【pytorch】torch.gather()函数

dim=0时

index=[ [x1,x2,x2],
		[y1,y2,y2],
		[z1,z2,z3] ]

如果dim=0
填入方式为：
index=[ [(x1,0),(x2,1),(x3,2)]
		[(y1,0),(y2,1),(y3,2)]
		[(z1,0),(z2,1),(z3,2)] ]
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input = [
    [1, 2, 3, 4],
    [5, 6, 7, 8],
    [9, 10, 11, 12]
] # shape（3,4）
input = torch.tensor(input)
length = torch.LongTensor([
    [2,2,2,2],
    [1,1,1,1],
    [0,0,0,0],
    [0,1,2,0]
])# shape（4,4）
out = torch.gather(input, dim=0, index=length)
print(out)
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tensor([[9, 10, 11, 12],
        [5, 6, 7, 8],
        [1, 2, 3, 4],
        [1, 6, 11, 4]])
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#### dim=0后，根据new_index对input进行索引
new_index=[ [(2,0),(2,1),(2,2),(2,3)],
			[(1,0),(1,1),(1,2),(1,3)],
			[(0,0),(0,1),(0,2),(0,3)],
			[(0,0),(1,1),(2,2),(0,3)] ]
			
可以观察到第四行，行索引变为0,所以当gather函数里的index超过input的唯独时，会从0重新计数。
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---

dim=1时

input = [
    [1, 2, 3, 4],
    [5, 6, 7, 8],
    [9, 10, 11, 12]
] # shape（3,4）
input = torch.tensor(input)
length = torch.LongTensor([
    [2,2,2,2],
    [1,1,1,1],
    [0,1,2,0]
]) # shape（3,4）
out = torch.gather(input, dim=1, index=length)
print(out)
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tensor([[3, 3, 3, 3],
        [6, 6, 6, 6],
        [9, 10, 11, 9]])

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new_index = [
	[(0,2),(0,2),(0,2),(0,2)],
	[(1,1),(1,1),(1,1),(1,1)],
	[(2,0),(2,1),(2,2)(2,0)]
]
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