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)) # tensorboard directory where the summaries are saved during training
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, "") # 同样的方法载入val数据集