如何在numpy中进行分散和收集操作?

How to do scatter and gather operations in numpy?(如何在numpy中进行分散和收集操作?)

问题描述

我想在 Numpy 中实现 Tensorflow 或 PyTorch 的分散和收集操作.我一直在挠头一段时间.任何指针都非常感谢！

I want to implement scatter and gather operations of Tensorflow or PyTorch in Numpy. I have been scratching my head for a while. Any pointers are greatly appreciated!

• PyTorch 散射

PyTorch 聚会

推荐答案

结果证明 scatter 方法比我预期的要多得多.我没有在 NumPy 中找到任何现成的函数.我在这里分享它是为了任何可能需要用 NumPy 实现它的人的利益.(ps self 是方法的目标或输出.)

The scatter method turned out to be way more work than I anticipated. I did not find any ready made function in NumPy for it. I'm sharing it here in the interest of anyone who may need to implement it with NumPy. (p.s. self is the destination or output of the method.)

def scatter_numpy(self, dim, index, src):
    """
    Writes all values from the Tensor src into self at the indices specified in the index Tensor.

    :param dim: The axis along which to index
    :param index: The indices of elements to scatter
    :param src: The source element(s) to scatter
    :return: self
    """
    if index.dtype != np.dtype('int_'):
        raise TypeError("The values of index must be integers")
    if self.ndim != index.ndim:
        raise ValueError("Index should have the same number of dimensions as output")
    if dim >= self.ndim or dim < -self.ndim:
        raise IndexError("dim is out of range")
    if dim < 0:
        # Not sure why scatter should accept dim < 0, but that is the behavior in PyTorch's scatter
        dim = self.ndim + dim
    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 output should be the same size")
    if (index >= self.shape[dim]).any() or (index < 0).any():
        raise IndexError("The values of index must be between 0 and (self.shape[dim] -1)")

    def make_slice(arr, dim, i):
        slc = [slice(None)] * arr.ndim
        slc[dim] = i
        return slc

    # We use index and dim parameters to create idx
    # idx is in a form that can be used as a NumPy advanced index for scattering of src param. in self
    idx = [[*np.indices(idx_xsection_shape).reshape(index.ndim - 1, -1),
            index[make_slice(index, dim, i)].reshape(1, -1)[0]] for i in range(index.shape[dim])]
    idx = list(np.concatenate(idx, axis=1))
    idx.insert(dim, idx.pop())

    if not np.isscalar(src):
        if index.shape[dim] > src.shape[dim]:
            raise IndexError("Dimension " + str(dim) + "of index can not be bigger than that of src ")
        src_xsection_shape = src.shape[:dim] + src.shape[dim + 1:]
        if idx_xsection_shape != src_xsection_shape:
            raise ValueError("Except for dimension " +
                             str(dim) + ", all dimensions of index and src should be the same size")
        # src_idx is a NumPy advanced index for indexing of elements in the src
        src_idx = list(idx)
        src_idx.pop(dim)
        src_idx.insert(dim, np.repeat(np.arange(index.shape[dim]), np.prod(idx_xsection_shape)))
        self[idx] = src[src_idx]

    else:
        self[idx] = src

    return self

对于 gather 可能有一个更简单的解决方案，但这就是我的解决方案:
(这里的 self 是从中收集值的 ndarray.)

There could be a simpler solution for gather, but this is what I settled on:
(here self is the ndarray that the values are gathered from.)

def gather_numpy(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

    :param dim: The axis along which to index
    :param index: A tensor of indices of elements to gather
    :return: tensor of gathered values
    """
    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)

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