R-CNN笔记2：rcnn_train.m文件

rcnn_train.m

function [rcnn_model, rcnn_k_fold_model] = ...    rcnn_train(imdb, varargin)% [rcnn_model, rcnn_k_fold_model] = rcnn_train(imdb, varargin)%   Trains an R-CNN detector for all classes in the imdb.%   %   Keys that can be passed in:%%   svm_C             SVM regularization parameter%   bias_mult         Bias feature value (for liblinear)%   pos_loss_weight   Cost factor on hinge loss for positives%   layer             Feature layer to use (either 5, 6 or 7)%   k_folds           Train on folds of the imdb%   checkpoint        Save the rcnn_model every checkpoint images%   crop_mode         Crop mode (either 'warp' or 'square')%   crop_padding      Amount of padding in crop%   net_file          Path to the Caffe CNN to use%   cache_name        Path to the precomputed feature cache% AUTORIGHTS% ---------------------------------------------------------% Copyright (c) 2014, Ross Girshick% % This file is part of the R-CNN code and is available % under the terms of the Simplified BSD License provided in % LICENSE. Please retain this notice and LICENSE if you use % this file (or any portion of it) in your project.% ---------------------------------------------------------% TODO:%  - allow training just a subset of classesip = inputParser;ip.addRequired('imdb', @isstruct);ip.addParamValue('svm_C',           10^-3,  @isscalar);ip.addParamValue('bias_mult',       10,     @isscalar);ip.addParamValue('pos_loss_weight', 2,      @isscalar);ip.addParamValue('layer',           7,      @isscalar);ip.addParamValue('k_folds',         2,      @isscalar);ip.addParamValue('checkpoint',      0,      @isscalar);ip.addParamValue('crop_mode',       'warp', @isstr);ip.addParamValue('crop_padding',    16,     @isscalar);ip.addParamValue('net_file', ...    './data/caffe_nets/finetune_voc_2007_trainval_iter_70k', ...    @isstr);ip.addParamValue('cache_name', ...    'v1_finetune_voc_2007_trainval_iter_70000', @isstr);ip.parse(imdb, varargin{:});opts = ip.Results;opts.net_def_file = './model-defs/rcnn_batch_256_output_fc7.prototxt';conf = rcnn_config('sub_dir', imdb.name);% Record a log of the training and test proceduretimestamp = datestr(datevec(now()), 'dd.mmm.yyyy:HH.MM.SS');diary_file = [conf.cache_dir 'rcnn_train_' timestamp '.txt'];diary(diary_file);fprintf('Logging output in %s\n', diary_file);fprintf('\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n');fprintf('Training options:\n');disp(opts);fprintf('~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n');% ------------------------------------------------------------------------% Create a new rcnn modelrcnn_model = rcnn_create_model(opts.net_def_file, opts.net_file, opts.cache_name);rcnn_model = rcnn_load_model(rcnn_model, conf.use_gpu);rcnn_model.detectors.crop_mode = opts.crop_mode;rcnn_model.detectors.crop_padding = opts.crop_padding;rcnn_model.classes = imdb.classes;% ------------------------------------------------------------------------% ------------------------------------------------------------------------% Get the average norm of the features% 获得特征的平均规范值opts.feat_norm_mean = rcnn_feature_stats(imdb, opts.layer, rcnn_model);fprintf('average norm = %.3f\n', opts.feat_norm_mean);rcnn_model.training_opts = opts;% ------------------------------------------------------------------------% ------------------------------------------------------------------------% Get all positive examples% We cache only the pool5 features and convert them on-the-fly to% fc6 or fc7 as required% 获得所有正例样本% 我们把pool5层的特征保存，并且把它们转换成fc6和fc7save_file = sprintf('./feat_cache/%s/%s/gt_pos_layer_5_cache.mat', ...    