test.py

import argparse
import os
import os.path as osp

import torch
from mmcv import Config
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 mmdet3d.apis import single_gpu_test
from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet3d.models import build_model
from mmdet.datasets import replace_ImageToTensor
from mmdet_test import multi_gpu_test
from mmdet_train import set_random_seed


def parse_args():
    parser = argparse.ArgumentParser(
        description='MMDet test (and eval) a model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', type=str, help='checkpoint file')
    parser.add_argument('--split', type=str, required=True, help='which split to test on')
    parser.add_argument('--work-dir', help='the dir to save logs and models')
    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(
        '--eval',
        action='store_true',
        help='whether to run evaluation.')
    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('--seed', type=int, default=0, help='random seed')
    parser.add_argument(
        '--deterministic',
        action='store_true',
        help='whether to set deterministic options for CUDNN backend.')
    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():
    args = parse_args()

    if args.split not in ['val', 'test']:
        raise ValueError('Please choose "val" or "test" split for testing')

    if (args.eval and args.format_only) or (not args.eval and not args.format_only):
        raise ValueError('Please specify exactly one operation (eval/format) '
                         'with the argument "--eval" or "--format-only"')

    if args.eval and args.split == 'test':
        raise ValueError('Cannot evaluate on test set')

    cfg = Config.fromfile(args.config)
    # import modules from string list.
    if cfg.get('custom_imports', None):
        from mmcv.utils import import_modules_from_strings
        import_modules_from_strings(**cfg['custom_imports'])
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True

    # import modules from plguin/xx, registry will be updated
    import sys
    sys.path.append(os.path.abspath('.'))
    if hasattr(cfg, 'plugin'):
        if cfg.plugin:
            import importlib
            if hasattr(cfg, 'plugin_dir'):
                def import_path(plugin_dir):
                    _module_dir = os.path.dirname(plugin_dir)
                    _module_dir = _module_dir.split('/')
                    _module_path = _module_dir[0]

                    for m in _module_dir[1:]:
                        _module_path = _module_path + '.' + m
                    print(f'importing {_module_path}/')
                    plg_lib = importlib.import_module(_module_path)

                plugin_dirs = cfg.plugin_dir
                if not isinstance(plugin_dirs, list):
                    plugin_dirs = [plugin_dirs, ]
                for plugin_dir in plugin_dirs:
                    import_path(plugin_dir)

            else:
                # import dir is the dirpath for the config file
                _module_dir = os.path.dirname(args.config)
                _module_dir = _module_dir.split('/')
                _module_path = _module_dir[0]
                for m in _module_dir[1:]:
                    _module_path = _module_path + '.' + m
                print(f'importing {_module_path}/')
                plg_lib = importlib.import_module(_module_path)

    cfg_data_dict = cfg.data.get(args.split)

    cfg.model.pretrained = None
    # in case the test dataset is concatenated
    samples_per_gpu = 1
    cfg_data_dict.test_mode = True
    samples_per_gpu = cfg_data_dict.pop('samples_per_gpu', 1)
    if samples_per_gpu > 1:
        # Replace 'ImageToTensor' to 'DefaultFormatBundle'
        cfg_data_dict.pipeline = replace_ImageToTensor(
            cfg_data_dict.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)

    # set random seeds
    if args.seed is not None:
        set_random_seed(args.seed, deterministic=args.deterministic)

    # build the dataloader
    if args.work_dir is not None:
        # update configs according to CLI args if args.work_dir is not None
        cfg.work_dir = args.work_dir
    elif cfg.get('work_dir', None) is None:
        # use config filename as default work_dir if cfg.work_dir is None
        cfg.work_dir = osp.join('./work_dirs',
                                osp.splitext(osp.basename(args.config))[0])

    cfg_data_dict.work_dir = cfg.work_dir
    print('work_dir: ', cfg.work_dir)
    dataset = build_dataset(cfg_data_dict)
    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_model(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, args.checkpoint, map_location='cpu')
    if args.fuse_conv_bn:
        model = fuse_conv_bn(model)
    if not distributed:
        model = MMDataParallel(model, device_ids=[0])
        outputs = single_gpu_test(model, data_loader)
    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.format_results(outputs, prefix=cfg.work_dir)
        elif args.eval:
            print('start evaluation!')
            print(dataset.evaluate(outputs))


if __name__ == '__main__':
    main()