com.microsoft - Pad#

Pad - 1 (com.microsoft)#

Version

  • name: Pad (GitHub)

  • domain: com.microsoft

  • since_version: 1

  • function:

  • support_level:

  • shape inference:

This version of the operator has been available since version 1 of domain com.microsoft.

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: Three modes: constant`(default) - 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 is ?.

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] or a 2D tensor of shape [1, 2 * input_rank]. pads format (1D example) should be as follow [x1_begin, x2_begin,…,x1_end, x2_end,…], where xi_begin is 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 (optional, heterogeneous) - T: (Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is constant (by default it is 0.0).

Outputs

  • output (heterogeneous) - T: Tensor after padding.

Examples

_constant_pad

node = onnx.helper.make_node(
    'Pad',
    inputs=['x', 'pads', 'value'],
    outputs=['y'],
    mode='constant'
)
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype(np.int64)  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
value = np.float32(1.2)
y = pad_impl(
    x,
    pads,
    'constant',
    1.2
)

expect(node, inputs=[x, pads, value], outputs=[y],
       name='test_constant_pad')

_reflection_and_edge_pad

for mode in ['edge', 'reflect']:
    node = onnx.helper.make_node(
        'Pad',
        inputs=['x', 'pads'],
        outputs=['y'],
        mode=mode
    )
    x = np.random.randn(1, 3, 4, 5).astype(np.int32)
    pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype(np.int64)  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
    y = pad_impl(
        x,
        pads,
        mode
    )

    expect(node, inputs=[x, pads], outputs=[y],
           name=f'test_{mode}_pad')