예제 #1
0
    args = parse_args()
    arg_net = args.network_name
    train_datset_name = args.train_dataset_name  # 测试数据集名称
    val_dataset_name = args.val_dataset_name  # 验证数据集名称
    set_cfgs = [
        'ANCHOR_SCALES',
        '[8,16,32]',
        'ANCHOR_RATIOS',
        '[0.5,1,2]',
        'TRAIN.STEPSIZE',
        '[50000]',
    ]
    project_path = os.path.abspath('.')
    pre_train_weight = project_path + "/data/pre_train_weight/" + arg_net + ".ckpt"

    cfg_from_file(project_path + "/experiments/cfgs/" + arg_net +
                  ".yml")  #载入参数配置
    cfg_from_list(set_cfgs)  #修改参数配置
    print('Using config:')
    pprint.pprint(cfg)

    # roidb:所有训练图片的gt_boxes
    # imdb:训练数据集的相关信息:包括类别列表,所有的图片名称的索引,数据集名称等等
    imdb, roidb = combined_roidb("gridsum_car_train")
    print(roidb[0]['boxes'])
    print(roidb[0])
    print('{:d} roidb entries'.format(len(roidb)))
    # output directory where the models are saved
    output_dir = get_output_dir(imdb, "")
    print('Output will be saved to `{:s}`'.format(output_dir))
    # tensorboard directory where the summaries are saved during training
    tb_dir = get_output_tb_dir(imdb, "")
예제 #2
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            roidb.extend(r)
        tmp = get_imdb(imdb_names.split('+')[1])
        imdb = lib.datasets.imdb.imdb(imdb_names, tmp.classes)
    else:
        imdb = get_imdb(imdb_names)
    return imdb, roidb


if __name__ == '__main__':
    args = parse_args()

    print('Called with args:')
    print(args)

    if args.cfg_file is not None:
        cfg_from_file(args.cfg_file)
    if args.set_cfgs is not None:
        cfg_from_list(args.set_cfgs)

    print('Using config:')
    pprint.pprint(cfg)

    np.random.seed(cfg.RNG_SEED)

    # train set
    imdb, roidb = combined_roidb(args.imdb_name)
    print('{:d} roidb entries'.format(len(roidb)))

    # output directory where the models are saved
    output_dir = get_output_dir(imdb, args.tag)
    print('Output will be saved to `{:s}`'.format(output_dir))
예제 #3
0

if __name__ == '__main__':
    args = parse_args()
    test_dataset_name = args.test_dataset_name  #测试数据集名称
    set_cfgs = [
        'ANCHOR_SCALES', '[8,16,32]', 'ANCHOR_RATIOS', '[0.5,1,2]',
        'TRAIN.STEPSIZE', '[50000]'
    ]
    project_path = os.path.abspath('.')
    #测试迭代训练好的参数路径
    test_iter_weight_path = os.path.join(
        project_path, "output", args.network_name, args.train_dataset_name,
        args.network_name + "_faster_rcnn_iter_" + args.iter_number + ".ckpt")

    cfg_from_file(project_path + "/experiments/cfgs/" + args.network_name +
                  ".yml")  # 载入参数配置
    cfg_from_list(set_cfgs)  #修改参数配置
    print('Using config:')
    pprint.pprint(cfg)

    # if has model, get the name from it
    # if does not, then just use the initialization weights
    filename = 'default/' + args.network_name

    imdb = get_imdb(test_dataset_name)

    #配置Session参数
    tfconfig = tf.ConfigProto(allow_soft_placement=True)
    tfconfig.gpu_options.allow_growth = True

    # init session