if args.cfg_file is not None: cfg_from_file(args.cfg_file) print('Using config:') pprint.pprint(cfg) # while not os.path.exists(args.model) and args.wait: # print('Waiting for {} to exist...'.format(args.model)) # time.sleep(1000) weights_filename = os.path.splitext(os.path.basename(args.model))[0] imdb = get_imdb(args.imdb_name) imdb.competition_mode(args.comp_mode) device_name = '/gpu:{:d}'.format(args.gpu_id) print device_name network = get_network(args.network_name) print 'Use network `{:s}` in training'.format(args.network_name) cfg.GPU_ID = args.gpu_id # start a session saver = tf.train.Saver() sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) saver.restore(sess, args.model) print('Loading model weights from {:s}').format(args.model) test_net(sess, network, imdb, weights_filename)
if args.cfg_file is not None: cfg_from_file(args.cfg_file) print('Using config:') pprint.pprint(cfg) while not os.path.exists(args.model) and args.wait: print('Waiting for {} to exist...'.format(args.model)) time.sleep(10) weights_filename = os.path.splitext(os.path.basename(args.model))[0] imdb = get_imdb(args.imdb_name) imdb.competition_mode(args.comp_mode) device_name = '/gpu:{:d}'.format(args.gpu_id) print device_name network = get_network(args.network_name) print 'Use network `{:s}` in training'.format(args.network_name) cfg.GPU_ID = args.gpu_id # start a session saver = tf.train.Saver() sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) saver.restore(sess, args.model) print('Loading model weights from {:s}').format(args.model) test_net(sess, network, imdb, weights_filename, vis=args.vis)
dest='imdb_name', choices=['caltech_test', 'caltech_train'], default='caltech_test', type=str) parser.add_argument('--network', dest='network_name', choices=['VGGnet_test', 'MSnet_test'], default='VGGnet_test') parser.add_argument('--vis', action='store_true') if len(sys.argv) == 1: parser.print_help() args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() if args.cfg_file is not None: cfg_from_file(args.cfg_file) pprint.pprint(cfg) weights_filename = os.path.splitext(os.path.basename(args.model))[0] imdb = get_imdb(args.imdb_name) print 'Use device /gpu:0' network = get_network(args.network_name) print 'Use network `{:s}` in training'.format(args.network_name) saver = tf.train.Saver() sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) saver.restore(sess, args.model) print('Loading model weights from {:s}').format(args.model) test_net(sess, network, imdb, weights_filename, vis=args.vis, thresh=0.05)
while not os.path.exists(args.model) and args.wait: print(('Waiting for {} to exist...'.format(args.model))) time.sleep(1000) weights_filename = os.path.splitext(os.path.basename(args.model))[0] imdb = get_imdb(args.imdb_name) imdb.competition_mode(args.comp_mode) device_name = '/gpu:{:d}'.format(args.gpu_id) print(device_name) with tf.device(device_name): network = get_network(args.network_name) print(('Use network `{:s}` in training'.format(args.network_name))) cfg.GPU_ID = args.gpu_id # import os # os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu_id) # start a session saver = tf.train.Saver() c = tf.ConfigProto(allow_soft_placement=True) c.gpu_options.visible_device_list = str(args.gpu_id) sess = tf.Session(config=c) saver.restore(sess, tf.train.latest_checkpoint(args.model)) print((('Loading model weights from {:s}').format(args.model))) test_net(sess, network, imdb, weights_filename, thresh=0.7) # load_test_net(sess, network, imdb, weights_filename)
if args.cfg_file is not None: cfg_from_file(args.cfg_file) print('Using config:') pprint.pprint(cfg) while not os.path.exists(args.model + '.index') and args.wait: print('Waiting for {} to exist...'.format(args.model)) time.sleep(1000) weights_filename = os.path.splitext(os.path.basename(args.model))[0] imdb = get_imdb(args.imdb_name) imdb.competition_mode(args.comp_mode) device_name = '/gpu:{:d}'.format(args.gpu_id) print device_name network = get_network(args.network_name) print 'Use network `{:s}` in training'.format(args.network_name) cfg.GPU_ID = args.gpu_id # start a session saver = tf.train.Saver() sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) saver.restore(sess, args.model) print ('Loading model weights from {:s}').format(args.model) test_net(sess, network, imdb, weights_filename, output_dir=args.output_dir)
if args.cfg_file is not None: cfg_from_file(args.cfg_file) print('Using config:') pprint.pprint(cfg) while not os.path.exists(args.model) and args.wait: print('Waiting for {} to exist...'.format(args.model)) time.sleep(1000) weights_filename = os.path.splitext(os.path.basename(args.model))[0] imdb = get_imdb(args.imdb_name) imdb.competition_mode(args.comp_mode) device_name = '/gpu:{:d}'.format(args.gpu_id) print device_name network = get_network(args.network_name) print 'Use network `{:s}` in training'.format(args.network_name) cfg.GPU_ID = args.gpu_id # start a session saver = tf.train.Saver() sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) saver.restore(sess, args.model) print ('Loading model weights from {:s}').format(args.model) test_net(sess, network, imdb, weights_filename)