test.py

"""Inference a pretrained model."""

import argparse
import os

import datasets  # pylint: disable=unused-import
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (
    get_dist_info,
    init_dist,
    load_checkpoint,
    wrap_fp16_model,
)
from mmdet.apis import multi_gpu_test, single_gpu_test
from mmdet.datasets import (
    build_dataloader,
    build_dataset,
    replace_ImageToTensor,
)
from mmdet.models import build_detector

MODEL_SERVER = "https://dl.cv.ethz.ch/bdd100k/det/models/"


def parse_args() -> argparse.Namespace:
    """Arguements definitions."""
    parser = argparse.ArgumentParser(
        description="MMDet test (and eval) a model"
    )
    parser.add_argument("config", help="test config file path")
    parser.add_argument(
        "--work-dir",
        help="the directory to save the file containing evaluation metrics",
    )
    parser.add_argument(
        "--fuse-conv-bn",
        action="store_true",
        help="Whether to fuse conv and bn, this will slightly increase"
        "the inference speed",
    )
    parser.add_argument(
        "--format-only",
        action="store_true",
        help="Format the output results without perform evaluation. It is"
        "useful when you want to format the result to a specific format and "
        "submit it to the test server",
    )
    parser.add_argument(
        "--format-dir", help="directory where the outputs are saved."
    )
    parser.add_argument("--show", action="store_true", help="show results")
    parser.add_argument(
        "--show-dir", help="directory where painted images will be saved"
    )
    parser.add_argument(
        "--show-score-thr",
        type=float,
        default=0.3,
        help="score threshold (default: 0.3)",
    )
    parser.add_argument(
        "--gpu-collect",
        action="store_true",
        help="whether to use gpu to collect results.",
    )
    parser.add_argument(
        "--tmpdir",
        help="tmp directory used for collecting results from multiple "
        "workers, available when gpu-collect is not specified",
    )
    parser.add_argument(
        "--cfg-options",
        nargs="+",
        action=DictAction,
        help="override some settings in the used config, the key-value pair "
        "in xxx=yyy format will be merged into config file. If the value to "
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        "Note that the quotation marks are necessary and that no white space "
        "is allowed.",
    )
    parser.add_argument(
        "--launcher",
        choices=["none", "pytorch", "slurm", "mpi"],
        default="none",
        help="job launcher",
    )
    parser.add_argument("--local_rank", type=int, default=0)
    args = parser.parse_args()
    if "LOCAL_RANK" not in os.environ:
        os.environ["LOCAL_RANK"] = str(args.local_rank)
    return args


def main() -> None:
    """Main function for model inference."""
    args = parse_args()

    assert 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 '--format-only', "
        "'--show' or '--show-dir'"
    )

    cfg = Config.fromfile(args.config)
    if cfg.load_from is None:
        cfg_name = os.path.split(args.config)[-1].replace(".py", ".pth")
        cfg.load_from = MODEL_SERVER + cfg_name
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    # set cudnn_benchmark
    if cfg.get("cudnn_benchmark", False):
        torch.backends.cudnn.benchmark = True

    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 = 1
    if isinstance(cfg.data.test, dict):
        cfg.data.test.test_mode = True  # type: ignore
        samples_per_gpu = cfg.data.test.pop("samples_per_gpu", 1)
        if samples_per_gpu > 1:
            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
            cfg.data.test.pipeline = replace_ImageToTensor(  # type: ignore
                cfg.data.test.pipeline  # type: ignore
            )
    elif isinstance(cfg.data.test, list):
        for ds_cfg in cfg.data.test:
            ds_cfg.test_mode = True
        samples_per_gpu = max(
            [ds_cfg.pop("samples_per_gpu", 1) for ds_cfg in cfg.data.test]
        )
        if samples_per_gpu > 1:
            for ds_cfg in cfg.data.test:
                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)

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

    rank, _ = get_dist_info()

    # build the dataloader
    dataset = build_dataset(cfg.data.test)
    data_loader = build_dataloader(
        dataset,
        samples_per_gpu=samples_per_gpu,
        workers_per_gpu=cfg.data.workers_per_gpu,
        dist=distributed,
        shuffle=False,
    )

    # build the model and load checkpoint
    cfg.model.train_cfg = None
    model = build_detector(cfg.model, test_cfg=cfg.get("test_cfg"))
    fp16_cfg = cfg.get("fp16", None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    checkpoint = load_checkpoint(model, cfg.load_from, map_location="cpu")
    if args.fuse_conv_bn:
        model = fuse_conv_bn(model)
    # old versions did not save class info in checkpoints, this walkaround is
    # for backward compatibility
    if "CLASSES" in checkpoint.get("meta", {}):
        model.CLASSES = checkpoint["meta"]["CLASSES"]
    else:
        model.CLASSES = dataset.CLASSES

    if not distributed:
        model = MMDataParallel(model, device_ids=[0])
        outputs = single_gpu_test(
            model, data_loader, args.show, args.show_dir, 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.format_only:
            dataset.convert_format(outputs, args.format_dir)


if __name__ == "__main__":
    main()