Python loader.TestDataLoader方法代碼示例

本文整理匯總了Python中core.loader.TestDataLoader方法的典型用法代碼示例。如果您正苦於以下問題：Python loader.TestDataLoader方法的具體用法？Python loader.TestDataLoader怎麽用？Python loader.TestDataLoader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.loader的用法示例。

在下文中一共展示了loader.TestDataLoader方法的2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_deeplab

# 需要導入模塊: from core import loader [as 別名]
# 或者: from core.loader import TestDataLoader [as 別名]
def test_deeplab(network, dataset, image_set, root_path, dataset_path,
              ctx, prefix, epoch,
              vis, logger=None, output_path=None):
    if not logger:
        assert False, 'require a logger'

    # print config
    pprint.pprint(config)
    logger.info('testing config:{}\n'.format(pprint.pformat(config)))

    # load symbol and testing data
    sym = eval('get_' + network + '_test')(num_classes=config.dataset.NUM_CLASSES)
    imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
    segdb = imdb.gt_segdb()

    # get test data iter
    test_data = TestDataLoader(segdb, batch_size=len(ctx))

    # load model
    # arg_params, aux_params = load_param(prefix, epoch, convert=True, ctx=ctx, process=True)
    arg_params, aux_params = load_param(prefix, epoch, process=True)

    # infer shape
    data_shape_dict = dict(test_data.provide_data_single)
    arg_shape, _, aux_shape = sym.infer_shape(**data_shape_dict)
    arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
    aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape))

    # check parameters
    for k in sym.list_arguments():
        if k in data_shape_dict or k in ['softmax_label']:
            continue
        assert k in arg_params, k + ' not initialized'
        assert arg_params[k].shape == arg_shape_dict[k], \
            'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
    for k in sym.list_auxiliary_states():
        assert k in aux_params, k + ' not initialized'
        assert aux_params[k].shape == aux_shape_dict[k], \
            'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)

    # decide maximum shape
    data_names = [k[0] for k in test_data.provide_data_single]
    label_names = ['softmax_label']
    max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.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 detection
    pred_eval(predictor, test_data, imdb, vis=vis, logger=logger)

開發者ID:tonysy，項目名稱:Deep-Feature-Flow-Segmentation，代碼行數:55，代碼來源:test_deeplab.py

示例2: test_deeplab

# 需要導入模塊: from core import loader [as 別名]
# 或者: from core.loader import TestDataLoader [as 別名]
def test_deeplab():
    epoch = config.TEST.test_epoch
    ctx = [mx.gpu(int(i)) for i in config.gpus.split(',')]
    image_set = config.dataset.test_image_set
    root_path = config.dataset.root_path
    dataset = config.dataset.dataset
    dataset_path = config.dataset.dataset_path

    logger, final_output_path = create_logger(config.output_path, args.cfg, image_set)
    prefix = os.path.join(final_output_path, '..', '_'.join([iset for iset in config.dataset.image_set.split('+')]), config.TRAIN.model_prefix)

    # print config
    pprint.pprint(config)
    logger.info('testing config:{}\n'.format(pprint.pformat(config)))

    # load symbol and testing data
    sym_instance = eval(config.symbol + '.' + config.symbol)()
    sym = sym_instance.get_symbol(config, is_train=False)

    imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=final_output_path)
    segdb = imdb.gt_segdb()

    # get test data iter
    test_data = TestDataLoader(segdb, config=config, batch_size=len(ctx))

    # infer shape
    data_shape_dict = dict(test_data.provide_data_single)
    sym_instance.infer_shape(data_shape_dict)

    # load model and check parameters
    arg_params, aux_params = load_param(prefix, epoch, process=True)

    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_single]
    label_names = ['softmax_label']
    max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.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 detection
    pred_eval(predictor, test_data, imdb, vis=args.vis, ignore_cache=args.ignore_cache, logger=logger)

開發者ID:tonysy，項目名稱:Deep-Feature-Flow-Segmentation，代碼行數:49，代碼來源:test.py

注：本文中的core.loader.TestDataLoader方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。