torch.max用法（指定维度dim）

如何理解torch.max中的dim

理解torch.max主要是一点，即dim的指定。
其实torch中的dim和numpy中的axis是一个东西，可以点击这个链接查看axis的详细解释，理解了axis后，dim就迎刃而解了。

pytorch文档解释

torch.max = max(...)
    max(input) -> Tensor

    Returns the maximum value of all elements in the ``input`` tensor.

    .. warning::
        This function produces deterministic (sub)gradients unlike ``max(dim=0)``

    Args:
        input (Tensor): the input tensor.

    Example::

        >>> a = torch.randn(1, 3)
        >>> a
        tensor([[ 0.6763,  0.7445, -2.2369]])
        >>> torch.max(a)
        tensor(0.7445)

    .. function:: max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)

    Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum
    value of each row of the :attr:`input` tensor in the given dimension
    :attr:`dim`. And ``indices`` is the index location of each maximum value found
    (argmax).

    .. warning::
        ``indices`` does not necessarily contain the first occurrence of each
        maximal value found, unless it is unique.
        The exact implementation details are device-specific.
        Do not expect the same result when run on CPU and GPU in general.
        For the same reason do not expect the gradients to be deterministic.

    If ``keepdim`` is ``True``, the output tensors are of the same size
    as ``input`` except in the dimension ``dim`` where they are of size 1.
    Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting
    in the output tensors having 1 fewer dimension than ``input``.

    Args:
        input (Tensor): the input tensor.
        dim (int): the dimension to reduce.
        keepdim (bool): whether the output tensor has :attr:`dim` retained or not. Default: ``False``.
        out (tuple, optional): the result tuple of two output tensors (max, max_indices)

    Example::

        >>> a = torch.randn(4, 4)
        >>> a
        tensor([[-1.2360, -0.2942, -0.1222,  0.8475],
                [ 1.1949, -1.1127, -2.2379, -0.6702],
                [ 1.5717, -0.9207,  0.1297, -1.8768],
                [-0.6172,  1.0036, -0.6060, -0.2432]])
        >>> torch.max(a, 1)
        torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1]))

    .. function:: max(input, other, out=None) -> Tensor

    Each element of the tensor ``input`` is compared with the corresponding
    element of the tensor ``other`` and an element-wise maximum is taken.

    The shapes of ``input`` and ``other`` don't need to match,
    but they must be :ref:`broadcastable <broadcasting-semantics>`.

    .. math::
        text{out}_i = max(text{tensor}_i, text{other}_i)

    .. note:: When the shapes do not match, the shape of the returned output tensor
              follows the :ref:`broadcasting rules <broadcasting-semantics>`.

    Args:
        input (Tensor): the input tensor.
        other (Tensor): the second input tensor
        out (Tensor, optional): the output tensor.

    Example::

        >>> a = torch.randn(4)
        >>> a
        tensor([ 0.2942, -0.7416,  0.2653, -0.1584])
        >>> b = torch.randn(4)
        >>> b
        tensor([ 0.8722, -1.7421, -0.4141, -0.5055])
        >>> torch.max(a, b)
        tensor([ 0.8722, -0.7416,  0.2653, -0.1584])

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