Pad¶

Pad - 23¶

Version¶

name: Pad (GitHub)
domain: main
since_version: 23
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 23.

Summary¶

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

Example 1 (constant mode):

Insert 0 pads to the beginning of the second dimension.

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'constant'

constant_value = 0.0

output = [
    [0.0, 0.0, 1.0, 1.2],
    [0.0, 0.0, 2.3, 3.4],
    [0.0, 0.0, 4.5, 5.7],
]

Example 2 (reflect mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'reflect'

output = [
    [1.0, 1.2, 1.0, 1.2],
    [2.3, 3.4, 2.3, 3.4],
    [4.5, 5.7, 4.5, 5.7],
]

Example 3 (edge mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'edge'

output = [
    [1.0, 1.0, 1.0, 1.2],
    [2.3, 2.3, 2.3, 3.4],
    [4.5, 4.5, 4.5, 5.7],
]

Example 4 (wrap mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [2, 1, 1, 1]

mode = 'wrap'

output = [
    [3.4, 2.3, 3.4, 2.3],
    [5.7, 4.5, 5.7, 4.5],
    [1.2, 1.0, 1.2, 1.0],
    [3.4, 2.3, 3.4, 2.3],
    [5.7, 4.5, 5.7, 4.5],
    [1.2, 1.0, 1.2, 1.0],
]

Attributes¶

mode - STRING (default is 'constant'):

Supported modes: constant(default), reflect, edge, wrap

Inputs¶

Between 2 and 4 inputs.

data (heterogeneous) - T:

Input tensor.
pads (heterogeneous) - tensor(int64):

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[i] and xi_end, the number of pad values added at the end of axis axes[i].
constant_value (optional, heterogeneous) - T:

(Optional) 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:

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]).

Outputs¶

output (heterogeneous) - T:

Tensor after padding.

Type Constraints¶

T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(float4e2m1), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint4), tensor(uint64), tensor(uint8) ):

Constrain input and output types to all tensor types up to IRv11.
Tind in ( tensor(int32), tensor(int64) ):

Constrain indices to integer types

Pad - 21 vs 23

Pad - 21¶

Version¶

name: Pad (GitHub)
domain: main
since_version: 21
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 21.

Summary¶

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

Example 1 (constant mode):

Insert 0 pads to the beginning of the second dimension.

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'constant'

constant_value = 0.0

output = [
    [0.0, 0.0, 1.0, 1.2],
    [0.0, 0.0, 2.3, 3.4],
    [0.0, 0.0, 4.5, 5.7],
]

Example 2 (reflect mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'reflect'

output = [
    [1.0, 1.2, 1.0, 1.2],
    [2.3, 3.4, 2.3, 3.4],
    [4.5, 5.7, 4.5, 5.7],
]

Example 3 (edge mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'edge'

output = [
    [1.0, 1.0, 1.0, 1.2],
    [2.3, 2.3, 2.3, 3.4],
    [4.5, 4.5, 4.5, 5.7],
]

Example 4 (wrap mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [2, 1, 1, 1]

mode = 'wrap'

output = [
    [3.4, 2.3, 3.4, 2.3],
    [5.7, 4.5, 5.7, 4.5],
    [1.2, 1.0, 1.2, 1.0],
    [3.4, 2.3, 3.4, 2.3],
    [5.7, 4.5, 5.7, 4.5],
    [1.2, 1.0, 1.2, 1.0],
]

Attributes¶

mode - STRING (default is 'constant'):

Supported modes: constant(default), reflect, edge, wrap

Inputs¶

Between 2 and 4 inputs.

data (heterogeneous) - T:

Input tensor.
pads (heterogeneous) - tensor(int64):

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[i] and xi_end, the number of pad values added at the end of axis axes[i].
constant_value (optional, heterogeneous) - T:

(Optional) 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:

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]).

Outputs¶

output (heterogeneous) - T:

Tensor after padding.

Type Constraints¶

T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint4), tensor(uint64), tensor(uint8) ):

Constrain input and output types to all tensor types up to IRv10.
Tind in ( tensor(int32), tensor(int64) ):

Constrain indices to integer types

Pad - 19¶

Version¶

name: Pad (GitHub)
domain: main
since_version: 19
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 19.

Summary¶

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

Example 1 (constant mode):

Insert 0 pads to the beginning of the second dimension.

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'constant'

constant_value = 0.0

output = [
    [0.0, 0.0, 1.0, 1.2],
    [0.0, 0.0, 2.3, 3.4],
    [0.0, 0.0, 4.5, 5.7],
]

Example 2 (reflect mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'reflect'

output = [
    [1.0, 1.2, 1.0, 1.2],
    [2.3, 3.4, 2.3, 3.4],
    [4.5, 5.7, 4.5, 5.7],
]

Example 3 (edge mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'edge'

output = [
    [1.0, 1.0, 1.0, 1.2],
    [2.3, 2.3, 2.3, 3.4],
    [4.5, 4.5, 4.5, 5.7],
]

Example 4 (wrap mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [2, 1, 1, 1]

mode = 'wrap'

output = [
    [3.4, 2.3, 3.4, 2.3],
    [5.7, 4.5, 5.7, 4.5],
    [1.2, 1.0, 1.2, 1.0],
    [3.4, 2.3, 3.4, 2.3],
    [5.7, 4.5, 5.7, 4.5],
    [1.2, 1.0, 1.2, 1.0],
]

Attributes¶

mode - STRING (default is 'constant'):

Supported modes: constant(default), reflect, edge, wrap

Inputs¶

Between 2 and 4 inputs.

data (heterogeneous) - T:

Input tensor.
pads (heterogeneous) - tensor(int64):

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[i] and xi_end, the number of pad values added at the end of axis axes[i].
constant_value (optional, heterogeneous) - T:

(Optional) 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:

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]).

