.Tensor.requires_grad_() or torch.Tensor.detach()。如果您有 NumPy ndarray 并且想要避免复制，请使用 torch.as_tensor()。

When data is a tensor x, torch.tensor() reads out the data from whatever it is passed, and constructs a leaf variable. Therefore torch.tensor(x) is equivalent to x.clone().detach() and torch.tensor(x, requires_grad=True) is equivalent to x.clone().detach().requires_grad_(True). The equivalents using clone() and detach() are recommended.
当数据是 tensor x 时，torch.tensor() 从传递的任何数据中读取 the data，并构造一个叶子变量。因此torch.tensor(x) 等同于 x.clone().detach()。而 torch.tensor(x, requires_grad=True) 等同于 x.clone().detach().requires_grad_(True)。推荐使用 clone() and detach() 的等效项。

1. Parameters

data (array_like) - Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.
张量的初始数据。

dtype (torch.dtype, optional) - the desired data type of returned tensor. Default: if None, infers data type from data.
返回张量的所需数据类型。默认值：如果为 None，则从 data 推断数据类型。

device (torch.device, optional) - the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
返回张量的所需设备。默认值：如果为None，则使用当前设备作为默认张量类型 (see torch.set_default_tensor_type())。对于 CPU 张量类型，设备将是 CPU。对于 CUDA 张量类型，device 将是当前 CUDA 设备。

requires_grad (bool, optional) - If autograd should record operations on the returned tensor. Default: False.
如果 autograd 应该在返回的张量上记录操作。

pin_memory (bool, optional) - If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.
如果设置，返回的张量将在固定的内存中分配。

2. Example

>>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
tensor([[ 0.1000,  1.2000],[ 2.2000,  3.1000],[ 4.9000,  5.2000]])>>> torch.tensor([0, 1])  # Type inference on data
tensor([ 0,  1])>>> torch.tensor([[0.11111, 0.222222, 0.3333333]],dtype=torch.float64,device=torch.device('cuda:0'))  # creates a torch.cuda.DoubleTensor
tensor([[ 0.1111,  0.2222,  0.3333]], dtype=torch.float64, device='cuda:0')>>> torch.tensor(3.14159)  # Create a scalar (zero-dimensional tensor)
tensor(3.1416)>>> torch.tensor([])  # Create an empty tensor (of size (0,))
tensor([])

(pt-1.4_py-3.6) yongqiang@yongqiang:~$ python
Python 3.6.10 |Anaconda, Inc.| (default, May  8 2020, 02:54:21)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> tensor_data = torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
>>> tensor_data.data
tensor([[0.1000, 1.2000],[2.2000, 3.1000],[4.9000, 5.2000]])
>>>
>>> tensor_data.dtype
torch.float32
>>>
>>> tensor_data.device
device(type='cpu')
>>>
>>> tensor_data.requires_grad
False
>>>
>>> tensor_data.pin_memory
<built-in method pin_memory of Tensor object at 0x7f83df13dab0>
>>>
>>> exit()
(pt-1.4_py-3.6) yongqiang@yongqiang:~$