import argparse import copy import os import warnings import mmcv import torch from torchpack.utils.config import configs from torchpack import distributed as dist 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 mmdet3d.apis import single_gpu_test from mmdet3d.datasets import build_dataloader, build_dataset from mmdet3d.models import build_model from mmdet.apis import multi_gpu_test, set_random_seed from mmdet.datasets import replace_ImageToTensor from mmdet3d.utils import recursive_eval 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", help="checkpoint file") parser.add_argument("--out", help="output result file in pickle format") 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", type=str, nargs="+", help='evaluation metrics, which depends on the dataset, e.g., "bbox",' ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC', ) parser.add_argument("--show", action="store_true", help="show results") parser.add_argument("--show-dir", help="directory where results will be saved") 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( "--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( "--options", nargs="+", action=DictAction, help="custom options for evaluation, the key-value pair in xxx=yyy " "format will be kwargs for dataset.evaluate() function (deprecate), " "change to --eval-options instead.", ) parser.add_argument( "--eval-options", nargs="+", action=DictAction, help="custom options for evaluation, the key-value pair in xxx=yyy " "format will be kwargs for dataset.evaluate() function", ) 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) if args.options and args.eval_options: raise ValueError( "--options and --eval-options cannot be both specified, " "--options is deprecated in favor of --eval-options" ) if args.options: warnings.warn("--options is deprecated in favor of --eval-options") args.eval_options = args.options return args def main(): args = parse_args() dist.init() torch.backends.cudnn.benchmark = True torch.cuda.set_device(dist.local_rank()) 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.") configs.load(args.config, recursive=True) cfg = Config(recursive_eval(configs), filename=args.config) print(cfg) 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 # in case the test dataset is concatenated samples_per_gpu = 1 if isinstance(cfg.data.test, dict): cfg.data.test.test_mode = True 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(cfg.data.test.pipeline) 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. distributed = True # set random seeds if args.seed is not None: set_random_seed(args.seed, deterministic=args.deterministic) # 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_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) # 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) 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)) if __name__ == "__main__": main()