torch.tensor() - v1.5.0

清疚 2023-02-16 01:40 7阅读 0赞

# **torch.tensor - v1.5.0** #

**PyTorch documentation**  
[https://pytorch.org/docs/stable/][https_pytorch.org_docs_stable]

*  `torch.tensor` (Python function, in `torch`)
 *  `torch.Tensor` (Python class, in `torch.Tensor`)

`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:~$

[https_pytorch.org_docs_stable]: https://pytorch.org/docs/stable/