pytorch复制张量的首选方法 | pytorch复制张量的首选方法

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问题描述

似乎有几种方法可以在Pytorch中创建张量副本，包括

  y = tensor.new_tensor（ x）#a 
 
y = x.clone（）。detach（）#b 
 
y = torch.empty_like（x）.copy_（x）#c 
 
y = torch.tensor（x）#d

b 明显优于 a 和 d code> a 或 d 。为什么首选它？性能？我认为它的可读性较差。

是否/反对使用 c ？

解决方案

TL; DR

使用 .clone（）。detach（）（或者最好是 .detach（）。clone（））

as it's slightly fast and explicit in what it does.

Using perflot, I plotted the timing of various methods to copy a pytorch tensor.

y = tensor.new_tensor(x) # method a

y = x.clone().detach() # method b

y = torch.empty_like(x).copy_(x) # method c

y = torch.tensor(x) # method d

y = x.detach().clone() # method e

The x-axis is the dimension of tensor created, y-axis shows the time. The graph is in linear scale. As you can clearly see, the tensor() or new_tensor() takes more time compared to other three methods.

Note: In multiple runs, I noticed that out of b, c, e, any method can have lowest time. The same is true for a and d. But, the methods b, c, e consistently have lower timing than a and d.

import torch
import perfplot

perfplot.show(
    setup=lambda n: torch.randn(n),
    kernels=[
        lambda a: a.new_tensor(a),
        lambda a: a.clone().detach(),
        lambda a: torch.empty_like(a).copy_(a),
        lambda a: torch.tensor(a),
        lambda a: a.detach().clone(),
    ],
    labels=["new_tensor()", "clone().detach()", "empty_like().copy()", "tensor()", "detach().clone()"],
    n_range=[2 ** k for k in range(15)],
    xlabel="len(a)",
    logx=False,
    logy=False,
    title='Timing comparison for copying a pytorch tensor',
)

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