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]

• 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

• 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 input data tensor.

• reflect - padded values are a reflection of the input data tensor; values on the edges are not duplicated. pads_begin[D] and pads_end[D] must be not greater than data.shape[D] – 1 for any valid D.

• symmetric - padded values are symmetrically added from the input data tensor. This method is similar to the reflect, but values on edges are duplicated. Refer to the examples above for more details. pads_begin[D] and pads_end[D] must be not greater than data.shape[D] for any valid D.

• 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 the pad_mode = "constant" only. All new elements are populated with this value or with 0 if input not provided. Shouldn’t be set for other pad_mode values. Optional.

Outputs

• 1 : Output padded tensor of type T with dimensions pads_begin[D] + data.shape[D] + pads_end[D] for each D from 0 to len(data.shape) - 1.

Types

• T : any numeric type.

• T_INT : any non-negative integer type.

Example : constant mode

<layer ... type="Pad" ...>
<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">
</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" ...>
<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>