Python loader.TestLoader方法代码示例

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
def test_rcnn(cfg, dataset, image_set, root_path, dataset_path,
              ctx, prefix, epoch,
              vis, ignore_cache, shuffle, has_rpn, proposal, thresh, logger=None, output_path=None):
    if not logger:
        assert False, 'require a logger'

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

    # load symbol and testing data
    key_sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
    cur_sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
    key_sym = key_sym_instance.get_key_test_symbol(cfg)
    cur_sym = cur_sym_instance.get_cur_test_symbol(cfg)
    imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
    roidb = imdb.gt_roidb()

    # get test data iter
    # split roidbs
    gpu_num = len(ctx)
    roidbs = [[] for x in range(gpu_num)]
    roidbs_seg_lens = np.zeros(gpu_num, dtype=np.int)
    for x in roidb:
        gpu_id = np.argmin(roidbs_seg_lens)
        roidbs[gpu_id].append(x)
        roidbs_seg_lens[gpu_id] += x['frame_seg_len']

    # get test data iter
    test_datas = [TestLoader(x, cfg, batch_size=1, shuffle=shuffle, has_rpn=has_rpn) for x in roidbs]

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

    # create predictor
    key_predictors = [get_predictor(key_sym, key_sym_instance, cfg, arg_params, aux_params, test_datas[i], [ctx[i]]) for i in range(gpu_num)]
    cur_predictors = [get_predictor(cur_sym, cur_sym_instance, cfg, arg_params, aux_params, test_datas[i], [ctx[i]]) for i in range(gpu_num)]

    # start detection
    #pred_eval(0, key_predictors[0], cur_predictors[0], test_datas[0], imdb, cfg, vis=vis, ignore_cache=ignore_cache, thresh=thresh, logger=logger)
    pred_eval_multiprocess(gpu_num, key_predictors, cur_predictors, test_datas, imdb, cfg, vis=vis, ignore_cache=ignore_cache, thresh=thresh, logger=logger) 

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
def test_net_thread(imdb, mtcnn_detector):
    test_data = TestLoader(imdb)
    detections = mtcnn_detector.detect_face(imdb, test_data, vis=False, mode='train')
    return detections 

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
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) 

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
def test_rcnn(cfg, dataset, image_set, root_path, dataset_path,
              ctx, prefix, epoch,
              vis, ignore_cache, shuffle, has_rpn, proposal, thresh, logger=None, output_path=None):
    if not logger:
        assert False, 'require a logger'

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

    # load symbol and testing data
    if has_rpn:
        sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
        sym = sym_instance.get_symbol(cfg, is_train=False)
        imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        roidb = imdb.gt_roidb()
    else:
        sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
        sym = sym_instance.get_symbol_rcnn(cfg, is_train=False)
        imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        gt_roidb = imdb.gt_roidb()
        roidb = eval('imdb.' + proposal + '_roidb')(gt_roidb)

    # get test data iter
    test_data = TestLoader(roidb, cfg, batch_size=len(ctx), shuffle=shuffle, has_rpn=has_rpn)

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

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

    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 = None
    max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES])))]]
    if not has_rpn:
        max_data_shape.append(('rois', (cfg.TEST.PROPOSAL_POST_NMS_TOP_N + 30, 5)))

    # 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, cfg, vis=vis, ignore_cache=ignore_cache, thresh=thresh, logger=logger) 

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
def test_rcnn(cfg, dataset, image_set, root_path, dataset_path,
              ctx, prefix, epoch,
              vis, ignore_cache, shuffle, has_rpn, proposal, thresh, logger=None, output_path=None):
    if not logger:
        assert False, 'require a logger'

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

    # load symbol and testing data
    if has_rpn:
        sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
        sym = sym_instance.get_symbol(cfg, is_train=False)
        imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        roidb = imdb.gt_roidb()
    else:
        sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
        sym = sym_instance.get_symbol_rfcn(cfg, is_train=False)
        imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        gt_roidb = imdb.gt_roidb()
        roidb = eval('imdb.' + proposal + '_roidb')(gt_roidb)

    # get test data iter
    test_data = TestLoader(roidb, cfg, batch_size=len(ctx), shuffle=shuffle, has_rpn=has_rpn)

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

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

    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 = None
    max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES])))]]
    if not has_rpn:
        max_data_shape.append(('rois', (cfg.TEST.PROPOSAL_POST_NMS_TOP_N + 30, 5)))

    # 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, cfg, vis=vis, ignore_cache=ignore_cache, thresh=thresh, logger=logger) 

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
def test_rcnn(cfg, dataset, image_set, root_path, dataset_path, motion_iou_path,
              ctx, prefix, epoch,
              vis, ignore_cache, shuffle, has_rpn, proposal, thresh, logger=None, output_path=None, enable_detailed_eval=True):
    if not logger:
        assert False, 'require a logger'

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

    # load symbol and testing data

    feat_sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
    aggr_sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()

    feat_sym = feat_sym_instance.get_feat_symbol(cfg)
    aggr_sym = aggr_sym_instance.get_aggregation_symbol(cfg)

    imdb = eval(dataset)(image_set, root_path, dataset_path, motion_iou_path, result_path=output_path, enable_detailed_eval=enable_detailed_eval)
    roidb = imdb.gt_roidb()