Outputs¶

output (heterogeneous) - T:

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

Pad - 18¶

Version¶

name: Pad (GitHub)
domain: main
since_version: 18
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 18.

Summary¶

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

Example 1 (constant mode):

Insert 0 pads to the beginning of the second dimension.

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'constant'

constant_value = 0.0

output = [
    [0.0, 0.0, 1.0, 1.2],
    [0.0, 0.0, 2.3, 3.4],
    [0.0, 0.0, 4.5, 5.7],
]

Example 2 (reflect mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'reflect'

output = [
    [1.0, 1.2, 1.0, 1.2],
    [2.3, 3.4, 2.3, 3.4],
    [4.5, 5.7, 4.5, 5.7],
]

Example 3 (edge mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'edge'

output = [
    [1.0, 1.0, 1.0, 1.2],
    [2.3, 2.3, 2.3, 3.4],
    [4.5, 4.5, 4.5, 5.7],
]

Attributes¶

mode - STRING (default is 'constant'):

Supported modes: constant(default), reflect, edge

Inputs¶

Between 2 and 4 inputs.

data (heterogeneous) - T:

Input tensor.
pads (heterogeneous) - tensor(int64):

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[i] and xi_end, the number of pad values added at the end of axis axes[i].
constant_value (optional, heterogeneous) - T:

(Optional) 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:

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]).

Outputs¶

output (heterogeneous) - T:

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

Pad - 13¶

Version¶

name: Pad (GitHub)
domain: main
since_version: 13
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 13.

Summary¶

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

Example 1 (constant mode): Insert 0 pads to the beginning of the second dimension.

data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ]

pads = [0, 2, 0, 0]

mode = ‘constant’

constant_value = 0.0

output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ]

Example 2 (reflect mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ]

pads = [0, 2, 0, 0]

mode = ‘reflect’

output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ]

Example 3 (edge mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ]

pads = [0, 2, 0, 0]

mode = ‘edge’

output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ]

Attributes¶

mode - STRING (default is 'constant'):

Supported modes: constant(default), reflect, edge

Inputs¶

Between 2 and 3 inputs.

data (heterogeneous) - T:

Input tensor.
pads (heterogeneous) - tensor(int64):

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 * input_rank]. 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 i and xi_end, the number of pad values added at the end of axis i.
constant_value (optional, heterogeneous) - T:

(Optional) A scalar value to be used if the mode chosen is constant (by default it is 0, empty string or False).

Outputs¶

output (heterogeneous) - T:

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.

Pad - 11¶

Version¶

name: Pad (GitHub)
domain: main
since_version: 11
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 11.

Summary¶

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)
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

Example 1 (constant mode): Insert 0 pads to the beginning of the second dimension.

data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ]

pads = [0, 2, 0, 0]

mode = ‘constant’

constant_value = 0.0

output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ]

Example 2 (reflect mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ]

pads = [0, 2, 0, 0]

mode = ‘reflect’

output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ]

Example 3 (edge mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ]

pads = [0, 2, 0, 0]

mode = ‘edge’

output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ]

Attributes¶

mode - STRING (default is 'constant'):

Supported modes: constant(default), reflect, edge

Inputs¶

Between 2 and 3 inputs.

data (heterogeneous) - T:

Input tensor.
pads (heterogeneous) - tensor(int64):

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 * input_rank]. 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 i and xi_end, the number of pad values added at the end of axis i.
constant_value (optional, heterogeneous) - T:

(Optional) A scalar value to be used if the mode chosen is constant (by default it is 0).

Outputs¶

output (heterogeneous) - T:

Tensor after padding.

Type Constraints¶

T in ( tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

Constrain input and output to only numeric types.

Pad - 2¶

Version¶

name: Pad (GitHub)
domain: main
since_version: 2
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 2.

Summary¶

Given data tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ]

Attributes¶

mode - STRING (default is 'constant'):

Three modes: constant(default), reflect, edge
pads - INTS (required) :

List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. pads rank should be double of the input’s rank. pads format should be as follow [x1_begin, x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added at the beginning of axis i and xi_end, the number of pixels added at the end of axis i.
value - FLOAT (default is '0.0'):

One float, indicates the value to be filled.

Inputs¶

data (heterogeneous) - T:

Input tensor.

Outputs¶

output (heterogeneous) - T:

Tensor after padding.

Type Constraints¶

T in ( tensor(double), tensor(float), tensor(float16) ):

Constrain input and output types to float tensors.

Pad - 1¶

Version¶

name: Pad (GitHub)
domain: main
since_version: 1
function: False
support_level: SupportType.COMMON
shape inference: False

This version of the operator has been available since version 1.

Summary¶

Given data tensor, paddings, mode, and value. Example: Insert 0 paddings to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] paddings = [0, 0, 2, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ]

Attributes¶

mode - STRING (default is 'constant'):

Three modes: constant(default), reflect, edge
paddings - INTS (required) :

List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. paddings rank should be double of the input’s rank. paddings format should be as follow [x1_begin, x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added at the beginning of axis i and xi_end, the number of pixels added at the end of axis i.
value - FLOAT (default is '0.0'):

One float, indicates the value to be filled, default is 0

Inputs¶

data (heterogeneous) - T:

Input tensor.

Outputs¶

output (heterogeneous) - T:

Tensor after padding.

Type Constraints¶

T in ( tensor(double), tensor(float), tensor(float16) ):

Constrain input and output types to float tensors.