np.meshgrid()函数常用于生成二维网格，比如图像的坐标点。
pytorch中也有一个类似的函数torch.meshgrid()，功能也类似，但是两者的用法有区别，使用时需要注意（刚踩坑，因此记录一下。。。）
比如我要生成一张图像(h=6, w=10)的xy坐标点，看下两者的实现方式：
np.meshgrid()
>>> import numpy as np
>>> h = 6
>>> w = 10
>>> xs, ys = np.meshgrid(np.arange(w), np.arange(h))
>>> xs.shape
(6, 10)
>>> ys.shape
(6, 10)
>>> xs
array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
>>> ys
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
[5, 5, 5, 5, 5, 5, 5, 5, 5, 5]])
>>> xys = np.stack([xs, ys], axis=-1)
>>> xys.shape
(6, 10, 2)
torch.meshgrid()
>>> import torch
>>> h = 6
>>> w = 10
>>> ys,xs = torch.meshgrid(torch.arange(h), torch.arange(w))
>>> xs.shape
torch.Size([6, 10])
>>> ys.shape
torch.Size([6, 10])
>>> xs
tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
>>> ys
tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
[5, 5, 5, 5, 5, 5, 5, 5, 5, 5]])
>>> xys = torch.stack([xs, ys], dim=-1)
>>> xys.shape
torch.Size([6, 10, 2])
从python交互式窗口可以清晰的看出numpy和pytorch中meshgrid()函数的区别，就不用文字总结了，自己体会哈哈哈。