module onnxrt.ops_cpu.op_pad#
Short summary#
module mlprodict.onnxrt.ops_cpu.op_pad
Runtime operator.
Classes#
class |
truncated documentation |
|---|---|
Pad === Given a tensor containing the data to be padded (data), a tensor containing the number of start and end pad … |
|
Pad === Given a tensor containing the data to be padded (data), a tensor containing the number of start and end pad … |
Functions#
function |
truncated documentation |
|---|---|
Implements numpy.pad based on ONNX signature. |
Properties#
property |
truncated documentation |
|---|---|
|
Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
|
Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
|
Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
|
Returns the list of modified parameters. |
|
Returns the list of modified parameters. |
|
Returns the list of modified parameters. |
|
Returns the list of optional arguments. |
|
Returns the list of optional arguments. |
|
Returns the list of optional arguments. |
|
Returns the list of optional arguments. |
|
Returns the list of optional arguments. |
|
Returns the list of optional arguments. |
|
Returns all parameters in a dictionary. |
|
Returns all parameters in a dictionary. |
|
Returns all parameters in a dictionary. |
Methods#
method |
truncated documentation |
|---|---|
|
|
|
|
Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_pad.Pad_1(onnx_node, desc=None, **options)#
Bases:
OpRun- __init__(onnx_node, desc=None, **options)#
- _run(data, pads, constant_value=None, attributes=None, verbose=0, fLOG=None)#
Should be overwritten.
- class mlprodict.onnxrt.ops_cpu.op_pad.Pad_18(onnx_node, desc=None, **options)#
Bases:
Pad_1Pad#
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
- 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: Supported modes: constant`(default), `reflect, edge Default value is
namemodesconstanttypeSTRING(STRING)
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 tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.
Tind tensor(int32), tensor(int64): Constrain indices to integer types
Version
Onnx name: Pad
This version of the operator has been available since version 18.
Runtime implementation:
Pad- _run(data, pads, constant_value=None, axes=None, attributes=None, verbose=0, fLOG=None)#
Should be overwritten.
- mlprodict.onnxrt.ops_cpu.op_pad._pad_impl(data, raw_pads, mode, constant_values=0.0, axes=None)#
- mlprodict.onnxrt.ops_cpu.op_pad.onnx_pad(data, pads, constant_value=None, mode='constant')#
Implements numpy.pad based on ONNX signature.
- Parameters:
data – data to pad
pads – 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 – A scalar value to be used if the mode chosen is constant (by default it is 0, empty string or False).
mode – Supported modes: constant`(default), `reflect, edge
- Returns:
tensor after padding