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| # -------------------------------------------------------- | |
| # InternVL | |
| # Copyright (c) 2023 OpenGVLab | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # -------------------------------------------------------- | |
| import argparse | |
| import os | |
| import os.path as osp | |
| import shutil | |
| import time | |
| import warnings | |
| import mmcv | |
| import mmcv_custom # noqa: F401,F403 | |
| import mmseg_custom # noqa: F401,F403 | |
| import torch | |
| from mmcv.parallel import MMDataParallel, MMDistributedDataParallel | |
| from mmcv.runner import (get_dist_info, init_dist, load_checkpoint, | |
| wrap_fp16_model) | |
| from mmcv.utils import DictAction | |
| from mmseg.apis import multi_gpu_test, single_gpu_test | |
| from mmseg.datasets import build_dataloader, build_dataset | |
| from mmseg.models import build_segmentor | |
| 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( | |
| '--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.') | |
| 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.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 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 | |
| # 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) | |
| 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) | |
| data_loader = build_dataloader( | |
| dataset, | |
| samples_per_gpu=1, | |
| 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_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 | |
| if not distributed: | |
| model = MMDataParallel(model, device_ids=[0]) | |
| 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 = MMDistributedDataParallel( | |
| model.cuda(), | |
| device_ids=[torch.cuda.current_device()], | |
| 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() | |