# Source code for mars.tensor.merge.dstack

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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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.
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# http://www.apache.org/licenses/LICENSE-2.0
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from ..base import atleast_3d
from .concatenate import concatenate
[docs]def dstack(tup):
"""
Stack tensors in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2-D tensors
of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
`(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
`dsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of tensors
The tensors must have the same shape along all but the third axis.
1-D or 2-D arrays must have the same shape.
Returns
-------
stacked : Tensor
The array formed by stacking the given tensors, will be at least 3-D.
See Also
--------
stack : Join a sequence of tensors along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split tensor along third axis.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array((1,2,3))
>>> b = mt.array((2,3,4))
>>> mt.dstack((a,b)).execute()
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = mt.array([[1],[2],[3]])
>>> b = mt.array([[2],[3],[4]])
>>> mt.dstack((a,b)).execute()
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return concatenate([atleast_3d(t) for t in tup], axis=2)