Pytorch的设计哲学：
- GPU版的Numpy
- 高效灵活的深度学习平台
numpy中运算，函数，索引的规则和方法可以无缝应用到pytorch的tensor操作。但需要特别注意的一点是，pytorch中in place操作如果有下划线的话，会改变调用该操作的变量。具体参见下面的例子。
基本函数注意点
-
以下划线为后缀的In-place函数会改变其对象! 如果没有下划线后缀则改变其调用对象，具体见下例。
-
Pytorch中关于tensor的操作在这
import torch
x = torch.rand(4, 3) #randomly generated tensors
print x
x.t_() #transpose of x, note that x has been changed!
print x
0.8031 0.0763 0.4798
0.6118 0.6341 0.4783
0.0423 0.9399 0.1805
0.0696 0.5616 0.8898
[torch.FloatTensor of size 4x3]
0.8031 0.6118 0.0423 0.0696
0.0763 0.6341 0.9399 0.5616
0.4798 0.4783 0.1805 0.8898
[torch.FloatTensor of size 3x4]
print x.t()
print x #the x is still the previous modified, which is not mutated by x.t()
0.8031 0.0763 0.4798
0.6118 0.6341 0.4783
0.0423 0.9399 0.1805
0.0696 0.5616 0.8898
[torch.FloatTensor of size 4x3]
0.8031 0.6118 0.0423 0.0696
0.0763 0.6341 0.9399 0.5616
0.4798 0.4783 0.1805 0.8898
[torch.FloatTensor of size 3x4]
z = torch.Tensor(3, 4)
z.copy_(x) #copy x to z
print x
print z
0.8031 0.6118 0.0423 0.0696
0.0763 0.6341 0.9399 0.5616
0.4798 0.4783 0.1805 0.8898
[torch.FloatTensor of size 3x4]
0.8031 0.6118 0.0423 0.0696
0.0763 0.6341 0.9399 0.5616
0.4798 0.4783 0.1805 0.8898
[torch.FloatTensor of size 3x4]
print z.add_(x)
print x
print z #note that z is replaced with the sum, but x stays the same
2.4094 1.8353 0.1268 0.2087
0.2290 1.9023 2.8198 1.6848
1.4393 1.4348 0.5415 2.6693
[torch.FloatTensor of size 3x4]
0.8031 0.6118 0.0423 0.0696
0.0763 0.6341 0.9399 0.5616
0.4798 0.4783 0.1805 0.8898
[torch.FloatTensor of size 3x4]
2.4094 1.8353 0.1268 0.2087
0.2290 1.9023 2.8198 1.6848
1.4393 1.4348 0.5415 2.6693
[torch.FloatTensor of size 3x4]
## the add() operation is not changing either x or zz
zz = x
print zz.add(x)
print x
print zz
1.6063 1.2235 0.0845 0.1391
0.1527 1.2682 1.8798 1.1232
0.9595 0.9565 0.3610 1.7796
[torch.FloatTensor of size 3x4]
0.8031 0.6118 0.0423 0.0696
0.0763 0.6341 0.9399 0.5616
0.4798 0.4783 0.1805 0.8898
[torch.FloatTensor of size 3x4]
0.8031 0.6118 0.0423 0.0696
0.0763 0.6341 0.9399 0.5616
0.4798 0.4783 0.1805 0.8898
[torch.FloatTensor of size 3x4]
### note that both x and zzz have been changed
zzz = x
print zzz.add_(x)
print x
print zzz
1.6063 1.2235 0.0845 0.1391
0.1527 1.2682 1.8798 1.1232
0.9595 0.9565 0.3610 1.7796
[torch.FloatTensor of size 3x4]
1.6063 1.2235 0.0845 0.1391
0.1527 1.2682 1.8798 1.1232
0.9595 0.9565 0.3610 1.7796
[torch.FloatTensor of size 3x4]
1.6063 1.2235 0.0845 0.1391
0.1527 1.2682 1.8798 1.1232
0.9595 0.9565 0.3610 1.7796
[torch.FloatTensor of size 3x4]
跟Numpy的联接
pytorch跟numpy的衔接还表现在二者之间的轻松转换，Pytorch中tensor支持所有numpy中的索引操作，并包含几乎所有基础操作函数。例如：
a= torch.ones(3)
print a
1
1
1
[torch.FloatTensor of size 3]
b = a.numpy()
c = torch.from_numpy(b)
print type(b), b #b is numpy array converted from a
print type(a), a #a is torch tensor
print type(c), c #c is torch tensor converted from b
<type 'numpy.ndarray'> [ 1. 1. 1.]
<class 'torch.FloatTensor'>
1
1
1
[torch.FloatTensor of size 3]
<class 'torch.FloatTensor'>
1
1
1
[torch.FloatTensor of size 3]
注意上例中a和b共享内存，当对a进行in place操作时，不仅a的值发生了变化，b也跟着变了。
a.add_(2)
print a, b #Note that both a and b are changed!
3
3
3
[torch.FloatTensor of size 3]
[ 3. 3. 3.]
tensors可以很简便地利用 .cuda 函数移植到GPU上进行运算
if torch.cuda.is_available():
a = a.cuda()
b = b.cuda()
c = a + b