pytorch repeat 和 expand 函数的使用场景，区别

x = torch.tensor([0, 1, 2, 3]).float().view(4, 1)


def test_assign(x):
    # 赋值操作
    x_expand = x.expand(-1, 3)
    x_repeat = x.repeat(1, 3)
    x_expand[:, 1] = torch.tensor([0, -1, -2, -3])
    x_repeat[:, 1] = torch.tensor([0, -1, -2, -3])
    print(x_expand, '\n', x_repeat)
    """
    x_expand, 每一列的值都被改了，因为是操作引用，一个变化全部变化
    tensor([[ 0.,  0.,  0.],
            [-1., -1., -1.],
            [-2., -2., -2.],
            [-3., -3., -3.]]) 
    x_repeat， 只有选中的列发生了改变，因为是内存都是复制来的
     tensor([[ 0.,  0.,  0.],
            [ 1., -1.,  1.],
            [ 2., -2.,  2.],
            [ 3., -3.,  3.]])   

    """


def other(x):
    # 引用值做其他操作
    x_expand = x.expand(-1, 3)  # x，ref
    x_repeat = x.repeat(1, 3)  # real copy
    y = torch.rand_like(x_expand) * 10  # real mem
    expand = x_expand - y  # x, ref - real mem
    x = x_expand - y  # assign x
    repeat = x_repeat - y
    print(expand, '\n', x, '\n', repeat)
    print(expand == repeat, '\n', x == repeat)
    print(id(x)==id(x_expand))
    """
         expand
         tensor([[ -6.6548,  -9.2567,  -3.7804],
                [ -7.5785,  -9.4398,  -5.6251],
                [ -6.5088,  -5.3956,  -3.9644],
                [-12.3324,  -7.5420, -12.4954]]) 
         x
         tensor([[ -6.6548,  -9.2567,  -3.7804],
                [ -7.5785,  -9.4398,  -5.6251],
                [ -6.5088,  -5.3956,  -3.9644],
                [-12.3324,  -7.5420, -12.4954]]) 
         repeat
         tensor([[ -6.6548,  -9.2567,  -3.7804],
                [ -7.5785,  -9.4398,  -5.6251],
                [ -6.5088,  -5.3956,  -3.9644],
                [-12.3324,  -7.5420, -12.4954]])

         expand==repeat
         tensor([[True, True, True],
                [True, True, True],
                [True, True, True],
                [True, True, True]]) 
         x==repeat
         tensor([[True, True, True],
                [True, True, True],
                [True, True, True],
                [True, True, True]])
    """


test_assign(x)
other(x)