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# Correlation¶

class mmcv.ops.Correlation(kernel_size: int = 1, max_displacement: int = 1, stride: int = 1, padding: int = 0, dilation: int = 1, dilation_patch: int = 1)[source]

Correlation operator

This correlation operator works for optical flow correlation computation.

There are two batched tensors with shape $$(N, C, H, W)$$, and the correlation output’s shape is $$(N, max\_displacement \times 2 + 1, max\_displacement * 2 + 1, H_{out}, W_{out})$$

where

$H_{out} = \left\lfloor\frac{H_{in} + 2 \times padding - dilation \times (kernel\_size - 1) - 1} {stride} + 1\right\rfloor$
$W_{out} = \left\lfloor\frac{W_{in} + 2 \times padding - dilation \times (kernel\_size - 1) - 1} {stride} + 1\right\rfloor$

the correlation item $$(N_i, dy, dx)$$ is formed by taking the sliding window convolution between input1 and shifted input2,

$Corr(N_i, dx, dy) = \sum_{c=0}^{C-1} input1(N_i, c) \star \mathcal{S}(input2(N_i, c), dy, dx)$

where $$\star$$ is the valid 2d sliding window convolution operator, and $$\mathcal{S}$$ means shifting the input features (auto-complete zero marginal), and $$dx, dy$$ are shifting distance, $$dx, dy \in [-max\_displacement \times dilation\_patch, max\_displacement \times dilation\_patch]$$.

Parameters
• kernel_size (int) – The size of sliding window i.e. local neighborhood representing the center points and involved in correlation computation. Defaults to 1.

• max_displacement (int) – The radius for computing correlation volume, but the actual working space can be dilated by dilation_patch. Defaults to 1.

• stride (int) – The stride of the sliding blocks in the input spatial dimensions. Defaults to 1.

• padding (int) – Zero padding added to all four sides of the input1. Defaults to 0.

• dilation (int) – The spacing of local neighborhood that will involved in correlation. Defaults to 1.

• dilation_patch (int) – The spacing between position need to compute correlation. Defaults to 1.

forward(input1: torch.Tensor, input2: torch.Tensor)torch.Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

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