Question

下面的代码是正确的，但我想对其进行矢量化（并可能转换为GPU）以提高速度。

如何将其转换为矢量形式？

RF = 4;     
inhibatory = 0;    
overlap=3;   
act_funct = 'sig';
gap = RF-overlap;    
Image1 = rand(30,22);  
Image2 = rand(27,19); % size_image2 is equal to 27x19
Image3 = rand(30,22); 
de_act_output = de_activate_Mat(Image1,act_funct); % finding derivative of the matrix. e.g. de_act_output = act_output.*(1-act_output) in case of sigmoid. 
for u=1:size(Image1,1)
    for v=1:size(Image1,2)
        sum_val=0;
        iLowMax=max(ceil((u-(RF+inhibatory))/(gap-inhibatory)),1);
        iHighMax=min(floor((u-1)/(gap-inhibatory))+1, size_image2(1));
        jLowMax=max(ceil((v-(RF+inhibatory))/(gap-inhibatory)),1);
        jHighMax = min(floor((v-1)/(gap-inhibatory))+1, size_image2(2));
        sum_sens = sum(sum(Image2(iLowMax:iHighMax,jLowMax:jHighMax)));
        sum_val = sum_sens(:,:) .* Image3(u,v);
        result(u,v) = de_act_output(u,v) .* sum_val;
    end
end

Answer 1

您在嵌套循环内创建的parallelogram-like块结构iLowMax:iHighMax,jLowMax:jHighMax不会导致任何简单的矢量化代码。但是如果性能对你的情况至关重要，那么你就可以全力以赴地进行矢量化，看起来convolution在那里很有用。这里列出的是一些调整通过预先计算大多数其他东西来加快这一步的速度，这必然会带来明显的加速。这是实施 -

U = 1:size(Image1,1); %// Create arrays of iteration steps
V = 1:size(Image1,2);

%// Calculate arrays of low-high row and column indices 
iLowMax=max(ceil((U-(RF+inhibatory))/(gap-inhibatory)),1);
iHighMax=min(floor((U-1)/(gap-inhibatory))+1, size_image2(1));

jLowMax=max(ceil((V-(RF+inhibatory))/(gap-inhibatory)),1);
jHighMax = min(floor((V-1)/(gap-inhibatory))+1, size_image2(2));

sens_sums(size(Image1,1),size(Image1,2)) = 0; %// Pre-allocation
for u=1:size(Image1,1)
    for v=1:size(Image1,2)
        sens = Image2(iLowMax(u):iHighMax(u),jLowMax(v):jHighMax(v));
        sens_sums(u,v) = sum(sens(:));
    end
end
result = sens_sums.*Image3.*de_act_output;

如何通过下面的代码向量化来避免循环？

1 个答案: