231 lines
8.2 KiB
Python
231 lines
8.2 KiB
Python
import argparse
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import copy
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import os
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import warnings
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import mmcv
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import torch
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from torchpack.utils.config import configs
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from torchpack import distributed as dist
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from mmcv import Config, DictAction
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from mmcv.cnn import fuse_conv_bn
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import get_dist_info, init_dist, load_checkpoint, wrap_fp16_model
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from mmdet3d.apis import single_gpu_test
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from mmdet3d.datasets import build_dataloader, build_dataset
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from mmdet3d.models import build_model
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from mmdet.apis import multi_gpu_test, set_random_seed
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from mmdet.datasets import replace_ImageToTensor
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from mmdet3d.utils import recursive_eval
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def parse_args():
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parser = argparse.ArgumentParser(description="MMDet test (and eval) a model")
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parser.add_argument("config", help="test config file path")
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parser.add_argument("checkpoint", help="checkpoint file")
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parser.add_argument("--out", help="output result file in pickle format")
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parser.add_argument(
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"--fuse-conv-bn",
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action="store_true",
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help="Whether to fuse conv and bn, this will slightly increase"
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"the inference speed",
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)
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parser.add_argument(
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"--format-only",
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action="store_true",
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help="Format the output results without perform evaluation. It is"
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"useful when you want to format the result to a specific format and "
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"submit it to the test server",
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)
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parser.add_argument(
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"--eval",
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type=str,
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nargs="+",
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help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
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' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC',
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)
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parser.add_argument("--show", action="store_true", help="show results")
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parser.add_argument("--show-dir", help="directory where results will be saved")
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parser.add_argument(
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"--gpu-collect",
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action="store_true",
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help="whether to use gpu to collect results.",
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)
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parser.add_argument(
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"--tmpdir",
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help="tmp directory used for collecting results from multiple "
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"workers, available when gpu-collect is not specified",
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)
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parser.add_argument("--seed", type=int, default=0, help="random seed")
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parser.add_argument(
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"--deterministic",
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action="store_true",
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help="whether to set deterministic options for CUDNN backend.",
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)
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parser.add_argument(
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"--cfg-options",
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nargs="+",
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action=DictAction,
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help="override some settings in the used config, the key-value pair "
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"in xxx=yyy format will be merged into config file. If the value to "
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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"Note that the quotation marks are necessary and that no white space "
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"is allowed.",
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)
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parser.add_argument(
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"--options",
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nargs="+",
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action=DictAction,
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help="custom options for evaluation, the key-value pair in xxx=yyy "
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"format will be kwargs for dataset.evaluate() function (deprecate), "
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"change to --eval-options instead.",
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)
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parser.add_argument(
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"--eval-options",
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nargs="+",
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action=DictAction,
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help="custom options for evaluation, the key-value pair in xxx=yyy "
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"format will be kwargs for dataset.evaluate() function",
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)
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parser.add_argument(
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"--launcher",
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choices=["none", "pytorch", "slurm", "mpi"],
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default="none",
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help="job launcher",
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)
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parser.add_argument("--local_rank", type=int, default=0)
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args = parser.parse_args()
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if "LOCAL_RANK" not in os.environ:
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os.environ["LOCAL_RANK"] = str(args.local_rank)
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if args.options and args.eval_options:
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raise ValueError(
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"--options and --eval-options cannot be both specified, "
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"--options is deprecated in favor of --eval-options"
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)
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if args.options:
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warnings.warn("--options is deprecated in favor of --eval-options")
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args.eval_options = args.options
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return args
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def main():
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args = parse_args()
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dist.init()
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torch.backends.cudnn.benchmark = True
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torch.cuda.set_device(dist.local_rank())
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assert args.out or args.eval or args.format_only or args.show or args.show_dir, (
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"Please specify at least one operation (save/eval/format/show the "
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'results / save the results) with the argument "--out", "--eval"'
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', "--format-only", "--show" or "--show-dir"'
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)
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if args.eval and args.format_only:
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raise ValueError("--eval and --format_only cannot be both specified")
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if args.out is not None and not args.out.endswith((".pkl", ".pickle")):
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raise ValueError("The output file must be a pkl file.")
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configs.load(args.config, recursive=True)
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cfg = Config(recursive_eval(configs), filename=args.config)
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print(cfg)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# set cudnn_benchmark
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if cfg.get("cudnn_benchmark", False):
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torch.backends.cudnn.benchmark = True
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cfg.model.pretrained = None
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# in case the test dataset is concatenated
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samples_per_gpu = 1
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if isinstance(cfg.data.test, dict):
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cfg.data.test.test_mode = True
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samples_per_gpu = cfg.data.test.pop("samples_per_gpu", 1)
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if samples_per_gpu > 1:
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# Replace 'ImageToTensor' to 'DefaultFormatBundle'
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cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
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elif isinstance(cfg.data.test, list):
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for ds_cfg in cfg.data.test:
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ds_cfg.test_mode = True
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samples_per_gpu = max(
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[ds_cfg.pop("samples_per_gpu", 1) for ds_cfg in cfg.data.test]
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)
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if samples_per_gpu > 1:
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for ds_cfg in cfg.data.test:
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ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
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# init distributed env first, since logger depends on the dist info.
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distributed = True
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# set random seeds
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if args.seed is not None:
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set_random_seed(args.seed, deterministic=args.deterministic)
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# build the dataloader
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dataset = build_dataset(cfg.data.test)
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=samples_per_gpu,
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workers_per_gpu=cfg.data.workers_per_gpu,
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dist=distributed,
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shuffle=False,
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)
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# build the model and load checkpoint
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cfg.model.train_cfg = None
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model = build_model(cfg.model, test_cfg=cfg.get("test_cfg"))
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fp16_cfg = cfg.get("fp16", None)
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu")
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if args.fuse_conv_bn:
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model = fuse_conv_bn(model)
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# old versions did not save class info in checkpoints, this walkaround is
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# for backward compatibility
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if "CLASSES" in checkpoint.get("meta", {}):
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model.CLASSES = checkpoint["meta"]["CLASSES"]
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else:
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model.CLASSES = dataset.CLASSES
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if not distributed:
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model = MMDataParallel(model, device_ids=[0])
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outputs = single_gpu_test(model, data_loader)
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else:
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model = MMDistributedDataParallel(
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model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False,
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)
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outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect)
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rank, _ = get_dist_info()
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if rank == 0:
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if args.out:
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print(f"\nwriting results to {args.out}")
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mmcv.dump(outputs, args.out)
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kwargs = {} if args.eval_options is None else args.eval_options
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if args.format_only:
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dataset.format_results(outputs, **kwargs)
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if args.eval:
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eval_kwargs = cfg.get("evaluation", {}).copy()
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# hard-code way to remove EvalHook args
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for key in [
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"interval",
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"tmpdir",
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"start",
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"gpu_collect",
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"save_best",
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"rule",
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]:
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eval_kwargs.pop(key, None)
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eval_kwargs.update(dict(metric=args.eval, **kwargs))
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print(dataset.evaluate(outputs, **eval_kwargs))
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if __name__ == "__main__":
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main()
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