rcnn.tar.gz_RCNN_caffe_object detection

function [rcnn_model, rcnn_k_fold_model] = ... my_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 classes ip = 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'; opts.net_def_file = './model-defs/pascal_finetune_deploy.prototxt'; conf = rcnn_config('sub_dir', imdb.name); conf done_fname = fullfile(conf.cache_dir, 'done_training'); if exist(done_fname, 'file') fprintf('done training, skipping training phase\n'); model_data = load([conf.cache_dir 'rcnn_model']); rcnn_model = model_data.rcnn_model; rcnn_k_fold_model = []; if opts.k_folds > 0 model_data = load([conf.cache_dir 'rcnn_k_fold_model']); rcnn_k_fold_model = model_data.rcnn_k_fold_model; end return; end % Record a log of the training and test procedure timestamp = 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 model rcnn_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 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 save_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 [X_pos, keys_pos] = get_positive_pool5_features(imdb, opts); save(save_file, 'X_pos', 'keys_pos', '-v7.3'); end % Init training caches caches = {}; for i = imdb.class_ids fprintf('%14s has %6d positive instances\n', ... imdb.classes{i}, size(X_pos{i},1)); 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 mining first_time = true; % one pass over the data is enough max_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) [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 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 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)); [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 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 easy = find(z_neg < caches{j}.evict_thresh); caches{j}.X_neg(easy,:) = []; caches{j}.keys_neg(easy,:) = []; fprintf(' Pruning easy negatives\n');