torch.Tensor - v1.5.0

torch.Tensor - v1.5.0

torch.Tensor
https://pytorch.org/docs/stable/tensors.html

A torch.Tensor is a multi-dimensional matrix containing elements of a single data type.
torch.Tensor 是一種包含單一數據類型元素的多維矩陣。

Torch defines nine CPU tensor types and nine GPU tensor types:
Torch 定義了九種 CPU 張量類型和九種 GPU 張量類型：

Data type	dtype	CPU tensor	GPU tensor
32-bit floating point	torch.float32 or torch.float	torch.FloatTensor	torch.cuda.FloatTensor
64-bit floating point	torch.float64 or torch.double	torch.DoubleTensor	torch.cuda.DoubleTensor
16-bit floating point	torch.float16 or torch.half	torch.HalfTensor	torch.cuda.HalfTensor
8-bit integer (unsigned)	torch.uint8	torch.ByteTensor	torch.cuda.ByteTensor
8-bit integer (signed)	torch.int8	torch.CharTensor	torch.cuda.CharTensor
16-bit integer (signed)	torch.int16 or torch.short	torch.ShortTensor	torch.cuda.ShortTensor
32-bit integer (signed)	torch.int32 or torch.int	torch.IntTensor	torch.cuda.IntTensor
64-bit integer (signed)	torch.int64 or torch.long	torch.LongTensor	torch.cuda.LongTensor
Boolean	torch.bool	torch.BoolTensor	torch.cuda.BoolTensor

torch.Tensor is an alias for the default tensor type (torch.FloatTensor).
torch.Tensor 是默認的 tensor 類型 (torch.FloatTensor) 的簡稱。

A tensor can be constructed from a Python list or sequence using the torch.tensor() constructor:
Tensor 可以用 torch.tensor() 轉換 Python 的 list 或序列​​生成：

(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
>>> import numpy as np
>>>
>>> torch.tensor([[1., -1.], [1., -1.]])
tensor([[ 1., -1.],
        [ 1., -1.]])
>>>
>>> torch.tensor(np.array([[1, 2, 3], [4, 5, 6]]))
tensor([[1, 2, 3],
        [4, 5, 6]])
>>>
>>> exit()
(pt-1.4_py-3.6) yongqiang@yongqiang:~$

torch.tensor() always copies data. If you have a Tensor data and just want to change its requires_grad flag, use requires_grad_() or detach() to avoid a copy. If you have a numpy array and want to avoid a copy, use torch.as_tensor().
torch.tensor() 總是拷貝 data。如果你有一個 Tensor data 並且僅僅想改變它的 requires_grad 屬性, 可用 requires_grad_() or detach() 來避免拷貝。如果你有一個 numpy 數組並且想避免拷貝, 請使用 torch.as_tensor().

A tensor of specific data type can be constructed by passing a torch.dtype and/or a torch.device to a constructor or tensor creation op:
指定數據類型的 Tensor 可以通過傳遞參數 torch.dtype 和/或 torch.device 到構造函數生成：

>>> torch.zeros([2, 4], dtype=torch.int32)
tensor([[ 0,  0,  0,  0],
        [ 0,  0,  0,  0]], dtype=torch.int32)
>>> cuda0 = torch.device('cuda:0')
>>> torch.ones([2, 4], dtype=torch.float64, device=cuda0)
tensor([[ 1.0000,  1.0000,  1.0000,  1.0000],
        [ 1.0000,  1.0000,  1.0000,  1.0000]], dtype=torch.float64, device='cuda:0')

The contents of a tensor can be accessed and modified using Python’s indexing and slicing notation:
Tensor 的內容可以通過 Python 索引或者切片訪問以及修改：

>>> x = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> print(x[1][2])
tensor(6)
>>> x[0][1] = 8
>>> print(x)
tensor([[ 1,  8,  3],
        [ 4,  5,  6]])

Use torch.Tensor.item() to get a Python number from a tensor containing a single value:
使用 torch.Tensor.item() 從只有一個值的 Tensor 中獲取 Python Number：

>>> x = torch.tensor([[1]])
>>> x
tensor([[ 1]])
>>> x.item()
1
>>> x = torch.tensor(2.5)
>>> x
tensor(2.5000)
>>> x.item()
2.5

A tensor can be created with requires_grad=True so that torch.autograd records operations on them for automatic differentiation.
Tensor 可以通過參數 requires_grad=True 創建, 這樣 torch.autograd 會記錄相關的運算實現自動求導。

(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
>>> data = torch.tensor([[1., -1.], [1., 1.]], requires_grad=True)
>>> data
tensor([[ 1., -1.],
        [ 1.,  1.]], requires_grad=True)
>>>
>>> data_pow = data.pow(2)
>>> data_pow
tensor([[1., 1.],
        [1., 1.]], grad_fn=<PowBackward0>)
>>>
>>> data_sum = data_pow.sum()
>>> data_sum
tensor(4., grad_fn=<SumBackward0>)
>>>
>>> data_sum.backward()
>>>
>>> data.grad
tensor([[ 2., -2.],
        [ 2.,  2.]])
>>> exit()
(pt-1.4_py-3.6) yongqiang@yongqiang:~$

Each tensor has an associated torch.Storage, which holds its data. The tensor class also provides multi-dimensional, strided view of a storage and defines numeric operations on it.
每一個 tensor 都有一個相應的 torch.Storage 保存其數據。tensor 類提供了一個多維的、strided 視圖，並定義了數值操作。

Methods which mutate a tensor are marked with an underscore suffix. For example, torch.FloatTensor.abs_() computes the absolute value in-place and returns the modified tensor, while torch.FloatTensor.abs() computes the result in a new tensor.
修改 tensor 的方法可以用一個下劃線後綴來標示。比如 torch.FloatTensor.abs_() 會在原地計算絕對值並返回修改的張量，而 torch.FloatTensor.abs() 將會在新張量中計算結果.

To change an existing tensor’s torch.device and/or torch.dtype, consider using to() method on the tensor.
爲了改變已有的 tensor 的 torch.device 和/或 torch.dtype，考慮使用 to() 方法。