pytorch中矩阵和向量之间的加/减

Question

我想在pytorch中的矩阵和向量之间做+ / - /*。我该怎么做才能有好的表现？我尝试使用 expand，但它真的很慢（我使用的是带有小向量的大矩阵）。

a = torch.rand(2,3)
print(a)
 0.7420  0.2990  0.3896
 0.0715  0.6719  0.0602
[torch.FloatTensor of size 2x3]
b = torch.rand(2)
print(b)
 0.3773
 0.6757
[torch.FloatTensor of size 2]
a.add(b)
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3066, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-17-a1cb1b03d031>", line 1, in <module>
    a.add(b)
RuntimeError: inconsistent tensor size, expected r_ [2 x 3], t [2 x 3] and src [2] to have the same number of elements, but got 6, 6 and 2 elements respectively at c:\miniconda2\conda-bld\pytorch-cpu_1519449358620\work\torch\lib\th\generic/THTensorMath.c:1021

预期结果：

 0.7420-0.3773  0.2990-0.3773  0.3896-0.3773
 0.0715-0.6757  0.6719-0.6757  0.0602-0.6757

Answer 1

要使用广播，您需要将张量的维度提升到二维，b因为张量a是二维的。

In [43]: a
Out[43]: 
tensor([[ 0.9455,  0.2088,  0.1070],
        [ 0.0823,  0.6509,  0.1171]])

In [44]: b
Out[44]: tensor([ 0.4321,  0.8250])

# subtraction    
In [46]: a - b[:, None]
Out[46]: 
tensor([[ 0.5134, -0.2234, -0.3252],
        [-0.7427, -0.1741, -0.7079]])

# alternative way to do subtraction
In [47]: a.sub(b[:, None])
Out[47]: 
tensor([[ 0.5134, -0.2234, -0.3252],
        [-0.7427, -0.1741, -0.7079]])

# yet another approach
In [48]: torch.sub(a, b[:, None])
Out[48]: 
tensor([[ 0.5134, -0.2234, -0.3252],
        [-0.7427, -0.1741, -0.7079]])

其他操作 ( +, *) 可以类似地完成。

在性能方面，使用一种方法似乎没有其他方法的优势。只需使用三种方法中的任何一种即可。

In [49]: a = torch.rand(2000, 3000)
In [50]: b = torch.rand(2000)

In [51]: %timeit torch.sub(a, b[:, None])
2.4 ms ± 8.31 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [52]: %timeit a.sub(b[:, None])
2.4 ms ± 6.94 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [53]: %timeit a - b[:, None]
2.4 ms ± 12 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Answer 2

那么重塑呢？

In [2]: a = torch.rand(2,3)
   ...: 

In [3]: a
Out[3]: 
tensor([[ 0.2207,  0.1203,  0.7908],
        [ 0.6702,  0.7557,  0.0708]])

In [4]: b = torch.rand(2)
   ...: 

In [5]: b
Out[5]: tensor([ 0.5953,  0.7925])

In [6]: a.add(b.reshape(-1,1))
Out[6]: 
tensor([[ 0.8160,  0.7156,  1.3861],
        [ 1.4627,  1.5481,  0.8632]])

还要检查广播：https://pytorch.org/docs/stable/notes/broadcasting.html