You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: script_faster_rcnn_VOC2012_VGG16.m
    path of file: ./repos/faster_rcnn/experiments
    
    the code of the file until where you have to start completion: function script_faster_rcnn_VOC2012_VGG16()
% script_faster_rcnn_VOC2012_VGG16()
% Faster rcnn training and testing with VGG16 model
% --------------------------------------------------------
% Faster R-CNN
% Copyright (c) 2015, Shaoqing Ren
% Licensed under The MIT License [see LICENSE for details]
% --------------------------------------------------------

clc;
clear mex;
clear is_valid_handle; % to clear init_key
run(fullfile(fileparts(fileparts(mfilename('fullpath'))), 'startup'));
%% -------------------- CONFIG --------------------
opts.caffe_version          = 'caffe_faster_rcnn';
opts.gpu_id                 = auto_select_gpu;
active_caffe_mex(opts.gpu_id, opts.caffe_version);

% do validation, or not 
opts.do_val                 = false; 
% model
model                       = Model.VGG16_for_Faster_RCNN_VOC2012;
% cache base
cache_base_proposal         = 'faster_rcnn_VOC2012_vgg_16layers';
cache_base_fast_rcnn        = '';
% train/test data
dataset                     = [];
use_flipped                 = true;
dataset                     = Dataset.voc2012_trainval(dataset, 'train', use_flipped);
dataset                     = Dataset.voc2012_test(dataset, 'test', false);

%% -------------------- TRAIN --------------------
% conf
conf_proposal               = proposal_config('image_means', model.mean_image, 'feat_stride', model.feat_stride);
conf_fast_rcnn              = fast_rcnn_config('image_means', model.mean_image);
% set cache folder for each stage
model                       = Faster_RCNN_Train.set_cache_folder(cache_base_proposal, cache_base_fast_rcnn, model);
% generate anchors and pre-calculate output size of rpn network 
[conf_proposal.anchors, conf_proposal.output_width_map, conf_proposal.output_height_map] ...
                            = proposal_prepare_anchors(conf_proposal, model.stage1_rpn.cache_name, model.stage1_rpn.test_net_def_file);

%%  stage one proposal
fprintf('\n***************\nstage one proposal \n***************\n');
% train
model.stage1_rpn            = Faster_RCNN_Train.do_proposal_train(conf_proposal, dataset, model.stage1_rpn, opts.do_val);
% test
dataset.roidb_train         = cellfun(@(x, y) Faster_RCNN_Train.do_proposal_test(conf_proposal, model.stage1_rpn, x, y), dataset.imdb_train, dataset.roidb_train, 'UniformOutput', false);
dataset.roidb_test       	= Faster_RCNN_Train.do_proposal_test(conf_proposal, model.stage1_rpn, dataset.imdb_test, dataset.roidb_test);

%%  stage one fast rcnn
fprintf('\n***************\nstage one fast rcnn\n***************\n');
% train
model.stage1_fast_rcnn      = Faster_RCNN_Train.do_fast_rcnn_train(conf_fast_rcnn, dataset, model.stage1_fast_rcnn, opts.do_val);
% test

    
function script_faster_rcnn_VOC2012_VGG16()
% script_faster_rcnn_VOC2012_VGG16()
% Faster rcnn training and testing with VGG16 model
% --------------------------------------------------------
% Faster R-CNN
% Copyright (c) 2015, Shaoqing Ren
% Licensed under The MIT License [see LICENSE for details]
% --------------------------------------------------------

clc;
clear mex;
clear is_valid_handle; % to clear init_key
run(fullfile(fileparts(fileparts(mfilename('fullpath'))), 'startup'));
%% -------------------- CONFIG --------------------
opts.caffe_version          = 'caffe_faster_rcnn';
opts.gpu_id                 = auto_select_gpu;
active_caffe_mex(opts.gpu_id, opts.caffe_version);

