213 lines
7.7 KiB
Python
213 lines
7.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import shutil
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import warnings
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import mmcv
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import torch
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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,
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wrap_fp16_model)
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from mmcv.utils import DictAction
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from mmseg.apis import multi_gpu_test, single_gpu_test
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from mmseg.datasets import build_dataloader, build_dataset
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from mmseg.models import build_segmentor
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def parse_args():
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parser = argparse.ArgumentParser(
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description='mmseg 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(
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'--aug-test', action='store_true', help='Use Flip and Multi scale aug')
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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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'--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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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., "mIoU"'
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' for generic datasets, and "cityscapes" for Cityscapes')
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parser.add_argument('--show', action='store_true', help='show results')
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parser.add_argument(
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'--show-dir', help='directory where painted images 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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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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parser.add_argument(
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'--options', nargs='+', action=DictAction, help='custom options')
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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')
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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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parser.add_argument(
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'--opacity',
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type=float,
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default=0.5,
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help='Opacity of painted segmentation map. In (0, 1] range.')
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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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return args
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def main():
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args = parse_args()
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assert args.out or args.eval or args.format_only or args.show \
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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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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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cfg = mmcv.Config.fromfile(args.config)
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if args.options is not None:
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cfg.merge_from_dict(args.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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if args.aug_test:
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# hard code index
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cfg.data.test.pipeline[1].img_ratios = [
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0.5, 0.75, 1.0, 1.25, 1.5, 1.75
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]
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cfg.data.test.pipeline[1].flip = True
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cfg.model.pretrained = None
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cfg.data.test.test_mode = True
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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init_dist(args.launcher, **cfg.dist_params)
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# build the dataloader
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# TODO: support multiple images per gpu (only minor changes are needed)
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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=1,
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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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# build the model and load checkpoint
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cfg.model.train_cfg = None
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model = build_segmentor(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 'CLASSES' in checkpoint.get('meta', {}):
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model.CLASSES = checkpoint['meta']['CLASSES']
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else:
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print('"CLASSES" not found in meta, use dataset.CLASSES instead')
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model.CLASSES = dataset.CLASSES
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if 'PALETTE' in checkpoint.get('meta', {}):
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model.PALETTE = checkpoint['meta']['PALETTE']
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else:
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print('"PALETTE" not found in meta, use dataset.PALETTE instead')
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model.PALETTE = dataset.PALETTE
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# clean gpu memory when starting a new evaluation.
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torch.cuda.empty_cache()
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eval_kwargs = {} if args.eval_options is None else args.eval_options
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# Deprecated
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efficient_test = eval_kwargs.get('efficient_test', False)
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if efficient_test:
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warnings.warn(
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'``efficient_test=True`` does not have effect in tools/test.py, '
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'the evaluation and format results are CPU memory efficient by '
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'default')
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eval_on_format_results = (
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args.eval is not None and 'cityscapes' in args.eval)
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if eval_on_format_results:
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assert len(args.eval) == 1, 'eval on format results is not ' \
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'applicable for metrics other than ' \
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'cityscapes'
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if args.format_only or eval_on_format_results:
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if 'imgfile_prefix' in eval_kwargs:
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tmpdir = eval_kwargs['imgfile_prefix']
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else:
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tmpdir = '.format_cityscapes'
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eval_kwargs.setdefault('imgfile_prefix', tmpdir)
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mmcv.mkdir_or_exist(tmpdir)
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else:
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tmpdir = None
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if not distributed:
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model = MMDataParallel(model, device_ids=[0])
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results = single_gpu_test(
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model,
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data_loader,
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args.show,
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args.show_dir,
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False,
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args.opacity,
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pre_eval=args.eval is not None and not eval_on_format_results,
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format_only=args.format_only or eval_on_format_results,
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format_args=eval_kwargs)
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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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results = multi_gpu_test(
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model,
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data_loader,
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args.tmpdir,
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args.gpu_collect,
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False,
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pre_eval=args.eval is not None and not eval_on_format_results,
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format_only=args.format_only or eval_on_format_results,
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format_args=eval_kwargs)
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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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warnings.warn(
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'The behavior of ``args.out`` has been changed since MMSeg '
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'v0.16, the pickled outputs could be seg map as type of '
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'np.array, pre-eval results or file paths for '
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'``dataset.format_results()``.')
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print(f'\nwriting results to {args.out}')
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mmcv.dump(results, args.out)
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if args.eval:
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dataset.evaluate(results, args.eval, **eval_kwargs)
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if tmpdir is not None and eval_on_format_results:
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# remove tmp dir when cityscapes evaluation
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shutil.rmtree(tmpdir)
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if __name__ == '__main__':
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main()
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