Question

我想在指定的轴中收集指定索引的元素，如下所示。

x = [[1,2,3], [4,5,6]]
index = [[2,1], [0, 1]]
x[:, index] = [[3, 2], [4, 5]]

这基本上是在pytorch中的聚集操作，但是如你所知，这在numpy中是不可能实现的。我想知道在numpy中是否有这样的“聚集”操作？

Answer 1

我前一段时间写这是为了在Numpy中复制PyTorch的{{1}}。在这种情况下，gather是您的self

这些是测试用例：

def gather(self, dim, index):
    """
    Gathers values along an axis specified by ``dim``.

    For a 3-D tensor the output is specified by:
        out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
        out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
        out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2

    Parameters
    ----------
    dim:
        The axis along which to index
    index:
        A tensor of indices of elements to gather

    Returns
    -------
    Output Tensor
    """
    idx_xsection_shape = index.shape[:dim] + \
        index.shape[dim + 1:]
    self_xsection_shape = self.shape[:dim] + self.shape[dim + 1:]
    if idx_xsection_shape != self_xsection_shape:
        raise ValueError("Except for dimension " + str(dim) +
                         ", all dimensions of index and self should be the same size")
    if index.dtype != np.dtype('int_'):
        raise TypeError("The values of index must be integers")
    data_swaped = np.swapaxes(self, 0, dim)
    index_swaped = np.swapaxes(index, 0, dim)
    gathered = np.choose(index_swaped, data_swaped)
    return np.swapaxes(gathered, 0, dim)

Answer 2

>>> x = np.array([[1,2,3], [4,5,6]])
>>> index = np.array([[2,1], [0, 1]])
>>> x_axis_index=np.tile(np.arange(len(x)), (index.shape[1],1)).transpose() 
>>> print x_axis_index
[[0 0]
 [1 1]]
>>> print x[x_axis_index,index]
[[3 2]
 [4 5]]

Answer 3

numpy.take_along_axis是我所需要的，根据索引获取元素。可以像PyTorch中的collect方法一样使用。

这是手册中的示例：

>>> a = np.array([[10, 30, 20], [60, 40, 50]])
>>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
>>> ai
array([[1],
       [0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[30],
       [60]])

Answer 4

使用numpy.take()函数，该函数具有PyTorch的大多数collect函数功能。

如何在numpy中收集特定指数的元素？

4 个答案: