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
