索引超过训练模型时遇到的矩阵维数

Question

我使用PASCAL开发工具包与Felzenszwalb，D.McAllester，D.Ramaman及其团队开发的Discriminatively deformable part model system系统训练模型存在问题在Matlab中。

目前我有这种输出错误，当我试图训练一个1分量模型'猫'使用10个正面和10个负面图像。

Error: 

??? Index exceeds matrix dimensions. 

Error in ==> pascal_train at 48 
models{i} = train(cls, models{i}, spos{i}, neg(1:maxneg), 
0, 0, 4, 3, ... 

Error in ==> pascal at 28 
model = pascal_train(cls, n, note); 

这是pascal_train文件

function model = pascal_train(cls, n, note) 

% model = pascal_train(cls, n, note) 
% Train a model with 2*n components using the PASCAL dataset. 
% note allows you to save a note with the trained model 
% example: note = 'testing FRHOG (FRobnicated HOG) 

% At every "checkpoint" in the training process we reset the 
% RNG's seed to a fixed value so that experimental results are 
% reproducible. 
initrand(); 

if nargin < 3 
    note = ''; 
end 

globals; 
[pos, neg] = pascal_data(cls, true, VOCyear); 
% split data by aspect ratio into n groups 
spos = split(cls, pos, n); 

cachesize = 24000; 
maxneg = 200; 

% train root filters using warped positives & random negatives 
try 
    load([cachedir cls '_lrsplit1']); 
catch 
    initrand(); 
    for i = 1:n 
    % split data into two groups: left vs. right facing instances 
    models{i} = initmodel(cls, spos{i}, note, 'N'); 
    inds = lrsplit(models{i}, spos{i}, i); 
    models{i} = train(cls, models{i}, spos{i}(inds), neg, i, 1, 1, 1, ... 
         cachesize, true, 0.7, false, ['lrsplit1_' num2str(i)]); 
    end 
    save([cachedir cls '_lrsplit1'], 'models'); 
end 

% train root left vs. right facing root filters using latent detections 
% and hard negatives 
try 
    load([cachedir cls '_lrsplit2']); 
catch 
    initrand(); 
    for i = 1:n 
    models{i} = lrmodel(models{i}); 
    models{i} = train(cls, models{i}, spos{i}, neg(1:maxneg), 0, 0, 4, 3, ... 
         cachesize, true, 0.7, false, ['lrsplit2_' num2str(i)]); 
    end 
    save([cachedir cls '_lrsplit2'], 'models'); 
end 

% merge models and train using latent detections & hard negatives 
try 
    load([cachedir cls '_mix']); 
catch 
    initrand(); 
    model = mergemodels(models); 
48: model = train(cls, model, pos, neg(1:maxneg), 0, 0, 1, 5, ... 
       cachesize, true, 0.7, false, 'mix'); 


save([cachedir cls '_mix'], 'model'); 
end 

% add parts and update models using latent detections & hard negatives. 
try 
    load([cachedir cls '_parts']); 
catch 
    initrand(); 
    for i = 1:2:2*n 
    model = model_addparts(model, model.start, i, i, 8, [6 6]); 
    end 
    model = train(cls, model, pos, neg(1:maxneg), 0, 0, 8, 10, ... 
       cachesize, true, 0.7, false, 'parts_1'); 
    model = train(cls, model, pos, neg, 0, 0, 1, 5, ... 
       cachesize, true, 0.7, true, 'parts_2'); 
    save([cachedir cls '_parts'], 'model'); 
end 

save([cachedir cls '_final'], 'model'); 

我强调的，在哪里发生行错误的代码行48

我敢肯定，该系统是在阅读正确和负面的图像训练正确。我不知道这个错误发生在哪里，因为matlab没有精确地指出哪个索引超出了矩阵的尺寸。

我试图尽可能地整理代码，指导我如果我在某处做错了。

任何建议，我应该开始看？

好吧，我试着用display来检查pascal_train中使用的变量; （i）; disp（size（models））; disp（size（spos））; disp（length（neg））; disp（maxneg）;

所以返回的结果是;

1 

1  1 

1  1 

10 

Answer 1

我没有回答你的问题，但这里是一个建议，可以帮助您调试自己这个问题。

在Matlab菜单中，进入调试 - >停止，如果错误/警告...并选择“总是停止如果错误（dbstop如果错误）”。现在再次运行你的脚本，这次当你遇到错误时，matlab将停在发生错误的那一行，就像你在那里放置一个断点一样。此时，您可以使用整个工作空间，并且可以检查所有变量和矩阵大小，以查看哪个变量会给出您所看到的错误。

Answer 2

只需更换：

models{i} = train(cls, models{i}, spos{i}, neg(1:maxneg), 

为

models{i} = train(cls, models{i}, spos{i}, neg(1:min(length(neg),maxneg)), 

还有其他地方几个类似的句子在这个脚本，你应该修改它们。

的原因是，你的训练样本集很小，所以你列出“负”短比maxneg(200)