|
- # Copyright (c) OpenMMLab. All rights reserved.
- import argparse
- import os
- import os.path as osp
- import shutil
- import time
- import warnings
-
- import mmcv
- import torch
- from mmcv.cnn.utils import revert_sync_batchnorm
- from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
- wrap_fp16_model)
- from mmcv.utils import DictAction
-
- from mmseg import digit_version
- from mmseg.apis import multi_gpu_test, single_gpu_test
- from mmseg.datasets import build_dataloader, build_dataset
- from mmseg.models import build_segmentor
- from mmseg.utils import build_ddp, build_dp, get_device, setup_multi_processes
-
-
- def parse_args():
- parser = argparse.ArgumentParser(
- description='mmseg test (and eval) a model')
- parser.add_argument('config', help='test config file path')
- parser.add_argument('checkpoint', help='checkpoint file')
- parser.add_argument(
- '--work-dir',
- help=('if specified, the evaluation metric results will be dumped'
- 'into the directory as json'))
- parser.add_argument(
- '--aug-test', action='store_true', help='Use Flip and Multi scale aug')
- parser.add_argument('--out', help='output result file in pickle format')
- 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., "mIoU"'
- ' for generic datasets, and "cityscapes" for Cityscapes')
- 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(
- '--gpu-collect',
- action='store_true',
- help='whether to use gpu to collect results.')
- parser.add_argument(
- '--gpu-id',
- type=int,
- default=0,
- help='id of gpu to use '
- '(only applicable to non-distributed testing)')
- parser.add_argument(
- '--tmpdir',
- help='tmp directory used for collecting results from multiple '
- 'workers, available when gpu_collect is not specified')
- parser.add_argument(
- '--options',
- nargs='+',
- action=DictAction,
- help="--options is deprecated in favor of --cfg_options' and it will "
- 'not be supported in version v0.22.0. 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(
- '--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(
- '--eval-options',
- nargs='+',
- action=DictAction,
- help='custom options for evaluation')
- parser.add_argument(
- '--launcher',
- choices=['none', 'pytorch', 'slurm', 'mpi'],
- default='none',
- help='job launcher')
- parser.add_argument(
- '--opacity',
- type=float,
- default=0.5,
- help='Opacity of painted segmentation map. In (0, 1] range.')
- # When using PyTorch version >= 2.0.0, the `torch.distributed.launch`
- # will pass the `--local-rank` parameter to `tools/train.py` instead
- # of `--local_rank`.
- parser.add_argument('--local_rank', '--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.cfg_options:
- raise ValueError(
- '--options and --cfg-options cannot be both '
- 'specified, --options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
- if args.options:
- warnings.warn('--options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
- args.cfg_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 = mmcv.Config.fromfile(args.config)
- if args.cfg_options is not None:
- cfg.merge_from_dict(args.cfg_options)
-
- # set multi-process settings
- setup_multi_processes(cfg)
-
- # set cudnn_benchmark
- if cfg.get('cudnn_benchmark', False):
- torch.backends.cudnn.benchmark = True
- if args.aug_test:
- # hard code index
- cfg.data.test.pipeline[1].img_ratios = [
- 0.5, 0.75, 1.0, 1.25, 1.5, 1.75
- ]
- cfg.data.test.pipeline[1].flip = True
- cfg.model.pretrained = None
- cfg.data.test.test_mode = True
-
- if args.gpu_id is not None:
- cfg.gpu_ids = [args.gpu_id]
-
- # init distributed env first, since logger depends on the dist info.
- if args.launcher == 'none':
- cfg.gpu_ids = [args.gpu_id]
- distributed = False
- if len(cfg.gpu_ids) > 1:
- warnings.warn(f'The gpu-ids is reset from {cfg.gpu_ids} to '
- f'{cfg.gpu_ids[0:1]} to avoid potential error in '
- 'non-distribute testing time.')
