flownet中correlation代码

以下是Flownet中的correlation代码示例：

from torch.nn.functional import unfold

def correlation(input1, input2, kernel_size=1, max_displacement=1, stride1=1, stride2=1, padding=0, dilation=1):
    b, c, h, w = input1.size()
    # Pad input2
    padded_input2 = F.pad(input2, (padding, padding, padding, padding))
    # Unfold input2
    unfolded_input2 = unfold(padded_input2, kernel_size=kernel_size, dilation=dilation, stride=stride2)
    # Reshape unfolded input2
    unfolded_input2 = unfolded_input2.view(b, c, kernel_size**2, h, w)
    # Create empty output tensor
    output = torch.zeros(b, (2 * max_displacement + 1) ** 2, h // stride1, w // stride1).to(input1.device)
    # Loop over displacements
    for i in range(-max_displacement, max_displacement + 1):
        for j in range(-max_displacement, max_displacement + 1):
            # Shift input2
            shifted_input2 = padded_input2[:, :, padding + i:h + padding + i, padding + j:w + padding + j]
            # Unfold shifted input2
            unfolded_shifted_input2 = unfold(shifted_input2, kernel_size=kernel_size, dilation=dilation, stride=stride2)
            # Reshape unfolded shifted input2
            unfolded_shifted_input2 = unfolded_shifted_input2.view(b, c, kernel_size**2, h, w)
            # Compute correlation
            corr = (input1 * unfolded_shifted_input2).sum(dim=1)
            # Add to output tensor
            output[:, (i + max_displacement) * (2 * max_displacement + 1) + j + max_displacement, :, :] = corr
    return output

这段代码实现了correlation操作，它是Flownet中的关键操作之一。在此操作中，两个输入张量被移位并相乘，然后求和以计算它们之间的相似性。这个操作在光流估计中非常有用，因为它可以帮助找到两个图像中的相同区域。