MirrorPad

tensorflow C++ API

tensorflow::ops::MirrorPad

Pads a tensor with mirrored values.

Summary

This operation pads ainputwith mirrored values according to thepaddingsyou specify.paddingsis an integer tensor with shape[n, 2], where n is the rank ofinput. For each dimension D ofinput,paddings[D, 0]indicates how many values to add before the contents ofinputin that dimension, andpaddings[D, 1]indicates how many values to add after the contents ofinputin that dimension. Bothpaddings[D, 0]andpaddings[D, 1]must be no greater thaninput.dim_size(D)(orinput.dim_size(D) - 1) ifcopy_borderis 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]] ```

Arguments:

• scope: A Scope object
• input: The input tensor to be padded.
• paddings: A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.
• mode: 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.

Returns:

• Output : The padded tensor.

MirrorPad block

Source link :https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_array_ops.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input input : A N-D Tensor.
• Input paddings : A Tensor type of int32,int64(Must be one integer type). A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input. ex) input shape -> [3][3][3], paddings shape -> [3][2]
• StringPiece mode : 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.

Output:

• Output output : Output object of MirrorPad class object.

Result:

• std::vector(Tensor) result_output: A Tensor . paddings(D, 0) + input.dim_size(D) + paddings(D, 1)

Using Method

※input의 shape가 [2][3], 값이 [[1,2,3][4,5,6]]이고, paddings가 [2][2]에 [[0,0][2,2]], mode가 "SYMMETRIC"일 때 결과는 [[2,1,1,2,3,3,2][5,4,4,5,6,6,5]]이다. mode가 "REFLECT"일 때 결과는 [[3,2,1,2,3,2,1][6,5,4,5,6,5,4]] 이다.

※paddings에 들어가는 값은 [D][0]은 input에 들어있는 값의 앞에 들어가는 padding값 이고(input이 [1,2,3]이고, padding이 [2,0], mode 가 SYMMETRIC일 때 값은 [2,1,1,2,3]이다. mode가 REFLECT라면 [3,2,1,2,3]이다.), [D][1]은 input에 들어있는 값의 뒤에 들어가는 padding값이다(input이 [1,2,3]이고, padding이 [0,2], mode 가 SYMMETRIC일 때 값은 [1,2,3,3,2]이다. mode가 REFLECT라면 [1,2,3,2,1]이다.).