UpdatedAugust 22, 2025

Pad

Description

Given a tensor containing the data to be padded (data), a tensor containing the number of start and end pad values for axis (pads), (optionally) a mode, and (optionally) constant_value, a padded tensor (output) is generated.

The three supported modes are (similar to corresponding modes supported by numpy.pad):

constant(default) – pads with a given constant value as specified by constant_value (which defaults to 0, empty string, or False)
reflect – pads with the reflection of the vector mirrored on the first and last values of the vector along each axis
edge – pads with the edge values of array
wrap – wrap-around padding as if the data tensor forms a torus

Input parameters

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

Graphs in : cluster, ONNX model architecture.

data (heterogeneous) – T : object, input tensor.
pads (heterogeneous) – tensor(int64) : object, tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. pads should be a 1D tensor of shape [2 * num_axes] where num_axes refers to the number of elements in the axes input or the input rank if axes are not provided explicitly. pads format should be: [x1_begin, x2_begin, …, x1_end, x2_end,…], where xi_begin is the number of pad values added at the beginning of axis axes and xi_end, the number of pad values added at the end of axis axes.
constant_value (optional, heterogeneous) – T : object, a scalar value to be used if the mode chosen is constant (by default it is 0, empty string or False).
axes (optional, heterogeneous) – Tind : object, 1-D tensor of axes that pads apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed ([0, 1, ..., input_rank-1]).

Parameters : cluster,

mode : enum, three modes : `constant`- pads with a given constant value, `reflect`- pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge`- pads with the edge values of array.
Default value “constant”.
training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

name (optional) : string, name of the node.

Output parameters

output (heterogeneous) – T : object, tensor after padding.

Type Constraints

T in (tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16),
tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)) : Constrain input and output types to all tensor types.

Tind in (tensor(int32), tensor(int64)) : Constrain indices to integer types.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).

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