Category: Data movement operations

Short description: Pad operation extends an input tensor on edges. The amount and value of padded elements are defined by inputs and attributes.

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 operation attribute.
• 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 below for more details. pads_begin[D] and pads_end[D] must be not greater than data.shape[D] for any valid D.
• Type: string
• Default value: None
• Required: yes

Inputs

• 1: data - input tensor to be padded. Required.
• 2: pads_begin - specifies the number of padding elements at the beginning of each axis. A list of non-negative integers. The length of the list must be equal to the number of dimensions in the input tensor. Required.
• 3: pads_end - specifies the number of padding elements at the beginning of each axis. A list of non-negative integers. The length of the list must be equal to the number of dimensions in the input tensor. Required.
• 4: pad_value - scalar tensor of type matching type of elements in data tensor to be replicated in padded area. Used with the pad_mode = "constant" only. All new elements are populated with this value. Optional for pad_mode = "constant". If not provided, 0 of appropriate type is used. Shouldn't be set for other pad_mode values.

Outputs

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

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:

• 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 ]]

Example

<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">