看一下stack的直观解释，动词可以简单理解为：把......放成一堆、把......放成一摞。
torch.stack方法用于沿着一个新的维度 join(也可称为cat)一系列的张量(可以是2个张量或者是更多)，它会插入一个新的维度，并让张量按照这个新的维度进行张量的cat操作。值得注意的是：张量序列中的张量必须要有相同的shape和dimension。
import torch
ogfW = 50
fW = ogfW // 10 #5
ogfH = 40
fH = ogfH // 10 ##4
print("====>>xs"*8)
xs = torch.linspace(0, ogfW - 1, fW, dtype=torch.float).view(1, fW).expand(fH, fW)
print(torch.linspace(0, ogfW - 1, fW, dtype=torch.float))
print(torch.linspace(0, ogfW - 1, fW, dtype=torch.float).view(1, fW))
print(xs)
print("====>>ys"*8)
ys = torch.linspace(0, ogfH - 1, fH, dtype=torch.float).view(fH, 1).expand(fH, fW)
print(torch.linspace(0, ogfH - 1, fH, dtype=torch.float))
print(torch.linspace(0, ogfH - 1, fH, dtype=torch.float).view(fH, 1))
print(ys)
print("====>>frustum"*8)
print("===>>>shape xs=", xs.shape)
print("===>>>shape ys=", ys.shape)
frustum = torch.stack((xs, ys), -1)
print("===>>>shape frustum=", frustum.shape)
print(frustum)
====>>xs====>>xs====>>xs====>>xs====>>xs====>>xs====>>xs====>>xs
tensor([ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000])
tensor([[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000]])
tensor([[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000]])
====>>ys====>>ys====>>ys====>>ys====>>ys====>>ys====>>ys====>>ys
tensor([ 0., 13., 26., 39.])
tensor([[ 0.],
[13.],
[26.],
[39.]])
tensor([[ 0., 0., 0., 0., 0.],
[13., 13., 13., 13., 13.],
[26., 26., 26., 26., 26.],
[39., 39., 39., 39., 39.]])
====>>frustum====>>frustum====>>frustum====>>frustum====>>frustum====>>frustum====>>frustum====>>frustum
===>>>shape xs= torch.Size([4, 5])
===>>>shape ys= torch.Size([4, 5])
===>>>shape frustum= torch.Size([4, 5, 2])
tensor([[[ 0.0000, 0.0000],
[12.2500, 0.0000],
[24.5000, 0.0000],
[36.7500, 0.0000],
[49.0000, 0.0000]],
[[ 0.0000, 13.0000],
[12.2500, 13.0000],
[24.5000, 13.0000],
[36.7500, 13.0000],
[49.0000, 13.0000]],
[[ 0.0000, 26.0000],
[12.2500, 26.0000],
[24.5000, 26.0000],
[36.7500, 26.0000],
[49.0000, 26.0000]],
[[ 0.0000, 39.0000],
[12.2500, 39.0000],
[24.5000, 39.0000],
[36.7500, 39.0000],
[49.0000, 39.0000]]])
Process finished with exit code 03维
import torch
D = 3
ogfW = 50
fW = ogfW // 10 #5
ogfH = 40
fH = ogfH // 10 ##4
ds = torch.arange(*[-6,-3,1], dtype=torch.float).view(-1, 1, 1).expand(-1, fH, fW)
print("===>>>ds" * 5)
print(torch.arange(*[-6,-3,1], dtype=torch.float))
print(torch.arange(*[-6,-3,1], dtype=torch.float).view(-1, 1, 1))
print(ds)
print("===>>>xs" * 5)
xs = torch.linspace(0, ogfW - 1, fW, dtype=torch.float).view(1, 1, fW).expand(D, fH, fW)
print(torch.linspace(0, ogfW - 1, fW, dtype=torch.float))
print(torch.linspace(0, ogfW - 1, fW, dtype=torch.float).view(1, 1, fW))
print(xs)
ys = torch.linspace(0, ogfH - 1, fH, dtype=torch.float).view(1, fH, 1).expand(D, fH, fW)
print("===>>>ys" * 5)
print(torch.linspace(0, ogfH - 1, fH, dtype=torch.float))
print(torch.linspace(0, ogfH - 1, fH, dtype=torch.float).view(1, fH, 1))
print(ys)
print("==>> "*20)
print("===>>>shape ds=", ds.shape)
print("===>>>shape xs=", xs.shape)
print("===>>>shape ys=", ys.shape)
frustum = torch.stack((xs, ys, ds), -1)
print("===>>>shape frustum=", frustum.shape)
print(frustum)
===>>>ds===>>>ds===>>>ds===>>>ds===>>>ds
tensor([-6., -5., -4.])
tensor([[[-6.]],
[[-5.]],
[[-4.]]])
tensor([[[-6., -6., -6., -6., -6.],
[-6., -6., -6., -6., -6.],
[-6., -6., -6., -6., -6.],
[-6., -6., -6., -6., -6.]],
[[-5., -5., -5., -5., -5.],
[-5., -5., -5., -5., -5.],
[-5., -5., -5., -5., -5.],
[-5., -5., -5., -5., -5.]],
[[-4., -4., -4., -4., -4.],
[-4., -4., -4., -4., -4.],
[-4., -4., -4., -4., -4.],
[-4., -4., -4., -4., -4.]]])