    # get test data iter
    # split roidbs
    gpu_num = len(ctx)
    roidbs = [[] for x in range(gpu_num)]
    roidbs_seg_lens = np.zeros(gpu_num, dtype=np.int)
    for x in roidb:
        gpu_id = np.argmin(roidbs_seg_lens)
        roidbs[gpu_id].append(x)
        roidbs_seg_lens[gpu_id] += x['frame_seg_len']

    # get test data iter
    test_datas = [TestLoader(x, cfg, batch_size=1, shuffle=shuffle, has_rpn=has_rpn) for x in roidbs]

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

    # create predictor
    feat_predictors = [get_predictor(feat_sym, feat_sym_instance, cfg, arg_params, aux_params, test_datas[i], [ctx[i]]) for i in range(gpu_num)]
    aggr_predictors = [get_predictor(aggr_sym, aggr_sym_instance, cfg, arg_params, aux_params, test_datas[i], [ctx[i]]) for i in range(gpu_num)]

    # start detection
    pred_eval_multiprocess(gpu_num, feat_predictors, aggr_predictors, test_datas, imdb, cfg, vis=vis, ignore_cache=ignore_cache, thresh=thresh, logger=logger) 

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
def test_rcnn(cfg, dataset, image_set, root_path, dataset_path,
              ctx, prefix, epoch,
              vis, ignore_cache, shuffle, has_rpn, proposal, thresh, logger=None, output_path=None):
    if not logger:
        assert False, 'require a logger'

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

    # load symbol and testing data
    sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
    sym = sym_instance.get_test_symbol(cfg)
    imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
    roidb = imdb.gt_roidb()

    # get test data iter
    test_data = TestLoader(roidb, cfg, batch_size=len(ctx), shuffle=shuffle, has_rpn=has_rpn)

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

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

    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 = None
    max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES])))]]
    if not has_rpn:
        max_data_shape.append(('rois', (cfg.TEST.PROPOSAL_POST_NMS_TOP_N + 30, 5)))

    # 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, cfg, vis=vis, ignore_cache=ignore_cache, thresh=thresh, logger=logger) 

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
def test_rcnn_poly(cfg, dataset, image_set, root_path, dataset_path,
              ctx, prefix, epoch,
              vis, draw, ignore_cache, shuffle, has_rpn, proposal, thresh, logger=None, output_path=None):
    if not logger:
        assert False, 'require a logger'

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

    # load symbol and testing data
    if has_rpn:
        sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
        sym = sym_instance.get_symbol(cfg, is_train=False)
        imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        roidb = imdb.gt_roidb()
    else:
        sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
        sym = sym_instance.get_symbol_rcnn(cfg, is_train=False)
        imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        gt_roidb = imdb.gt_roidb()
        roidb = eval('imdb.' + proposal + '_roidb')(gt_roidb)

    # get test data iter
    test_data = TestLoader(roidb, cfg, batch_size=len(ctx), shuffle=shuffle, has_rpn=has_rpn)

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

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

    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 = None
    max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES])))]]
    if not has_rpn:
        max_data_shape.append(('rois', (cfg.TEST.PROPOSAL_POST_NMS_TOP_N + 30, 5)))

    # 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)

    # ignore_cache = True
    # start detection
    if cfg.network.RRoI_REGRESSION:
        pred_eval_dota_rotbox_Rroi(predictor, test_data, imdb, cfg, vis=False, draw=True, ignore_cache=ignore_cache,
                                      thresh=thresh, logger=logger)
    else:
        # start detection
        pred_eval_dota_rotbox(predictor, test_data, imdb, cfg, vis=vis, draw=draw, ignore_cache=ignore_cache, thresh=thresh, logger=logger) 

# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestLoader [as 别名]
def test_rcnn(cfg, dataset, image_set, root_path, dataset_path,
              ctx, prefix, epoch,
              vis, ignore_cache, shuffle, has_rpn, proposal, thresh, logger=None, output_path=None):
    if not logger:
        assert False, 'require a logger'

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

    # load symbol and testing data
    if has_rpn:
        sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
        sym = sym_instance.get_symbol(cfg, is_train=False)
        # imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        # imdb = eval(dataset)(image_set, root_path, '/data/ARD/test', result_path=output_path)
        imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        # imdb = eval(dataset)(image_set, root_path, '/data/ARD/dota/dota1024/little/', result_path=output_path)
        roidb = imdb.gt_roidb()
    else:
        sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
        sym = sym_instance.get_symbol_rcnn(cfg, is_train=False)
        imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
        gt_roidb = imdb.gt_roidb()
        roidb = eval('imdb.' + proposal + '_roidb')(gt_roidb)

    # get test data iter
    test_data = TestLoader(roidb, cfg, batch_size=len(ctx), shuffle=shuffle, has_rpn=has_rpn)
    print 'test_data size is :',test_data.size
    # load model
    arg_params, aux_params = load_param(prefix, epoch, process=True)

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

    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 = None
    max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES])))]]
    if not has_rpn:
        max_data_shape.append(('rois', (cfg.TEST.PROPOSAL_POST_NMS_TOP_N + 30, 5)))

    # 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, cfg, vis=vis, ignore_cache=ignore_cache, thresh=thresh, logger=logger)