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- # Copyright (c) OpenMMLab. All rights reserved.
- import argparse
- import mmcv
- import os
- import torch
- import warnings
- 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
-
-
- 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()
-
- 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)
- # 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.
- 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
- 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
- # palette for visualization in segmentation tasks
- if 'PALETTE' in checkpoint.get('meta', {}):
- model.PALETTE = checkpoint['meta']['PALETTE']
- elif hasattr(dataset, 'PALETTE'):
- # segmentation dataset has `PALETTE` attribute
- model.PALETTE = dataset.PALETTE
-
- if not distributed:
- model = MMDataParallel(model, device_ids=[0])
- outputs = single_gpu_test(model, data_loader, args.show, args.show_dir)
- 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()
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