Table Of Contents
Table Of Contents

# mxnet.ndarray.contrib.DeformableConvolution¶

mxnet.ndarray.contrib.DeformableConvolution(data=None, offset=None, weight=None, bias=None, kernel=_Null, stride=_Null, dilate=_Null, pad=_Null, num_filter=_Null, num_group=_Null, num_deformable_group=_Null, workspace=_Null, no_bias=_Null, layout=_Null, out=None, name=None, **kwargs)

Compute 2-D deformable convolution on 4-D input.

The deformable convolution operation is described in https://arxiv.org/abs/1703.06211

For 2-D deformable convolution, the shapes are

• data: (batch_size, channel, height, width)

• offset: (batch_size, num_deformable_group * kernel[0] * kernel[1], height, width)

• weight: (num_filter, channel, kernel[0], kernel[1])

• bias: (num_filter,)

• out: (batch_size, num_filter, out_height, out_width).

Define:

f(x,k,p,s,d) = floor((x+2*p-d*(k-1)-1)/s)+1


then we have:

out_height=f(height, kernel[0], pad[0], stride[0], dilate[0])
out_width=f(width, kernel[1], pad[1], stride[1], dilate[1])


If no_bias is set to be true, then the bias term is ignored.

The default data layout is NCHW, namely (batch_size, channle, height, width).

If num_group is larger than 1, denoted by g, then split the input data evenly into g parts along the channel axis, and also evenly split weight along the first dimension. Next compute the convolution on the i-th part of the data with the i-th weight part. The output is obtained by concating all the g results.

If num_deformable_group is larger than 1, denoted by dg, then split the input offset evenly into dg parts along the channel axis, and also evenly split out evenly into dg parts along the channel axis. Next compute the deformable convolution, apply the i-th part of the offset part on the i-th out.

Both weight and bias are learnable parameters.

Defined in src/operator/contrib/deformable_convolution.cc:L100

Parameters
• data (NDArray) – Input data to the DeformableConvolutionOp.

• offset (NDArray) – Input offset to the DeformableConvolutionOp.

• weight (NDArray) – Weight matrix.

• bias (NDArray) – Bias parameter.

• kernel (Shape(tuple), required) – Convolution kernel size: (h, w) or (d, h, w)

• stride (Shape(tuple), optional, default=[]) – Convolution stride: (h, w) or (d, h, w). Defaults to 1 for each dimension.

• dilate (Shape(tuple), optional, default=[]) – Convolution dilate: (h, w) or (d, h, w). Defaults to 1 for each dimension.

• pad (Shape(tuple), optional, default=[]) – Zero pad for convolution: (h, w) or (d, h, w). Defaults to no padding.

• num_filter (int (non-negative), required) – Convolution filter(channel) number

• num_group (int (non-negative), optional, default=1) – Number of group partitions.

• num_deformable_group (int (non-negative), optional, default=1) – Number of deformable group partitions.

• workspace (long (non-negative), optional, default=1024) – Maximum temperal workspace allowed for convolution (MB).

• no_bias (boolean, optional, default=0) – Whether to disable bias parameter.

• layout ({None, 'NCDHW', 'NCHW', 'NCW'},optional, default='None') – Set layout for input, output and weight. Empty for default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays