Custom conv2d operation Pytorch

Question

我尝试了一个自定义的Conv2d函数，该函数必须类似于nn.Conv2d，但在nn.Conv2d内部使用的乘法和加法被mymult（num1，num2）和myadd（num1，num2）取代。

根据非常有用的论坛1, 2的见解，我可以做的是尝试展开它，然后进行矩阵乘法。我该如何使用带mymult（）和myadd（）的循环在下面给出@部分，我相信这是@ matmul，尽管我不确定。

基本上，我想使用python循环实现res = torch.matmul（kernels_flat，imageunfold），以便代替*我可以使用mymult（num1，num2），对于+我可以使用myadd（num1，num2）

def convcheck():
    torch.manual_seed(123)
    batch_size = 2
    channels = 2

    h, w = 2, 2
    image = torch.randn(batch_size, channels, h, w) # input image
    out_channels = 3
    kh, kw = 1, 1# kernel size
    dh, dw = 1, 1 # stride
    size = int((h-kh+2*0)/dh+1)    #include padding in place of zero

    conv = nn.Conv2d(in_channels=channels, out_channels=out_channels, kernel_size=kw, padding=0,stride=dh ,bias=False)

    out = conv (image)
    #print('out', out)
    #print('out.size()', out.size())
    #print('')
    filt = conv.weight.data 


    imageunfold = F.unfold(image,kernel_size=kh,padding=0,stride=dh)

    print("Unfolded image","\n",imageunfold,"\n",imageunfold.shape)
    kernels_flat = filt.view(out_channels,-1)
    print("Kernel Flat=","\n",kernels_flat,"\n",kernels_flat.shape)
    res = kernels_flat @ imageunfold        # I have to replace this operation with mymult() and myadd()
    print(res,"\n",res.shape)
    #print(res.size(2),"\n",res.shape)
    res = res.view(-1, out_channels, size, size)
    #print("Same answer as buitlin function",res)

Answer 1

res = kernels_flat @ imageunfold可以替换为此。

     for m_batch in range(len(imageunfold)):
            #iterate through rows of X  
        for i in range(kernels_flat.size(0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            ):
            # iterate through columns of Y
            for j in range(imageunfold.size(2)):                   
                # iterate through rows of Y
                for k in range(imageunfold.size(1)):              
                    #print(result[m_batch][i][j]," +=",   kernels_flat[i][k], "*", imageunfold[m_batch][k][j])
                    result[m_batch][i][j] +=   kernels_flat[i][k] * imageunfold[m_batch][k][j]