roidb = rpn_train.get_training_roidb(imdb) output_dir = rpn_config.get_output_dir(imdb, None) print 'Output will be saved to `{:s}`'.format(output_dir) ### mix anothor dataset if args.mix_imdb_name != None: imdb_mix = get_imdb(args.mix_imdb_name) print 'Loaded dataset `{:s}` for training'.format(imdb_mix.name) roidb_mix = rpn_train.get_training_roidb(imdb_mix) roidb.extend(roidb_mix) ### stage1_model = rpn_train.train_net( train_cfg.stage1.solver, roidb, anchordb, output_dir, final_name=imdb.name, pretrained_model=train_cfg.common.pretrained_model, max_iters=train_cfg.stage1.max_iters) print 'Stage-1: training rpn finished!' print 're-store stage-1 final model...' dest_path = train_cfg.stage1.model_path.format(imdb.name) os.system('cp {:s} {:s}'.format(stage1_model, dest_path)) print 'done!' print 'stage-1 done!' # =============== stage-2 training fast-rcnn with proposals generated by rpn if current_stage <= 2: print 'start stage-2...' imdb = get_imdb(args.imdb_name)
roidb = rpn_train.get_training_roidb(imdb) output_dir = rpn_config.get_output_dir(imdb, None) print 'Output will be saved to `{:s}`'.format(output_dir) ### mix anothor dataset if args.mix_imdb_name != None: imdb_mix = get_imdb(args.mix_imdb_name) roidb_mix = rpn_train.get_training_roidb(imdb_mix) roidb.extend(roidb_mix) ### rpn_model = rpn_train.train_net(args.solver, roidb, anchordb, output_dir, final_name=imdb.name, pretrained_model=args.pretrained_model, max_iters=args.max_iters) print 'training rpn finished!' print 're-store rpn final model...' # dest_path = train_cfg.stage1.model_path.format(imdb.name) # os.system('cp {:s} {:s}'.format(rpn_model, dest_path)) print 'done!' # =============== stage-2 testing final_test_imdbname = args.test_imdb if not final_test_imdbname: print 'have no image set to test!\nexit!' sys.exit()
imdb = get_imdb(args.imdb_name) print 'Loaded dataset `{:s}` for training'.format(imdb.name) roidb = rpn_train.get_training_roidb(imdb) output_dir = rpn_config.get_output_dir(imdb, None) print 'Output will be saved to `{:s}`'.format(output_dir) ### mix anothor dataset if args.mix_imdb_name != None: imdb_mix = get_imdb(args.mix_imdb_name) roidb_mix = rpn_train.get_training_roidb(imdb_mix) roidb.extend(roidb_mix) ### rpn_model = rpn_train.train_net(args.solver, roidb, anchordb, output_dir, final_name=imdb.name, pretrained_model=args.pretrained_model, max_iters=args.max_iters) print 'training rpn finished!' print 're-store rpn final model...' # dest_path = train_cfg.stage1.model_path.format(imdb.name) # os.system('cp {:s} {:s}'.format(rpn_model, dest_path)) print 'done!' # =============== stage-2 testing final_test_imdbname = args.test_imdb if not final_test_imdbname: print 'have no image set to test!\nexit!' sys.exit() print 'start test...'
print 'Loaded dataset `{:s}` for training'.format(imdb.name) roidb = rpn_train.get_training_roidb(imdb) output_dir = rpn_config.get_output_dir(imdb, None) print 'Output will be saved to `{:s}`'.format(output_dir) ### mix anothor dataset if args.mix_imdb_name != None: imdb_mix = get_imdb(args.mix_imdb_name) print 'Loaded dataset `{:s}` for training'.format(imdb_mix.name) roidb_mix = rpn_train.get_training_roidb(imdb_mix) roidb.extend(roidb_mix) ### stage1_model = rpn_train.train_net(train_cfg.stage1.solver, roidb, anchordb, output_dir, final_name=imdb.name, pretrained_model=train_cfg.common.pretrained_model, max_iters=train_cfg.stage1.max_iters) print 'Stage-1: training rpn finished!' print 're-store stage-1 final model...' dest_path = train_cfg.stage1.model_path.format(imdb.name) os.system('cp {:s} {:s}'.format(stage1_model, dest_path)) print 'done!' print 'stage-1 done!' # =============== stage-2 training fast-rcnn with proposals generated by rpn if current_stage <= 2: print 'start stage-2...' imdb = get_imdb(args.imdb_name) print 'Loaded dataset `{:s}` for training'.format(imdb.name)