rcnn_model.cache_name, imdb.name);try  load(save_file);  fprintf('Loaded saved positives from ground truth boxes\n');catch  %获得正例样本的pool5层特征  [X_pos, keys_pos] = get_positive_pool5_features(imdb, opts);  save(save_file, 'X_pos', 'keys_pos', '-v7.3');end% Init training cachescaches = {};for i = imdb.class_ids  fprintf('%14s has %6d positive instances\n', ...      imdb.classes{i}, size(X_pos{i},1));  % 把pool5层特征转换成全连接层特征  X_pos{i} = rcnn_pool5_to_fcX(X_pos{i}, opts.layer, rcnn_model);  X_pos{i} = rcnn_scale_features(X_pos{i}, opts.feat_norm_mean);  caches{i} = init_cache(X_pos{i}, keys_pos{i});end% ------------------------------------------------------------------------% ------------------------------------------------------------------------% Train with hard negative miningfirst_time = true;% one pass over the data is enoughmax_hard_epochs = 1;for hard_epoch = 1:max_hard_epochs  for i = 1:length(imdb.image_ids)    fprintf('%s: hard neg epoch: %d/%d image: %d/%d\n', ...            procid(), hard_epoch, max_hard_epochs, i, length(imdb.image_ids));    % Get hard negatives for all classes at once (avoids loading feature cache    % more than once)    % 从所有的类中一次的获得难反例（避免超过一次的加载特征）    % 这里X的难反例样本的特征，keys是一个索引 keys[a b]，a is the clasee and b is index    [X, keys] = sample_negative_features(first_time, rcnn_model, caches, ...        imdb, i);    % Add sampled negatives to each classes training cache, removing    % duplicates    for j = imdb.class_ids      if ~isempty(keys{j})        if ~isempty(caches{j}.keys_neg)          [~, ~, dups] = intersect(caches{j}.keys_neg, keys{j}, 'rows');          assert(isempty(dups));        end        % 这里将难样本X合并到caches变量中，X[1*20 cell],caches[1*20 cell]        caches{j}.X_neg = cat(1, caches{j}.X_neg, X{j});        caches{j}.keys_neg = cat(1, caches{j}.keys_neg, keys{j});        caches{j}.num_added = caches{j}.num_added + size(keys{j},1);      end      % Update model if      %  - first time seeing negatives      %  - more than retrain_limit negatives have been added      %  - its the final image of the final epoch      % 更新模型 如果      % 第一次看到反例样本      % 超过retrain_limit数量的反例样本已经被添加      % 这是最后的时代的最终图像      is_last_time = (hard_epoch == max_hard_epochs && i == length(imdb.image_ids));      hit_retrain_limit = (caches{j}.num_added > caches{j}.retrain_limit);      if (first_time || hit_retrain_limit || is_last_time) && ...          ~isempty(caches{j}.X_neg)        fprintf('>>> Updating %s detector <<<\n', imdb.classes{j});        fprintf('Cache holds %d pos examples %d neg examples\n', ...                size(caches{j}.X_pos,1), size(caches{j}.X_neg,1));        % 跟新模型        [new_w, new_b] = update_model(caches{j}, opts);        rcnn_model.detectors.W(:, j) = new_w;        rcnn_model.detectors.B(j) = new_b;        caches{j}.num_added = 0;        z_pos = caches{j}.X_pos * new_w + new_b;        z_neg = caches{j}.X_neg * new_w + new_b;        caches{j}.pos_loss(end+1) = opts.svm_C * opts.pos_loss_weight * ...                                    sum(max(0, 1 - z_pos));        caches{j}.neg_loss(end+1) = opts.svm_C * sum(max(0, 1 + z_neg));        caches{j}.reg_loss(end+1) = 0.5 * new_w' * new_w + ...                                    0.5 * (new_b / opts.bias_mult)^2;        caches{j}.tot_loss(end+1) = caches{j}.pos_loss(end) + ...                                    caches{j}.neg_loss(end) + ...                                    caches{j}.reg_loss(end);        for t = 1:length(caches{j}.tot_loss)          fprintf('    %2d: obj val: %.3f = %.3f (pos) + %.3f (neg) + %.3f (reg)\n', ...                  t, caches{j}.tot_loss(t), caches{j}.pos_loss(t), ...                  