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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)[源代码]

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

注解

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.

参数
  • 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[源代码]

Deformable Convolutional forward function.

参数
  • 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)
    

返回

Output of the layer.

返回类型

Tensor

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