188 lines
6.6 KiB
Python
188 lines
6.6 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 os.path as osp
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from copy import deepcopy
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import mmengine
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from mmengine.config import Config, ConfigDict, DictAction
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from mmengine.hooks import Hook
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from mmengine.runner import Runner
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from mmcls.utils import register_all_modules
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMCLS 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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'--work-dir',
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help='the directory to save the file containing evaluation metrics')
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parser.add_argument('--out', help='the file to save metric results.')
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parser.add_argument(
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'--dump',
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type=str,
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help='dump predictions to a pickle file for offline evaluation')
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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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parser.add_argument(
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'--show-dir',
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help='directory where the visualization images will be saved.')
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parser.add_argument(
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'--show',
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action='store_true',
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help='whether to display the prediction results in a window.')
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parser.add_argument(
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'--interval',
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type=int,
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default=1,
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help='visualize per interval samples.')
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parser.add_argument(
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'--wait-time',
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type=float,
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default=2,
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help='display time of every window. (second)')
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parser.add_argument(
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'--no-pin-memory',
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action='store_true',
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help='whether to disable the pin_memory option in dataloaders.')
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parser.add_argument(
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'--tta',
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action='store_true',
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help='Whether to enable the Test-Time-Aug (TTA). If the config file '
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'has `tta_pipeline` and `tta_model` fields, use them to determine the '
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'TTA transforms and how to merge the TTA results. Otherwise, use flip '
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'TTA by averaging classification score.')
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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('--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 merge_args(cfg, args):
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"""Merge CLI arguments to config."""
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cfg.launcher = args.launcher
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# work_dir is determined in this priority: CLI > segment in file > filename
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if args.work_dir is not None:
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# update configs according to CLI args if args.work_dir is not None
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cfg.work_dir = args.work_dir
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elif cfg.get('work_dir', None) is None:
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# use config filename as default work_dir if cfg.work_dir is None
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cfg.work_dir = osp.join('./work_dirs',
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osp.splitext(osp.basename(args.config))[0])
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cfg.load_from = args.checkpoint
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# -------------------- visualization --------------------
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if args.show or (args.show_dir is not None):
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assert 'visualization' in cfg.default_hooks, \
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'VisualizationHook is not set in the `default_hooks` field of ' \
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'config. Please set `visualization=dict(type="VisualizationHook")`'
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cfg.default_hooks.visualization.enable = True
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cfg.default_hooks.visualization.show = args.show
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cfg.default_hooks.visualization.wait_time = args.wait_time
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cfg.default_hooks.visualization.out_dir = args.show_dir
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cfg.default_hooks.visualization.interval = args.interval
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# -------------------- Dump predictions --------------------
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if args.dump is not None:
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assert args.dump.endswith(('.pkl', '.pickle')), \
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'The dump file must be a pkl file.'
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dump_metric = dict(type='DumpResults', out_file_path=args.dump)
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if isinstance(cfg.test_evaluator, (list, tuple)):
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cfg.test_evaluator = list(cfg.test_evaluator).append(dump_metric)
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else:
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cfg.test_evaluator = [cfg.test_evaluator, dump_metric]
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# -------------------- TTA related args --------------------
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if args.tta:
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if 'tta_model' not in cfg:
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cfg.tta_model = dict(type='mmcls.AverageClsScoreTTA')
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if 'tta_pipeline' not in cfg:
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test_pipeline = cfg.test_dataloader.dataset.pipeline
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cfg.tta_pipeline = deepcopy(test_pipeline)
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flip_tta = dict(
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type='TestTimeAug',
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transforms=[
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[
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dict(type='RandomFlip', prob=1.),
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dict(type='RandomFlip', prob=0.)
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],
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[test_pipeline[-1]],
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])
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cfg.tta_pipeline[-1] = flip_tta
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cfg.model = ConfigDict(**cfg.tta_model, module=cfg.model)
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cfg.test_dataloader.dataset.pipeline = cfg.tta_pipeline
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# ----------------- Default dataloader args -----------------
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default_dataloader_cfg = ConfigDict(
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pin_memory=True,
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collate_fn=dict(type='default_collate'),
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)
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def set_default_dataloader_cfg(cfg, field):
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if cfg.get(field, None) is None:
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return
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dataloader_cfg = deepcopy(default_dataloader_cfg)
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dataloader_cfg.update(cfg[field])
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cfg[field] = dataloader_cfg
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if args.no_pin_memory:
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cfg[field]['pin_memory'] = False
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set_default_dataloader_cfg(cfg, 'test_dataloader')
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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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return cfg
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def main():
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args = parse_args()
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# register all modules in mmcls into the registries
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# do not init the default scope here because it will be init in the runner
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register_all_modules(init_default_scope=False)
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# load config
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cfg = Config.fromfile(args.config)
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cfg = merge_args(cfg, args)
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# build the runner from config
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runner = Runner.from_cfg(cfg)
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if args.out:
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class SaveMetricHook(Hook):
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def after_test_epoch(self, _, metrics=None):
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if metrics is not None:
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mmengine.dump(metrics, args.out)
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runner.register_hook(SaveMetricHook(), 'LOWEST')
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# start testing
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runner.test()
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if __name__ == '__main__':
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main()
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