===>>>xs===>>>xs===>>>xs===>>>xs===>>>xs
tensor([ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000])
tensor([[[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000]]])
tensor([[[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000]],
[[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000]],
[[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000],
[ 0.0000, 12.2500, 24.5000, 36.7500, 49.0000]]])
===>>>ys===>>>ys===>>>ys===>>>ys===>>>ys
tensor([ 0., 13., 26., 39.])
tensor([[[ 0.],
[13.],
[26.],
[39.]]])
tensor([[[ 0., 0., 0., 0., 0.],
[13., 13., 13., 13., 13.],
[26., 26., 26., 26., 26.],
[39., 39., 39., 39., 39.]],
[[ 0., 0., 0., 0., 0.],
[13., 13., 13., 13., 13.],
[26., 26., 26., 26., 26.],
[39., 39., 39., 39., 39.]],
[[ 0., 0., 0., 0., 0.],
[13., 13., 13., 13., 13.],
[26., 26., 26., 26., 26.],
[39., 39., 39., 39., 39.]]])
==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>> ==>>
===>>>shape ds= torch.Size([3, 4, 5])
===>>>shape xs= torch.Size([3, 4, 5])
===>>>shape ys= torch.Size([3, 4, 5])
===>>>shape frustum= torch.Size([3, 4, 5, 3])
tensor([[[[ 0.0000, 0.0000, -6.0000],
[12.2500, 0.0000, -6.0000],
[24.5000, 0.0000, -6.0000],
[36.7500, 0.0000, -6.0000],
[49.0000, 0.0000, -6.0000]],
[[ 0.0000, 13.0000, -6.0000],
[12.2500, 13.0000, -6.0000],
[24.5000, 13.0000, -6.0000],
[36.7500, 13.0000, -6.0000],
[49.0000, 13.0000, -6.0000]],
[[ 0.0000, 26.0000, -6.0000],
[12.2500, 26.0000, -6.0000],
[24.5000, 26.0000, -6.0000],
[36.7500, 26.0000, -6.0000],
[49.0000, 26.0000, -6.0000]],
[[ 0.0000, 39.0000, -6.0000],
[12.2500, 39.0000, -6.0000],
[24.5000, 39.0000, -6.0000],
[36.7500, 39.0000, -6.0000],
[49.0000, 39.0000, -6.0000]]],
[[[ 0.0000, 0.0000, -5.0000],
[12.2500, 0.0000, -5.0000],
[24.5000, 0.0000, -5.0000],
[36.7500, 0.0000, -5.0000],
[49.0000, 0.0000, -5.0000]],
[[ 0.0000, 13.0000, -5.0000],
[12.2500, 13.0000, -5.0000],
[24.5000, 13.0000, -5.0000],
[36.7500, 13.0000, -5.0000],
[49.0000, 13.0000, -5.0000]],
[[ 0.0000, 26.0000, -5.0000],
[12.2500, 26.0000, -5.0000],
[24.5000, 26.0000, -5.0000],
[36.7500, 26.0000, -5.0000],
[49.0000, 26.0000, -5.0000]],
[[ 0.0000, 39.0000, -5.0000],
[12.2500, 39.0000, -5.0000],
[24.5000, 39.0000, -5.0000],
[36.7500, 39.0000, -5.0000],
[49.0000, 39.0000, -5.0000]]],
[[[ 0.0000, 0.0000, -4.0000],
[12.2500, 0.0000, -4.0000],
[24.5000, 0.0000, -4.0000],
[36.7500, 0.0000, -4.0000],
[49.0000, 0.0000, -4.0000]],
[[ 0.0000, 13.0000, -4.0000],
[12.2500, 13.0000, -4.0000],
[24.5000, 13.0000, -4.0000],
[36.7500, 13.0000, -4.0000],
[49.0000, 13.0000, -4.0000]],
[[ 0.0000, 26.0000, -4.0000],
[12.2500, 26.0000, -4.0000],
[24.5000, 26.0000, -4.0000],
[36.7500, 26.0000, -4.0000],
[49.0000, 26.0000, -4.0000]],
[[ 0.0000, 39.0000, -4.0000],
[12.2500, 39.0000, -4.0000],
[24.5000, 39.0000, -4.0000],
[36.7500, 39.0000, -4.0000],
[49.0000, 39.0000, -4.0000]]]])
Process finished with exit code 0
部分转载于：https://blog.csdn.net/dongjinkun/article/details/132590205