caches{j}.neg_loss(t), caches{j}.reg_loss(t));        end        % store negative support vectors for visualizing later        % 为了之后的可视化存储反例支撑向量        SVs_neg = find(z_neg > -1 - eps);        rcnn_model.SVs.keys_neg{j} = caches{j}.keys_neg(SVs_neg, :);        rcnn_model.SVs.scores_neg{j} = z_neg(SVs_neg);        % evict easy examples        % 逐出容易的样本        easy = find(z_neg < caches{j}.evict_thresh);%where caches{j}.evict_thresh = -1.2        caches{j}.X_neg(easy,:) = [];        caches{j}.keys_neg(easy,:) = [];        fprintf('  Pruning easy negatives\n');        fprintf('  Cache holds %d pos examples %d neg examples\n', ...                size(caches{j}.X_pos,1), size(caches{j}.X_neg,1));        fprintf('  %d pos support vectors\n', numel(find(z_pos <  1 + eps)));        fprintf('  %d neg support vectors\n', numel(find(z_neg > -1 - eps)));      end    end    first_time = false;    if opts.checkpoint > 0 && mod(i, opts.checkpoint) == 0      save([conf.cache_dir 'rcnn_model'], 'rcnn_model');    end  endend% save the final rcnn_modelsave([conf.cache_dir 'rcnn_model'], 'rcnn_model');% ------------------------------------------------------------------------% ------------------------------------------------------------------------if opts.k_folds > 0  rcnn_k_fold_model = rcnn_model;  [W, B, folds] = update_model_k_fold(rcnn_model, caches, imdb);  rcnn_k_fold_model.folds = folds;  for f = 1:length(folds)    rcnn_k_fold_model.detectors(f).W = W{f};    rcnn_k_fold_model.detectors(f).B = B{f};  end  save([conf.cache_dir 'rcnn_k_fold_model'], 'rcnn_k_fold_model');else  rcnn_k_fold_model = [];end% ------------------------------------------------------------------------% ------------------------------------------------------------------------function [X_neg, keys] = sample_negative_features(first_time, rcnn_model, ...                                                  caches, imdb, ind)% ------------------------------------------------------------------------opts = rcnn_model.training_opts;d = rcnn_load_cached_pool5_features(opts.cache_name, ...    imdb.name, imdb.image_ids{ind});% d [1*1 struct]% d.gt [n*1 logical]% d.overlap [n*20 single]% d.boxes [n*4 double]% d.feat [n*9216 single]% d.class [n*1 uint8]class_ids = imdb.class_ids;if isempty(d.feat)  X_neg = cell(max(class_ids), 1);  keys = cell(max(class_ids), 1);  return;endd.feat = rcnn_pool5_to_fcX(d.feat, opts.layer, rcnn_model);d.feat = rcnn_scale_features(d.feat, opts.feat_norm_mean);neg_ovr_thresh = 0.3;if first_time  for cls_id = class_ids    % 找出    I = find(d.overlap(:, cls_id) < neg_ovr_thresh);% where neg_ovr_thresh = 0.3    X_neg{cls_id} = d.feat(I,:);    keys{cls_id} = [ind*ones(length(I),1) I];  endelse  zs = bsxfun(@plus, d.feat*rcnn_model.detectors.W, rcnn_model.detectors.B);  for cls_id = class_ids    z = zs(:, cls_id);    % 找到 得分大于难样本的阈值 并且 overlap 小于 反例overlap阈值的样本    I = find((z > caches{cls_id}.hard_thresh) & ...             (d.overlap(:, cls_id) < neg_ovr_thresh));    % Avoid adding duplicate features    % 避免增加重复的特征    keys_ = [ind*ones(length(I),1) I];    if ~isempty(caches{cls_id}.keys_neg) && ~isempty(keys_)      % 寻找在caches{cls_id}.keys_neg和keys_共同出现的元素      [~, ~, dups] = intersect(caches{cls_id}.keys_neg, keys_, 'rows');      % C = setdiff(A, B) returns the data in A that is not in B      % 也就是寻找在1:size(keys_,1)出现的元素而在dups中没有出现      keep = setdiff(1:size(keys_,1), dups);      % 这样I就完成了去重      I = I(keep);    end    % Unique hard negatives    X_neg{cls_id} = d.feat(I,:);    keys{cls_id} = [ind*ones(length(I),1) I];  endend% ------------------------------------------------------------------------function [w, b] = update_model(cache, opts, pos_inds, neg_inds)% ------------------------------------------------------------------------solver = 'liblinear';liblinear_type = 3;  % l2 regularized l1 hinge loss%liblinear_type = 5; % l1 regularized l2 hinge lossif ~exist('pos_inds', 'var') || isempty(pos_inds)  num_pos = size(cache.X_pos, 1); %正样本的数量  pos_inds = 1:num_pos;else  num_pos = length(pos_inds); %正样本的数量  fprintf('[subset mode] using %d out of %d total positives\n', ...      