Python 输入图像的小波二维散射变换

我正在尝试对输入图像进行2D散射变换。当我运行下面的代码时，我得到了这个错误：“过滤器不兼容乘法！”。有人能帮忙吗？ 塔克斯

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对于具有相同宽度和高度（正方形，而非矩形）的1KB

.png

小屏幕，似乎工作正常：

输出 但是，您在该方法中遇到的错误（

backend\u torch.py

）应该与张量大小有关：

def cdgmm(A, B, inplace=False):
    """
        Complex pointwise multiplication between (batched) tensor A and tensor B.

        Parameters
        ----------
        A : tensor
            input tensor with size (B, C, M, N, 2)
        B : tensor
            B is a complex tensor of size (M, N, 2)
        inplace : boolean, optional
            if set to True, all the operations are performed inplace

        Returns
        -------
        C : tensor
            output tensor of size (B, C, M, N, 2) such that:
            C[b, c, m, n, :] = A[b, c, m, n, :] * B[m, n, :]
    """
    A, B = A.contiguous(), B.contiguous()
    if A.size()[-3:] != B.size():
        raise RuntimeError('The filters are not compatible for multiplication!')

    if not iscomplex(A) or not iscomplex(B):
        raise TypeError('The input, filter and output should be complex')

    if B.ndimension() != 3:
        raise RuntimeError('The filters must be simply a complex array!')

    if type(A) is not type(B):
        raise RuntimeError('A and B should be same type!')


    C = A.new(A.size())

    A_r = A[..., 0].contiguous().view(-1, A.size(-2)*A.size(-3))
    A_i = A[..., 1].contiguous().view(-1, A.size(-2)*A.size(-3))

    B_r = B[...,0].contiguous().view(B.size(-2)*B.size(-3)).unsqueeze(0).expand_as(A_i)
    B_i = B[..., 1].contiguous().view(B.size(-2)*B.size(-3)).unsqueeze(0).expand_as(A_r)

    C[..., 0].view(-1, C.size(-2)*C.size(-3))[:] = A_r * B_r - A_i * B_i
    C[..., 1].view(-1, C.size(-2)*C.size(-3))[:] = A_r * B_i + A_i * B_r

    return C if not inplace else A.copy_(C)

来源

---

对于宽度和高度相等（正方形，而不是矩形）的1KB

.png

来说，似乎工作正常：

输出 但是，您在该方法中遇到的错误（

backend\u torch.py

）应该与张量大小有关：

def cdgmm(A, B, inplace=False):
    """
        Complex pointwise multiplication between (batched) tensor A and tensor B.

        Parameters
        ----------
        A : tensor
            input tensor with size (B, C, M, N, 2)
        B : tensor
            B is a complex tensor of size (M, N, 2)
        inplace : boolean, optional
            if set to True, all the operations are performed inplace

        Returns
        -------
        C : tensor
            output tensor of size (B, C, M, N, 2) such that:
            C[b, c, m, n, :] = A[b, c, m, n, :] * B[m, n, :]
    """
    A, B = A.contiguous(), B.contiguous()
    if A.size()[-3:] != B.size():
        raise RuntimeError('The filters are not compatible for multiplication!')

    if not iscomplex(A) or not iscomplex(B):
        raise TypeError('The input, filter and output should be complex')

    if B.ndimension() != 3:
        raise RuntimeError('The filters must be simply a complex array!')

    if type(A) is not type(B):
        raise RuntimeError('A and B should be same type!')


    C = A.new(A.size())

    A_r = A[..., 0].contiguous().view(-1, A.size(-2)*A.size(-3))
    A_i = A[..., 1].contiguous().view(-1, A.size(-2)*A.size(-3))

    B_r = B[...,0].contiguous().view(B.size(-2)*B.size(-3)).unsqueeze(0).expand_as(A_i)
    B_i = B[..., 1].contiguous().view(B.size(-2)*B.size(-3)).unsqueeze(0).expand_as(A_r)

    C[..., 0].view(-1, C.size(-2)*C.size(-3))[:] = A_r * B_r - A_i * B_i
    C[..., 1].view(-1, C.size(-2)*C.size(-3))[:] = A_r * B_i + A_i * B_r

    return C if not inplace else A.copy_(C)

来源

torch.Size([81, 19, 19])

def cdgmm(A, B, inplace=False):
    """
        Complex pointwise multiplication between (batched) tensor A and tensor B.

        Parameters
        ----------
        A : tensor
            input tensor with size (B, C, M, N, 2)
        B : tensor
            B is a complex tensor of size (M, N, 2)
        inplace : boolean, optional
            if set to True, all the operations are performed inplace

        Returns
        -------
        C : tensor
            output tensor of size (B, C, M, N, 2) such that:
            C[b, c, m, n, :] = A[b, c, m, n, :] * B[m, n, :]
    """
    A, B = A.contiguous(), B.contiguous()
    if A.size()[-3:] != B.size():
        raise RuntimeError('The filters are not compatible for multiplication!')

    if not iscomplex(A) or not iscomplex(B):
        raise TypeError('The input, filter and output should be complex')

    if B.ndimension() != 3:
        raise RuntimeError('The filters must be simply a complex array!')

    if type(A) is not type(B):
        raise RuntimeError('A and B should be same type!')


    C = A.new(A.size())

    A_r = A[..., 0].contiguous().view(-1, A.size(-2)*A.size(-3))
    A_i = A[..., 1].contiguous().view(-1, A.size(-2)*A.size(-3))

    B_r = B[...,0].contiguous().view(B.size(-2)*B.size(-3)).unsqueeze(0).expand_as(A_i)
    B_i = B[..., 1].contiguous().view(B.size(-2)*B.size(-3)).unsqueeze(0).expand_as(A_r)

    C[..., 0].view(-1, C.size(-2)*C.size(-3))[:] = A_r * B_r - A_i * B_i
    C[..., 1].view(-1, C.size(-2)*C.size(-3))[:] = A_r * B_i + A_i * B_r

    return C if not inplace else A.copy_(C)