DeformConv2d¶

class mmcv.ops.DeformConv2d(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, ...]], stride: Union[int, Tuple[int, ...]] = 1, padding: Union[int, Tuple[int, ...]] = 0, dilation: Union[int, Tuple[int, ...]] = 1, groups: int = 1, deform_groups: int = 1, bias: bool = False, im2col_step: int = 32)[source]¶

Deformable 2D convolution.

Applies a deformable 2D convolution over an input signal composed of several input planes. DeformConv2d was described in the paper Deformable Convolutional Networks

Note

The argument im2col_step was added in version 1.3.17, which means number of samples processed by the im2col_cuda_kernel per call. It enables users to define batch_size and im2col_step more flexibly and solved issue mmcv#1440.

Parameters

in_channels (int) – Number of channels in the input image.
out_channels (int) – Number of channels produced by the convolution.
kernel_size (int, tuple) – Size of the convolving kernel.
stride (int, tuple) – Stride of the convolution. Default: 1.
padding (int or tuple) – Zero-padding added to both sides of the input. Default: 0.
dilation (int or tuple) – Spacing between kernel elements. Default: 1.
groups (int) – Number of blocked connections from input. channels to output channels. Default: 1.
deform_groups (int) – Number of deformable group partitions.
bias (bool) – If True, adds a learnable bias to the output. Default: False.
im2col_step (int) – Number of samples processed by im2col_cuda_kernel per call. It will work when batch_size > im2col_step, but batch_size must be divisible by im2col_step. Default: 32. New in version 1.3.17.

forward(x: torch.Tensor, offset: torch.Tensor) → torch.Tensor [source]¶

Deformable Convolutional forward function.

Parameters

x (Tensor) – Input feature, shape (B, C_in, H_in, W_in)
offset (Tensor) –
Offset for deformable convolution, shape (B, deform_groups*kernel_size[0]*kernel_size[1]*2, H_out, W_out), H_out, W_out are equal to the output’s.

An offset is like [y0, x0, y1, x1, y2, x2, …, y8, x8]. The spatial arrangement is like:
```
(x0, y0) (x1, y1) (x2, y2)
(x3, y3) (x4, y4) (x5, y5)
(x6, y6) (x7, y7) (x8, y8)
```

Returns

Output of the layer.

Return type

Tensor