core/maxframe/tensor/misc/transpose.py

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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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import numpy as np

from ... import opcodes
from ...serialization.serializables import FieldTypes, KeyField, ListField
from ..core import TensorOrder
from ..datasource import tensor as astensor
from ..operators import TensorHasInput, TensorOperatorMixin
from ..utils import reverse_order


def _reorder(x, axes):
    if x is None:
        return
    return type(x)(x[ax] for ax in axes)


class TensorTranspose(TensorHasInput, TensorOperatorMixin):
    _op_type_ = opcodes.TRANSPOSE

    _input = KeyField("input")
    axes = ListField("axes", FieldTypes.int32, default=None)

    def __init__(self, axes=None, **kw):
        # transpose will create a view
        super().__init__(axes=axes, create_view=True, **kw)

    def __call__(self, a):
        shape = tuple(
            s if np.isnan(s) else int(s) for s in _reorder(a.shape, self.axes)
        )
        if self.axes == list(reversed(range(a.ndim))):
            # order reversed
            tensor_order = reverse_order(a.order)
        else:
            tensor_order = TensorOrder.C_ORDER
        return self.new_tensor([a], shape, order=tensor_order)

    def _set_inputs(self, inputs):
        super()._set_inputs(inputs)
        self._input = self._inputs[0]

    def on_output_modify(self, new_output):
        op = self.copy().reset_key()
        return op(new_output)

    def on_input_modify(self, new_input):
        op = self.copy().reset_key()
        return op(new_input)


def transpose(a, axes=None):
    """
    Returns an array with axes transposed.

    For a 1-D array, this returns an unchanged view of the original array, as a
    transposed vector is simply the same vector.
    To convert a 1-D array into a 2-D column vector, an additional dimension
    must be added, e.g., ``mt.atleast_2d(a).T`` achieves this, as does
    ``a[:, mt.newaxis]``.
    For a 2-D array, this is the standard matrix transpose.
    For an n-D array, if axes are given, their order indicates how the
    axes are permuted (see Examples). If axes are not provided, then
    ``transpose(a).shape == a.shape[::-1]``.

    Parameters
    ----------
    a : array_like
        Input array.
    axes : tuple or list of ints, optional
        If specified, it must be a tuple or list which contains a permutation
        of [0,1,...,N-1] where N is the number of axes of `a`. The `i`'th axis
        of the returned array will correspond to the axis numbered ``axes[i]``
        of the input. If not specified, defaults to ``range(a.ndim)[::-1]``,
        which reverses the order of the axes.

    Returns
    -------
    p : ndarray
        `a` with its axes permuted. A view is returned whenever possible.

    Notes
    -----
    Use ``transpose(a, argsort(axes))`` to invert the transposition of tensors
    when using the `axes` keyword argument.


    Examples
    --------
    >>> import maxframe.tensor as mt

    >>> x = mt.arange(4).reshape((2,2))
    >>> x.execute()
    array([[0, 1],
           [2, 3]])

    >>> mt.transpose(x).execute()
    array([[0, 2],
           [1, 3]])

    >>> x = mt.ones((1, 2, 3))
    >>> mt.transpose(x, (1, 0, 2)).shape
    (2, 1, 3)

    """
    a = astensor(a)
    if axes:
        if len(axes) != a.ndim:
            raise ValueError("axes don't match tensor")

    if not axes:
        axes = list(range(a.ndim))[::-1]
    else:
        axes = list(axes)
    op = TensorTranspose(axes, dtype=a.dtype)
    return op(a)