num_pos, size(cache.X_pos,1));endif ~exist('neg_inds', 'var') || isempty(neg_inds)  num_neg = size(cache.X_neg, 1); %反例样本的数量  neg_inds = 1:num_neg;else  num_neg = length(neg_inds); %反例样本的数量  fprintf('[subset mode] using %d out of %d total negatives\n', ...      num_neg, size(cache.X_neg,1));endswitch solver  case 'liblinear'    ll_opts = sprintf('-w1 %.5f -c %.5f -s %d -B %.5f', ...                      opts.pos_loss_weight, opts.svm_C, ...                      liblinear_type, opts.bias_mult);    fprintf('liblinear opts: %s\n', ll_opts);    X = sparse(size(cache.X_pos,2), num_pos+num_neg); %创造输入的稀疏矩阵X    X(:,1:num_pos) = cache.X_pos(pos_inds,:)'; %将正例样本导入X    X(:,num_pos+1:end) = cache.X_neg(neg_inds,:)'; %将反例样本导入X    y = cat(1, ones(num_pos,1), -ones(num_neg,1)); %创造标签变量y    llm = liblinear_train(y, X, ll_opts, 'col');  %更新模型    w = single(llm.w(1:end-1)'); %这里为什么会多一个w呢？    b = single(llm.w(end)*opts.bias_mult); %b的计算方法？  otherwise    error('unknown solver: %s', solver);end% ------------------------------------------------------------------------function [W, B, folds] = update_model_k_fold(rcnn_model, caches, imdb)% ------------------------------------------------------------------------opts = rcnn_model.training_opts;num_images = length(imdb.image_ids);folds = create_folds(num_images, opts.k_folds);W = cell(opts.k_folds, 1);B = cell(opts.k_folds, 1);fprintf('Training k-fold models\n');for i = imdb.class_ids  fprintf('\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n');  fprintf('Training folds for class %s\n', imdb.classes{i});  fprintf('~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n');  for f = 1:length(folds)    fprintf('Holding out fold %d\n', f);    [pos_inds, neg_inds] = get_cache_inds_from_fold(caches{i}, folds{f});    [new_w, new_b] = update_model(caches{i}, opts, ...        pos_inds, neg_inds);    W{f}(:,i) = new_w;    B{f}(i) = new_b;  endend% ------------------------------------------------------------------------function [pos_inds, neg_inds] = get_cache_inds_from_fold(cache, fold)% ------------------------------------------------------------------------pos_inds = find(ismember(cache.keys_pos(:,1), fold) == false);neg_inds = find(ismember(cache.keys_neg(:,1), fold) == false);% ------------------------------------------------------------------------function [X_pos, keys] = get_positive_pool5_features(imdb, opts)% ------------------------------------------------------------------------X_pos = cell(max(imdb.class_ids), 1);keys = cell(max(imdb.class_ids), 1);for i = 1:length(imdb.image_ids)  tic_toc_print('%s: pos features %d/%d\n', ...                procid(), i, length(imdb.image_ids));  d = rcnn_load_cached_pool5_features(opts.cache_name, ...      imdb.name, imdb.image_ids{i});  for j = imdb.class_ids    if isempty(X_pos{j})      X_pos{j} = single([]);      keys{j} = [];    end    sel = find(d.class == j);    if ~isempty(sel)      X_pos{j} = cat(1, X_pos{j}, d.feat(sel,:));      keys{j} = cat(1, keys{j}, [i*ones(length(sel),1) sel]);    end  endend% ------------------------------------------------------------------------function cache = init_cache(X_pos, keys_pos)% ------------------------------------------------------------------------cache.X_pos = X_pos;cache.X_neg = single([]);cache.keys_neg = [];cache.keys_pos = keys_pos;cache.num_added = 0;cache.retrain_limit = 2000;cache.evict_thresh = -1.2;cache.hard_thresh = -1.0001;cache.pos_loss = [];cache.neg_loss = [];cache.reg_loss = [];cache.tot_loss = [];