DeformConv#
Domain:
ai.onnxSince version: 22
Performs deformable convolution as described in https://arxiv.org/abs/1703.06211 and https://arxiv.org/abs/1811.11168. This operator specification supports the general N-D case. Note that most common use cases have 2D or 3D data.
Inputs
X (T): Input data tensor. For 2D image data, it has shape (N, C, H, W) where N is the batch size, C is the number of input channels, and H and W are the height and width. In general, the shape is (N, C, D1, D2, … , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes. Most common use cases have n = 2 or 3.
W (T): Weight tensor that will be used in the convolutions. It has shape (oC, C/group, kH, kW), where oC is the number of output channels and kH and kW are the kernel height and width. For more than 2 dimensions, it has shape (oC, C/group, k1, k2, … , kn).
offset (T): Offset tensor denoting the offset for the sampling locations in the convolution kernel. It has shape (N, offset_group * kH * kW * 2, oH, oW) for 2D data or (N, offset_group * k1 * k2 * … * kn * n, o1, o2, … , on) for nD data. Use linear interpolationfor fractional offset values. Sampling locations outside of the padded input tensor gives zero.
B (T): Optional 1D bias of length oC to be added to the convolution. Default is a tensor of zeros.
mask (T): The mask tensor to be applied to each position in the convolution kernel. It has shape (N, offset_group * kH * kW, oH, oW) for 2D data or (N, offset_group * k1 * k2 * … * kn * n, o1, o2, … , on) for nD data. Default is a tensor of ones.
Outputs
Y (T): Output data tensor that contains the result of convolution. It has shape (N, oC, oH, oW) for 2D data or (N, oC, o1, o2, …, on) for nD data
Type Constraints
T: Constrain input and output types to float tensors. Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16).
Examples#
test_cc_basic_deform_conv_with_padding
Node:
DeformConv(X, W, offset) -> (Y)
Attributes:
kernel_shape = [2, 2]
pads = [1, 1, 1, 1]
Inputs:
X: shape=(1, 1, 3, 3), dtype=float32
[[[[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]]]]
W: shape=(1, 1, 2, 2), dtype=float32
[[[[1., 1.],
[1., 1.]]]]
offset: shape=(1, 8, 4, 4), dtype=float32
[[[[ 0.5, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]],
[[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]],
[[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]],
...,
[[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , -0.1, 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]],
[[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]],
[[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]]]]
Outputs:
Y: shape=(1, 1, 4, 4), dtype=float32
[[[[ 0. , 1. , 3. , 2. ],
[ 3. , 8. , 11.9, 7. ],
[ 9. , 20. , 24. , 13. ],
[ 6. , 13. , 15. , 8. ]]]]
test_cc_basic_deform_conv_without_padding
Node:
DeformConv(X, W, offset) -> (Y)
Attributes:
kernel_shape = [2, 2]
pads = [0, 0, 0, 0]
Inputs:
X: shape=(1, 1, 3, 3), dtype=float32
[[[[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]]]]
W: shape=(1, 1, 2, 2), dtype=float32
[[[[1., 1.],
[1., 1.]]]]
offset: shape=(1, 8, 2, 2), dtype=float32
[[[[ 0.5, 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , -0.1],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]]]]
Outputs:
Y: shape=(1, 1, 2, 2), dtype=float32
[[[[ 9.5, 11.9],
[20. , 24. ]]]]
test_cc_deform_conv_with_mask_bias
Node:
DeformConv(X, W, offset, B, mask) -> (Y)
Attributes:
kernel_shape = [2, 2]
pads = [0, 0, 0, 0]
Inputs:
X: shape=(1, 1, 3, 3), dtype=float32
[[[[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]]]]
W: shape=(1, 1, 2, 2), dtype=float32
[[[[1., 1.],
[1., 1.]]]]
offset: shape=(1, 8, 2, 2), dtype=float32
[[[[ 0.5, 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , -0.1],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]]]]
B: shape=(1,), dtype=float32
[1.]
mask: shape=(1, 4, 2, 2), dtype=float32
[[[[1. , 1. ],
[1. , 1. ]],
[[1. , 1. ],
[1. , 1. ]],
[[1. , 1. ],
[1. , 0.2]],
[[1. , 1. ],
[1. , 1. ]]]]
Outputs:
Y: shape=(1, 1, 2, 2), dtype=float32
[[[[10.5, 12.9],
[21. , 19.4]]]]
test_cc_deform_conv_with_multiple_offset_groups
Node:
DeformConv(X, W, offset) -> (Y)
Attributes:
kernel_shape = [2, 2]
pads = [0, 0, 0, 0]
offset_group = 2
Inputs:
X: shape=(1, 2, 3, 3), dtype=float32
[[[[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]],
[[8., 7., 6.],
[5., 4., 3.],
[2., 1., 0.]]]]
W: shape=(1, 2, 2, 2), dtype=float32
[[[[1., 1.],
[1., 1.]],
[[1., 1.],
[1., 1.]]]]
offset: shape=(1, 16, 2, 2), dtype=float32
[[[[ 0.5, 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , -0.1],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]],
[[ 0. , 0. ],
[ 0. , 0. ]]]]
Outputs:
Y: shape=(1, 1, 2, 2), dtype=float32
[[[[33.5, 32.1],
[32. , 32. ]]]]
Differences with previous version (19)#
SchemaDiff: DeformConv (domain 'ai.onnx')
old version: 19
new version: 22
breaking: no
Type constraints:
changed ‘T’: added types: [‘tensor(bfloat16)’]