- cfg.gpu_ids = cfg.gpu_ids[0:1]
- else:
- distributed = True
- init_dist(args.launcher, **cfg.dist_params)
-
- rank, _ = get_dist_info()
- # allows not to create
- if args.work_dir is not None and rank == 0:
- mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
- if args.aug_test:
- json_file = osp.join(args.work_dir,
- f'eval_multi_scale_{timestamp}.json')
- else:
- json_file = osp.join(args.work_dir,
- f'eval_single_scale_{timestamp}.json')
- elif rank == 0:
- work_dir = osp.join('./work_dirs',
- osp.splitext(osp.basename(args.config))[0])
- mmcv.mkdir_or_exist(osp.abspath(work_dir))
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
- if args.aug_test:
- json_file = osp.join(work_dir,
- f'eval_multi_scale_{timestamp}.json')
- else:
- json_file = osp.join(work_dir,
- f'eval_single_scale_{timestamp}.json')
-
- # build the dataloader
- # TODO: support multiple images per gpu (only minor changes are needed)
- dataset = build_dataset(cfg.data.test)
- # The default loader config
- loader_cfg = dict(
- # cfg.gpus will be ignored if distributed
- num_gpus=len(cfg.gpu_ids),
- dist=distributed,
- shuffle=False)
- # The overall dataloader settings
- loader_cfg.update({
- k: v
- for k, v in cfg.data.items() if k not in [
- 'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
- 'test_dataloader'
- ]
- })
- test_loader_cfg = {
- **loader_cfg,
- 'samples_per_gpu': 1,
- 'shuffle': False, # Not shuffle by default
- **cfg.data.get('test_dataloader', {})
- }
- # build the dataloader
- data_loader = build_dataloader(dataset, **test_loader_cfg)
-
- # build the model and load checkpoint
- cfg.model.train_cfg = None
- model = build_segmentor(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 'CLASSES' in checkpoint.get('meta', {}):
- model.CLASSES = checkpoint['meta']['CLASSES']
- else:
- print('"CLASSES" not found in meta, use dataset.CLASSES instead')
- model.CLASSES = dataset.CLASSES
- if 'PALETTE' in checkpoint.get('meta', {}):
- model.PALETTE = checkpoint['meta']['PALETTE']
- else:
- print('"PALETTE" not found in meta, use dataset.PALETTE instead')
- model.PALETTE = dataset.PALETTE
-
- # clean gpu memory when starting a new evaluation.
- torch.cuda.empty_cache()
- eval_kwargs = {} if args.eval_options is None else args.eval_options
-
- # Deprecated
- efficient_test = eval_kwargs.get('efficient_test', False)
- if efficient_test:
- warnings.warn(
- '``efficient_test=True`` does not have effect in tools/test.py, '
- 'the evaluation and format results are CPU memory efficient by '
- 'default')
-
- eval_on_format_results = (
- args.eval is not None and 'cityscapes' in args.eval)
- if eval_on_format_results:
- assert len(args.eval) == 1, 'eval on format results is not ' \
- 'applicable for metrics other than ' \
- 'cityscapes'
- if args.format_only or eval_on_format_results:
- if 'imgfile_prefix' in eval_kwargs:
- tmpdir = eval_kwargs['imgfile_prefix']
- else:
- tmpdir = '.format_cityscapes'
- eval_kwargs.setdefault('imgfile_prefix', tmpdir)
- mmcv.mkdir_or_exist(tmpdir)
- else:
- tmpdir = None
-
- cfg.device = get_device()
- if not distributed:
- warnings.warn(
- 'SyncBN is only supported with DDP. To be compatible with DP, '
- 'we convert SyncBN to BN. Please use dist_train.sh which can '
- 'avoid this error.')
- if not torch.cuda.is_available():
- assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
- 'Please use MMCV >= 1.4.4 for CPU training!'
- model = revert_sync_batchnorm(model)
- model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
- results = single_gpu_test(
- model,
- data_loader,
- args.show,
- args.show_dir,
- False,
- args.opacity,
- pre_eval=args.eval is not None and not eval_on_format_results,
- format_only=args.format_only or eval_on_format_results,
- format_args=eval_kwargs)
- else:
- model = build_ddp(
- model,
- cfg.device,
- device_ids=[int(os.environ['LOCAL_RANK'])],
- broadcast_buffers=False)
- results = multi_gpu_test(
- model,
- data_loader,
- args.tmpdir,
- args.gpu_collect,
- False,
- pre_eval=args.eval is not None and not eval_on_format_results,
- format_only=args.format_only or eval_on_format_results,
- format_args=eval_kwargs)
-
- rank, _ = get_dist_info()
- if rank == 0:
- if args.out:
- warnings.warn(
- 'The behavior of ``args.out`` has been changed since MMSeg '
- 'v0.16, the pickled outputs could be seg map as type of '
- 'np.array, pre-eval results or file paths for '
- '``dataset.format_results()``.')
- print(f'\nwriting results to {args.out}')
- mmcv.dump(results, args.out)
- if args.eval:
- eval_kwargs.update(metric=args.eval)
- metric = dataset.evaluate(results, **eval_kwargs)
- metric_dict = dict(config=args.config, metric=metric)
- mmcv.dump(metric_dict, json_file, indent=4)
- if tmpdir is not None and eval_on_format_results:
- # remove tmp dir when cityscapes evaluation
- shutil.rmtree(tmpdir)
-
-
- if __name__ == '__main__':
- main()
|