def run_default_validation_server(datadir='../dataset', prefix='val_'): ''' run a validation server with mostly default values. ''' try: datapath = glob.glob(os.path.join(datadir, 'camera', prefix) + "*") if len(datapath) == 0: print 'no files found to validate with' else: print 'validating with', len(datapath), 'files' gen = datagen(datapath, time_len=1, batch_size=256, ignore_goods=False, show_time=False) start_server(gen, port=5556, hwm=20) except KeyboardInterrupt: pass
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-02-02--10-16-58.h5' % (str_data), 'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-02-08--14-56-28.h5' % (str_data), 'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-02-11--21-32-47.h5' % (str_data), 'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-03-29--10-50-20.h5' % (str_data), 'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-04-21--14-48-08.h5' % (str_data), 'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-05-12--22-20-00.h5' % (str_data), ] # 2 for validation validation_path = [ 'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-06-02--21-39-29.h5' % (str_data), 'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-06-08--11-46-01.h5' % (str_data) ] if args.validation: datapath = validation_path else: datapath = train_path gen = datagen(datapath, time_len=args.time, batch_size=args.batch, ignore_goods=args.nogood) start_server(gen, port=args.port, hwm=args.buffer)
help='Serve validation dataset instead.') parser.add_argument('--small', dest='small', action='store_true', default=False) args, more = parser.parse_known_args() if config.UseFeat: str_data = "feat" else: str_data = "cam" if args.small: # 10% of dataset if args.validation: filenames = os.path.join(config.h5path, 'val_small.txt') else: filenames = os.path.join(config.h5path, 'train_small.txt') else: if args.validation: filenames = os.path.join(config.h5path, 'val.txt') else: filenames = os.path.join(config.h5path, 'train.txt') with open(filenames, 'r') as f: file_paths = [ '%s%s/%s.h5' % (config.h5path, str_data, x.strip()) for x in f.readlines() ] gen = datagen(file_paths, time_len=config.timelen, batch_size=config.batch_size, ignore_goods=args.nogood) start_server(gen, port=args.port, hwm=args.buffer)
# 9 for training train_path = [ './extract-bagfiles/retas_1.h5', './extract-bagfiles/retas_2.h5', './extract-bagfiles/curvas_suaves_1.h5', './extract-bagfiles/curvas_suaves_2.h5', './extract-bagfiles/curvas_em_T_1.h5', ] # 2 for validation validation_path = [ './extract-bagfiles/retas_1.h5', './extract-bagfiles/retas_2.h5', './extract-bagfiles/curvas_suaves_1.h5', './extract-bagfiles/curvas_suaves_2.h5', './extract-bagfiles/curvas_em_T_1.h5', ] if args.validation: datapath = validation_path else: datapath = train_path gen = datagen(datapath, time_len=args.time, batch_size=args.batch, ignore_goods=args.nogood, data_set=args.DataTrainAndTest) start_server(gen, port=args.port, hwm=args.buffer)
# 2 for validation validation_path = [ './camera/2016-06-02--21-39-29.h5', './camera/2016-06-08--11-46-01.h5' ] # 2 for test test_path = [ './camera/2016-01-30--13-46-00.h5', './camera/2016-05-12--22-20-00.h5', ] datapath = test_path time_length = 30 pack_size = 256 # 每个tfrecords文件中有256个视频序列 gen = datagen(datapath, time_len=time_length, batch_size=pack_size, ignore_goods=False) data = next(gen) dataset_num = data[3] count = 0 while count * 30 < dataset_num / 25: #dataset_num : try: data = next(gen) # 取出256*30张图片 starts = count ends = starts + pack_size - 1 count = count + pack_size tfrecords_filename = '../video_prediction/data/comma/test/traj_%d_to_%d.tfrecords' % ( starts, ends) writer = python_io.TFRecordWriter(
parser.add_argument('--validation', dest='validation', action='store_true', default=False, help='Serve validation dataset instead.') args, more = parser.parse_known_args() # 9 for training train_path = [ './dataset/camera/2016-01-30--11-24-51.h5', './dataset/camera/2016-01-30--13-46-00.h5', './dataset/camera/2016-01-31--19-19-25.h5', './dataset/camera/2016-02-02--10-16-58.h5', './dataset/camera/2016-02-08--14-56-28.h5', './dataset/camera/2016-02-11--21-32-47.h5', './dataset/camera/2016-03-29--10-50-20.h5', './dataset/camera/2016-04-21--14-48-08.h5', './dataset/camera/2016-05-12--22-20-00.h5', ] # 2 for validation validation_path = [ './dataset/camera/2016-06-02--21-39-29.h5', './dataset/camera/2016-06-08--11-46-01.h5' ] if args.validation: datapath = validation_path else: datapath = train_path gen = datagen(datapath, time_len=args.time, batch_size=args.batch, ignore_goods=args.nogood) start_server(gen, port=args.port, hwm=args.buffer)