本文整理匯總了Python中core.tester.generate_proposals方法的典型用法代碼示例。如果您正苦於以下問題：Python tester.generate_proposals方法的具體用法？Python tester.generate_proposals怎麽用？Python tester.generate_proposals使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類core.tester的用法示例。
在下文中一共展示了tester.generate_proposals方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_rpn
# 需要導入模塊: from core import tester [as 別名]
# 或者: from core.tester import generate_proposals [as 別名]
def test_rpn(cfg, dataset, image_set, root_path, dataset_path,
ctx, prefix, epoch,
vis, shuffle, thresh, logger=None, output_path=None):
# set up logger
if not logger:
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# rpn generate proposal cfg
cfg.TEST.HAS_RPN = True
# print cfg
pprint.pprint(cfg)
logger.info('testing rpn cfg:{}\n'.format(pprint.pformat(cfg)))
# load symbol
sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
sym = sym_instance.get_symbol_rpn(cfg, is_train=False)
# load dataset and prepare imdb for training
imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
roidb = imdb.gt_roidb()
test_data = TestLoader(roidb, cfg, batch_size=len(ctx), shuffle=shuffle, has_rpn=True)
# load model
arg_params, aux_params = load_param(prefix, epoch)
# infer shape
data_shape_dict = dict(test_data.provide_data_single)
sym_instance.infer_shape(data_shape_dict)
# check parameters
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict, is_train=False)
# decide maximum shape
data_names = [k[0] for k in test_data.provide_data[0]]
label_names = None if test_data.provide_label[0] is None else [k[0] for k in test_data.provide_label[0]]
max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES])))]]
# create predictor
predictor = Predictor(sym, data_names, label_names,
context=ctx, max_data_shapes=max_data_shape,
provide_data=test_data.provide_data, provide_label=test_data.provide_label,
arg_params=arg_params, aux_params=aux_params)
# start testing
imdb_boxes = generate_proposals(predictor, test_data, imdb, cfg, vis=vis, thresh=thresh)
all_log_info = imdb.evaluate_recall(roidb, candidate_boxes=imdb_boxes)
logger.info(all_log_info)