在PyTorch中Tensor的查找和筛选例子

> x = torch.randn(3, 4) # 目标矩阵
> x
tensor([[ 0.1427, 0.0231, -0.5414, -1.0009],
    [-0.4664, 0.2647, -0.1228, -1.1068],
    [-1.1734, -0.6571, 0.7230, -0.6004]])
> indices = torch.tensor([0, 2]) # 在轴上筛选坐标
> torch.index_select(x, dim=0, indices) # 指定筛选对象、轴、筛选坐标
tensor([[ 0.1427, 0.0231, -0.5414, -1.0009],
    [-1.1734, -0.6571, 0.7230, -0.6004]])
> torch.index_select(x, dim=1, indices)
tensor([[ 0.1427, -0.5414],
    [-0.4664, -0.1228],
    [-1.1734, 0.7230]])

> x = torch.randn(3, 2)
> y = torch.ones(3, 2)
> torch.where(x > 0, x, y)
tensor([[1.4013, 1.0000],
    [1.0000, 0.9267],
    [1.0000, 0.4302]])
> x
tensor([[ 1.4013, -0.9960],
    [-0.3715, 0.9267],
    [-0.7163, 0.4302]])

> x = torch.arange(5)  
> x
tensor([0, 1, 2, 3, 4])
> torch.gt(x,1) # 大于
tensor([0, 0, 1, 1, 1], dtype=torch.uint8)
> x>1   # 大于
tensor([0, 0, 1, 1, 1], dtype=torch.uint8)
> torch.ne(x,1) # 不等于
tensor([1, 0, 1, 1, 1], dtype=torch.uint8)
> x!=1  # 不等于
tensor([1, 0, 1, 1, 1], dtype=torch.uint8)
> torch.lt(x,3) # 小于
tensor([1, 1, 1, 0, 0], dtype=torch.uint8)
> x<3   # 小于
tensor([1, 1, 1, 0, 0], dtype=torch.uint8)
> torch.eq(x,3) # 等于
tensor([0, 0, 0, 1, 0], dtype=torch.uint8)
> x==3  # 等于
tensor([0, 0, 0, 1, 0], dtype=torch.uint8)

> x = torch.arange(5)
> x  # 1维
tensor([0, 1, 2, 3, 4])
> torch.nonzero(x)
tensor([[1],
    [2],
    [3],
    [4]])
> x = torch.Tensor([[0.6, 0.0, 0.0, 0.0],[0.0, 0.4, 0.0, 0.0],[0.0, 0.0, 1.2, 0.0],[0.0, 0.0, 0.0,-0.4]])
> x  # 2维
tensor([[ 0.6000, 0.0000, 0.0000, 0.0000],
    [ 0.0000, 0.4000, 0.0000, 0.0000],
    [ 0.0000, 0.0000, 1.2000, 0.0000],
    [ 0.0000, 0.0000, 0.0000, -0.4000]])
> torch.nonzero(x)
tensor([[0, 0],
    [1, 1],
    [2, 2],
    [3, 3]])

> x=torch.arange(12).view(3,4)
> x
tensor([[ 0, 1, 2, 3],
    [ 4, 5, 6, 7],
    [ 8, 9, 10, 11]])
> (x>4).nonzero()
tensor([[1, 1],
    [1, 2],
    [1, 3],
    [2, 0],
    [2, 1],
    [2, 2],
    [2, 3]])