Exemplo n.º 1
0
def test_single_gpu_test_kie_novisual(cfg_file):
    curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
    config_file = os.path.join(curr_dir, cfg_file)
    cfg = Config.fromfile(config_file)
    meta_keys = list(cfg.data.test.pipeline[-1]['meta_keys'])
    must_keys = ['img_norm_cfg', 'ori_filename', 'img_shape']
    for key in must_keys:
        meta_keys.append(key)

    cfg.data.test.pipeline[-1]['meta_keys'] = tuple(meta_keys)

    with tempfile.TemporaryDirectory() as tmpdirname:
        out_dir = osp.join(tmpdirname, 'tmp')
        model, data_loader = gene_sdmgr_model_dataloader(cfg,
                                                         out_dir,
                                                         curr_dir,
                                                         empty_img=True)
        results = single_gpu_test(model,
                                  data_loader,
                                  out_dir=out_dir,
                                  is_kie=True)
        assert check_argument.is_type_list(results, dict)

        model, data_loader = gene_sdmgr_model_dataloader(
            cfg, out_dir, curr_dir)
        results = single_gpu_test(model,
                                  data_loader,
                                  out_dir=out_dir,
                                  is_kie=True)
        assert check_argument.is_type_list(results, dict)
Exemplo n.º 2
0
def test_single_gpu_test_kie(cfg_file):
    curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
    config_file = os.path.join(curr_dir, cfg_file)
    cfg = Config.fromfile(config_file)

    with tempfile.TemporaryDirectory() as tmpdirname:
        out_dir = osp.join(tmpdirname, 'tmp')
        model, data_loader = gene_sdmgr_model_dataloader(
            cfg, out_dir, curr_dir)
        results = single_gpu_test(model,
                                  data_loader,
                                  out_dir=out_dir,
                                  is_kie=True)
        assert check_argument.is_type_list(results, dict)
Exemplo n.º 3
0
def test_single_gpu_test_det(cfg_file):
    curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
    config_file = os.path.join(curr_dir, cfg_file)
    cfg = Config.fromfile(config_file)

    model = build_model(cfg)
    img_prefix = 'data/toy_dataset/imgs'
    ann_file = 'data/toy_dataset/instances_test.json'
    data_loader = generate_sample_dataloader(cfg, curr_dir, img_prefix,
                                             ann_file)

    with tempfile.TemporaryDirectory() as tmpdirname:
        out_dir = osp.join(tmpdirname, 'tmp')
        results = single_gpu_test(model, data_loader, out_dir=out_dir)
        assert check_argument.is_type_list(results, dict)
Exemplo n.º 4
0
def main():
    args = parse_args()

    assert (
        args.out or args.eval or args.format_only or args.show
        or args.show_dir), (
            'Please specify at least one operation (save/eval/format/show the '
            'results / save the results) with the argument "--out", "--eval"'
            ', "--format-only", "--show" or "--show-dir".')

    if args.eval and args.format_only:
        raise ValueError('--eval and --format_only cannot be both specified.')

    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
        raise ValueError('The output file must be a pkl file.')

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    setup_multi_processes(cfg)

    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    if cfg.model.get('pretrained'):
        cfg.model.pretrained = None
    if cfg.model.get('neck'):
        if isinstance(cfg.model.neck, list):
            for neck_cfg in cfg.model.neck:
                if neck_cfg.get('rfp_backbone'):
                    if neck_cfg.rfp_backbone.get('pretrained'):
                        neck_cfg.rfp_backbone.pretrained = None
        elif cfg.model.neck.get('rfp_backbone'):
            if cfg.model.neck.rfp_backbone.get('pretrained'):
                cfg.model.neck.rfp_backbone.pretrained = None

    # in case the test dataset is concatenated
    samples_per_gpu = (cfg.data.get('test_dataloader', {})).get(
        'samples_per_gpu', cfg.data.get('samples_per_gpu', 1))
    if samples_per_gpu > 1:
        cfg = disable_text_recog_aug_test(cfg)
        cfg = replace_image_to_tensor(cfg)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        cfg.gpu_ids = [args.gpu_id]
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # build the dataloader
    dataset = build_dataset(cfg.data.test, dict(test_mode=True))
    # step 1: give default values and override (if exist) from cfg.data
    default_loader_cfg = {
        **dict(seed=cfg.get('seed'), drop_last=False, dist=distributed),
        **({} if torch.__version__ != 'parrots' else dict(
               prefetch_num=2,
               pin_memory=False,
           ))
    }
    default_loader_cfg.update({
        k: v
        for k, v in cfg.data.items() if k not in [
            'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
            'test_dataloader'
        ]
    })
    test_loader_cfg = {
        **default_loader_cfg,
        **dict(shuffle=False, drop_last=False),
        **cfg.data.get('test_dataloader', {}),
        **dict(samples_per_gpu=samples_per_gpu)
    }

    data_loader = build_dataloader(dataset, **test_loader_cfg)

    # build the model and load checkpoint
    cfg.model.train_cfg = None
    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
    model = revert_sync_batchnorm(model)
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    load_checkpoint(model, args.checkpoint, map_location='cpu')
    if args.fuse_conv_bn:
        model = fuse_conv_bn(model)

    if not distributed:
        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
        is_kie = cfg.model.type in ['SDMGR']
        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
                                  is_kie, args.show_score_thr)
    else:
        model = MMDistributedDataParallel(
            model.cuda(),
            device_ids=[torch.cuda.current_device()],
            broadcast_buffers=False)
        outputs = multi_gpu_test(model, data_loader, args.tmpdir,
                                 args.gpu_collect)

    rank, _ = get_dist_info()
    if rank == 0:
        if args.out:
            print(f'\nwriting results to {args.out}')
            mmcv.dump(outputs, args.out)
        kwargs = {} if args.eval_options is None else args.eval_options
        if args.format_only:
            dataset.format_results(outputs, **kwargs)
        if args.eval:
            eval_kwargs = cfg.get('evaluation', {}).copy()
            # hard-code way to remove EvalHook args
            for key in [
                    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
                    'rule'
            ]:
                eval_kwargs.pop(key, None)
            eval_kwargs.update(dict(metric=args.eval, **kwargs))
            print(dataset.evaluate(outputs, **eval_kwargs))