Pad#
Versioned name: Pad-1
Category: Data movement
Short description: Pad operation extends an input tensor on edges. The amount and value of padded elements are defined by inputs and attributes.
Detailed Description: The attributes specify a number of elements to add along each axis and a rule by which new element values are generated: for example, whether they are filled with a given constant or generated based on the input tensor content.
The following examples illustrate how output tensor is generated for the Pad layer for a given input tensor:
INPUT =
[[ 1 2 3 4 ]
[ 5 6 7 8 ]
[ 9 10 11 12 ]]
with the following attributes:
pads_begin = [0, 1]
pads_end = [2, 3]
depending on the pad_mode.
pad_mode = "constant"
:
OUTPUT =
[[ 0 1 2 3 4 0 0 0 ]
[ 0 5 6 7 8 0 0 0 ]
[ 0 9 10 11 12 0 0 0 ]
[ 0 0 0 0 0 0 0 0 ]
[ 0 0 0 0 0 0 0 0 ]]
pad_mode = "edge"
:
OUTPUT =
[[ 1 1 2 3 4 4 4 4 ]
[ 5 5 6 7 8 8 8 8 ]
[ 9 9 10 11 12 12 12 12 ]
[ 9 9 10 11 12 12 12 12 ]
[ 9 9 10 11 12 12 12 12 ]]
pad_mode = "reflect"
:
OUTPUT =
[[ 2 1 2 3 4 3 2 1 ]
[ 6 5 6 7 8 7 6 5 ]
[ 10 9 10 11 12 11 10 9 ]
[ 6 5 6 7 8 7 6 5 ]
[ 2 1 2 3 4 3 2 1 ]]
pad_mode = "symmetric"
:
OUTPUT =
[[ 1 1 2 3 4 4 3 2 ]
[ 5 5 6 7 8 8 7 6 ]
[ 9 9 10 11 12 12 11 10 ]
[ 9 9 10 11 12 12 11 10 ]
[ 5 5 6 7 8 8 7 6 ]]
Attributes
pad_mode
Description: pad_mode specifies the method used to generate new element values.
Range of values: Name of the method in string format:
constant
- padded values are equal to the value of the pad_value input, if input not provided zero value is padded.edge
- padded values are copied from the respective edge of the inputdata
tensor.reflect
- padded values are a reflection of the inputdata
tensor; values on the edges are not duplicated.pads_begin[D]
andpads_end[D]
must be not greater thandata.shape[D] – 1
for any validD
.symmetric
- padded values are symmetrically added from the inputdata
tensor. This method is similar to thereflect
, but values on edges are duplicated. Refer to the examples above for more details.pads_begin[D]
andpads_end[D]
must be not greater thandata.shape[D]
for any validD
.
Type:
string
Required: yes
Inputs
1:
data
tensor of arbitrary shape and type T. Required.2:
pads_begin
1D tensor of type T_INT. Number of elements matches the number of indices in data attribute. Specifies the number of padding elements at the beginning of each axis. Required.3:
pads_end
1D tensor of type T_INT. Number of elements matches the number of indices in data attribute. Specifies the number of padding elements at the ending of each axis. Required.4:
pad_value
scalar tensor of type T. Used with thepad_mode = "constant"
only. All new elements are populated with this value or with 0 if input not provided. Shouldn’t be set for otherpad_mode
values. Optional.
Outputs
1: Output padded tensor of type T with dimensions
pads_begin[D] + data.shape[D] + pads_end[D]
for eachD
from0
tolen(data.shape) - 1
.
Types
T: any numeric type.
T_INT: any non-negative integer type.
Example: constant mode
<layer ... type="Pad" ...>
<data pad_mode="constant"/>
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>32</dim>
<dim>40</dim>
</port>
<port id="1">
<dim>4</dim> <!-- pads_begin = [0, 5, 2, 1] -->
</port>
<port id="2">
<dim>4</dim> <!-- pads_end = [1, 0, 3, 7] -->
</port>
<port id="3">
<!-- pad_value = 15.0 -->
</port>
</input>
<output>
<port id="0">
<dim>2</dim> <!-- 2 = 0 + 1 + 1 = pads_begin[0] + input.shape[0] + pads_end[0] -->
<dim>8</dim> <!-- 8 = 5 + 3 + 0 = pads_begin[1] + input.shape[1] + pads_end[1] -->
<dim>37</dim> <!-- 37 = 2 + 32 + 3 = pads_begin[2] + input.shape[2] + pads_end[2] -->
<dim>48</dim> <!-- 48 = 1 + 40 + 7 = pads_begin[3] + input.shape[3] + pads_end[3] -->
<!-- all new elements are filled with 15.0 value -->
</port>
</output>
</layer>
Example: edge mode
<layer ... type="Pad" ...>
<data pad_mode="edge"/>
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>32</dim>
<dim>40</dim>
</port>
<port id="1">
<dim>4</dim> <!-- pads_begin = [0, 5, 2, 1] -->
</port>
<port id="2">
<dim>4</dim> <!-- pads_end = [1, 0, 3, 7] -->
</port>
</input>
<output>
<port id="0">
<dim>2</dim> <!-- 2 = 0 + 1 + 1 = pads_begin[0] + input.shape[0] + pads_end[0] -->
<dim>8</dim> <!-- 8 = 5 + 3 + 0 = pads_begin[1] + input.shape[1] + pads_end[1] -->
<dim>37</dim> <!-- 37 = 2 + 32 + 3 = pads_begin[2] + input.shape[2] + pads_end[2] -->
<dim>48</dim> <!-- 48 = 1 + 40 + 7 = pads_begin[3] + input.shape[3] + pads_end[3] -->
</port>
</output>
</layer>