% do validation, or not 
opts.do_val                 = false; 
% model
model                       = Model.VGG16_for_Faster_RCNN_VOC2012;
% cache base
cache_base_proposal         = 'faster_rcnn_VOC2012_vgg_16layers';
cache_base_fast_rcnn        = '';
% train/test data
dataset                     = [];
use_flipped                 = true;
dataset                     = Dataset.voc2012_trainval(dataset, 'train', use_flipped);
dataset                     = Dataset.voc2012_test(dataset, 'test', false);

%% -------------------- TRAIN --------------------
% conf
conf_proposal               = proposal_config('image_means', model.mean_image, 'feat_stride', model.feat_stride);
conf_fast_rcnn              = fast_rcnn_config('image_means', model.mean_image);
% set cache folder for each stage
model                       = Faster_RCNN_Train.set_cache_folder(cache_base_proposal, cache_base_fast_rcnn, model);
% generate anchors and pre-calculate output size of rpn network 
[conf_proposal.anchors, conf_proposal.output_width_map, conf_proposal.output_height_map] ...
                            = proposal_prepare_anchors(conf_proposal, model.stage1_rpn.cache_name, model.stage1_rpn.test_net_def_file);

%%  stage one proposal
fprintf('\n***************\nstage one proposal \n***************\n');
% train
model.stage1_rpn            = Faster_RCNN_Train.do_proposal_train(conf_proposal, dataset, model.stage1_rpn, opts.do_val);
% test
dataset.roidb_train         = cellfun(@(x, y) Faster_RCNN_Train.do_proposal_test(conf_proposal, model.stage1_rpn, x, y), dataset.imdb_train, dataset.roidb_train, 'UniformOutput', false);
dataset.roidb_test       	= Faster_RCNN_Train.do_proposal_test(conf_proposal, model.stage1_rpn, dataset.imdb_test, dataset.roidb_test);

%%  stage one fast rcnn
fprintf('\n***************\nstage one fast rcnn\n***************\n');
% train
model.stage1_fast_rcnn      = Faster_RCNN_Train.do_fast_rcnn_train(conf_fast_rcnn, dataset, model.stage1_fast_rcnn, opts.do_val);
% test
% opts.mAP                    = Faster_RCNN_Train.do_fast_rcnn_test(conf_fast_rcnn, model.stage1_fast_rcnn, dataset.imdb_test, dataset.roidb_test);

%%  stage two proposal
% net proposal
fprintf('\n***************\nstage two proposal\n***************\n');
% train
model.stage2_rpn.init_net_file = model.stage1_fast_rcnn.output_model_file;
model.stage2_rpn            = Faster_RCNN_Train.do_proposal_train(conf_proposal, dataset, model.stage2_rpn, opts.do_val);
% test
dataset.roidb_train        	= cellfun(@(x, y) Faster_RCNN_Train.do_proposal_test(conf_proposal, model.stage2_rpn, x, y), dataset.imdb_train, dataset.roidb_train, 'UniformOutput', false);
dataset.roidb_test         	= Faster_RCNN_Train.do_proposal_test(conf_proposal, model.stage2_rpn, dataset.imdb_test, dataset.roidb_test);

%%  stage two fast rcnn
fprintf('\n***************\nstage two fast rcnn\n***************\n');
% train
model.stage2_fast_rcnn.init_net_file = model.stage1_fast_rcnn.output_model_file;
model.stage2_fast_rcnn      = Faster_RCNN_Train.do_fast_rcnn_train(conf_fast_rcnn, dataset, model.stage2_fast_rcnn, opts.do_val);

%% final test
fprintf('\n***************\nfinal test\n***************\n');
     
model.stage2_rpn.nms        = model.final_test.nms;
dataset.roidb_test       	= Faster_RCNN_Train.do_proposal_test(conf_proposal, model.stage2_rpn, dataset.imdb_test, dataset.roidb_test);
opts.final_mAP              = Faster_RCNN_Train.do_fast_rcnn_test(conf_fast_rcnn, model.stage2_fast_rcnn, dataset.imdb_test, dataset.roidb_test);

% save final models, for outside tester
Faster_RCNN_Train.gather_rpn_fast_rcnn_models(conf_proposal, conf_fast_rcnn, model, dataset);
end