deform_conv2d

torchvision.ops.deform_conv2d(input: torch.Tensor, offset: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1), padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), mask: Optional[torch.Tensor] = None) → torch.Tensor

Performs Deformable Convolution v2, described in Deformable ConvNets v2: More Deformable, Better Results if mask is not None and Performs Deformable Convolution, described in Deformable Convolutional Networks if mask is None.

Parameters

• input (Tensor[batch_size, in_channels, in_height, in_width]) – input tensor

• offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]) – offsets to be applied for each position in the convolution kernel.

• weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]) – convolution weights, split into groups of size (in_channels // groups)

• bias (Tensor[out_channels]) – optional bias of shape (out_channels,). Default: None

• stride (int or Tuple[int, int]) – distance between convolution centers. Default: 1

• padding (int or Tuple[int, int]) – height/width of padding of zeroes around each image. Default: 0

• dilation (int or Tuple[int, int]) – the spacing between kernel elements. Default: 1

• mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]) – masks to be applied for each position in the convolution kernel. Default: None

Returns

result of convolution

Return type

Tensor[batch_sz, out_channels, out_h, out_w]

Examples::

>>> input = torch.rand(4, 3, 10, 10)
>>> kh, kw = 3, 3
>>> weight = torch.rand(5, 3, kh, kw)
>>> # offset and mask should have the same spatial size as the output
>>> # of the convolution. In this case, for an input of 10, stride of 1
>>> # and kernel size of 3, without padding, the output size is 8
>>> offset = torch.rand(4, 2 * kh * kw, 8, 8)
>>> mask = torch.rand(4, kh * kw, 8, 8)
>>> out = deform_conv2d(input, offset, weight, mask=mask)
>>> print(out.shape)
>>> # returns
>>>  torch.Size([4, 5, 8, 8])