Pads a tensor with mirrored values.
tf.raw_ops.MirrorPad(
input, paddings, mode, name=None
)
This operation pads a input
with mirrored values according to the paddings
you specify. paddings
is an integer tensor with shape [n, 2]
, where n is
the rank of input
. For each dimension D of input
, paddings[D, 0]
indicates
how many values to add before the contents of input
in that dimension, and
paddings[D, 1]
indicates how many values to add after the contents of input
in that dimension. Both paddings[D, 0]
and paddings[D, 1]
must be no greater
than input.dim_size(D)
(or input.dim_size(D) - 1
) if copy_border
is true
(if false, respectively).
The padded size of each dimension D of the output is:
paddings(D, 0) + input.dim_size(D) + paddings(D, 1)
For example:
# 't' is [[1, 2, 3], [4, 5, 6]].
# 'paddings' is [[1, 1]], [2, 2]].
# 'mode' is SYMMETRIC.
# rank of 't' is 2.
pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2]
[2, 1, 1, 2, 3, 3, 2]
[5, 4, 4, 5, 6, 6, 5]
[5, 4, 4, 5, 6, 6, 5]]
Args | |
---|---|
input
|
A Tensor . The input tensor to be padded.
|
paddings
|
A Tensor . Must be one of the following types: int32 , int64 .
A two-column matrix specifying the padding sizes. The number of
rows must be the same as the rank of input .
|
mode
|
A string from: "REFLECT", "SYMMETRIC" .
Either REFLECT or SYMMETRIC . In reflect mode the padded regions
do not include the borders, while in symmetric mode the padded regions
do include the borders. For example, if input is [1, 2, 3] and paddings
is [0, 2] , then the output is [1, 2, 3, 2, 1] in reflect mode, and
it is [1, 2, 3, 3, 2] in symmetric mode.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as input .
|