import scipy.io from transforms3d.quaternions import quat2mat # start rendering imdb.data_queue = Queue(maxsize=100) meta_data = scipy.io.loadmat(roidb[0]['meta_data']) intrinsic_matrix = meta_data['intrinsic_matrix'].astype(np.float32, copy=True) if cfg.TRAIN.SYN_CLASS_INDEX >= 0: t = threading.Thread(target=render_one, args=(imdb.data_queue, intrinsic_matrix, imdb._extents_all, imdb._points_all)) else: t = threading.Thread(target=render, args=(imdb.data_queue, intrinsic_matrix, imdb._points_all)) t.start() else: imdb.data_queue = [] from networks.factory import get_network network = get_network(args.network_name) print 'Use network `{:s}` in training'.format(args.network_name) if cfg.TRAIN.SEGMENTATION: train_net(network, imdb, roidb, roidb_val, output_dir, pretrained_model=pretrained_model, pretrained_ckpt=args.pretrained_ckpt, iters_train=iters[0], iters_val=iters[1]) else: train_net_det(network, imdb, roidb, output_dir, pretrained_model=pretrained_model, pretrained_ckpt=args.pretrained_ckpt, max_iters=args.max_iters)
args=(imdb.data_queue, intrinsic_matrix, imdb._extents_all, imdb._points_all)) else: t = threading.Thread(target=render, args=(imdb.data_queue, intrinsic_matrix, imdb._points_all)) t.start() else: imdb.data_queue = [] from networks.factory import get_network network = get_network(args.network_name) print 'Use network `{:s}` in training'.format(args.network_name) if cfg.TRAIN.SEGMENTATION: train_net(network, imdb, roidb, output_dir, pretrained_model=pretrained_model, pretrained_ckpt=args.pretrained_ckpt, max_iters=args.max_iters) else: train_net_det(network, imdb, roidb, output_dir, pretrained_model=pretrained_model, pretrained_ckpt=args.pretrained_ckpt, max_iters=args.max_iters)
print('Called with args:') print(args) if args.cfg_file is not None: cfg_from_file(args.cfg_file) print('Using config:') pprint.pprint(cfg) if not args.randomize: # fix the random seeds (numpy and caffe) for reproducibility np.random.seed(cfg.RNG_SEED) imdb = get_imdb(args.imdb_name) print 'Loaded dataset `{:s}` for training'.format(imdb.name) roidb = get_training_roidb(imdb) output_dir = get_output_dir(imdb, None) print 'Output will be saved to `{:s}`'.format(output_dir) device_name = '/gpu:{:d}'.format(args.gpu_id) cfg.GPU_ID = args.gpu_id print device_name network = get_network(args.network_name, args.pretrained_model) print 'Use network `{:s}` in training'.format(args.network_name) train_net(network, imdb, roidb, output_dir, pretrained_model=args.pretrained_model, max_iters=args.max_iters)