if args.cpu_mode: caffe.set_mode_cpu() else: caffe.set_mode_gpu() caffe.set_device(args.gpu_id) if args.curr_stage < 0: print 'The number of stage must more than or equal to 0!' sys.exit() current_stage = args.curr_stage # ============== stage-pre print 'start stage-pre...' # calculate ouput size map and prepare anchors output_w, output_h = rpn_train.proposal_calc_output_size( args.imdb_name, train_cfg.stage1.test_net) anchors, anchors_file = rpn_train.proposal_generate_anchors(args.imdb_name) anchordb = { 'anchors': anchors, 'output_width_map': output_w, 'output_height_map': output_h } print 'stage-pre done!' # =============== stage-1 training rpn with imagenet parameters if current_stage <= 1: print 'start stage-1...' imdb = get_imdb(args.imdb_name) print 'Loaded dataset `{:s}` for training'.format(imdb.name)
np.random.seed(rpn_config.cfg.RNG_SEED) caffe.set_random_seed(rpn_config.cfg.RNG_SEED) if args.cpu_mode: caffe.set_mode_cpu() else: caffe.set_mode_gpu() caffe.set_device(args.gpu_id) # ============== stage-pre print 'start stage-pre...' if not os.path.exists(args.test_def): raise IOError(('{:s} not found!').format(args.test_def)) # calculate ouput size map and prepare anchors output_w, output_h = rpn_train.proposal_calc_output_size( args.imdb_name, args.test_def) anchors, anchors_file = rpn_train.proposal_generate_anchors(args.imdb_name) anchordb = { 'anchors': anchors, 'output_width_map': output_w, 'output_height_map': output_h } print 'stage-pre done!' # =============== stage-1 training rpn with imagenet parameters print 'start train rpn model...' imdb = get_imdb(args.imdb_name) print 'Loaded dataset `{:s}` for training'.format(imdb.name) roidb = rpn_train.get_training_roidb(imdb)
np.random.seed(rpn_config.cfg.RNG_SEED) caffe.set_random_seed(rpn_config.cfg.RNG_SEED) if args.cpu_mode: caffe.set_mode_cpu() else: caffe.set_mode_gpu() caffe.set_device(args.gpu_id) # ============== stage-pre print 'start stage-pre...' if not os.path.exists(args.test_def): raise IOError(('{:s} not found!').format(args.test_def)) # calculate ouput size map and prepare anchors output_w, output_h = rpn_train.proposal_calc_output_size(args.imdb_name, args.test_def) anchors, anchors_file = rpn_train.proposal_generate_anchors(args.imdb_name) anchordb = {'anchors': anchors, 'output_width_map': output_w, 'output_height_map': output_h } print 'stage-pre done!' # =============== stage-1 training rpn with imagenet parameters print 'start train rpn model...' 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)
if args.cpu_mode: caffe.set_mode_cpu() else: caffe.set_mode_gpu() caffe.set_device(args.gpu_id) if args.curr_stage < 0: print 'The number of stage must more than or equal to 0!' sys.exit() current_stage = args.curr_stage # ============== stage-pre print 'start stage-pre...' # calculate ouput size map and prepare anchors output_w, output_h = rpn_train.proposal_calc_output_size(args.imdb_name, train_cfg.stage1.test_net) anchors, anchors_file = rpn_train.proposal_generate_anchors(args.imdb_name) anchordb = {'anchors': anchors, 'output_width_map': output_w, 'output_height_map': output_h } print 'stage-pre done!' # =============== stage-1 training rpn with imagenet parameters if current_stage <= 1: print 'start stage-1...' imdb = get_imdb(args.imdb_name) print 'Loaded dataset `{:s}` for training'.format(imdb.name)